Implementing optimization with process simulation

For the past few years, the pulp and paper industry has faced the difficult challenge of doing more with less, and commercial simulation has become a valuable tool for this purpose. Doing "more" often 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, optimization is often performed manually by using combinations of trial-error variables. Most simulators do not make use of mathematically robust optimization procedures that search for the optimal combination of process variables automatically and systematically. In a previous article, we presented a potential solution to the qualitative metrics problem by developing and implementing a neural network-based module that can perform independently of the classic heat and material balance of process variables [5]. In this paper, we tackle the problem of optimizing some important process quality metrics appearing in new models by systematically searching the "space" of process parameter values that yield their maximum or minimum. We used a simulated annealing process, described in this paper, which demonstrates that well-known simplex method for this purpose. We also include and discuss an example simulation that uses a newly developed optimization module. Application: Mills can benefit from incorporating this type of optimizer into process simulation models to help achieve specific target goals, such as improved product quality and lower production costs.