Introduction to Process Simulation Series v1.0
Complete Practical Guide to Chemical Process Simulation - From Material and Energy Balances to Flowsheet Creation
Series Overview
This series is a comprehensive 5-chapter educational content that allows you to learn the fundamentals to practical applications of chemical process simulation step by step. You will master both Sequential Modular and Equation-Oriented approaches and become capable of building simulations of actual chemical processes (distillation columns, reactors, heat exchangers).
Features:
- ✅ Practice-Oriented: 40 executable Python code examples
- ✅ Systematic Structure: 5-chapter structure covering from fundamental theory to industrial applications step by step
- ✅ Industrial Applications: Complete implementation of distillation columns, CSTR, and heat exchanger networks
- ✅ Latest Technologies: scipy, CoolProp, Cantera, Python integration frameworks
Total Learning Time: 150-180 minutes (including code execution and exercises)
How to Progress Through the Learning
Recommended Learning Sequence
For Beginners (learning process simulation for the first time):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Time Required: 150-180 minutes
For Chemical Engineering Practitioners (with basic knowledge of unit operations):
- Chapter 1 (light review) → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Time Required: 120-150 minutes
For Simulation Experts (experience with Aspen Plus, etc.):
- Chapter 3 → Chapter 4 → Chapter 5
- Time Required: 80-100 minutes
Details of Each Chapter
Chapter 1: Fundamentals of Process Simulation
Learning Content
- Overview of Process Simulation
- Sequential Modular vs Equation-Oriented Approach
- Components of Process Simulators
- Fundamentals of Material and Energy Balances
- Overview of Industrial Simulators (Aspen Plus, HYSYS, PRO/II)
- Selection of Thermodynamic Models
- Ideal Gas Model (Ideal Gas Law)
- Equations of State (SRK, Peng-Robinson)
- Activity Coefficient Models (NRTL, UNIQUAC, Wilson)
- Guidelines for Model Selection
- Stream Property Calculation
- Calculation of Enthalpy, Entropy, and Density
- Utilizing CoolProp Library
- Property Estimation for Mixtures
- Flash Calculation (VLE Equilibrium)
- Fundamentals of Convergence Calculation
- Successive Substitution Method
- Newton-Raphson Method
- Selection of Tear Streams
- Convergence Criteria and Acceleration Techniques
Learning Objectives
- ✅ Understand the difference between Sequential Modular and Equation-Oriented approaches
- ✅ Be able to select thermodynamic models appropriately
- ✅ Be able to calculate stream properties in Python
- ✅ Be able to implement convergence calculation algorithms
- ✅ Understand and implement flash calculations
Chapter 2: Unit Operation Modeling
Learning Content
- Heat Exchanger
- Log Mean Temperature Difference (LMTD) Method
- NTU-ε Method
- Shell and Tube, Plate Heat Exchangers
- Heat Loss and Pressure Drop Models
- Reactor
- CSTR (Continuous Stirred Tank Reactor) Model
- PFR (Plug Flow Reactor) Model
- Reaction Rate Equations and Arrhenius Equation
- Material Balance for Multi-Component Reaction Systems
- Separation Operations (Separator)
- Flash Drum
- Simplified Model of Distillation Column
- Vapor-Liquid Equilibrium (VLE) Calculation
- Rachford-Rice Equation
- Other Unit Operations
- Pumps and Compressors (Pressure Change)
- Mixers and Splitters
- Valves and Pressure Control
Learning Objectives
- ✅ Be able to implement major unit operation models
- ✅ Be able to calculate heat balance and LMTD method for heat exchangers
- ✅ Be able to solve material and energy balances for reactors
- ✅ Understand flash calculation and VLE equilibrium
- ✅ Be able to implement unit operations as Python classes
Chapter 3: Flowsheet Creation and Stream Connection
Learning Content
- Flowsheet Configuration
- How to Read PFD (Process Flow Diagram)
- Connection of Streams and Units
- Handling Recycle Loops
- Graph Representation of Flowsheets
- Sequential Modular Method
- Determining Calculation Order (Topological Sort)
- Identification of Recycle Streams
- Tear Stream Selection Algorithm
- Improving Calculation Efficiency
- Stream Class Design
- Implementation of Stream Objects
- Property Calculation Methods
- Operations Between Streams (Mixing, Splitting)
- Integration with Unit Operations
- Building a Process Flow Simulator
- Registry of Unit Operations
- Management of Connection Information
- Automatic Determination of Execution Order
- Visualization of Results
Learning Objectives
- ✅ Be able to interpret PFD and build flowsheets
- ✅ Be able to determine calculation order using Sequential Modular method
- ✅ Be able to design and implement stream classes
- ✅ Be able to solve flowsheets containing recycle loops
- ✅ Be able to build a simple process simulator in Python
Chapter 4: Convergence Calculation and Optimization
Learning Content
- Convergence of Recycle Loops
- Direct Substitution Method
- Wegstein Acceleration Method
- Broyden's Method
- Convergence Stability and Effect of Initial Values
- Equation-Oriented Approach
- Simultaneous Solution of All Equations
- Construction of Jacobian Matrix
- Utilization of Sparse Matrices
- Comparison with Sequential Modular
- Sensitivity Analysis and Uncertainty Evaluation
- Calculation of Parameter Sensitivity
- Monte Carlo Simulation
- Propagation of Uncertainty
- Robustness Evaluation
- Integration with Process Optimization
- Setting Objective Functions (Economic, Environmental)
- Formulation of Constraints
- Simulation-Based Optimization
- Integration with scipy.optimize
Learning Objectives
- ✅ Be able to implement convergence algorithms for recycle loops
- ✅ Be able to master Wegstein and Broyden methods
- ✅ Understand the Equation-Oriented approach
- ✅ Be able to practice sensitivity analysis and Monte Carlo methods
- ✅ Be able to integrate simulation and optimization
Chapter 5: Case Studies - Complete Process Simulation
Learning Content
- Case Study 1: Distillation Process
- Complete Simulation of Continuous Distillation Column
- MESH Equations (Material, Equilibrium, Summation, Heat balance)
- Optimization of Number of Stages and Reflux Ratio
- Economic Evaluation (CAPEX, OPEX)
- Case Study 2: Reaction Process
- Reactor + Separation + Recycle System
- Yield Maximization and By-Product Minimization
- Optimization of Reaction Temperature and Pressure
- Energy Integration (Heat Recovery)
- Case Study 3: Heat Exchanger Network
- Pinch Analysis
- Heat Exchanger Network Design
- Achievement of Minimum Utility Cost
- HEN (Heat Exchanger Network) Optimization
- Development to Industrial Implementation
- Integration with Aspen Plus (COM API)
- Database Integration
- Real-Time Simulation
- Concept of Digital Twin
Learning Objectives
- ✅ Be able to implement complete simulation of distillation column
- ✅ Be able to build reaction-separation-recycle system
- ✅ Be able to perform Pinch Analysis and heat exchanger network design
- ✅ Be able to integrate commercial simulators with Python
- ✅ Be able to complete simulation projects for real processes
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Understand the difference between Sequential Modular and Equation-Oriented approaches
- ✅ Know the selection criteria for thermodynamic models
- ✅ Understand the theory of major unit operation models
- ✅ Know the principles of convergence calculation algorithms
- ✅ Understand industrial applications of process simulation
Practical Skills (Doing)
- ✅ Be able to calculate stream properties in Python
- ✅ Be able to model heat exchangers, reactors, and separators
- ✅ Be able to build flowsheets and perform convergence calculations
- ✅ Be able to solve processes containing recycle loops
- ✅ Be able to practice sensitivity analysis and optimization
- ✅ Be able to utilize CoolProp, scipy, and Cantera libraries
Application Ability (Applying)
- ✅ Be able to simulate actual chemical processes
- ✅ Be able to optimize design parameters for distillation columns and reactors
- ✅ Be able to design heat exchanger networks
- ✅ Be able to integrate commercial simulators with Python
- ✅ Be able to handle simulation tasks as a process engineer
FAQ (Frequently Asked Questions)
Q1: How much prior knowledge of chemical engineering is required?
A: Basic knowledge of material and energy balances, unit operations (distillation, reaction, heat exchange), and thermodynamics is required. It is assumed that you have completed chemical engineering courses at the university 2nd-3rd year level.
Q2: What is the difference from commercial simulators (Aspen Plus, etc.)?
A: Commercial simulators are highly complete and optimal for industrial use, but they are black boxes. In this series, by implementing internal algorithms in Python, you can gain a deep understanding of how simulation works. Chapter 5 also covers how to integrate with commercial simulators.
Q3: Which Python libraries are required?
A: Mainly NumPy, SciPy, Pandas, Matplotlib, CoolProp (property calculation), and Cantera (reaction systems) are used. All can be installed via pip.
Q4: What is the relationship with the Process Optimization series?
A: By applying the optimization methods learned in the Process Optimization series to the simulation models in this series, optimal design and optimal operating condition searches become possible. By combining both series, you can master the complete process design workflow.
Q5: Can it be applied to actual chemical plants?
A: Yes. Chapter 5 covers the complete workflow for application to real processes through practical case studies. However, careful verification of safety and process constraints is necessary during implementation.
Next Steps
Recommended Actions After Series Completion
Immediate (within 1 week):
1. ✅ Publish Chapter 5 case studies on GitHub
2. ✅ Evaluate simulation opportunities for your company's processes
3. ✅ Try implementing simple unit operation models
Short-term (1-3 months):
1. ✅ Validate simulation models with real process data
2. ✅ Practice Python integration with Aspen Plus
3. ✅ Projects integrating with optimization series
4. ✅ Learn dynamic simulation (Modelica, gPROMS)
Long-term (6 months or more):
1. ✅ Build digital twin systems
2. ✅ Real-time process optimization
3. ✅ Conference presentations and paper writing
4. ✅ Career development as a process simulation engineer
Feedback and Support
About This Series
This series was created as part of the PI Knowledge Hub project under Dr. Yusuke Hashimoto at Tohoku University.
Created: October 26, 2025
Version: 1.0
We Welcome Your Feedback
We welcome your feedback to improve this series:
- Typos, omissions, technical errors: Please report via Issues in GitHub repository
- Improvement suggestions: New topics, code examples you'd like to see added, etc.
- Questions: Parts that were difficult to understand, areas where additional explanation is needed
- Success stories: Projects where you used what you learned in this series
Contact: yusuke.hashimoto.b8@tohoku.ac.jp
License and Terms of Use
This series is published under the CC BY 4.0 (Creative Commons Attribution 4.0 International) license.
What you can do:
- ✅ Free viewing and downloading
- ✅ Use for educational purposes (classes, study sessions, etc.)
- ✅ Modifications and derivative works (translation, summarization, etc.)
Conditions:
- 📌 Author credit must be displayed
- 📌 Modifications must be clearly indicated
- 📌 Please contact us in advance for commercial use
Details: CC BY 4.0 License Full Text
Let's Get Started!
Are you ready? Start from Chapter 1 and begin your journey into the world of process simulation!
Chapter 1: Fundamentals of Process Simulation →
Update History
- 2025-10-26: v1.0 Initial Release
Your process simulation learning journey starts here!