Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst

Abstract

This paper presents a comparative study of two artificial intelligence based hybrid process modeling and optimization strategies, namely ANN-GA and SVR-GA, for modeling and optimization of benzene isopropylation on Hbeta catalytic process. In the ANN-GA approach [Ind. Eng. Chem. Res. 41 (2002) 2159], an artificial neural network model is constructed for correlating process data comprising values of operating and output variables. Next, model inputs describing process operating variables are optimized using genetic algorithms (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient-based deterministic optimization algorithms. In the second hybrid methodology, a novel machine learning formalism, namely support vector regression (SVR), has been utilized for developing process models and the input space of these models is optimized again using GAs. The SVR-GA is a new strategy for chemical process modeling and optimization. The major advantage of the two hybrid strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, rate constants, etc.) is not required. Using ANN-GA and SVR-GA strategies, a number of sets of optimized operating conditions leading to maximized yield and selectivity of the benzene isopropylation reaction product, namely cumene, were obtained. The optimized solutions when verified experimentally resulted in a significant improvement in the cumene yield and selectivity.