Printer Friendly
The Free Library
22,741,889 articles and books

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 Years, The

the seven decades of Eleanor Pargiter’s life. [Br. Lit.: Benét, 1109]

See : Time
 pulp and paper industry The global pulp and paper industry is dominated by North American (United States, Canada), northern European (Finland, Sweden) and East Asian countries (such as Japan). Australasia and Latin America also have significant pulp and paper industries.  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 Performance evaluation

The assessment of a manager's results, which involves, first, determining whether the money manager added value by outperforming the established benchmark (performance measurement) and, second, determining how the money manager achieved the calculated return
 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 In mathematics, robust optimization is an approach in optimization to deal with uncertainty. It is similar to the recourse model of stochastic programming, in that some of the parameters are random variables, except that feasibility for all possible realizations (called scenarios)  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 simulated annealing - A technique which can be applied to any minimisation or learning process based on successive update steps (either random or deterministic) where the update step length is proportional to an arbitrarily set parameter which can play the role of a temperature.  version of the well-known simplex method simplex method

Standard technique in linear programming for solving an optimization problem, typically one involving a function and several constraints expressed as inequalities.
 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 British Columbia, province (2001 pop. 3,907,738), 366,255 sq mi (948,600 sq km), including 6,976 sq mi (18,068 sq km) of water surface, W Canada. Geography
; Email Laperriere at, or Wasik at
COPYRIGHT 2002 Paper Industry Management Association
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2002, Gale Group. All rights reserved. Gale Group is a Thomson Corporation Company.

 Reader Opinion




Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Process Optimization: summary of peer-reviewed material.
Author:Wasik, Larry
Publication:Solutions - for People, Processes and Paper
Date:Jun 1, 2002
Previous Article:Dynamic modeling of potassium and chloride in the recovery area.
Next Article:Effect of cooking temperature on kraft pulping of hardwood.

Related Articles
Panel saw optimization: minimizes waste, maximizes yield.
Eliminating Modeling 'Trial & Error' with Casting Process Optimization.
"Intellligent" Business Process Solutions. (Tools).
CAE opens new frontiers in casting design: applying cutting-edge, integrated computer-aided engineering (CAE) technologies to optimize casting...
Modeling and simulation of pulp and paper quality characteristics using neural networks.
Invensys' SimSci-Esscor announces upgrade to next-generation dynamic process simulation solution.
MAGMA Foundry Technologies Inc.
Latest in 3D simulation highlighted at Moldflow's international user conference.
Simulation spots trouble before it starts.
Simulation training: the power of continuous performance optimization.

Terms of use | Copyright © 2014 Farlex, Inc. | Feedback | For webmasters