A Hybrid Entropy TOPSIS-based Reaction Engineering Framework for Multi-Criteria Decision-Making in Chemical and Process Systems

Multi-criteria decision-making (MCDM) is becoming necessary in chemical and process engineering because of the ingrained trade-offs among economic, environmental conditions, and objectives of performance. This article proposes a novel hybrid approach combining a weighting entropy, conceptual reaction engineering, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ranking approach for improving the accuracy of decisions in system-based complex chemical. Different from traditional frameworks, the proposed framework is used to embed physicochemical restrictions straight into the decision matrix, corroborating genuine and process-harmonious evaluation conditions. The proposed model is used to be validated utilizing a simulated reactor selection issue, including transformation efficiency, selectivity, consumption of energy, and emissions. The key results illustrate that combining the equations of thermodynamics and the kinetic with MCDM importantly enhances ranking strength and stability-based sensitivity. It confirms that the hybrid approach minimizes the variance of ranking by 18% compared to the conventional entropy-TOPSIS approaches. In addition, the modeled algorithm allows adaptive weighting over dynamic process environments, specifying higher-ranking flexibility for commercial applications. The contributions of this work involve a structured decision framework that spans optimization, reaction engineering, and theory-based decision-making. It offers a scalable solution for applications like reactor models, selection of energy systems, and potential process optimization.