On-line optimization of the Tennessee Eastman challenge problem

Abstract

Abstract On-line, optimal steady state operating conditions were determined for the six modes of operation (with and without disturbances) for the industrial recycle reactor problem proposed by Downs and Vogel (J.J. Downs, E.F. Vogel, A plant-wide industrial process control problem, Computers and Chemical Engineering 17 (1993) 245–255.). The optimization and modeling problem was formulated as a sequential solution of steady state material balances through the process. Optimal operating setpoints were obtained using NPSOL (P.E. Gill et al., User's Guide for NPSOL: A FORTRAN Package for Nonlinear Programming, Technical Report SOL86-2, Stanford University, CA, 1986.) and reactor feed material balances as equality, non-linear constraints. On-line optimization results, without disturbances, compared favorably with the results obtained by Ricker (N.L. Ricker, Optimal steady-state operation of the Tennessee Eastman challenge process, Computers and Chemical Engineering 19 (1995) 949–959.). The optimization objective function tended to be broad and flat around the optimal operating conditions for all six modes of operation. The on-line, steady state, optimization algorithm compared favorably with the more complicated optimization structure designed by Yan (M. Yan, N. L. Ricker, On-line optimization of the Tennessee Eastman challenge process, in: Proceedings of the 1997 American Control Conference.). However, the algorithm presented here required less computational effort and exhibited greater convergence reliability than the work of Yan. On-line optimization was performed every 8 h and required less than 5 min calculation time. Updated model parameters were calculated every minute and filtered using a first order filter with 15 min time constant. Net profit was introduced as a tool to compare economic performance of the plant operating under a knowledgeable operator and operating under an off-line/on-line optimization algorithm for all six modes of operation. For modes 1–3, operating at the setpoints generated by the optimization algorithm provided significant increases in production rate and net profit that amounted to a 16–45% decrease in product operating costs when compared to operation of the plant at the setpoints specified by an operator. By decreasing operating costs and increasing production rate while maintaining a specified G/H ratio, the optimization algorithm increased net profit by 9.3–0.5% for modes 4–6, respectively, when compared to knowledgeable operator optimization of the plant. Also for sustained disturbances, the optimization algorithm decreased the error in the desired G/H ratio and increased process stability when compared to knowledgeable operator optimization of the plant. On-line optimization provided a maximum 1% relative increase in production rate and 1.5% relative increase in net profit compared to off-line optimization for modes 4–6 only when certain sustained, disturbances occurred. The economic justification of on-line optimization over off-line optimization depends upon the type, magnitude, and frequency of the disturbances.