Applied Machine Learning in Chemical Process Engineering

Applied Machine Learning in Chemical Process Engineering, 1st Edition, edited by Zafar Said and Muhammad Farooq, offers a reference work on computational methods in process engineering. Subtitled A Practical Approach and published by Elsevier, this 2026 publication presents systematic methods for applying algorithms to chemical engineering challenges.

The text synthesizes primary thematic arcs starting with data handling and preprocessing engineered for chemical datasets. It examines predictive modeling techniques alongside unsupervised learning and pattern recognition to extract insights from operational metrics. Further sections address process optimization and control strategies suited to chemical processing environments.

The volume highlights physics-informed neural networks and explainable AI within chemical engineering contexts. Designed for researchers, graduate students, and industry professionals, this reference provides structured guidance across chemical and process engineering applications.