Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial safety implications. Previous efforts a few decades ago to combine artificial intelligence chemical engineering modeling were unable fulfill the expectations. In last five years, increasing availability of data computational resources has led resurgence in machine learning-based research. Many recent have facilitated roll-out learning techniques research field by developing databases, benchmarks, representations applications new frameworks. Machine significant advantages over traditional techniques, including flexibility, accuracy, execution speed. These strengths also come weaknesses, such as lack interpretability these black-box models. The greatest opportunities involve using time-limited real-time optimization planning that require high accuracy can build self-learning ability recognize patterns, learn from data, become more intelligent time. threat today is inappropriate use because most had limited training computer science analysis. Nevertheless, will definitely trustworthy element toolbox engineers.