Video Lecture
The whole series is available as a single video with chapter markers. Each chapter page starts this video at that chapter.
Chemical Engineering Thermodynamics
Energy, Equilibrium, and the Limits of Every Process
Rate laws tell you how fast; thermodynamics tells you how far — and how much it will cost in energy. This course builds the thermodynamic toolkit beneath our Chemical Engineering Introduction series: the laws that set every process's energy bill, the equilibria that cap every separation and reaction, and the property models that every process simulator quietly relies on.
Series Overview
- Account — the First Law turns energy conservation into the plant's bookkeeping
- Limit — the Second Law sets direction, the Carnot ceiling, and the minimum price of separation
- Coexist — vapor-liquid equilibrium, Raoult's law, and the azeotropes that stop distillation
- React — Gibbs energy and the equilibrium constant decide how far reactions go
- Compute — real-fluid equations of state connect all of it to the simulator
Energy"] --> B["Second Law
Entropy"] B --> C["Phase
Equilibrium"] C --> D["Chemical
Equilibrium"] D --> E["Real Fluids
& EOS"]
Chapters
| Chapter | Title | What You Will Learn |
|---|---|---|
| 1 | Energy and the First Law | State functions, enthalpy, sensible vs latent heat, energy balances in flowsheets |
| 2 | Entropy and the Second Law | Direction of processes, the Carnot limit, minimum work of separation, Gibbs energy |
| 3 | Phase Equilibrium | Vapor pressure, Raoult's law, relative volatility, activity coefficients, azeotropes |
| 4 | Chemical Equilibrium | The equilibrium constant, van 't Hoff behavior, Le Chatelier, the Haber–Bosch compromise |
| 5 | Real Fluids and Equations of State | Compressibility, van der Waals, SRK and Peng–Robinson, choosing a property package |
Who This Series is For
- Students who met unit operations and reactors in our Chemical Engineering Introduction and want the theory that governs them
- Engineers and researchers who use process simulators and want to understand — and distrust intelligently — the property models inside
- Data scientists building surrogates or soft sensors on top of simulation data, who need to know where that data's accuracy comes from
Recommended preparation: our Chemical Engineering Introduction series. Mathematics stays at algebra, logarithms, and exponentials; the few calculus expressions are explained in words.
Related Series
This course deepens the Chemical Engineering Introduction series and supports the data-driven series that build on simulation: Process Informatics Introduction, Digital Twin Construction Introduction, and Introduction to Bayesian Optimization.