Stochastic and global real time optimization of Tennessee Eastman challenge problem

Abstract

A stochastic real time optimization (SRTO) which has an efficient result has been implemented on the Tennessee Eastman (TE) challenging problem. In this article a novel stochastic optimization method, the so-called heuristic random optimization (HRO) proposed by Li & Rhinehart is used which attempts to rationally combine features of both deterministic and random (stochastic) methods. Further, an on-line nonlinear identifier via extended Kalman filter (EKF) is used to supply the plant model for model-based optimization algorithm. Using the information obtained from EKF an on-line HRO is accomplished by a random search method whose search directions and steps are considerably reduced by some heuristic rules. In order to compare and prove the performance of HRO method, the problem was solved again via sequential quadratic programming (SQP) which is the most efficient algorithms among the deterministic methods. The optimizer initiates every 8 h and determines the optimal set points of the PI controllers in the plant. The calculations are completed in about 15 s by HRO method. Simulations have been done using an Intel P4 2.8 GHz, and 256 MB of RAM.