A Systematic Approach for the Design of Optimal Monitoring Systems for Large Scale Processes

Abstract

In this work a new concept for designing an efficient monitoring system for large scale chemical plants is presented. It is considered that the monitoring problem must be solved integrated with the optimal sensor location together with the plant-wide control structure design. The solution of these problems involves deciding among a great number of possible combinations between the input-output variables. It is done supported by the application of genetic algorithm (GA). The key new idea is to propose an adequate objective function, within the GA, that takes into account a fault detectability index based on combined statistics. Additionally, by using a specific penalty function, it is possible to drive the search to the less expensive structure, that is by using the lowest number of sensors. The well-known benchmark case of the Tennessee Eastman plant (TE) is chosen for testing this methodology and for discussion purposes. Since several authors have studied the TE case, the results obtained here can be rigorously compared with those already published. All of the previous works considered that every TE output variables were available for the abnormal events detection for designing the monitoring system.