The Application of Machine Learning in Chemical Engineering: A Literature Review

In chemical engineering research, to develop a Physico-chemical model is expensive.Also, it's very difficult for a computationally tractable model to perfectly describe many complex phenomena.Nowadays, machine learning can learn complicated behavior.Also, the development of its model is more economical.Machine learning offers significant advantages over traditional modeling techniques, including flexibility, accuracy, and speed of execution.This paper conducted a literature review in the application of machine learning (ML) in chemical engineering (CE).Four questions were being researched.What are the CE fields that ML applied mostly to?For what purposes is ML used in CE research?What are the algorithms that are mostly used in CE research?What are the difficulties of these applications?After examining 48 papers, the conclusion was that chemical process engineering was the field ML was applied mostly, the primary purposes for applying ML algorithms in CE were prediction and optimization, Artificial Neural Network was the most used algorithm in CE research, and the limited application field was the main existing difficulty.This paper can be a groundwork for future CE and ML research efforts as a result of these findings.