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Implementing optimization with process simulation.

Application: Mills can benefit from incorporating this type of optimizer into commercial process simulation models to help achieve specific target goals, such as improved product quality and lower production costs.

For the past few years, the pulp and paper industry has faced the difficult challenge of doing better with less. Commercial simulation has turned out to be a valuable tool for this purpose. Nevertheless, doing better means optimizing the process, which in turn means finding combinations of process parameter values that yield optimum measures of process performance with the least sensitivity to process disturbances.

In the context of most current commercial simulators, this view of optimization presents two important problems. First, the category of process variables often used as qualitative metrics in process performance evaluation and optimization are usually absent from commercial simulator models, i.e. paper strength or pulp color do not lend themselves to mass and energy balances. Second, operators often perform optimizations manually by "playing" with the simulator, performing trial and error combinations of variables. That is because most simulators do not make use of mathematically robust optimization procedures that search the optimal combination of process variables automatically and systematically.

In a previous article (abstracted in the October 2001 Solutions!, Vol. 84, No. 10, p. 59) we presented a potential solution to the first problem by developing and implementing a neural network-based module that can be independent of the classic heat and material balance of process variables upon which the model is based. In this paper, we tackle the second problem of optimizing some important process quality metrics appearing in the new models by systematically searching the space of process parameter values that yield their maximum or minimum. We used a simulated annealing version of the well-known simplex method for this purpose. In the paper, we present and discuss an example simulation that uses the newly developed module.

Laperriere is with Universite du Quebec a Trois-Rivieres, Pulp and Paper Research Center, Trois-Rivieres, Quebec, Canada; Wasik is with Aurel Systems Inc., Burbaby, British Columbia; Email Laperriere at, or Wasik at
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Title Annotation:Process Optimization: summary of peer-reviewed material.
Author:Wasik, Larry
Publication:Solutions - for People, Processes and Paper
Date:Jun 1, 2002
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