This paper presents an interdisciplinary study combining computer science and physicochemical analysis methods for catalytic complexes based on clinoptilolite (Cl) modified with divalent cations (Co²⁺, Cu²⁺, Cr²⁺, Mo²⁺). The primary objective of the study is to develop a digital approach to interpreting and predicting the properties of catalysts used in methanol conversion processes. Natural clinoptilolite from the Aydag deposit (Azerbaijan), enriched in a zeolite phase by more than 90 wt%, was used as the starting material. To study the influence of the cation nature on the activity and selectivity of the catalysts, ion-exchange modifications, catalytic testing, and subsequent processing of the experimental data using digital analysis tools were performed. An analysis of experimental results, including conversion (X), selectivity (S), and yields of the main products (DME, DMM, CO, CO₂), was performed using correlation and visual analysis methods in Microsoft Excel and Python. The use of a Pearson correlation matrix and heat maps allowed us to identify quantitative relationships between the catalyst system parameters and determine the dominant influence of acidity and cation nature on methyl ester selectivity. The results showed that catalysts modified with copper ions (Cu²⁺) exhibit the best balance of activity and selectivity, due to the optimal distribution of active sites and a high degree of dispersion. Cobalt-based catalysts (Co²⁺) exhibit stable but less pronounced characteristics, while systems containing chromium and molybdenum exhibit reduced activity. Thus, the use of digital data processing methods not only improves the accuracy of experimental data interpretation but also forms the basis for intelligent modeling of next-generation catalysts. The proposed methodology integrates experimental chemistry and computer analysis, opening up prospects for developing "digital catalysis" as an independent field of materials science and chemical engineering focused on accelerating the development and optimization of catalysts with predictable properties.

doi.org/10.32737/0005-2531-2026-1-111-118