Process Informatics Introduction Series v1.0

Complete Guide to Data-Driven Chemical Process Optimization - From History to Practice and Career

Series Overview

This series is a comprehensive 4-chapter educational content designed for learners ranging from complete Process Informatics (PI) beginners to those seeking practical skills.

Features:
- ✅ Chapter Independence: Each chapter can be read as a standalone article
- ✅ Systematic Structure: Comprehensive content progressing through 4 chapters
- ✅ Practice-Oriented: 35 executable code examples, 5 detailed case studies
- ✅ Career Support: Concrete career paths and learning roadmaps provided

Total Learning Time: 90-120 minutes (including code execution and exercises)

How to Learn

Recommended Learning Sequence

graph TD
    A[Chapter 1: Why PI?] --> B[Chapter 2: PI Fundamentals]
    B --> C[Chapter 3: Python Hands-On]
    C --> D[Chapter 4: Real-World Applications]

    style A fill:#e3f2fd
    style B fill:#fff3e0
    style C fill:#f3e5f5
    style D fill:#e8f5e9

For Complete Beginners:
- Chapter 1 → Chapter 2 → Chapter 3 (partial skip possible) → Chapter 4
- Duration: 70-90 minutes

For Python Experienced Learners:
- Chapter 2 → Chapter 3 → Chapter 4
- Duration: 60-80 minutes

For Practical Skill Enhancement:
- Chapter 3 (intensive) → Chapter 4
- Duration: 50-65 minutes

Chapter Details

Chapter 1: Why Process Informatics?

Difficulty: Introductory
Reading Time: 15-20 minutes

Learning Content

1. History of Chemical Process Development
   - From ancient distillation to modern process control
   - Evolution of development methods: Trial & error → Empirical rules → Theory-driven → Data-driven

2. Limitations of Traditional Methods
   - Time: 1-3 years for scale-up
   - Cost: Billions for plant construction
   - Batch-to-batch variation: Quality consistency issues

3. Detailed Case Study: Chemical Plant Optimization
   - Yield improvement: 70% → 85%
   - Energy consumption: 30% reduction
   - PI can shorten development time to 1/3

4. Comparison Diagram (Traditional vs PI)
   - Mermaid diagram: Workflow visualization
   - Timing comparison: 6 months/condition vs 1 week/condition

5. Column: "A Day in the Life"
   - 1990 process engineer: 1 experiment/week, manual data analysis
   - 2025 process engineer: 50 experiments/week (automation), AI optimization suggestions

6. "Why Now?" - 3 Converging Factors
   - Sensor technology: IoT, real-time monitoring
   - Data infrastructure: Cloud, big data processing
   - Social urgency: Carbon neutrality, quality assurance, DX

Learning Objectives

• ✅ Explain historical transitions in chemical process development
• ✅ List 3 limitations of traditional methods with specific examples
• ✅ Understand social and technical backgrounds requiring PI

Read Chapter 1 →

Chapter 2: PI Fundamentals - Concepts, Methods, Ecosystem

Difficulty: Introductory to Intermediate
Reading Time: 20-25 minutes

Learning Content

1. Definition and Related Fields
   - Etymology and history of Process Informatics
   - Relationship with Industry 4.0 and Smart Factory
   - Differences from Quality Engineering (QE) and Design of Experiments (DoE)

2. 20 PI Terminology Glossary
   - 3 categories: Basic, Method, Application terms
   - Each term: Japanese, English, 1-2 sentence explanation

3. Types of Major Process Data
   - Process parameters: Temperature, pressure, flow rate, residence time
   - Product characteristics: Yield, selectivity, purity, quality indicators
   - Operational data: Energy consumption, equipment status

4. PI Ecosystem Diagram
   - Mermaid diagram: Sensors → Data collection → ML → Optimization → Process control
   - Feedback loop visualization

5. 5-Step Workflow (Detailed)
   - Step 0: Problem formulation (yield improvement? cost reduction?)
   - Step 1: Data collection (time series data, experimental data)
   - Step 2: Model building (regression, classification, time series prediction)
   - Step 3: Optimization (Bayesian optimization, multi-objective optimization)
   - Step 4: Implementation & validation (pilot scale, actual plant)
   - Each step: Substeps, common pitfalls, time estimates

6. Process Descriptors Deep Dive
   - Physicochemical parameters: Concentration, temperature, pressure, pH
   - Equipment characteristics: Reactor size, stirring speed, residence time
   - Operating conditions: Feed rate, heating rate, cooling rate

Learning Objectives

• ✅ Explain PI definition and differences from related fields
• ✅ Understand major data types in chemical processes
• ✅ Detail PI workflow 5 steps including substeps
• ✅ Explain types of process descriptors with examples
• ✅ Appropriately use 20 PI technical terms

Read Chapter 2 →

Chapter 3: Experiencing PI with Python - Process Optimization Practice

Difficulty: Intermediate
Reading Time: 30-40 minutes
Code Examples: 35 (all executable)

Learning Content

1. Environment Setup (3 Options)
   - Option 1: Anaconda (recommended for beginners, with GUI)
   - Option 2: venv (Python standard, lightweight)
   - Option 3: Google Colab (no installation required, cloud-based)

2. 6 Machine Learning Models (Full Implementation)
   - Example 1: Linear Regression (yield prediction, R²=0.75)
   - Example 2: Random Forest (yield/selectivity prediction, R²=0.88)
   - Example 3: LightGBM (gradient boosting, R²=0.91)
   - Example 4: SVR (nonlinear process optimization, R²=0.86)
   - Example 5: Time Series Analysis (ARIMA, Prophet)
   - Example 6: Bayesian Optimization (reaction condition optimization)

3. Model Performance Comparison
   - Comparison table: MAE, R², training time, interpretability
   - Visualization: Bar charts for each metric
   - Model selection flowchart

4. Process Optimization Methods
   - Grid Search: Exhaustive search (temperature × pressure × concentration)
   - Bayesian Optimization: Efficient search (optimal conditions in 10-20 experiments)
   - Multi-objective Optimization: Yield vs cost trade-offs

5. Feature Engineering
   - Process parameter interaction terms
   - Time series features (moving average, lag variables)
   - Derived variables from quality indicators

6. Troubleshooting Guide
   - 7 common errors and solutions
   - 5-step debugging checklist
   - Performance improvement strategies

7. Project Challenge
   - Goal: Chemical reactor yield optimization (yield > 80%)
   - 6-step guide

Learning Objectives

• ✅ Set up Python environment using one of 3 methods
• ✅ Implement and compare 6 ML models
• ✅ Execute Bayesian optimization
• ✅ Perform multi-objective optimization (yield vs cost)
• ✅ Troubleshoot common errors independently
• ✅ Complete chemical reactor optimization project

Read Chapter 3 →

Chapter 4: Real-World PI Applications - Success Stories and Future Outlook

Difficulty: Intermediate to Advanced
Reading Time: 20-25 minutes

Learning Content

1. 5 Detailed Case Studies

Case Study 1: Catalyst Process Optimization (Yield Improvement)
- Technology: Bayesian Optimization, Random Forest
- Results: Yield 70% → 85% (+15%pt), development time 6 months → 2 months
- Impact: Annual revenue increase ¥2 billion
- Company: Chemical manufacturer A

Case Study 2: Polymerization Reaction Control (Molecular Weight Distribution)
- Technology: Time Series Analysis, PID control + ML hybrid
- Results: Molecular weight distribution std dev 50% reduction, defect rate 5% → 1%
- Impact: Waste cost reduction ¥500 million/year
- Company: Polymer manufacturer B

Case Study 3: Distillation Column Optimization (Energy Reduction)
- Technology: Multi-objective Optimization, Soft Sensor
- Results: Energy consumption 30% reduction, 99.5% purity maintained
- Impact: CO2 emission reduction, energy cost ¥300 million/year saved
- Company: Petrochemical manufacturer C

Case Study 4: Pharmaceutical Batch Process (Quality Consistency)
- Technology: Statistical Process Control (SPC), DoE + ML
- Results: Batch-to-batch variation 70% reduction, 100% regulatory compliance
- Impact: FDA inspection passed, market launch 3 months earlier
- Company: Pharmaceutical manufacturer D

Case Study 5: Bioprocess Optimization (Fermentation)
- Technology: Online Learning, metabolic model + ML
- Results: Cell concentration +40%, productivity +50%
- Impact: Biofuel cost 30% reduction, carbon neutrality contribution
- Company: Biotechnology company E

2. Future Trends (3 Major Trends)

Trend 1: Digital Twin
- Example: Real-time process simulation
- Prediction: 80% of major chemical companies adopt by 2030
- Initial investment: ¥500 million, ROI: 1-2 years

Trend 2: Autonomous Control
- Example: AI-driven 24/7 optimization
- Effect: 80% reduction in human operator intervention
- Prediction: 20% operation efficiency improvement by 2030

Trend 3: Sustainability DX
- LCA integration: Carbon footprint optimization
- Examples: Green chemistry, by-product recycling

3. Career Paths (3 Major Paths)

Path 1: Academia (Researcher)
- Route: Bachelor→Master→PhD→Postdoc→Assistant Professor
- Salary: ¥5-12M/year (Japan), $60-120K (US)
- Skills: Python, ML, Chemical Engineering, paper writing

Path 2: Industrial R&D
- Roles: Process Engineer, Data Scientist
- Salary: ¥7-15M/year (Japan), $70-200K (US)
- Companies: Mitsubishi Chemical, Asahi Kasei, Sumitomo Chemical, BASF

Path 3: Startup/DX Consulting
- Examples: Process DX consulting firms
- Salary: ¥6-12M/year + performance bonus
- Risk/Return: High risk, high impact

4. Skill Development Timeline
   - 3-month plan: Basics→Practice→Portfolio
   - 1-year plan: Advanced ML→Project→Conference
   - 3-year plan: Expert→Publication→Leadership

5. Learning Resources
   - Online courses: Coursera, edX, Udemy
   - Books: "Process Systems Engineering" by Seborg et al.
   - Communities: SCEJ, AIChE
   - Conferences: PSE, ESCAPE, SCEJ Annual Meeting

Learning Objectives

• ✅ Explain 5 real-world PI success cases with technical details
• ✅ List 3 PI future trends and evaluate industrial impact
• ✅ Describe 3 PI career paths and understand required skills
• ✅ Plan concrete learning timelines (3 months/1 year/3 years)
• ✅ Select appropriate learning resources for next steps

Read Chapter 4 →

Overall Learning Outcomes

Upon completing this series, you will acquire the following skills and knowledge:

Knowledge Level (Understanding)

• ✅ Explain historical background and necessity of PI
• ✅ Understand basic PI concepts, terms, and methods
• ✅ Understand types and handling of process data
• ✅ Detail 5+ real-world success cases

Practical Skills (Doing)

• ✅ Set up Python environment and install necessary libraries
• ✅ Implement and compare 6 ML models
• ✅ Optimize process conditions with Bayesian optimization
• ✅ Perform multi-objective optimization (yield vs cost)
• ✅ Analyze and predict time series data
• ✅ Debug errors independently

Application Ability (Applying)

• ✅ Design new chemical process optimization projects
• ✅ Evaluate industrial implementation cases and apply to own research
• ✅ Plan future career paths concretely
• ✅ Establish continuous learning strategies

Feedback and Support

About This Series

This series was created as part of the MI Knowledge Hub project under Dr. Yusuke Hashimoto at Tohoku University.

Created: October 16, 2025
Version: 1.0

We Welcome Your Feedback

• Errors/Technical issues: Report via GitHub repository
• Improvement suggestions: New topics, additional code examples
• Questions: Difficult sections, areas needing additional explanation
• Success stories: Projects using what you learned

Contact: yusuke.hashimoto.b8@tohoku.ac.jp

License and Terms of Use

This series is published under CC BY 4.0 (Creative Commons Attribution 4.0 International) license.

Allowed:
- ✅ Free viewing and downloading
- ✅ Educational use (classes, study groups, etc.)
- ✅ Modification and derivative works

Conditions:
- 📌 Author credit required
- 📌 Indicate if modified
- 📌 Contact before commercial use

Details: CC BY 4.0 License

Let's Begin!

Ready? Start from Chapter 1 and begin your journey into the world of PI!

Chapter 1: Why Process Informatics? →

Your PI learning journey starts here!