Biodegradable iron chelate for H₂S abatement: modeling and optimization using artificial intelligence strategies

Abstract

A batch reactor process for the abatement of a common pollutant, namely, H₂S using Fe³⁺-malic acid chelate (Fe³⁺-MA) catalyst has been developed. Further, process modeling and optimization was conducted in the three stages with a view to maximize the H₂S conversion: (i) sensitivity analysis of process inputs was performed to select the most influential process operating variables and parameters, (ii) an artificial neural network (ANN)-based data-driven process model was developed using the influential process variables and parameters as model inputs, and H₂S conversion (%) as the model output, and (iii) the input space of the ANN model was optimized using the artificial immune systems (AIS) formalism. The AIS is a recently proposed stochastic nonlinear search and optimization method based on the human biological immune system and has been introduced in this study for chemical process optimization. The AIS-based optimum process conditions have been compared with those obtained using the genetic algorithms (GA) formalism. The AIS-optimized process conditions leading to high (≈97%) H₂S conversion, were tested experimentally and the results obtained thereby show an excellent match with the AIS-maximized H₂S conversion. It was also observed that the AIS required lesser number of generations and function evaluations to reach the convergence when compared with the GA.