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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Fouling Studies
  5. Cleaning
  6. Novel Cleaning Approaches
  7. Measuring Cleaning
  8. Summary and Conclusion
  9. References

Fouling of food process plant surfaces and the subsequent cleaning needed is a significant industrial problem, and as the cost of water and chemical disposal increases, the problem is becoming more significant. Current literature on water-based cleaning is reviewed here according to the classification of 3 types of cleaning problems. By doing this, it is hoped that new knowledge can be highlighted applicable to improving industrial cleaning. (i) For type 1 deposits (that can be cleaned with water alone)Cleaning time appears related to Reynolds number and surface shear stress. An increase in Reynolds number seems to decrease cleaning time. Cleaning temperatures greater than 50 °C do not appear beneficial. (ii) For type 2 deposits (biofilms)—Removal behavior of biofilms seems to be dependent on the microbial aging time on the surface. Keeping a material hydrated on a surface enables easier removal of it with water. a. Water rinsing: Temperature and wall shear stress have varied effects on removal. b. Chemical rinsing: Flow and temperature were seen to have the biggest effect at the start of cleaning, but contact time was more important as cleaning progressed at a given sodium hydroxide solution flow and temperature. (iii) For type 3 deposits (that require a cleaning chemical)For specifically, protein-based systems excessive chemical forms a deposit difficult to remove. Increasing wall shear stress and temperature was most beneficial to cleaning rather than concentration. The action of temperature can reduce the use of a chemical for type 2 and type 3 soils. The findings suggest that the right combination of flow characteristics at a given temperature and concentration is crucial to achieving fast cleaning in all cases. There are a number of cleaning monitoring methods at various stages of commercialization that may be capable of monitoring bulk cleaning and cleaning at the surface. To optimize cleaning will require integration of measurement methods into the cleaning process.

Contents

 1. Introduction     1

 2. Fouling studies     3

  2.1 Adhesion of microorganisms to surfaces     4

  2.3 Preventing fouling     5

  2.3.1 Process surface modification     5

  2.3.2 Process alterations     6

 3. Cleaning     6

  3.1 Product recovery     9

  3.2 The effect of CIP parameters on type 1 removal     10

  3.2.1 Flow and wall shear stress     11

  3.2.2 Temperature     11

  3.2.3 Design     11

  3.3 The effect of CIP parameters on type 2 and type deposit removal     13

  3.3.1 Membrane cleaning     13

  3.3.2 Water-rinsing of hard surfaces     14

  3.3.3 Chemical effects on the cleaning of type 2 deposits     14

  3.3.4 Chemical effects on the cleaning of type 3 deposits     15

 4. Novel cleaning approaches     16

  4.1 Increasing boundary layer disruption     16

  4.2 Alternative cleaners to reduce environmental impact     16

  4.3. Other studies related to cleaning behavior     17

  4.3.1 Deposit shear     17

  4.3.2 Deposit deformation and strength     17

 5. Measuring cleaning     17

  5.1 Online bulk measurements     18

  5.2 Online surface measurements     19

  5.3 Measuring microbial cleanliness     19

 6. Summary and conclusion     19

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Fouling Studies
  5. Cleaning
  6. Novel Cleaning Approaches
  7. Measuring Cleaning
  8. Summary and Conclusion
  9. References

The success of a branded fast moving consumer goods (FMCGs) business depends fundamentally on product quality and safety conformance at a required level. Poor cleaning or hygiene conformance can be a result of fouling layers building up in a plant or other problems. Figure 1 illustrates a typical route by which a large FMCG manufacturer will ensure a hygienic plant. There should be fundamental research and development obtained from both practice and science that are integrated and applied in-plant to provide the optimum cleaning protocol. In food and beverage manufacturing operations, cleaning-in-place (CIP) is used to remove residual product, fouling, and microbes that remain in the process line from production. The act of cleaning therefore maintains product quality, safety, and production efficiency. During CIP, water and/or chemical solution is circulated around plant process equipment. With large-scale manufacturers, the process is generally fully automated. A typical CIP philosophy in industry is that of Scottish & Newcastle Breweries (2008):

ensure all production, processing, and packaging plant is cleaned by a standard regime and to a schedule which ensures cleanliness and microbiological integrity at all times; with minimum cost, energy, and delay to production in a manner which ensures human, plant, product, and environmental safety.”

image

Figure 1. Flow diagram illustrating the route taken by industry to ensure plant hygiene (Heineken personal communication 2012).

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A significant body of cleaning knowledge exists within individual manufacturers, equipment suppliers, and chemical companies; however, the determined cleaning regimens have often been kept confidential and plant-specific. This has resulted in independent development of cleaning operations. Organizations such as the European Hygienic Engineering and Design Group (EHEDG) have produced extensive guidelines on the types of surface and equipment that are easy to clean, such as detailed in the EHEDG Yearbook (2007).

CIP tends to follow a similar series of steps for a prescribed time and at a prescribed flow rate, temperature, and chemical concentration known to give a repeatable level of cleanliness. It is not yet possible to predict before an operation how a given piece of equipment could foul and be cleaned. It is difficult to identify the best way to clean a processing plant from experiments of different plants. The direct selection of cleaning protocols is not always possible. In practice, cleaning protocols can only be developed semiempirically in industry. In most cases, CIP cannot be optimized in situ because of the risk posed of compromising existing cleanliness.

Fryer and Asteriadou (2009) suggest a classification of cleaning problems in terms of cleaning cost and soil complexity. A diagrammatic representation of this relationship is presented in Figure 2. This classification enables the nature of a foulant to be related to the type of cleaning employed, and therefore, the cost. This classification also indicates the environmental impact of the type of cleaning employed; complex soils require chemical and thermal cleaning that lead to a high cleaning cost and high environmental impact. Three deposit types were chosen to represent a broad range of cleaning problems seen in food, beverage, and personal care products manufacturing:

  1. Type 1: Viscoelastic or viscoplastic fluids such as yogurt and toothpaste that can be rinsed from a process surface with water.
  2. Type 2: Microbial and gel-like films such as biofilms and polymers removed in part by water and in part by chemical.
  3. Type 3: Solid-like cohesive foulants formed during thermal processing such as milk pasteurization and brewery wort evaporation. These operations mostly require chemical removal.
image

Figure 2. Cleaning map; a classification of cleaning problems based on soil type and cleaning chemical use (from Fryer and Asteriadou 2009).

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Yang and others (2008) also classified cleaning optimization methods, here into 2 types of investigation:

  1. Engineering investigations: reducing energy, time, and cost in established cleaning operations.
  2. Scientific investigations: achieving cleanliness or a cleaning time as a function of influencing factors; for example, wall shear stress, temperature, surface type, and finish.

The aim of this review was to provide an overview of current knowledge on cleaning solutions classified by soil cleaning type. This novel classification is hoped to highlight new CIP optimization opportunities for industry and any future research in the field. Current knowledge of fouling prevention and novel cleaning methods are also discussed here.

Fouling Studies

  1. Top of page
  2. Abstract
  3. Introduction
  4. Fouling Studies
  5. Cleaning
  6. Novel Cleaning Approaches
  7. Measuring Cleaning
  8. Summary and Conclusion
  9. References

Fouling is defined as the unwanted buildup of material on a surface. The fouling process generally involves a number of steps (Epstein 1983):

  1. surface conditioning,
  2. mass transfer of species to the surface,
  3. surface deposition,
  4. deposit aging, and
  5. possible removal.

There is also a classification of fouling mechanisms demonstrated by Bott (1990) detailed in Table 1. Fouling problems that have been reported in the food and beverage industries include (but this is by no means a complete list):

  • Protein and mineral deposition in heat exchangers.
  • Ice buildup in freezers.
  • Scale buildup in cooling water systems.
  • Fat burn-on in ovens.
  • Product solidification.
  • Growth of biofilm—often after the formation of a “conditioning layer’’ of protein onto the surface.
  • Accumulation of material in stagnant or low-flow areas of equipment.
  • Loss of membrane activity.
Table 1. Fouling mechanisms: adapted from Bott (1990) and Sharma and others (1982)
Fouling mechanismUnderlying process
CrystallizationFormation of crystals on the surface formed from solutions of dissolved substances when the solubility limit is changed. Cooled surfaces are subject to fouling from normally soluble salts, fats, and waxes. Inversely soluble salts, such as calcium phosphate deposits on heated surfaces. Where the fluid or components of the fluid solidify onto the surface, this is called solidification fouling (Sharma and others 1982).
Particulate depositionSmall suspended particles such as clay, silt, or iron oxide deposit onto heat transfer surfaces. Where settling by gravity is the determining factor, this is then called sedimentation fouling.
Biological growth (biofouling)The deposition and growth of organic films consisting of microorganisms and their products, called biofilm.
Chemical reaction at fluid/surface interfaceReaction of some part of the flow to generate insoluble material. The deposit formed on the surface (particularly heat transfer surfaces) has a different composition to the process fluid (for example, in petroleum refining, polymer production, and dairy plants).
CorrosionThe material of the heat transfer surface is involved in reactions with components of the fluid to form corrosion products on the surface, a specific type of chemical reaction fouling.
FreezingDeposit formed from a frozen layer of the process fluid, for example, ice from water or solid fats from a food fluid.

Fouling is a costly problem in the food, beverage, and other industries, which is often unavoidable due to the heat treatment that often has to be given to products to develop certain colors and flavors and ensure safety. By definition, foods are sources of nutrients favorable not only to people but also to microbes that stick to process surfaces—so microbial adhesion to surfaces and subsequent growth are important phenomena. The economic penalties of fouling in heat exchangers were discussed by Müller-Steinhagen (2000) and can be summarized:

  1. Capital expenditure, due to:
    1. Excess heat transfer surface area compensating for the occurrence of fouling. This has been estimated as an average of 30% additional capital cost.
    2. Higher transport and installation costs for bigger and heavier equipment.
    3. Cleaning systems, including their installation and maintenance costs.
  1. Fuel cost—If extra energy (such as steam) is required to keep the fouled heat exchanger operating for the required performance.
  2. Maintenance cost—Of the heat exchanger, cleaning system, and any ancillary equipment in the process (and cleaning) loop, for example, chemical tank level probes, flow meters, interface probes, and boilers.
  3. Cost due to production loss—Cost of continuous production (without shut-down for cleaning or maintenance) as compared to the actual production cost.

Accurate measurement of the effects of fouling and the efficiency is critical. Changes in heat transfer efficiency have widely been recorded. Most common is following the change in heat transfer during fouling by including a fouling resistance, Rf, in the equation relating the initial clean heat transfer coefficient, (U0), to that at time t, (U):

  • display math(1)

And the extent of fouling may be expressed by a Biot number (Bi), which accounts for deposit thickness (x) and thermal conductivity (λ): Bi = Rf.U0, where Rf = x/λ for the deposit. Deposit resistance during cleaning can be described as the reverse process to (1) (Tuladhar 2001) as

  • display math(2)

where Ut is the heat transfer coefficient at time t and Uc the heat transfer coefficient of the final clean system, so the rate of change of this is a measure of cleaning.

The rate and extent of fouling and cleaning is often classified in terms of fluid flow, either in terms of the Reynolds number (Re = ρvd/μ, where ρ and μ are the density of viscosity of a fluid flowing at mean velocity v through a system of characteristic length d, such as pipe diameter) or the surface shear stress. In this paper, many correlations in terms of Reynolds number are discussed—to convert to velocity requires knowledge of density and viscosity of the fluid, which is simple for water but may be more complex for cleaning solutions.

Adhesion of microorganisms to surfaces

The principal factors responsible for adhesion between surface and foulant include: (i) van der Waals forces, (ii) electrostatic forces, and (iii) contact area effects; the larger the area, the greater the total attractive force (Bott 1995). Microbes have a natural affinity to surfaces. Numerous authors have reported the adhesion of bacteria to processing surfaces (for example, Geesey and others 1996; Bénézech 2001; Zhao and others 2007). If left to proliferate, individual microbes can grow into biofilms (adhesive and cohesive communities of microbes) that become difficult to remove from a surface (Jefferson 2004). Garrett and others (2008) summarize the occurrence of biofilms in industry, fouling mechanisms and methods of observing and probing structures. The adhesion of organisms usually follows the formation of a conditioning layer of protein (Lorite and others 2011) that makes subsequent adhesion and biofilm formation easier. The sequence of events that occur during film formation is discussed by Busscher and others (2010) and Chen and others (2010) who show the kinetics of film formation.

Other researchers have studied yeast adhesion and proliferation on processing surfaces, critical in brewing operations. Reynolds and Fink (2001) proved that Baker's yeast can initiate biofilm formation on plastic when in a low-glucose environment. Mozes and others (1987) found that yeast could attach and form a dense layer of cells on stainless steel and aluminum at pH 3 and pH 5 and 6. The authors also determined that a dense layer of yeast cells would attach to glass and plastics if the negative charge was reduced by treatment with ferric ions. The system pH will determine the surface charge of both the substrate and the adhering species. The isoelectric point, the pH where the material carries no charge, will also vary with surface and organism. Yeast has also been found by other authors to readily attach to stainless steel, plastics, elastomers (Guillemot and others 2006), and glass (Mercier-Bonin and others 2004), all of which are used extensively in FMCG industries. The effect of cleaning parameters on yeast removal from process surfaces is discussed in later sections.

Product contact surface finishes with a roughness (Ra) value of up to 0.8 μm are recommended (Lelieveld and others 2005), which is often called 2B finish of stainless steel. Surface roughness exists in 2 principal planes, one perpendicular to the surface described as height deviation and one in the plane of the surface described by spatial parameters. The effect of average surface roughness height, Ra, and surface topography on microbial retention has been investigated most thoroughly. Hilbert and others (2003) investigated the effect of stainless steel roughness (Ra 0.9 to 0.01 μm) on retaining various microbes. The surfaces also had a conditioning layer. The retention of microbes (measured by indirect conductometry) on the conditioned surfaces was similar over the range of Ra tested.

Cluett (2001) investigated the effect of stainless steel surface finish on the fouling and cleaning of a beer fermenter. Surface finishes investigated included 2B milled stainless steel and mechanically polished 120 grit, 240 grit, and electropolished (EP) stainless steel. The top surface of the fermenter was half EP, half 240 grit, and the cone was EP. The cylinder of the vessel had all finishes, one quarter of the vessel from top to bottom represented by each surface finish. After lager beer fermentation lasting 12 d, Cluett (2001) found that all surfaces fouled similarly and the level of deposition was heavy. He also found that all the surfaces cleaned similarly using a similar CIP regime with a spray ball (prerinse, caustic, water, acid, water, and sanitizer). However, the number of viable microbes was found to decrease in the cone at the bottom of the vessel.

Gallardo-Moreno and others (2004) investigated the effect of surface roughness by comparing yeast adhesion to glass (Ra 0.8 μm and hydrophilic) and silicone rubber (SR) (Ra 0.61 μm and hydrophobic). The authors found larger adhesion rates for SR, and at 37 °C rather than 22 °C. Whitehead and others (2006) investigated Pseudomonas aeruginosa (rods of 1 μm width and 3 μm length) and Staphylococcus aureus (1 μm sphere) retention on a titanium dioxide surface: smooth with defined surface features (pits) of 0.5 μm. S. aureus cells were removed more easily from the smooth surface, whereas P. aeruginosa cells were removed more easily from the defects. Whitehead and Verran (2006) also reviewed the effect of Ra and topography on microbial retention. Research suggests that surfaces with a Ra value close to the cell size see increased retention on the surface. For example, yeasts were found to require larger defects (5 μm) for retention and smaller daughter cells were retained in smaller defects (2 μm). Rod-shaped cells seemed to orient themselves in grains and grooves of similar size.

Preventing fouling

If fouling were not to occur, there would be little need for cleaning. Broadly speaking, 2 methods for preventing fouling have been approached in the literature:

  1. Functional surfaces—For example, smooth surfaces with specific finish, topography, hydrophobicity, or surface charge. “Nonstick surfaces” are designed to have a specific surface energy to minimize fouling.
  2. Processing alterations—For example, changing product flow characteristics, holding times, transient times, and other process parameters designed to minimize fouling.
Process surface modification

A hygienic surface needs to be smooth, easy to clean, able to resist wear, and retain its hygienic qualities. Stainless steel is the most common food contact material used in the industry, being stable at a variety of temperatures, inert, relatively resistant to corrosion, and it may be treated mechanically or electrolytically to obtain a range of finishes (Akhtar and others 2010). The wettability of a surface is dependent on its surface energy. A surface with a high surface energy is hydrophilic and a drop of cleaning fluid will spread over the surface. A low-energy surface is hydrophobic and a drop of water will not spread. Water partially wets glass and acrylic and does not wet Teflon (PTFE) surfaces—but surfactants are often added to commercial cleaning agents to improve wetting. Wetting is determined by the nature of both the liquid and the solid substrate. The cleanability (and disinfectability) of stainless steel has been compared with those of other materials, and is comparable to glass when cleaning microbes, and significantly better than polymers, aluminum, or copper (Akhtar and others 2010).

Microbes are known to readily attach to SR. Everaert and others (1998) absorbed long fluorocarbon chains (Ar-SR-C8F17) to SR used in prosthetics in an attempt to reduce the number of adhering microbes. They found that the initial adhesion rate of Streptococcus bacteria to the treated rubber was significantly reduced, from around 2500 to 900 cm−2 s−1, without a conditioning film of saliva and 400 cm−2 s−1 with a conditioning film of saliva. The adhesion rate of Candida species to treated rubber was also reduced compared to untreated rubber.

Dhadwar and others (2003) investigated the effect of oligopeptide treatment of glass (hydrophilic) and plastic (hydrophobic) on yeast adhesion. Overall surface energy was 50 to 60 and 25 mJ/m for cell adhesion on plastic and glass, respectively. Coating both surfaces changed the free energy of the system resulting in a decrease to 35 to 40 mJ/m for plastic and an increase to 30 to 40 mJ/m for glass. Yeast adhesion was significantly reduced on the plastic surface coated in the peptide and increased on the glass surface. Changes in surface roughness and hydrophobicity due to the coating will also have contributed to adhesion.

Quain and Storgårds (2009) mentioned the testing of “functional materials” in the lab and in brewery dispense lines such as hydrophobic fluoropolymer coatings, photocatalytic titanium dioxide coatings, and the inclusion of antimicrobial silver ions (0.042%) in stainless steel. The latter was shown to reduce the number of adhering bacteria by 99% compared to normal stainless steel. However, the effect decreased with time.

The influence of surface energy on adhesion is well known in marine and medical biofouling and is characterized by the “Baier curve” (Baier 1980). This curve demonstrates the weakest adhesive strength of bacteria to be at surface energies of around 25 mN/m.

Equations defining possible minimum adhesion energies between a deposit and the surface have been developed. The following equation has been derived:

  • display math(3)

where inline image, inline image, and inline image are the Lifshitz–van der Waals (LW) surface free energy of the surface, deposit, and fluid, respectively, and which can be quantified from contact angle measurements (Zhao and others 2004). Liu and others (2006) studied the interactions of 316 L stainless steel with baked and unbaked tomato deposit: a minimum removal energy range of 20 to 25 mN/m was found in both cases. Either side of this surface energy range, the adhesive strength of the deposit on the surface increased. Zhao and others (2005a) found that stainless steel surfaces coated with Ag-PTFE reduced Escherichia coli attachment by 94% to 98%, compared with silver coating, stainless steel, or titanium surfaces. A surface with energy of 24.5 mN/m roughly matching the theoretical minimum adhesion energy of E. coli, 28.3 mN/m, was achieved. Composite coatings using nickel, phosphorus, copper, and PTFE were also used by Zhao and others (2005b) and Zhao and Liu (2006) to create surfaces with specific energies shown to reduce biofouling. A major EU project (“MODSTEEL”) developed and studied a wide range of surfaces and how they might reduce fouling from milk (see Santos and others 2004 and Rosmaninho and others 2007).

Work by Pereni and others (2006) confirmed the effect of surface free energy in minimizing P. aeruginosa adhesion to a range of coatings including silicone, polished and nonpolished stainless steel, PFA (perfluoroalkoxy polymer) and PTFE nickel, phosphorus, and aluminum composite coatings. The total surface free energy was in the range 17.2 to 48.3 mN/m, as shown in Figure 3(A). Minimum retention of bacteria was found at 20 to 27 mN/m. Silicone had a surface free energy of around 20 mN/m and the lowest colony forming units (CFUs) count. Surface free energy has been shown to be the parameter dominating E. coli adhesion over a range of metal–polymer coatings, and a minimum adhesion energy of 25 mN/m was found (Zhao and others 2007), as shown in Figure 3(B).

image

Figure 3. (A) P. aeruginosa AK1 retention on investigated surfaces compared with the total surface free energy (adapted from Pereni and others 2006). (B) Effect of surface free energy on E. coli adhesion (adapted from Zhao and others 2007).

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Parbhu and others (2006) used a transient treatment to modify a stainless steel surface. The treatment was present during the processing cycle and removed at high pH during alkaline cleaning. The treatment was shown to reduce the interaction potential between stainless steel and phosphate anions resulting in significant reductions in fouling rates.

Akhtar and others (2010) compared adhesion of a range of food and personal care foulants to different surfaces. Particle tips of different materials were attached to an atomic force microscope (AFM) cantilever to study the detachment from toothpaste and some confectionery components: Turkish delight, caramel, and sweetened condensed milk (SCM). The study did reveal significantly different detachment forces for the same deposit from different surface types (see Figure 4). Caramel and SCM seemed to be more difficult to detach from glass than stainless steel. It was possible to relate data from the AFM to measurements taken on a millimeter scale using micromanipulation probes (Liu and others 2002, 2006, 2007). Akhtar and others (2012) describe further research using AFM to study food adhesion to process surfaces.

image

Figure 4. Force of attraction between stainless steel, PTFE (fluorinated low energy surface), and glass particles and different food materials obtained using AFM (from Akhtar and others 2010). F/R is the force/probe radius with units of N/m.

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Process alterations

Dror-Ehre and others (2010) tested the effect of biofilm development of P. aeruginosa when pretreated in an aqueous solution of molecularly capped silver nanoparticles (MCNPs). Under specific conditions, cells and surfaces incubated for 39 h at 37 °C, Ag-MCNPs retarded biofilm formation even when a high percentage of planktonic P. aeruginosa cells survived pretreatment with Ag-MCNPs. At the various incubation times, a stable, low value of biomass was formed that could be easily removed. The authors found, from micrographs of pretreated cells, that the intracellular material was pushed toward the peripheral parts of the cell; a potential survival strategy. Treatment of water systems with silver nanoparticles could prevent significant biofilm buildup.

Tse and others (2003) found that in a 2-phase (liquid-vapor) wort boiling system, the wall temperature did not significantly affect the rate of fouling. Under conditions where vapor was condensed at lower flow velocities (0.07 and 0.14 m/s), the initial fouling phase was more rapid at the higher flow velocity. The authors found that the initial fouling rate was halved as the flow velocity was doubled. These findings suggest that circulating fluid at a fast flow rate would reduce fouling. The authors also found that at the lowest flow rate, 0.07 m/s, and highest temperature, 170 °C, the foulant appeared the most severe. The fouling also had different makeup depending on its position in the column. At the top of the column, the deposit was light in color, smooth, and patchy; at the bottom, the deposit was dark brown and multilayered. The authors suggested 2 fouling mechanisms: chemical reaction of species in the wort forming polymers and crystallization of species from the wort due to evaporation at bubble nucleation sites in nucleate boiling regions. Liu and others (2004) compared fouling of 2-phase flow (liquid-vapor) and 3-phase flow (liquid–vapor–solid) during the evaporation of Gengnian'an extract. The solid phase was added as inert solid particles. The 2-phase flow system generated fouling in 15 h, whereas the 3-phase flow system generated fouling after 60 h.

Modifying the process by using electric fields has also been discussed. Ohmic heating occurs when an electric current is passed directly through milk to heat it, rather than it being heated by surface heat transfer. The process results in lower surface temperatures and less fouling initially. However, fouling in the bulk is easily transferred to the surface, resulting in fouling (Bansal and Chen 2006). Kim and others (2011) demonstrated that an electric field could be used to control membrane fouling with E. coli. E. coli cell suspensions were treated by an electric field prior to filtration. The flux of the suspension was maintained throughout the filtration period due to larger fouling particles reducing cake resistance. Cell death also increased with increasing electric field strength from 5 to 20 kV/cm. Flux of the untreated E. coli suspension decreased abruptly after the onset of filtration.

Xiaokai and others (2005) investigated the effect of electromagnetic treatment of water to minimize scale formation in the tubes of a plate heat exchanger (PHE). The technology is termed electromagnetic antifouling (EAF). The treatment was shown to aggregate particles in the flow that led to reduced precipitation at the wall.

Cleaning

  1. Top of page
  2. Abstract
  3. Introduction
  4. Fouling Studies
  5. Cleaning
  6. Novel Cleaning Approaches
  7. Measuring Cleaning
  8. Summary and Conclusion
  9. References

No economically viable fouling prevention method is yet to be demonstrated in industry. Should one of the modified surface methods prove economic, then the problem will be greatly reduced. Understanding the cleanability of surfaces requires combining understanding of surface chemistry and engineering, the deposit and the cleaning fluid (for a recent review of cleanability, see Detry and others 2010). Further research discussed here considers the findings of studies relevant to optimizing cleaning.

One significant issue is determination of the correct cleaning time. A deposit that has aged on a surface is more difficult to remove than fresh material on a surface, so cleaning is encouraged after production. Aging of a particular soil type could make a deposit harder to remove from a process surface. For example, a type 1 soil could become a type 2 soil over time and heating may result in a type 3 soil. Goode and others (2010) found that in beer fermentation vessels, there were 2 distinct deposit types to be cleaned, classified as type A and type B foulants:

  1. Type A—Formed during fermentation above the beer level at the top of the vessel,
  2. Type B—Residual yeast attached to the vessel wall and cone below the beer level during emptying.

Type B fouling is shown by Salo and others (2008), as seen in Figure 5(A), while an example of type A foulant viewed from a fermenter man way door at the top of the vessel is given in Figure 5(B). Type B fouling has a shorter aging time than type A fouling. As such, type B foulant can be removed by the falling film in a tank, whereas type A foulant may require a larger impact force for removal or a combination of water and chemical rinses for complete removal (Goode and others 2010). Similarly, Liu and others (2002) found that the force required to remove a tomato deposit from a surface increased with time until after about 200 min of heating it remained constant.

image

Figure 5. (A) 80 L stainless steel tank (0.8 m × 0.4 mm) with residual yeast fouling attached to the wall and the cone. The wall was also sampled by contact agar (adapted from Salo and others 2008). (B) Type A deposit seen at the top of a fermenter around the man way door and the gasket (Goode 2012).

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Automated CIP has been widely applied in dairies, food processing, brewing, and wine processing for the last 50 y to return the plant to a clean state (Stewart and Seiberling 1996). During CIP, water and chemicals are circulated around the plant for a prescribed duration (Tamine 2008). The CIP factors found to determine cleaning can be described by Sinner's circle, a circle of the cleaning parameters: mechanical action, chemical action, time, and temperature (Lelieveld and others 2005). Cleaning can also be dependent on geometry. In a pipe, the contribution of the cleaning factors is equal. In a pipe dead leg, time determines cleaning (Lelieveld and others 2005). A number of attempts have been made to try to incorporate computational models into the design process, as shown by Asteriadou and others (2006) and Jensen and Friis (2005). This approach will become more important as understanding of the processes in cleaning increases.

Rheological characterization of materials enables their classification. Materials within a similar class may have similar cleaning behavior, according to the classification by Fryer and Asteriadou (2009). Vinogradov and others (2004) characterized the rheology of a dental plaque biofilm. Biofilm rheology has been viscoelastic, temperature-dependent and/or time-dependent (Rao 1999). Characklis (1980) compared the elastic and viscous modules obtained for a biofilm and a cross-linked protein gel, fibrinogen. The elastic modulus was the same order of magnitude for the protein gel and the biofilm. The cleaning map, presented in Figure 2 (Fryer and Asteriadou 2009), is a useful cleaning problem classification tool and forms the basis for the structure of this review. Examples of each deposit type include:

  1. Type 1: toothpaste, tomato paste, yogurt, shampoo, beer, wine, milk, and yeast.
  2. Type 2: microbes and microbial films of bacteria, spores, and yeast species.
  3. Type 3: milk, whey protein concentrate (WPC), cooked SCM, starch, boiled wort, and egg albumin.

Some of the research that has considered the influence of cleaning parameters in flowing systems on the removal behavior of deposits is listed in Table 2 to 4. Table 2 details type 1 deposit removal studies, Table 3 details type 2 deposit removal studies, and Table 4 details type 3 deposit removal studies. The cleaned geometry, effect of CIP parameters, and the method of determining cleaning effectiveness are listed in each Table. The effect of flow has been studied both in terms of the Reynolds number (Re) and the surface shear stress. Both may provide further insight into the effect of removal behavior on flow velocity.

Table 2. Some CIP studies of type 1 deposit
DepositGeometryEffect of flow or τwEffect of temperatureEffect ReCleaning determinantReference
  1. OD = outer diameter, ID = inner diameter, ED = equivalent diameter, ss = stainless steel, MSS = mechatronic surface sensor, MHFS = microfoil heat flux sensor.

Toothpaste1 m long, 2″ OD, 316 L ss pipe (horizontal)Increase flow velocity (1 to 3 m/s, decrease cleaning time.Increase temperature (from 20 °C), decrease cleaning time (to a point approximately 40 °C).Increase Re (4000 to 250000) decreases cleaning time.Turbidity reaches 4 ppm.Cole and others (2010)
Shampoo316 ss plate (350 mm long, 30 mm ID, 18.3 mm ED) (vertical flow cell)(0.14 to 0.47 m/s) higher flow velocity, more efficient removal at the start of cleaning.(31 to 51 °C), removal of shampoo layers faster at higher temperatures as cleaning proceeds.Visual MSS and spectrophotometryPereira and others (2009)
Mustardglass T-piece (variable depth T, 4 and 6 cm)Increase flow velocity (1 to 1.88 m/s) increase removal rate.Above a certain Reynolds number, the recirculation zone length becomes constant.VisualJensen and others (2007)
Yeast cells rehydrated (aged 1 h at ambient)glass, polypropylene, and polystyrene surfaces (210 × 90 mm long) in horizontal flow cellIncrease τw, decrease number of cells.VisualGuillemot and others (2006)
  (i) linearly for plastics    
  (ii) as a curve for glass    
Tomato paste316 L ss coupons (circular: 26 mm D) horizontal flow cell(0.7, 1.5, 2.3 L/min) increase flow rate, the effect of temperature on cleaning time decreases(30, 50, and 70 °C) increase temperature, decrease the time to remove deposit(850 to 4800 Re) increase Re, decrease cleaning timeVisual, image analysis, and MHFSChristian (2004)
Table 3. Selected CIP studies of type 2 deposit
DepositGeometryEffect of flow or τwEffect of temperatureEffect of chemical/pHCleaning determinantReference
  1. OD = outer diameter, ID = inner diameter, ED = equivalent diameter, ss = stainless steel, MSS = mechatronic surface sensor, MHFS = microfoil heat flux sensor.

Yeast slurry (aged at 30 °C, 5 d)316 ss coupons (square: 30 × 30 mm long) in horizontal flow cellIncrease in flow velocity (0.26 to 0.5 m/s) decrease cleaning time at 50 and 70 °C. Limited effect beyond 0.4 m/s at 20 and 30 °C.Increase temperature, decrease cleaning time.1% NaOHVisual, image analysis, and MHFSGoode and others (2010)
B. cereus spores316 L ss pipe (20 cm long, 2.37 cm ID) and 2 way valve (entry to exist 18 cm long, 3.5 cm ID)NaOH 0.5% (w/w) at 60 °C, 2200 L/h, up to 30 min. The % residual spores decreased as cleaning time increased.Agar overlay technique using TTC (spores appear red)Le Gentil and others (2010)
Yeast cells rehydrated (aged 1 h at ambient)316 L ss, (210 × 90 mm long) in horizontal flow cellIncrease τw, decrease number of cells barely for stainless steel (10%).VisualGuillemot and others (2006)
B. cereus spores (in milk)304 L ss pipes (15 × 10−2 m long, 2.3 × 10−2 m ID) (horizontal)Increase τw (17.45 to 68.95 Pa, that is, 1.61 to 3.29 m/s) decrease number of spores (after 5 min). Contact time was more important in reducing spores.Rinsing at 60 °C revealed less spores compared to 20 °C at the same soaking times.0.5% w/w of NaOH at 60 °CAgar overlay technique using TTC (spores appear red)Lelièvre and others (2002)
B. cereus spores (in custard)Progressive-cavity pump (with axial or tangential exit pipe). Tangential was best. In the axial setup, the number of CFU was greater than 10 CFU/cm in the pump body and gaskets.Prerinse 0.5 m/s (6 min); 0.2% NaOH at 1.5 m/s, 60 °C (10 min); intermediate rinse 0.5 m/s (6 min); 0.2% HNO3 at 1.5 m/s, 60 °C (10 min); final rinse 0.5 m/s (6 min).Agar overlay technique using TTC (spores appear red)Bénézech and others (2002)
Table 4. Selected CIP studies of type 3 deposit
SoilGeometryEffect flowEffect of temperatureEffect ReEffect of chemical/pHCleaning determinantReference
  1. OD = outer diameter, ID = inner diameter, ED = equivalent diameter, ss = stainless steel, MSS = mechatronic surface sensor, MHFS = Microfoil heat flux sensor, wss = wall shear stress.

Starch (with phosphorescent tracer molecules)Continuous and abrupt expansions (ID 26 mm, expanding from 26 to 38 mm)Local cleaning time has a minimum where the wss shows a maximum and vice versaN/A, constantRe > 25000 investigated.N/A. Constant 0.5%.Visual, image analysisAugustin and others (2010)
Cooked SCM (sweet condensed milk)316 L ss coupons (square: 30 × 30 mm long)Increase flow velocity from 0.25 – 0.5 m/s, decrease cleaning time at all temperaturesAn increase in temperature (40, 60, and 80 °C) revealed a linear decrease in cleaning timeAn increase in Re (6500 to 27500) revealed a decrease in cleaning time according to Power law.An increase from 0.5 to 1.5% NaOH did not significantly affect cleaning time at higher flow velocities.Visual, image analysis and MHFSOthman and others (2010)
Egg albumin316 L ss coupons (circular: 26 mm D)Increase flow less significant at higher chemical concentrations.30 °C did not clean. Increase temperature, decrease in cleaning time. However, 50 °C removed more deposit at 1% NaOH than 70 °C.Increase Re (1090 to 4840) decreases cleaning time (at 50 °C, 0.1 to 1% NaOH). At 70 °C 0.1%, increase Re, increase cleaning time.No cleaning at 0.1 wt% NaOH. Concentration 0.25% to 3% (at 50 °C, 2.3 L/min) decreases cleaning time. Most significant at low flow (0.7 L/min)Visual, image analysis, and MHFSAziz (2008)
WPC316 L ss coupons (circular: 26 mm D) horizontal flow cellLimited benefit to increase flow velocity at 70 °C and 1% NaOH. Benefit if increase flow at low concentration.Increase temperature (30 to 70 °C) decrease cleaning time at all flow rates and chemical concentrations (0.7, 1.5, 2.3 L/min, 0.1%, 0.5%, 1% NaOH)Increasing Re (1090 to 4840) only beneficial at 0.1% NaOH.Limited benefit to increase concentration above 0.5%.Visual, image analysis, and MHFSChristian (2004)
WPC10 cm sections of sstubes (6 mm ID 0.15 mm thickness) fouled in counter current heat exchangerIncreasing flow rate does not necessarily decrease cleaning time. It is important to decay phase time.Wall temperature did not affect the plateau. Increasing the bulk temperature decreases cleaning time.Re 500 to 6500 investigated. As Re increases cleaning time decreases generally.N/A. Constant 0.5%.Thermal resistance using MHFS and massGillham and others (1999)

Milk processing is a large industry and fouling is a significant problem, as both protein aggregates and minerals are deposited; Burton (1967) classified the proteinaceous deposit seen in pasteurizers as type A and the mineral deposit seen at UHT temperatures as type B. Reviews of dairy fouling research are presented by Changani and others (1997) and Bansal and Chen (2006). Proteins have been identified as a major source of fouling deposits. Fickak and others (2011) found that increasing the protein concentration of whey protein increased the amount of fouling on a pilot-scale heat exchanger. Holding of milk before heating sections has been shown to aggregate β-lactoglobulin in the holding sections rather than the heating sections (de Jong and van der Linden 1992). Christian and others (2002) found that increasing the mineral content of whey protein decreased the extent of fouling on a PHE.

WPC is often used in research studies to represent a milk fouling deposit, because it is easier to handle and store than milk, and the fouling composition is thus easier to control and replicate. Robbins and others (1999) compared the cleaning of milk and WPC from a PHE. They found that in the pasteurization and UHT sections of the PHE, both materials fouled heavily. However, in the intermediate section, WPC also fouled excessively, whereas milk did not. Compositional analysis revealed protein fouling from both materials in the pasteurizer section. Increasing to UHT temperatures revealed milk fouling to become more mineral-based, whereas the WPC fouling remained predominantly protein-based, suggesting comparison of milk fouling and WPC fouling is not wise at UHT temperatures.

Yeast can exhibit type 1 (if in contact with glass) and type 2 (if in contact with stainless steel) cleaning behavior. Guillemot and others (2006) found that yeast cells could be wholly removed from glass using water but that yeast cells had strong adhesion to stainless steel. The wall shear stress required to remove 50% of the attached cells from stainless steel, denoted as τw50%, was 30 Pa, while for plastics τw50% ranged from 1 to 2 Pa.

The effect of CIP parameters on the removal of different deposit types is discussed in the following sections. Even though there is clear evidence that different deposit types are removed from surfaces differently, the approach to cleaning is typically the same:

  1. Prerinse (or product recovery stage); to remove loosely bound soil and product.
  2. Detergent phase (alkali or acid); to remove the fouling layers.
  3. Intermediate rinse; to remove chemical.
  4. Sanitization/disinfection step (chemical and/or thermal); to kill viable microbes and restore the hygienic condition of the system.
  5. Final water rinse; so product can be reintroduced to the system.

Although disinfection is done after the deposit has been removed from a process surface, this stage is often included as part of the CIP operation in industry. The purpose of this stage is to make the surface free of product spoilage microbes rather than to remove the foulant. Product may be recovered before cleaning, depending on the type of product, its value, and the geometry used in processing. This is discussed in the following section.

Product recovery

At the end of a process, there can be a significant amount of material left in pipes and tanks. This product may be saleable, in which case it should be recovered, or it may be considered waste. In both cases, the bulk of this material should be removed (generally in the first rinse phase) prior to the “cleaning” phase. Type 1 deposits will generally be saleable for products like toothpaste or shampoo, and recovery should be maximized. Type 2 and type 3 deposits will generally be formed as thin layers at the wall of a different composition to product. The layers need to be removed, to return the plant to a clean state, and tend not be recovered at the end of a process.

Palabiyik and others (2012) investigated the effect of the product recovery (using water) on overall cleaning time of toothpaste from a 1 m pipe. They determined that the amount of toothpaste removed during product recovery was not a function of pipe Reynolds number. A similar mass fraction was removed over the Re range 5000 to 25000. They did, however, find that product recovery conditions had a profound effect on overall cleaning time. The cleaning phase was conducted at the same condition: 0.55 m/s at 50 °C. High flow velocity and low temperature in the product recovery stage revealed the fastest cleaning times. The results suggested that the structure of the toothpaste film after the product recovery stage is important in determining the overall cleaning time.

Product recovery can be done by pigging, in which fluid is expelled from a system by the “pig” which could be solid, liquid, or gas. Solid pigs tend to be used in long sections of straight pipe work where complex geometries do not need to be navigated; for example, in crude oil pipelines to remove paraffin wax (Guo and others 2005). The use of crushed ice (with a freezing point depressant) in pigging systems has been developed and researched at the Univ. of Bristol to remove starch–water mixes (Quarini 2002). The void fraction of the ice is controlled so that the pig can navigate bends and T-pieces as well as straight pipe work. Application of this technology in the food and beverage industries is currently limited. The ice is expensive to make and store. A company called Aeolus promotes a “Whirlwind” technology that uses compressed air to remove soft deposits like fruit juice from pipe work with bends (see www.aeolustech.co.uk). Application of this technology in the food and beverage industry is also limited as the cost of compressed air is considerable. However, use of air in cleaning is likely to increase in the future as water becomes more precious.

The effect of CIP parameters on type 1 removal

Schlüsser (1976) compared cleaning behavior of 3 type 1 soils; beer, wine, and milk, illustrated in Figure 6. The products themselves were not heated. The cleaning profiles of each product were different. Type 1 products can have a complex rheology, but are often shear thinning, that is, they have an effective viscosity that is a function of shear rate. The shear-thinning rheology of yogurt was determined by Henningsson and others (2007) who also studied the use of water to displace the yoghurt. For flow velocities of 0.05 to 0.25 m/s, yogurt was observed and predicted to flow as a plug. If the process was set up so that yogurt flows as a plug, at changeover, the mixing zone between the 2 yogurts would be smaller and yield reduced losses. Prediction of the mixing zone of a Hershel–Bulkley material with and without wall slip at 0.19 m/s was also done by Henningsson and others (2007). With wall slip, it was predicted that the material would have a larger plug flow region. However, predicting the flow of a high-viscosity plug or wall layer is very difficult in practice.

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Figure 6. Cleaning characteristics of 3 type 1 products, beer, red wine, and milk with water (Schlüsser 1976).

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Flow and wall shear stress

Flow rate has an effect on the removal rate of type 1 materials. The rheology of tomato paste has been represented by the Carreau model (Bayod and others 2008), and the cleaning behavior of tomato paste in a flow cell has been investigated by Christian (2004). At 30 °C, it was found that by increasing the cleaning water flow rate from 0.7 to 1.5 to 2.3 L/min (Re 750 and 4840), the cleaning time decreased. The relationship appears linear. This was also true at 50 and 70 °C.

Shampoo (SUNSILK® color radiant, viscosity quoted as 7000 cP at 24 °C) was rinsed by water from a stainless steel plate in a vertical flow cell by Pereira and others (2009), and they found that the faster the initial flow rate (in the range of 0.14 to 0.47 m/s), the more shampoo was removed from a duct. The same effect was found for removing toothpaste (a Hershel–Bulkley fluid with a yield stress) from a pipe (Cole and others 2010). The effect of wall shear stress (τw) in the range of 0.5 to 10 Pa on toothpaste removal was studied. Shear stress is affected by both fluid density and Re that are both affected by temperature. Toothpaste cleaning time is governed by 2 removal phases by Cole and others (2010):

  1. Core removal—where most of the product is removed as a “slug’’ of product that can be recovered.
  2. Thin-film removal—where the remaining annular wall film of toothpaste is removed.

More recently, 3 phases were defined by a further investigation of the effect of product recovery on cleaning of toothpaste using water (Palabiyik and others 2012):

  1. Core removal—the first few seconds (a time comparable to the residence time of the fluid in the system) where approximately half the product mass is removed and the remaining toothpaste coats the pipe wall. Also called the product recovery stage.
  2. Film removal—further product is removed up to about 1000 s according to a process that is 1st order in deposit weight/thickness, leaving a thinner but continuous film of toothpaste remaining on the pipe wall.
  3. Patch removal—greater than 1000 s, the continuous film is broken up and only patches of toothpaste are left on the surface. These are gradually eroded away according to zero-order kinetics.

It was found in both cases that the time to remove the remaining patches of toothpaste was the rate-limiting step in overall cleaning time. For shampoo, Pereira and others (2009) found that flow velocity had the biggest impact on shampoo removal from the flow cell at the start of cleaning, less so as cleaning progressed. Palabiyik and others (2012) found that the shear stresses induced in the deposit during the core removal stage can affect the final cleaning time—creation of a wavy film in the product removal stage leads to much more rapid removal than if a smooth film is created. The authors also found that the remaining film thickness was independent of pipe length, suggesting that removal is uniform throughout the pipe, as also found by Cole and others (2010).

Temperature

For cleaning of tomato paste in a flow cell, Christian (2004) found that an increase in temperature decreased the cleaning time by a linear relationship. Both an increase in temperature from 30 to 70 °C and in flow velocity from 0.7 to 2.3 L/min decreased cleaning time. Cleaning time decreased by a factor of 6 from the lowest flow rate and temperature to the highest flow rate and temperature.

For tomato paste cleaning, it was found that cleaning time was also correlated with Reynolds number (Christian 2004). As the Re was increased from 800 to 4800, the cleaning time (tc) decreased according to a power law: tc = 2 × 106 (Re)−0.97. R2 = 0.81. Jackson and Low (1982) found a critical Re of 6300 for cleaning of dried tomato juice from a PHE, below which little deposit was removed.

Shampoo was rinsed at 0.14 m/s, at 31 and 51 °C, by Pereira and others (2009). After the initial bulk of shampoo was removed from the flow cell, it was found that the removal of shampoo layers occurred faster at higher temperatures. For toothpaste, Cole and others (2010) found that an increase in the water temperature from 20 to 40 °C decreased the cleaning time; however, increasing the temperature above 40 °C did not decrease cleaning time any further. The same effect may occur when rinsing shampoo; the investigators did not exceed a water temperature of 51 °C in their experiments.

Cole and others (2010) found that for toothpaste cleaning (from various length scales and diameters), a dimensionless cleaning time, inline image (where tc is the cleaning time and d is the pipe diameter), could be plotted as a function of Re, as a power law model: θc = 9 × 107 (Re)−0.78; with a similar fit, R2 = 0.84. Palabiyik and others (2012) found that temperature had a greater effect on toothpaste film removal than flow velocity, and fitted the data.

Design

The velocity of 1.5 m/s is the flow velocity most often reported to clean pipe lines effectively in industry CIP (EHEDG 1992). This is, however, anecdotal with no theoretical justification (Changani and others 1997; Tamine 2008). In industrial pipe systems, there are, however, more complicated geometries such as bends, valves, and T-pieces. It raises the question: does increasing the flow velocity decrease the cleaning time of other geometries? This gives a better indication of the effect of flow on the cleaning time of a whole system.

Jensen and others (2007) filled a variable depth “upstand” or “downstand” (also called a T-piece) made from glass with commercially available mustard and rinsed with ambient water. The geometry used in the study is shown in Figure 7 and is in the downstand position. The downstand depth was tested at 4 and 6 cm. The flow velocity was increased from 1 to 1.88 m/s to define the effect on cleaning the T-piece. Jensen and others (2007) found that:

  1. Increasing the flow velocity increased removal rate. However, the authors suggested that this was more likely due to greater acceleration of the water at 1.88 m/s into the T-piece. At the lower flow velocities, flow had not fully developed before entering the T-piece.
  2. Some areas of the T-piece were harder to clean than others. The position in the downstand most difficult to clean was always located in the same position (see Figure 8, shown as a downstand).
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Figure 7. Downstand geometry used for investigating the influence of different flow rates during CIP (flow was from left to right) (from Jensen and others 2007).

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Figure 8. CFD simulations of the flow field in 4 cm downstand T piece at (A) 0.5 , (B) 1, and (C) 2 m/s. Blue is low wall shear stress (0 Pa) and red is high wall shear stress (5 Pa). White represents wall shear stress in excess of 5 Pa. Water enters the section from the right and exits the T section on the left represented by the arrow in (a) (adapted from Jensen and others 2007).

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As expected, the top of the downstand was difficult to clean. However, an additional area located on the downstand pipe was always the last part to be cleaned in all the experiments, regardless of velocity. Jensen and others (2007) used computational fluid dynamics (CFD) simulations to predict the wall shear stress in the 4-cm downstand. Their CFD findings are illustrated in Figure 8(A) to 8(C) where blue is low wall shear stress (0 Pa) and red is high wall shear stress (5 Pa). As the flow velocity was increased, the blue area decreased in size. Within these simulations, the area most difficult to clean, the center of the downstand, is identified. Increasing the flow rate does not improve cleaning of this area. The wall shear stress achieved at this position is low at all 3 flow velocities. The other areas hardest to clean are circled.

Jensen and others (2007) examined the effect of pulsed flow in the downstand. They found that pulsing flow only affected the cleaning time of the 4-cm-depth T piece, not the 6-cm-depth T piece. They compared cleaning at 1 m/s (v1) and 2 m/s (v2) and pulsing at 15 s (p1) and 30 s (p2). The cleaning time of the 4-cm downstand was longer when rinsed at 1 m/s than when the flow was pulsed. However, rinsing the downstand at 2 m/s gave the quickest cleaning time. The authors concluded that at turbulent Re, the area of the recirculation zone in the T-piece did not change. A recirculation zone is typically located after a pipe expansion and depends on Reynolds number and the expansion ratio. At lower Re (less than 10000 in this case), the length of the recirculation zone may change; hence, cleaning times are shorter for pulsed flow at 1 m/s than using constant flow at 1 m/s.

Jensen and Friis (2005) used CFD simulations to predict the cleanability of a mix proof valve fouled with B. stearothermophilus spores in accordance with the EHEDG standard cleanability test (EHEDG 1992; Timperley and others 2000). In the EHEDG test, the apparatus is filled with sour milk and/or spores. An area “difficult to clean” is defined as an area that produces yellow agar in 3 consecutive tests (EHEDG 1992). Yellow agar shows the presence of spores. The study revealed that the valve was easier to clean than the radial flow cell (detailed by Jensen and Friis 2004). The study predicted that a critical wall shear stress of 3 Pa was necessary in both systems to ensure cleaning; however, areas of extremely low wall shear stress and some areas of wall shear stress higher than 3 Pa had spores remaining. The authors concluded that wall shear stress was not the only factor governing cleaning in this case. As spores are more likely a type 2 soil, this conclusion seems logical.

Bénézech and others (2002) rinsed spores in custard from a progressive cavity pump (a type of positive displacement pump) using a standard CIP operation in 2 configurations (i) with an axial exit pipe, where custard was pumped out of the top of the pump body on the same axis as entry, and (ii) with a tangential exit pipe, custard was pumped out of the body at the side off the axis of entry. The CIP consisted of a prerinse at 0.5 m/s for 6 min; 0.2% NaOH rinse at 1.5 m/s, 60 °C, for 10 min; intermediate rinse at 0.5 m/s for 6 min; 0.2% HNO3 rinse at 1.5 m/s, 60 °C, for 10 min; and final rinse at 0.5 m/s for 6 min. The group found that in the tangential setup, all parts of the pump were cleaned to the same number of CFU/cm, approximately 10 CFU/cm2. The authors defined a high level of hygiene as counts less than 18 CFU/cm2. In the axial set up not all components were cleaned to the same level. There was an increased number of CFU/cm2 in the pump body and gaskets (>18 CFU/cm2).

To clean tanks, spray devices typically called cleaning heads are used. The design of a cleaning head is of paramount importance to be effective in cleaning. There are 2 main choices:

  1. Static cleaning heads—These devices spray cleaning fluid onto the tank surface from a fixed position. The effectiveness of the cleaning head depends on cleaning fluid flow rate and the size and pattern of the holes.
  2. Dynamic cleaning heads—These devices spray cleaning fluid onto the tank surface using larger pressures, around 5 Bar (resulting in large wall shear stresses and direct impact force), and rotation to ensure full vessel coverage. The effectiveness of the cleaning head depends on the cleaning fluid pressure/flow rate to ensure that the preprogrammed pattern is achieved.

Examples of commercially available cleaning heads of both types are shown in Figure 9. Increasing the impact force of a jet stream of fluid onto a surface can overcome large deposit hydration times and reduce cleaning times. The fraction effect of time, physical action, temperature, and chemical action delivered to the tank by a static cleaning head (spray ball) and a dynamic cleaning head (high-pressure cleaning head) are given in Figure 10 (Tamine 2008). For spray ball cleaning, time is required to achieve deposit removal. Cleaning time is required to achieve product removal using a static head, and mechanical action is required to achieve product removal using a dynamic cleaning head. Dynamic heads enable cleaning behaviors that are less reliant on contact time with the chemical at high temperature. A type 3 soil could be cleaned in a similar time as a type 1 soil. It should, however, be noted that impingement jets from a rotary device or from many small jets in a static device may cause corrosion problems due to “rouging,” from small iron particles worn from the orifices of the thin walled static spray devices that then deposit on the tank wall.

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Figure 9. Commercially available (A) spray ball static device (GEA, Warrington, U.K.), (B) rotary spray head dynamic device (Alfa Laval, Minworth, U.K.), and (C) rotary jet head dynamic device (Alfa Laval, U.K.).

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Figure 10. The fractional importance of different factors: time, coverage, physical action (impact), temperature, and chemical action (chemistry) required for effective tank cleaning by (A) spray ball and (B) rotary jet head (adapted from Tamine 2008).

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Morrison and Thorpe (2002) defined the wetting rate at the mass flow rate (kg/s) required to completely wet a surface of width W (in meter). Wetting rates achieved by single jets from a spray ball were 0.1 to 0.3 kg/ms. The act of removing deposit from a vessel involves initial wetting and subsequent softening (or dissolution) of the deposit, followed by complete removal by further impingement. Morrison and Thorpe (2002) measured the dimensions of the wet area by the impaction of single water jets onto a sheet of painted acrylic for a range of pressures and distances from spray balls of different sized holes. They found that if the jet directly impacted the area to be cleaned, then this area was cleaned within 60 s. The point of impact was smaller than the total area being wetted; however, certain areas were not cleaned by the spray ball. The width of the falling film from the point of impact remained the same size throughout rinsing. Jet breakup was observed at 45 °C, which increased the distribution of the jet and cleaned a larger area. An interesting model for the flow behavior of jets is given by Wilson and others (2012).

The effect of CIP parameters on type 2 and type 3 deposit removal

Type 2 deposits can be viscoelastic, temperature-dependent, and/or time-dependent (Rao 1999). Type 3 deposits tend to be thermally induced and precipitate from the process stream onto the heat exchanger surface over time. For example, wort is a complex fluid with several components that change structure and solubility upon heating, including carbohydrates, proteins, vitamins, minerals, and lipids. The deposits formed during wort boiling are solid and dissimilar to the process stream (Tse and others 2003).

Membrane cleaning

There are many types of filtration processes in food and beverage manufacturing operations. The fouling of membranes alters permeability and selectivity and can be characterized by increased pressure differential and decreased membrane flux. Membranes used in the food and bioprocess industries include reverse osmosis (RO), nanofiltration (NF), ultrafiltration (UF), and microfiltration (MF) (Cui and Muralidhara 2010). In the brewing industry, beer is clarified using MF in which yeast readily fouls the membranes. Membrane cleaning is complex as it is necessary to both remove the surface layers and open the pores in the structure—this must be done without the cleaning agent damaging the material.

Güell and others (1999) found that when yeast cells were present on cellulose acetate membrane (CAM) as a layer (yeast cake), the yeast was believed to have formed a secondary membrane. Increasing the thickness of the yeast cake reduced permeate flux and protein transmission through the membrane. Increasing the yeast concentration in the feed solution resulted in lower fluxes and protein transmission through the CAM. Hughes and Field (2006) discussed the fouling of MF and UF membranes with yeast at subcritical fluxes where fouling is negligible. For the MF membrane, the rate of fouling increased with increasing feed concentration, increasing membrane pore size, and decreasing shear stress. The UF membrane could not be cleaned effectively.

Mores and Davis (2002) examined the effect of pulsing flow through a CAM to clean it. They found that the flux increased with increasing shear rate, back pulse pressure, and back pulse duration. At higher shear rate and back pulse pressure, multiple short back pulses were more effective in cleaning the membrane. At low shear rate and back pulse pressure, fewer longer back pulses were more effective. Longer, weaker back pulses led to the highest recovered fluxes.

Shorrock and Bird (1998) fouled a MF membrane (hydrophilic polyethersulfone, 0.1 μm pore diameter), with yeast cake. Water rinsing was found to remove most of the deposit and an increase in temperature from 30 to 60 °C was found to decrease fouling resistance (at 0.74 m/s cross-flow velocity (CFV)). At 40 °C, using NaOH as optimum concentration, there was optimum flux through the membrane, 0.01% to 0.025%. Formulated sodium hydroxide solution was found to restore membrane flux completely.

Cleaning of MF membranes with WPC was considered by Bird and Bartlett (2002) using a flat plate stainless steel membrane and by Blanpain-Avet and others (2009) using a tubular ceramic membrane. An optimum alkaline detergent concentration of 0.02% NaOH was found to give maximum flux after cleaning of the stainless steel membrane at 50 °C, 1.67 m/s. Increasing the CFV from 1 to 6 m/s decreased fouling resistance of the ceramic membrane and gave the least amount of fouling present on the membrane after 20 min.

Water rinsing of hard surfaces

For the type 3 deposits WPC and egg albumin, Christian (2004) and Aziz (2008) found that neither deposit was removed with water rinsing at the temperatures and flow velocities investigated; 30 to 70 °C and 0.7 to 2.3 L/min. The authors determined that chemical action was required for their removal.

Guillemot and others (2006) rinsed rehydrated Saccharomyces cerevisiae cells from stainless steel in a flow cell over a wall shear stress range of 0 to 80 Pa. They found that as the wall shear stress was increased, the number of cells remaining on the steel decreased. However, only a 10% reduction in the number of yeast cells was achieved in this range of wall stress. Goode and others (2010) rinsed aged yeast slurry from stainless steel coupons using water in a flow cell, and they found that increasing the flow velocity did not significantly affect the amount of deposit removed from the surface at ambient temperature; this was over a wall shear stress range of 0 to 1.24 Pa. Rinsing removed around 50% of the deposit area. Goode and others (2010) also found that increasing the temperature of the water rinse removed more deposit up to 50 °C with flow rate having a negligible effect. However, at 70 °C, decreased removal efficiency was observed, particularly at the highest flow velocity, 0.5 m/s.

The yeast was aged at different temperatures and for different times in the work by Guillemot and others (2006) and Goode and others (2010); 20 and 30 °C and 1 h and 5 d, respectively. The cell concentration was also different at 0.0065 g/mL for Guillemot and others (2006) and 1 g/mL for Goode and others (2010). These findings suggest that fouling conditions dictate cleaning behavior, as already found from milk fouling (Changani and others 1997).

Chemical effects on the cleaning of type 2 deposits

Various authors have investigated the removal behavior of bacterial spores from stainless steel. The effect of shear on adhesion has been studied using devices such as the radial flow cell (Detry and others 2007, 2009) that can, when used correctly, allow ranges of shears to be studied. Le Gentil and others (2010) cleaned Bacillus cereus spores from 316 L stainless steel pipes using 0.5% (w/w) NaOH at 60 °C at 2.2 L/min. The test was carried out over 30 min. As the cleaning time increased, the number of spores decreased as expected. In the first 10 min, up to 70% of the spores were removed, less so in the remaining 20 min. Lelièvre and others (2002) investigated the removal of B. cereus spores from 304 L stainless steel pipes, similar in length and diameter to the pipes used in the study by Le Gentil and others (2010) and 0.5% (w/w) of NaOH was used at 60 °C to rinse the pipe. In this study, the effect of flow velocity and temperature was investigated over a 30 min clean. The researchers found that cleaning at 60 °C removed more spores than rinsing at 20 °C at each 5-min time interval, at the same flow velocity of 1.97 m/s. They found that increasing the flow velocity from 1.61 to 3.29 m/s (τw = 17.45 to 68.95 Pa) at 60 °C decreased the number of attached spores in the first 5 min. However, after this time, the contact time was more important in removing the spores. The increased acceleration at higher flow rates may be controlling the number of spores removed in the first 5 min of cleaning, as found by Jensen and Friis (2004).

Bremer and others (2006) investigated the effect of alkali rinses and acid rinses (formulated and nonformulated) on removing a biofilm generated by recirculating skimmed milk powder in a CIP skid for 18 h in 15 mm stainless steel tubes. There were a number of conclusions:

  1. Rinsing with 1% NaOH (for 10 min, 65 °C, 1.5 m/s) followed by 1% nitric acid (for 10 min, 65 °C, 1.5 m/s) reduced the number of cells to a similar level than that found after rinsing with only NaOH (at the same conditions).
  2. Formulated detergents (with surfactants, chelating agents, and sequestrants) decreased cell numbers to the same level as rinsing with NaOH (at the same conditions).
  3. Addition of a surface-active agent to the caustic solution significantly reduced the number of cells compared to standard CIP (NaOH and nitric acid in (i)).
  4. Nitric acid with surfactants removed significantly more cells than just nitric acid.
  5. Addition of a sanitizer step after CIP did not significantly reduce viable bacteria numbers.

This suggests that the concentration of the alkali, the flow velocity, and the temperature could be optimized to give the most efficient cleaning regime where all cells can be removed.

Goode and others (2010) investigated the effect of chemical on yeast removal from stainless steel coupons in a flow cell using 2% Advantis 210 (1% NaOH equivalent). They found that increasing the temperature from 20 to 70 °C decreased the cleaning time. An increase in flow velocity at 50 and 70 °C from 0.26 to 0.5 m/s also decreased the cleaning time; however, at 20 and 30 °C, an increase in flow velocity from 0.4 to 0.5 m/s did not significantly decrease cleaning time.

Chemical effects on the cleaning of type 3 deposits

The effect of chemical cleaning of WPC from milk has largely been characterized in the literature as uneven. The cleaning process has 3 distinct phases seen by many independent researchers, for example, by Bird (1992), Gillham (1997), Grasshoff (1997), Tuladhar (2001), and Christian (2004):

  1. Swelling—alkali solution contacts the deposit and causes swelling, forming a protein matrix of high void fraction.
  2. Erosion—uniform removal of deposit by shear stress forces and diffusion. There may be a plateau region of constant cleaning rate, but this depends on the balance between swelling and removal.
  3. Decay—the swollen deposit is thin and no longer uniform so that removal of isolated islands occurs by shear stress and mass transport.

Many authors quote 0.5% NaOH to be optimal for WPC removal from stainless steel, although the existence of cleaning optima has not been categorically proved in all cases. Bird and Fryer (1991) found that increasing the NaOH concentration to 2% can produce a deposit with a less open (dissolved) structure than at 0.5%, thus lengthening the swelling phase—Yoo and others (2007) and Saikhwan and others (2010) explained the processes that underpin this observation. Plett (1985) reported that a maximum cleaning rate occurs when cleaning with detergent. The contribution of flow rate is hard to determine in chemical cleaning because both shear stress imposed on the deposit and mass flow to the deposit are dependent on the flow rate. In general, the higher the flow rate, the shorter the cleaning time. Timperley and Smeulders (1988) found that the cleaning time of a PHE decreased with increasing flow velocity from 0.2 to 0.5 m/s. There are arguments supporting higher flow rates that create turbulent conditions. This is because turbulent conditions are known to make the flow patterns of the microscopic boundary layer unstable. However, Bird and Fryer (1991) found that there was no significant change in cleaning rate when moving from laminar to turbulent flow. Disruption of the boundary layer is further discussed in Section 4.1. Generally increasing the temperature decreases the cleaning time. Gillham and others (1999) found that removal of whey protein deposits from stainless steel pipes was strongly dependent on temperature (less so the swelling phase).

SCM is an intermediate in the manufacture of some confectionary products, made by evaporating water from milk and adding sugar to lower the water activity of the product. SCM has 70% to 74% total solids of which 40% to 45% is sucrose (Fisher and Rice 1924) leaving 29% to 30% milk solids. In the study of Othman and others (2010), SCM was cooked for 4 h at 85 to 90 °C on stainless steel coupons and washed by chemical cleaning in a flow cell. It was found that increasing the flow velocity from 0.25 to 0.5 m/s decreased the cleaning time at all temperatures. An increase in temperature from 40 to 80 °C decreased the cleaning time linearly. Interestingly, the authors found that increasing the NaOH concentration from 0.5% to 1.5% did not significantly affect the cleaning time at each temperature. This agrees with findings for WPC cleaning that quote 0.5% NaOH as the optimum concentration. It was the increase in temperature rather than the increase in chemical concentration that decreased cleaning time.

Cleaning time was plotted compared with Re for SCM at 1% NaOH (40, 60, and 80 °C) by Othman and others (2010) and at 0.1%, 0.5%, and 1% NaOH (at 30, 50, and 70 °C) for WPC by Christian (2004) as shown in Figure 11(A) and 11(B). For WPC, the range of investigated Re was around 800 to 4840. There were separate groups of data at each temperature that could not be plotted on one master curve. This suggests that temperature was the dominant parameter in controlling cleaning time. Christian (2004) concluded that an increase in Re was only beneficial to cleaning time at low concentration. Jennings and others (1957) suggested the existence of a threshold Re of 25000 for cleaning a pipe surface of dry milk deposit before an increase in Re resulted in increased cleaning rate. For SCM, the Re range investigated was much higher, from 6500 to 27000. All the data collapsed onto one curve. As the Re increased, the cleaning time decreased, suggesting that Re was the dominant parameter controlling cleaning time. Othman and others (2010) did find, however, that the effect of Re on cleaning time became less significant as the temperature was increased. Gillham and others (1999) found that tc was proportional to Ren, where n was in the range of 0.2 to 0.35 for 0.5% NaOH. For SCM at 1% NaOH, tc was again proportional to Ren where n = −1.28 (R2 = 0.92).

The cleaning of egg albumin was characterized by Aziz (2008). Generally, an increase in temperature decreased the cleaning time. However, at 1% NaOH, cleaning time was faster at 50 °C than at 70 °C. Deposit was not removed at 30 °C at any flow velocity or NaOH concentration investigated. An increase in NaOH concentration from 0.25% to 3% NaOH decreased the cleaning time (at 50 °C, 2.3 L/min); however, increasing the flow rate had a less significant impact on cleaning time at higher chemical concentration. The author concluded a high temperature, mid to high flow velocity, and mid-range chemical concentration appeared to be the optimum, similarly to WPC cleaning optima. For egg albumin, the range of Re investigated was 1090 to 4840. There were separate groups of data at 50 and 70 °C that could not be plotted on one master curve. This suggests that temperature was the dominant parameter in controlling cleaning time similarly to WPC.

In Christian (2004), WPC cleaning experiments were conducted using 0.5% NaOH at 30, 50, and 70 °C and 0.7, 1.5, and 2.3 L/min. Rd (the fouling resistance) is a measure of resistance to the flow of heat to the sensor. Rd measured at the same flow rate reduced Rd more rapidly as the temperature was increased from 30 to 70 °C. An increase in flow rate from 1.5 to 2.3 L/min revealed similar Rd profiles, suggesting that temperature dictated the cleaning time in this case.

For all type 3 deposits detailed here, temperature seems to be the dominant contributor to cleaning time at both low and high flow velocity and low and high concentration. Reaction rate, solubility, and possible phase transitions (such as in fats) will all be affected by temperature.

Novel Cleaning Approaches

  1. Top of page
  2. Abstract
  3. Introduction
  4. Fouling Studies
  5. Cleaning
  6. Novel Cleaning Approaches
  7. Measuring Cleaning
  8. Summary and Conclusion
  9. References

There have been various methods reported in the literature to improve cleaning.

Increasing boundary layer disruption

Various authors have considered pulsing flow in pipes to enhance wall shear stress at lower average flow velocities to enhance cleaning. Gillham and others (2000) showed that pulsing flow at a relatively low frequency (2 Hz) enhanced cleaning of a tubular heat exchanger compared to the same steady-flow velocity. A pulsed flow creates high periodic accelerations of the liquid flow. The directional change in the flow can increase mass transfer of the cleaning fluid to the surface, thus decreasing cleaning times. A pulsed flow is characterized by a stationary base flow on which an oscillating fluid movement, wos, is superimposed, as illustrated in Figure 12.

image

Figure 11. Re compared with visual cleaning time of (A) SCM at 40, 60, and 80 °C using 1% NaOH (from Othman and others 2010) and (B) WPC at 30, 50, and 70 °C, using 0.1%, 0.5%, and 1% NaOH (from Christian 2004). Shading denotes the flow temperature: Black: 30 °C, Gray: 50 °C, and Open: 70 °C.

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image

Figure 12. Stationary flow and oscillating components of flow, wos symbolizes an oscillating fluid movement, wos,max is maximum oscillating fluid velocity, wstat is mean fluid velocity, and ωt is frequency of oscillation (Augustin and others 2010).

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The intensity of a superimposed pulsation (wos,max + wstat) can be quantified using waviness, W, the ratio of the maximum oscillating (wos,max) and the stationary or mean flow velocities, w:

  • display math(4)

w for an oscillation interval is defined by Augustin and others (2010) as:

  • display math(5)

A higher value of waviness is believed to result in a separation of the viscous sublayer and the formation of eddy currents; flow reversal is critical. This effect can decrease the thickness of the laminar sublayer at the pipe surface when applying a turbulent flow. The temporary maximum velocity, wos,max, can occur near the pipe wall resulting in large shear rates and high wall shear stresses. Bode and others (2007) characterized a linear decrease of cleaning time with increasing waviness from 1 to 5. Below a waviness of 1, there is limited flow reversal in the pipe and the cleaning time increases. Augustin and others (2010) compared the cleaning rate of deposit at a flow velocity of 1 m/s (Re 25000), where waviness was 0, and pulsing the flow, where waviness was 1. They found that the amount of deposit in the pipe became asymptotic at 4 min using the pulsed-flow regime and 6 min using the stationary flow regime. Augustin and others (2010) have validated CFD models accompanying their cleaning data. Phosphorescent zinc sulfide crystals were used as an optical tracer enabling accurate characterization of flow patterns.

Alternative cleaners to reduce environmental impact

The efficacy of enzymes in cleaning has been investigated over the last decade. Grasshoff (2002) investigated the efficacy of Savinase, a protease (optimum temperature 50 °C, pH 9.5), in cleaning a milk pasteurizer following a 15-min acid wash. Increasing the concentration of the enzyme from 0.0025% to 0.05% at 60 °C showed that residual soil was removed faster. In the plant, the heat exchanger was opened and inspected after 45 min of enzyme cleaning and 6 min of water rinsing (30 L/min). The interior surfaces were clean. Microbiological product samples collected showed no indication of microbial or enzyme contamination after CIP.

The use of commercial enzymes to clean UF membranes has been discussed by Petrus and others (2008) and Allie and others (2003). Petrus and others (2008) used proteases to clean proteins (BSA and β-Lg) and defined an optimum concentration of 0.1% for 60 min. The enzyme deposited on the membrane when rinsed for longer than 60 min. Allie and others (2003) used proteases and lipases to clean abattoir effluent. Up to 55% of fouling was removed with lipases, and up to 70% by using lipases and proteases and a flux recovery of up to 100%. However, to apply enzyme cleaning in industry enzyme dosage, process control, and the overall economic burden need to be considered.

Orgaz and others (2006) tested the efficacy of fungal enzymes at breaking Pseudomonas fluorescens biofilm bonds. Out of the tested enzymes, a T. viride enzyme (cultured on pectin) was most effective at removing the biofilm by 84% (± 2%). The enzyme was cellulose-pectinesterase-based. The least effective enzyme removed 19% (± 6%) of the biofilm and was mostly cellulose.

Other studies related to cleaning behavior

Various authors have quantified parameters related to cleaning behavior by other methods rather than in a flow cell or pilot plant. Some techniques used to infer cleaning data are discussed here.

Deposit shear

Simões and others (2005) fouled stainless steel cylinders with P. fluorescens biofilm in a bioreactor. Three cylinders were rotated at 500, 1000, 1500, and 2000 min−1 sequentially (ReA 2400 to 16100) for 30 s each in phosphate buffer to assess the effect of rotation speed on biofilm removal. There was an optimum ReA of 8100 where 45% of biofilm was removed. Either side of this ReA removal was less and similar at around 15%. Cylinders were also submerged in different chemical solutions and rotated at 300 min−1 for 30 min and then were rotated at 500, 1000, 1500, and 2000 min−1 sequentially in phosphate buffer to test cleaning effectiveness. NaOH and sodium hypochlorite (NaClO) representing a CIP detergent and CIP sterilant were investigated. Irrespective of ReA, NaOH and NaClO removed a similar percentage of biofilm at the same concentrations (50, 200, and 300 mg/L); however, at the highest concentration, 500 mg/L, NaOH removed approximately 5% more biofilm.

Demilly and others (2006) characterized the removal of yeast cells as a function of wall shear stress from different stainless steel surfaces using a radial flow chamber, in which flow trajectory was from the center of the plate outwards toward the edges of the plate. This is similar to the impingement of a water jet on a surface. Radial flow investigations may relate to the action of a cleaning head in a tank, to provide a microscale method of investigating water jets, and deposit removal. The steel surfaces were micropolished and electrochemically etched with different sizes of grain and depths. The number of cells remaining from the original number of cells was defined. Interestingly, the authors found a threshold shear stress above which cells were removed regardless of topography; that detachment of yeast cells was faster from etched steel than mirror-polished steel.

Deposit deformation and strength

Two methods have been independently developed at Birmingham and Cambridge to determine the strength and deformation behavior of soft-solid fouling layers on hard surfaces immersed in liquid: the micromanipulation technique and the fluid dynamic gauging (FDG) technique, respectively. The micromanipulation technique employs controlled strain parallel to the deposit, where a T-shaped probe is pulled across a horizontal circular plate at a constant height, removing the fouling deposit by a shoveling action. The FDG was developed to measure the thickness of soft-solid meso- and macrolayers on surfaces. The surface is immersed in a Newtonian liquid and a nozzle is brought close to the surface. A suction pressure differential is applied so that liquid is drawn into the nozzle. The gauging suction is increased until deformation is observed. Deformation of the deposit results in changes in h, the distance between the deposit and the gauge tip, which can be readily detected. The 2 techniques showed similar trends and complementary phenomenological detail when removing tomato paste in parallel studies (Hooper and others 2006). As the baking time increased, the adhesive strength increased, and as the hydration time increased, the adhesive strength decreased.

The FDG technique has been used to study the effect of alkaline cleaning chemical concentration (0.3% to 2%), temperature (20 to 50 °C), and velocity (0.03 to 0.3 m/s, Re: 500 to 10000) on whey protein (Tuladhar and others 2002), the effect of drying time on tomato paste (Chew and others 2004), and the effect of temperature and pH on gelatin film swelling (Gorden and others 2010). The micromanipulation technique has been used to study the strength of P. fluorescens biofilm with growth medium velocity (Chen and others 1998), the effect of baking, hydration time, and temperature on tomato paste (Liu and others 2002), and surface energy and cut height on tomato paste (Liu and others 2006). The same study considered the effect of drying/aging and cut height on bread dough, egg albumin, and whey protein. The effect of ovalbumin concentration, temperature, and NaOH concentration on egg albumin adhesion is presented by Liu and others (2007). Tomato paste, bread dough, and egg albumin deposits have been found to have a lower adhesive than cohesive strength, while whey protein was found to have lower cohesive than adhesive strength.

Atomic force microscopy (AFM) has also been used to study deposit–surface interactions. In this technique, a cantilever contacts the deposit and the force of detaching the cantilever is determined. Beech and others (2002) demonstrated the effective use of AFM for studying the adhesion of bacteria to a wide range of surfaces. Bowen and others (2001) used AFM to study yeast cell detachment from hydrophobic and hydrophilic silicate surfaces with and without protein. The authors found that as yeast cells aged, adhesion force changed. Also, cells in the stationary phase adhered most strongly to the surface. Surface contact was demonstrated to be important where after 5 min, adhesion force increased. Elofsson and others (1997) used AFM to study the coverage of β-Lg and WPC to mica sheets at different surface loadings in the range of 0.03 to 3 kg/cm2. The use of AFM enabled the authors to distinguish between different states of protein aggregation at the submicrometer level.

Measuring Cleaning

  1. Top of page
  2. Abstract
  3. Introduction
  4. Fouling Studies
  5. Cleaning
  6. Novel Cleaning Approaches
  7. Measuring Cleaning
  8. Summary and Conclusion
  9. References

Ensuring good performance of the cleaning process is vital in maintaining reliable product shelf life and quality. Figure 1 demonstrates implementation of a CIP standard in industry, revealing that CIP measurement and control are fundamental to ensuring hygiene on a daily basis. Processes used to establish and run CIP operations include:

  1. Validation—determining the right method of cleaning and setting it as a standard; it is done before implementation of a new method and after alterations in an existing operation. It should always be up-to-date.
  2. Verification—are the results correct and accepted? Checks that the system behaves in the predetermined and expected way, after validation.
  3. Monitoring—continuous monitoring of specific points of a process determining that the process is under control, after verification.

When defects in the system have been identified, appropriate action should always be taken as detailed in Figure 1. Typical CIP monitoring tools include visual detection methods, microbial enumeration of CIP rinse water, and in-line probes that measure temperature and the proportion of hydrogen ions (pH) or electrolytes (conductivity) in the cleaning fluid. The conductivity of acid and alkali is different to that of water so that the chemical phases of cleaning can be clearly identified and the chemical recovered. The first step in optimizing CIP is ensuring that the used monitoring techniques are giving reliable data. Yang and others (2008) described the importance of forecasting models in the approach to optimize cleaning. Data obtained at the beginning of CIP (where the confidence limit of variation is high) are used to predict the end point of cleaning (where the confidence limit of variation is low). Online application of such a tool would be valuable in optimizing CIP. A range of measurement devices have been considered in the literature and have been used to assess either the cleaning fluid (bulk measurements) or the surface. Figure 13 illustrates where in a process of line bulk and surface measurements, CIP measurements may be taken. Flow, temperature, conductivity, and pH are bulk measurements.

image

Figure 13. Potential locations of online CIP measurement techniques that could be used in a process line.

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Online bulk measurements

Online bulk measures monitor the fluid during cleaning. Typically temperature, flow, pressure, conductivity, and pH are monitored during CIP to ensure that the process is under control. Pressure drop across a system (∆P) is defined as: ∆P = PIPO, where PI and PO are the inlet and outlet pressures. PO increases with time during fouling and decreases during cleaning generally. The rate of change of pressure can be used as a measure of cleaning. Fryer and others (2006) illustrated that pressure drop initially increased across a PHE during cleaning. When the cleaning chemical came into contact with the deposit, the deposit swelled and further increased the pressure drop through the processing plant before cleaning commenced. This effect has been well characterized in pulsed-flow cleaning by Christian and Fryer (2006). Robbins and others (1999) also used pressure drop across a PHE to characterize the fouling nature of milk and whey protein fouling.

Van Asselt and others (2002) demonstrated online monitoring of a dairy evaporator CIP by conductivity and pH. The chemical and water interfaces were clear. Conductivity can also be used to indicate product–water interfaces in nonchemical cleaning. Fickak and others (2011) measured conductivity during water rinsing (at 0.01 m/s) of a heated rod fouling with heat-induced whey protein gel (HIWPG). The conductivity was seen to increase to 1200 μS/cm in the first 100 to 200 s and decrease thereafter to 200 μS/cm in around 1000 s. The decrease in conductivity was a smooth slope. Fickak and others (2011) also used turbidity at the outlet of the fouled rod to indicate removal behavior during the prerinse. The fouled rod was rinsed until the turbidity measurement was at 0.5 to 1 Nephelometric Turbidity Units (NTU), indicating drinking water quality. The turbidity increased at the onset of the prerinse to approximately 28 NTU in the first 200 s and decreased thereafter. At 1200 s, however, the turbidity value increased from 3 to 7 NTU before decreasing further. The turbidity profile contained numerous jagged peaks that may suggest the deposit removal was nonuniform. The profile is quite different from conductivity. Van Asselt and others (2002) tested an inline calcium probe (CHEMFET) during cleaning of a tubular heat exchanger against offline measurements of calcium (g/L) and pH. During the prerinse, no difference in the online measurement was detected. Offline measurements revealed a decrease in calcium from 0.5 to 0 g/L in approximately 3 min and an increase in pH from approximately 6 to 8 in 6 min. Conductivity that may have been measured during the prerinse was not presented.

Van Asselt and others (2002) also measured turbidity and conductivity online and offline during cleaning of a dairy evaporator. The evaporator was not prerinsed with water. The conductivity revealed, as expected, an increase during chemical phases and a decrease during water phases. Turbidity determined in-line and offline gave different deposit removal profiles. For example, during the second alkaline rinse online turbidity revealed 2 considerable peaks. This was also seen at the onset of acid to a lesser extent. Offline measurements did not reveal these peaks. The offline turbidity samples taken during CIP were analyzed at a later stage which the authors think altered the particle size of the samples, and thus the turbidity measurement. Particles of different sizes scatter light differently, for example, as a function of smaller particle size (Clauberg and Marciniak 2009). However, nonproduct substances (such as air bubbles and detergent) can also absorb light giving misleading data.

CellFacts (Coventry, U.K.) has a patented offline sampling technology which they have demonstrated determines particle size and density in rinse water samples. This was demonstrated in case studies on their website (http://www.cellfacts.com/Cleaning-In-Place.php). The company used this technology to determine particle size and density during CIP of a beer maturation vessel (Scottish & Newcastle Breweries 2009). Malvern Instruments (Malvern, U.K.) have made commercially available technologies that could be used to monitor particle sizes and counts online during CIP of a process line.

An online electrical resistance method has been developed at the Univ. of Auckland to monitor fouling buildup and removal. Using the fouling surface as an electrode and a reference electrode on the opposing wall of the test section, the difference in electrical resistance (RE) across the flow channel can be monitored online (Chen and others 2004). During milk fouling, electrical resistance increased, similar to thermal resistance (Rf) measurement. During cleaning RE decreased rapidly before any changes in Rf were detected. The authors determined that NaOH chemical penetration into the deposit was not the rate-limiting step in cleaning. Winquist and others (2005) described the use on a voltammetric “electronic tongue” that consisted of a series of electrodes implanted in a surface. A measurement series is based on successive voltage pulses of gradually changing amplitude between which the base potential is applied, and the current is continuously measured. Electronic tongues were integrated into a dairy process line and online measurements were taken. A difference in current was observed between the process stream, water, alkali, and acid, although the investigation back to the clean state was not presented. The authors suggested that the “dirtiness” of the solutions could be distinguished by the tongue.

Online surface measurements

Various authors have reported heat transfer measurements during cleaning to assess the effect of cleaning parameters on the heat transfer coefficient and fouling resistance, Rd, including Gillham and others (1999, 2000), Christian (2004), and Aziz (2008). An example of Rd measured during the cleaning of (a) egg albumin and (b) whey protein using 0.5% NaOH at 1.5 L/min is illustrated in Figure 14. Rinsing egg albumin at 30 °C did not clean the surface. Deposit remained on the surface even after 3 h of rinsing.

image

Figure 14. Rd profiles for (A) egg albumin gel (from Aziz 2008) and (B) whey protein (from Christian 2004) with different flow temperatures: 30, 50, and 70 °C using 0.5 wt% NaOH and a flow rate of 1.5 L/min.

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Klahre and others (2000) used differential turbidity to monitor biofouling on the pipe walls of water systems. The technique could be considered to study biofouling removal during cleaning. The use of a Mechatronic Surface Sensor (MSS) was being tested to monitor milk components such as calcium phosphate and whey proteins (Pereira and others 2006) and shampoo (Pereira and others 2009). The sensor measures changes in the vibration properties of surfaces due to the buildup or removal of fouling layers.

Measuring microbial cleanliness

Microbiological enumeration techniques tend to be offline retrospective techniques. Rinse water samples and surface swabs are plated on selective agar and viable microbes will present themselves within 3 to 7 d, depending on the organism. Most informative swab and plate methods include contact agar method where the agar plate is pressed directly onto the surface and microbes enumerated directly (Salo and others 2008). The cleanliness of a surface can be verified more quickly using ATP bioluminescence that indicates the presence (or absence) of microbes that are alive. However, the measure of ATP does not indicate microbe specificity. Microbes have been identified on surfaces using infrared spectroscopy (Fornalik 2008) and Raman spectroscopy (Rösch and others 2003). Visual assessment, image analysis, and mass can all be used to determine the amount of deposit removed (or remaining) on a surface after cleaning; however, these methods are offline.

Janknecht and Melo (2003) published a review discussing online biofilm monitoring measurement techniques, known detection limits, and applicability to practical situations. The techniques discussed that have been investigated in an industrial setting include:

  • Differential turbidimetry in a paper mill (Klahre and others 2000),
  • Light scattering detection (using a fiber optic probe) in a brewery (Tamachkiarow and Flemming 2003),
  • Biofilm respiration measurement in a bed reactor (Carrión and others 2003).

Goode and others (2010) were able to monitor yeast cleaning from stainless steel coupons using a heat flux sensor and the image analysis technique. Less commercially applied techniques discussed by Janknecht and Melo (2003) include measuring radiation signals (spectroscopy, fluorometry, and photoacoustic spectroscopy) and electric and mechanical (vibration) signals.

The critical part of optimizing a cleaning process would be the incorporation of cleaning measurements into the process schedule. At present, it is usual that cleaning times are set automatically and are not changed in operation. If measurements made at the start of cleaning could be used to determine the end point of cleaning with high confidence, it would be possible to develop some form of a process control strategy that could minimize the cleaning cost (Yang and others 2008). This approach can be successful if measurements are robust, inexpensive, and taken at the dirtiest point within a system.

Summary and Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Fouling Studies
  5. Cleaning
  6. Novel Cleaning Approaches
  7. Measuring Cleaning
  8. Summary and Conclusion
  9. References

Published information describing the adhesion of fouling materials and microorganisms to a range of surfaces used throughout the food and beverage industries has been presented and discussed. Cleaning operations such as CIP are ubiquitously applied to remove unwanted fouling layers in a processing plant to maintain product safety and process efficiency. However, cost benefit analysis of CIP is not often done; as a result, the route of optimization is unclear. The grouping of deposits into 3 types has enabled a clear presentation of recent studies that have investigated the effect of CIP parameters. This has enabled parallels in the literature to be drawn.

  1. For type 1 deposits, cleaning time seems to be related to Re. An increase in Re seems to decrease cleaning time according to the power law model. It was also seen that increasing the flow rate or wall shear stress and cleaning temperature to a mid-range temperature (up to 50 °C) decreases cleaning time.
  2. For type 2 deposits, water rinsing parameters, temperature, and wall shear stress seemed to have varied effects on removal. Removal behavior seemed to be dependent on the microbial aging time on the surface. NaOH solution removed type 2 deposits in flowing systems. When considering one chemical concentration, flow and temperature were seen to have the biggest effect at the start of cleaning, but it was clear that contact time was an important factor as cleaning progressed.
  3. For type 3 deposits, specifically protein, an optimum NaOH concentration has been found to occur in numerous studies where excessive chemical material causes formation of a deposit difficult to remove. However, increasing wall shear stress and temperature were most beneficial to cleaning.

The findings suggest that optimizing the flow characteristics at a given temperature and concentration is crucial to achieving fast cleaning in all soil cases. This parameter should be optimized in CIP before temperature and or chemical concentration is increased.

Novel surface coatings for stainless steel and alternative chemicals for cleaning are being actively researched at an academic scale. However, application of these findings has not been adopted in industry. The factors affecting the application of research in industry include cost, maintenance, product safety, product quality, and process reliability. The longevity of surface coatings and the traceability of enzymes out of a test system have not been fully demonstrated. However, research in this area is so extensive that it is probably only a matter of time before effective solutions are found. The pulsing of flow to achieve higher wall shear stress within a system looks like a promising route to improve cleanability of process lines. This could be achieved at very low cost because the required equipment is already used in CIP. However, longevity of pumps as a result of pulse cleaning has not been fully determined. In the future, minimizing the water load and environmental impact of cleaning will only become more important. Current issues surrounding novel approaches to cleaning will need to be overcome for application in industrial CIP that will become more important in the future as water becomes less available and/or more expensive.

There are a number of methods at various stages of commercialization for monitoring bulk cleaning and cleaning at the surface. Any probe use to monitor cleaning needs to be robust and of low cost because cleaning cost is believed to be a relatively low cost of the total cost of production. The robustness and applicability of such novel measurement systems again need to be determined in an industrial setting.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Fouling Studies
  5. Cleaning
  6. Novel Cleaning Approaches
  7. Measuring Cleaning
  8. Summary and Conclusion
  9. References
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