New Dynamic Response Surface Methodology for Modeling Nonlinear Processes over Semi-infinite Time Horizons

Abstract

We follow up on the recently proposed Dynamic Response Surface Methodology (DRSM) [Klebanov and Georgakis Ind. Eng. Chem. Res. 2016, 55(14), 4022] as an effective data-driven approach for modeling time-varying outputs of batch processes with finite time durations. The present new DRSM methodology, DRSM-2, is capable of accurately modeling nonlinear continuous processes over both finite and semi-infinite time horizons as easily and accurately as modeling batch processes, as DRSM-1 did in the initial publication, cited above. The key innovation here is the introduction of an exponential transformation of time, converting the semi-infinite time duration into a finite one. We also propose a systematic model selection procedure to determine the optimal values of the decision parameters affecting the accuracy of the obtained DRSM-2 model. We demonstrate the power of the DRSM-2 approach in two representative processes, a continuous propylene polymerization and a semibatch penicillin fermentation. It is shown that the obtained data-driven models accurately represent the time-varying process outputs in both of the examined, and quite different, case studies.