Nieng Yordan Lee Gaitan

Python and Machine Learning for Sustainable Thermochemical Optimization

Chemical engineering still relies heavily on costly, slow experimental trials to evaluate operating conditions in thermochemical processes. This talk proposes a practical approach based on Python and machine learning to accelerate that process: building predictive models from physicochemical data that estimate key outcomes without testing every scenario in the lab. A complete flow oriented toward real applications will be shown, from data to decisions, with the goal of reducing analysis time, lowering experimental costs, and supporting process optimization with environmental impact.