Practical Python for Chemical Engineering: A Problem-Solving Approach

Practical Python for Chemical EngineeringBy Dhan Lord B. Fortela & Ashley P. MikolajczykA modern, computation‑first chemical engineering textbook for the next generation of problem solvers.Practical Python for Chemical Engineering is a comprehensive, practice‑driven textbook that reimagines the entire chemical engineering curriculum through the lens of Python, numerical methods, and reproducible scientific computing. Designed for undergraduate and graduate students, instructors, and practicing engineers, this book bridges classical theory with modern computational workflows—empowering readers to solve real engineering problems with clarity, confidence, and precision.Built from the ground up as a hands‑on, example‑rich, Python‑powered learning resource, the book covers everything from introductory programming to advanced multiphysics modeling. Every chapter includes annotated scripts, visualizations, and step‑by‑step derivations that translate equations into executable code. Students learn not only what the equations mean, but how to implement them, troubleshoot them, and extend them to real‑world systems.What makes this book differentA unified computational approach to the entire chemical engineering curriculumHundreds of worked examples, each paired with clean, reproducible Python scriptsModern numerical methods integrated directly into core topicsVisualization‑first pedagogy using Matplotlib, NumPy, and FiPyAdvanced applications including optimization, PDEs, machine learning, network modeling, and reaction–transport couplingDesigned for instructors with clear learning outcomes, structured problem sets, and consistent scaffoldingInside the bookPart I — Python PreliminariesA complete introduction to Python for engineers: data types, control flow, functions, plotting, GUIs, file handling, and scientific scripting.Part II — Core Problem Types in Chemical EngineeringLinear and nonlinear equation solvingRegression and data analysisOptimization (LP, MILP, NLP, MOO)Dynamical systems, ODEs, DAEs, stiffness, and solver strategiesPDE fundamentals and numerical methodsPart III — Topical Area ApplicationsMass and energy balancesMulticomponent distillationMixing, draining, and transient tank systemsThermodynamics, VLE regression, EOS modelingTransport processes (momentum, heat, mass) with full FiPy implementationsHeat transfer correlations, HTU–NTU, and dimensionless analysisPart IV — Advanced Applications and Case StudiesMachine learning and deep neural networksReaction engineering and reaction–transport couplingAxially dispersed PFRsGibbs energy minimizationFluid mechanics, Moody charts, rheology, pipe networks, packed bedsMultiphysics PDE case studies including reaction–diffusion, advection–dispersion, and combustion with impinging jetsWho this book is forUndergraduate and graduate chemical engineering studentsInstructors seeking a modern, computation‑first textbookPracticing engineers transitioning to Python‑based workflowsResearchers needing a reproducible, transparent modeling foundationA textbook built for the future of engineeringBy integrating classical principles with modern Python tools, Practical Python for Chemical Engineering equips readers with the conceptual understanding and computational fluency needed for research, industry, and advanced study.Whether you are learning Python for the first time or modernizing your engineering toolkit, this book provides a clear, rigorous, and highly practical path forward. Read more