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 firstname.lastname@example.org, or Wasik at email@example.com