The article "Application of Machine Learning in Chemical Engineering: Outlook and Perspectives" by Ashraf Al Sharah et al. explores the growing integration of machine learning (ML) in chemical engineering. The authors highlight the importance of mathematical modeling in chemical engineering and discuss how ML can enhance these models by leveraging large datasets and advanced algorithms. They emphasize the benefits of ML in real-time optimization, planning, and pattern recognition, while also addressing the challenges and limitations, such as interpretability and data quality. The article reviews the current state of ML applications in chemical engineering, including the use of deep learning, unsupervised learning, and supervised learning techniques. It also discusses the need for collaboration between IT professionals and chemical engineers to ensure the effective and ethical use of ML in the field. The authors conclude by emphasizing the importance of accessible data, understandable models, and a deep understanding of algorithmic theory to make ML more credible and trustworthy in chemical engineering research and practice.The article "Application of Machine Learning in Chemical Engineering: Outlook and Perspectives" by Ashraf Al Sharah et al. explores the growing integration of machine learning (ML) in chemical engineering. The authors highlight the importance of mathematical modeling in chemical engineering and discuss how ML can enhance these models by leveraging large datasets and advanced algorithms. They emphasize the benefits of ML in real-time optimization, planning, and pattern recognition, while also addressing the challenges and limitations, such as interpretability and data quality. The article reviews the current state of ML applications in chemical engineering, including the use of deep learning, unsupervised learning, and supervised learning techniques. It also discusses the need for collaboration between IT professionals and chemical engineers to ensure the effective and ethical use of ML in the field. The authors conclude by emphasizing the importance of accessible data, understandable models, and a deep understanding of algorithmic theory to make ML more credible and trustworthy in chemical engineering research and practice.