Applying decision trees to investigate the operating regimes of a production process

Abstract

Nowadays beside the improvement of the overall process performance, the maintenance of the safe operation conditions is the key element in the development of process control systems. To improve the quality of products, to reduce energy and materials waste, and to increase the flexibility of production the process operators require more insight in the behavior of the process. While the optimal operating conditions of production processes are getting closer and closer to the physical constrains, more and more important is the development of knowledge based expert systems for supporting the operators to keep the operation conditions in this narrow range. Next to this requirement an expert system has to be able to detect failures, discover the sources of failures and forecast the false operations to prevent from the development of production breakdowns. The aim of this work is to propose a novel approach based on process models and decision tree induction technique to discover and isolate the operating regimes of dynamic processes. The novelty of this approach is the application of a classical machine learning tool (decision tree induction) for the extraction of the hidden knowledge of process models into easily interpretable rule base that describes the operation regions of the process. In order to emphasize applicability of decision trees in extracting the relevant information from the model of a technology and how the rules represent operating regimes a detailed case study was performed based on a sophisticated model of an industrial heterocatalytic reactor.