Практический Python для химической инженерии: подход к решению проблем

Practical Python for Chemical Engineering By Dhan Lord B. Fortela & Ashley P. Mikolajczyk A 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 different A unified computational approach to the entire chemical engineering curriculum A unified computational approach to the entire chemical engineering curriculum Hundreds of worked examples, each paired with clean, reproducible Python scripts Hundreds of worked examples , each paired with clean, reproducible Python scripts Modern numerical methods integrated directly into core topics Modern numerical methods integrated directly into core topics Visualization‑first pedagogy using Matplotlib, NumPy, and FiPy Visualization‑first pedagogy using Matplotlib, NumPy, and FiPy Advanced applications including optimization, PDEs, machine learning, network modeling, and reaction–transport coupling Advanced applications including optimization, PDEs, machine learning, network modeling, and reaction–transport coupling Designed for instructors with clear learning outcomes, structured problem sets, and consistent scaffolding Designed for instructors with clear learning outcomes, structured problem sets, and consistent scaffolding Inside the book Part I — Python Preliminaries A 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 Engineering Linear and nonlinear equation solving Linear and nonlinear equation solving Regression and data analysis Regression and data analysis Optimization (LP, MILP, NLP, MOO) Optimization (LP, MILP, NLP, MOO) Dynamical systems, ODEs, DAEs, stiffness, and solver strategies Dynamical systems, ODEs, DAEs, stiffness, and solver strategies PDE fundamentals and numerical methods PDE fundamentals and numerical methods Part III — Topical Area Applications Mass and energy balances Mass and energy balances Multicomponent distillation Multicomponent distillation Mixing, draining, and transient tank systems Mixing, draining, and transient tank systems Thermodynamics, VLE regression, EOS modeling Thermodynamics, VLE regression, EOS modeling Transport processes (momentum, heat, mass) with full FiPy implementations Transport processes (momentum, heat, mass) with full FiPy implementations Heat transfer correlations, HTU–NTU, and dimensionless analysis Heat transfer correlations, HTU–NTU, and dimensionless analysis Part IV — Advanced Applications and Case Studies Machine learning and deep neural networks Machine learning and deep neural networks Reaction engineering and reaction–transport coupling Reaction engineering and reaction–transport coupling Axially dispersed PFRs Axially dispersed PFRs Gibbs energy minimization Gibbs energy minimization Fluid mechanics, Moody charts, rheology, pipe networks, packed beds Fluid mechanics, Moody charts, rheology, pipe networks, packed beds Multiphysics PDE case studies including reaction–diffusion, advection–dispersion, and combustion with impinging jets Multiphysics PDE case studies including reaction–diffusion, advection–dispersion, and combustion with impinging jets Who this book is for Undergraduate and graduate chemical engineering students Undergraduate and graduate chemical engineering students Instructors seeking a modern, computation‑first textbook Instructors seeking a modern, computation‑first textbook Practicing engineers transitioning to Python‑based workflows Practicing engineers transitioning to Python‑based workflows Researchers needing a reproducible, transparent modeling foundation Researchers needing a reproducible, transparent modeling foundation A textbook built for the future of engineering By 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.