Optimal Sensor Location for Chemical Process Accounting the Best Control Configuration

DAVID ZUMOFFEN; MARTA BASUALDO

Computer Aided Chemical Engineering

Elsevier

Año: 2009; p. 1593 - 1598

In this work a new methodology for solving simultaneously the problems of optimal sensor location and control structure selection for large scale chemical processes is presented. Here, it is considered the need of guaranteing the best plant-wide control structure before answering about which is the best sensor net able for achieving that objective. In this work it is demonstrated the importance of answer both questions as an integrated problem because of the strong impact in the initial investment and the future controlled process performance. Most of the previous works in this area analyze these problems as separated subjects. Here, genetic algorithms (GA) are used because they represent a valuable tool for support the decisions about the sensor placement, possible pairing of input output variables among a great number of combinations, since the interaction effect point of view. It allows to avoid the expert knowledge as decision criteria for pairing selection. The preliminary study is done on a simplified plant model obtained by subspace identification techniques (4sid). The final testing is performed on the rigorous dynamic model with the obtained plant-wide structure where the controllers tuning is performed through the internal model control (IMC) theory. The well-known case of the Tennessee Eastman (TE) benchmark is adopted for testing the methodology described here and compared with other strategies.