Chapter 5. Statistical Models in Chemical Engineering

  1. Prof. Dr. Ing. Tanase G. Dobre1 and
  2. Dr. José G. Sanchez Marcano2

Published Online: 10 MAY 2007

DOI: 10.1002/9783527611096.ch5

Chemical Engineering: Modelling, Simulation and Similitude

Chemical Engineering: Modelling, Simulation and Similitude

How to Cite

Dobre, T. G. and Sanchez Marcano, J. G. (2007) Statistical Models in Chemical Engineering, in Chemical Engineering: Modelling, Simulation and Similitude, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527611096.ch5

Author Information

  1. 1

    Politechnic University of Bucharest, Chemical Engineering Department, Polizu 1-3, 78126 Bucharest, Sector 1, Romania

  2. 2

    Institut Européen des Membranes, I. E. M., UMII, cc 0047, 2, place Bataillon, 34095 Montpellier, France

Publication History

  1. Published Online: 10 MAY 2007
  2. Published Print: 11 MAY 2007

ISBN Information

Print ISBN: 9783527306077

Online ISBN: 9783527611096



  • chemical engineering;
  • statistical models;
  • statistical selection;
  • correlation analysis;
  • regression analysis;
  • experimental design methods;
  • analysis of variances;
  • interaction of factors;
  • neural net computing statistical modelling


This chapter contains sections titled:

  • Basic Statistical Modelling

  • Characteristics of the Statistical Selection

    • The Distribution of Frequently Used Random Variables

    • Intervals and Limits of Confidence

      • A Particular Application of the Confidence Interval to a Mean Value

      • An Actual Example of the Calculation of the Confidence Interval for the Variance

    • Statistical Hypotheses and Their Checking

  • Correlation Analysis

  • Regression Analysis

    • Linear Regression

      • Application to the Relationship between the Reactant Conversion and the Input Concentration for a CSR

    • Parabolic Regression

    • Transcendental Regression

    • Multiple Linear Regression

      • Multiple Linear Regressions in Matrix Forms

    • Multiple Regression with Monomial Functions

  • Experimental Design Methods

    • Experimental Design with Two Levels (2k Plan)

    • Two-level Experiment Plan with Fractionary Reply

    • Investigation of the Great Curvature Domain of the Response Surface: Sequential Experimental Planning

    • Second Order Orthogonal Plan

      • Second Order Orthogonal Plan, Example of the Nitration of an Aromatic Hydrocarbon

    • Second Order Complete Plan

    • Use of Simplex Regular Plan for Experimental Research

      • SRP Investigation of a Liquid–Solid Extraction in Batch

    • On-line Process Analysis by the EVOP Method

      • EVOP Analysis of an Organic Synthesis

      • Some Supplementary Observations

  • Analysis of Variances and Interaction of Factors

    • Analysis of the Variances for a Monofactor Process

    • Analysis of the Variances for Two Factors Processes

    • Interactions Between the Factors of a Process

      • Interaction Analysis for a CFE 2n Plan

      • Interaction Analysis Using a High Level Factorial Plan

      • Analysis of the Effects of Systematic Influences

  • Use of Neural Net Computing Statistical Modelling

    • Short Review of Artificial Neural Networks

    • Structure and Threshold Functions for Neural Networks

    • Back-propagation Algorithm

    • Application of ANNs in Chemical Engineering

  • References