Optimization of Industrial Chemical Processes Using Machine Learning Algorithms: Case Studies in Catalytic Reactions
Keywords:
Machine learning, chemical processes, catalysis, artificial intelligence, industrial optimizationAbstract
This literature review aims to analyze the use of machine learning (ML) algorithms in the optimization of industrial chemical processes, with a particular focus on applications in catalytic reactions. The digital transformation of the chemical industry in Latin America demands innovative tools to improve efficiency, selectivity, and sustainability in production systems. In this context, ML offers a flexible and powerful approach to modeling and predicting complex behaviors under variable conditions. Through the analysis of scientific literature published up to 2024, several Latin American studies were identified that apply models such as neural networks, support vector machines, and random forests to optimize key variables in catalytic processes. The findings show significant improvements in reactant conversion, waste reduction, and energy consumption. However, challenges remain, including limited access to high-quality data, weak collaboration with industry, and a lack of interpretable models that facilitate implementation. The review concludes that ML use in catalytic industrial processes is a strategic opportunity for sustainable development in the region. Strengthening academic-industrial collaboration, promoting technical training in data science for chemistry, and implementing pilot programs in real environments are recommended as essential steps forward
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Copyright (c) 2025 Roger Audes Baltazar Flores, Manuel Enrique Neciosup Prieto (Autor/a)
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