A simple technique for the optimization of lay-out and location for chemical plant safety

Authors

  • P. F. Nolan,

    1. Department of Chemical Engineering, Polytechnic of the South Bank, Borough Road, London SE1 0AA, England
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    • Philip F. Nolan obtained his B.Sc. in chemical engineering from the University of Surrey and his Ph.D. in chemical engineering from the University of Sheffield, England. He has always held a strong interest in industrial safety and loss prevention and in 1977 started an ever expanding research group at the South Bank Polytechnic, London to investigate fires, explosions and toxic releases.

  • C. W. J. Bradley

    1. Department of Chemical Engineering, Polytechnic of the South Bank, Borough Road, London SE1 0AA, England
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    • Charles W.J. Bradley obtained his B.Sc. in chemical engineering from South Bank Polytechnic, London, and his Ph.D. on chemical plant layout at the same college under the supervision of Dr. Nolan. He has subsequently worked on fire product dispersion with the South Bank group and atmospheric pollution with the Greater London Council and is currently employed on hazard assessment with a firm of consulting engineers.


Abstract

A computer program to optimize the use of land and ensure safe operation of plant. A logical process flow is maintained, while the position of plant items is optimized using a hill-climbing technique.

A number of quantitative techniques exist for the measurement of hazard values for comparison of process routes in chemical plant design and operation. Such numerical values can also be used as a basis in lay-out considerations. There is a need for a simple technique, which allows for the optimization of separation distances between plant by determining the minimum requirement for safe operation. This requirement determines the cost of land and that of piping and control lines. It is then possible to carry out economic comparisons between using separation distances and the installation of mobile and fixed prevention and protection systems. An optimization technique based on a modified hill-climbing method is described.

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