Evaluation of the efficiency of methane pyrolysis technology within a decision support system

This paper presents the development of a concept and operating algorithm for a decision support system (DSS) designed for comprehensive analysis and identification of directions for improving the methane pyrolysis process. The authors propose a modular DSS architecture whose key element is a multifactor-analysis module based on the super-efficiency Data Envelopment Analysis (DEA) model. This module evaluates and ranks various technological operating modes of methane pyrolysis. Applying DEA models enables the identification of efficient operating modes and, crucially, the pinpointing of those with the greatest potential for practical deployment owing to their technological robustness. The study analyzes the influence of technological parameters on methane-conversion degree and hydrogen productivity in the pyrolysis process, and demonstrates how the proposed approach can rank efficient modes and generate well-grounded recommendations for their further optimization. The developed DSS can serve as an effective tool for enhancing the technological and economic efficiency of methane pyrolysis, facilitating its industrial adoption and strengthening its competitiveness as an environmentally oriented hydrogen-production method. The results contribute to the advancement of methodologies for managing complex technological systems.