Machine Learning Tools for Chemical Engineering

Machine Learning Tools for Chemical Engineering by Francisco López-Flores delivers a focused, practical guide that brings modern data-driven methods into the chemical engineering toolbox. Whether you are a practicing engineer, graduate student, or researcher, this book shows how machine learning transforms process modeling, optimization, and control with clarity and real-world relevance.

Start with a compelling overview of why data-centric approaches matter today: increased sensor data, tighter performance targets, and the need for faster model development. The text builds interest through accessible explanations of core machine learning concepts tailored to chemical engineering challenges—predictive modeling, feature selection, surrogate models, and performance evaluation—without unnecessary jargon.

Desire grows from concrete examples and applications that demonstrate measurable gains: improved yield prediction, fault detection, energy-efficient operation, and scale-up support. The narrative emphasizes transferable techniques and best practices that engineers can apply across industries—from petrochemicals and pharmaceuticals to food processing and environmental engineering—making it valuable for professionals in Europe, North America, Latin America, and beyond.

Finish with confidence: clear takeaways, implementation tips, and a problem-solving mindset that empowers you to prototype solutions faster and make data-driven decisions with certainty. Machine Learning Tools for Chemical Engineering is a practical, insightful resource that bridges theory and practice, helping teams convert data into robust process improvements.

Order your copy today to modernize your workflow, reduce development time, and unlock the potential of machine learning in chemical engineering.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.