The demand for high-quality sulfur that is used in medicine, chemistry, and other industries is growing. The technological processes for extracting sulfur from harmful acid gases in oil refining are characterized by complex, nonlinear, and fuzzy relationships between input and output parameters, complicating the development of their models. Therefore, solving the problems of modeling and optimizing sulfur production processes under uncertainty, as they occur in sulfur recovery units (SRUs), is a highly relevant scientific and practical task. To address these issues, we propose a method for synthesizing a neuro-fuzzy model for assessing the integrated quality and volume of sulfur, enabling the development of a highly adequate model under fuzzy conditions. The developed hybrid model, based on the proposed method, is trained on historical data and adapts its fuzzy rules, enabling the modeling of complex nonlinear, fuzzy relationships between the input and output parameters of sulfur production processes. An ANFIS architecture for a neuro-fuzzy model for assessing the quality and volume of sulfur from the reactor outlet of the Atyrau refinery SRU was developed. A fuzzy Pareto optimization method was proposed, which, based on the developed neuro-fuzzy model, enables vector optimization of sulfur production processes, taking into account the constraints, and determines a Pareto-optimal solution in a fuzzy environment. The best solution selected by the decision-maker from the Pareto set, depending on the current situation, ensures a balance between the sulfur volume and its integrated quality. As a result of multi-criteria optimization of sulfur production processes at the Atyrau refinery SRU based on the proposed methods, the volume of high-quality sulfur increased by 7.39%, hydrogen by 10.71%, and energy consumption decreased by 80 kW/h, demonstrating the effectiveness of the proposed methods.
Orazbayev et al. (Thu,) studied this question.