Chemische processen versterken met Artificiële Intelligentie: Auto-encoders, zwermintelligentie en Reinforcement Learning voor modellering en optimalisatie van complexe reacties

Artificial Intelligence (AI) is now applied successfully to a number of disciplines including chemical engineering. With the help of AI, it is possible to predict an optimal chemical process unit as well as to obtain the optimal factors of the chemical process unit based on the existing data sources. This thesis uses AI methods, such as artificial neural networks to predict the optimal conditions of chemical processes and to give accurate parametric suggestions. For this purpose, the economic and operational optimization of the chemical processes can be achieved.