A new approach to real time optimization of the Tennessee Eastman challenge problem

On-line optimization for the base case of the Tennessee Eastman (TE) challenge problem is presented; furthermore, an interesting operating condition near base case has been obtained, which results in a lower cost function. The proposed method is based on the estimation of the internal states and the time varying parameters of the process model based on an Extended Kalman filter. The sequential quadratic programming method has been used to accomplish the non-linear programming (NLP) task. The objective function is the operational cost while the constraints are the reactor mass balance, safe operation of the process equipment, and the conditions that satisfy the product quality and flow. The optimizer is triggered every 8 h, and determines an optimal set of process operating conditions. Note, the calculations are completed in some 5-15 s by an Intell PIII 800 MHz with 256 MB of RAM. The study shows that the proposed algorithm outperforms the alternative algorithms developed by other researchers both in speed and results.