Chemical Engineering Introduction
Unit Operations, Reactors, Control, Design — and Where AI Fits
A reaction that works beautifully in a 100 mL flask does not simply scale to a 100 m³ reactor. Chemical engineering is the discipline that bridges that gap: designing and operating processes that transform matter and energy at industrial scale — safely, economically, and increasingly with the help of AI. This series is a bird's-eye introduction for students and researchers who want to understand how the process industries actually work, and where data-driven methods fit in.
Series Overview
The series follows the life of a process, from its building blocks to its intelligent operation:
- Decompose — every process breaks down into reusable unit operations, glued together by mass and energy balances
- React — the reactor sets conversion and selectivity, dictating everything downstream
- Control — feedback keeps the plant at its target despite constant disturbances
- Design — hierarchical decisions turn chemistry into an economic, safe flowsheet
- Learn — soft sensors, Bayesian optimization, and digital twins make the plant intelligent
& Balances"] --> B["Reaction
Engineering"] B --> C["Process
Control"] C --> D["Process
Design"] D --> E["AI & Process
Informatics"]
Chapters
| Chapter | Title | What You Will Learn |
|---|---|---|
| 1 | What is Chemical Engineering? | The scale-up problem, unit operations, mass and energy balances, flowsheets, transport phenomena |
| 2 | Reaction Engineering Fundamentals | Rate laws and Arrhenius behavior, batch/CSTR/PFR reactors, conversion and selectivity |
| 3 | Process Control Fundamentals | Feedback loops, PID control, process dynamics, cascade and plant-wide control |
| 4 | How Processes are Designed | The design hierarchy, separations and distillation, pinch analysis, inherently safer design |
| 5 | AI and the Future of Chemical Engineering | Soft sensors, Bayesian optimization, digital twins, the road to autonomous plants |
Who This Series is For
- Students in chemistry, materials science, or engineering who want the process-scale picture their coursework may not cover
- Researchers collaborating with industry who need to speak the language of plants, flowsheets, and control loops
- Data scientists and informatics practitioners entering the process industries who want the domain fundamentals beneath process informatics
No prior chemical engineering background is assumed; the mathematics stays at the level of algebra, logarithms, and a few worked calculations; the one calculus expression (the PID equation) is explained in words.
Related Series
This series is the classical-fundamentals companion to our data-driven process series: Process Informatics Introduction, Introduction to Bayesian Optimization, Introduction to Process Monitoring and Control, Digital Twin Construction Introduction, and Self-Driving Labs Introduction. Chapter 5 connects the two worlds explicitly.