Implementation of a Neural Network Modeling Module in a Simulation Software Package, 2000 Engineering Conference Proceedings

Luc Laperrière, Larry Wasik--Simulation of pulp and paper processes has become a very valuable tool to the pulp and paper industries. Whether used for predictive analysis of the consequences of process configuration changes or for process optimization by virtually changing combinations of process parameter settings, or simply for gaining better understanding of an existing process, simulation is now one of the most powerful tools for these purposes. But what can we simulate exactly? Most commercial simulators are limited to the static treatment of a subset of process variables that explicitly appear in mass and energy balance equations. Other more versatile software will also handle the time dimension (dynamic simulation) in their balance equations, providing dynamic changes of the balanced variables. Numerical methods are then used for solving the resulting differential equations pertaining to certain types of process modules such as tanks and controllers. However, an important category of process variables often used as qualitative metrics in process performance evaluation and optimization are usually absent from these models, due to the fact that they do not lend themselves to treatment in the typical mass and energy balance equations of most simulators. This article presents a potential solution to this problem and describes the development of 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. One valuable application of this technology would be the prediction of paper strength and quality parameters. An example simulation that uses the newly developed module is presented and discussed.