Somnath Nandi

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

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.

Hydrodynamics of Pilot Plant Scale Airlift Reactor in Presence of Alcohols

Abstract The overall gas hold-up of a pilot plant scale internal loop airlift reactor was studied in presence of different alcohols in varied concentration. It has been observed that these simple alcohols can enhance overall gas hold-up of the airlift assembly possibly due to formation of protective thin layer over smaller gas bubbles thereby reducing chance of their coalescence. As the alcohols can be used as food source for the micro organisms present in the system, this green and environment friendly process have potential to replace usage of hazardous surfactants often used for enhancing overall hold-up in order to obtain desired mass transfer characteristics. An empirical relationship encompassing overall gas hold-up of the reactor as a function of superficial gas velocity and alcohol concentration is also developed.