Process Informatics Introduction Series v1.0
Data-Driven Approach in Process Industries - Complete Guide from Fundamentals to Practice
Series Overview
This series is a 4-chapter educational content designed for progressive learning, from beginners to those seeking practical skills in Process Informatics (PI).
Features:
- ✅ Independent Chapters: Each chapter can be read as a standalone article
- ✅ Systematic Structure: Comprehensive content for progressive learning across 4 chapters
- ✅ Practice-Oriented: 35 executable code examples, case studies using real process data
- ✅ Industrial Application Focus: Rich examples from chemical plants and manufacturing processes
Total Learning Time: 90-120 minutes (including code execution and exercises)
How to Learn
Recommended Learning Path
For Beginners (First time with PI):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4
- Duration: 90-120 minutes
Python Experienced (Basic data analysis knowledge):
- Chapter 2 → Chapter 3 → Chapter 4
- Duration: 60-80 minutes
Practical Skill Enhancement (Familiar with PI concepts):
- Chapter 3 (Intensive study) → Chapter 4
- Duration: 45-60 minutes
Chapter Details
Chapter 1: PI Fundamentals and Data Utilization in Process Industries
Difficulty: Beginner
Reading Time: 20-25 minutes
Learning Content
-
What is Process Informatics (PI)?
- Definition and purpose of PI
- Differences from Materials Informatics (MI)
- Importance in process industries -
Characteristics of Process Industries
- Continuous process vs Batch process
- Characteristics of chemical, petrochemical, pharmaceutical, food, and semiconductor industries
- Process complexity: Multivariable, nonlinear, time delays -
Types of Process Data
- Sensor data (temperature, pressure, flow rate, concentration)
- Operating condition data (setpoints, control parameters)
- Quality data (product characteristics, purity, yield)
- Event data (alarms, anomaly detection) -
Data-Driven Process Improvement Case Studies
- Case Study: Chemical plant yield improvement (5% increase)
- Case Study: Energy consumption reduction (15% reduction)
- ROI analysis: Return on investment in data analysis -
Introduction to Process Data Visualization with Python
- Time series data plotting (Matplotlib)
- Correlation analysis between process variables (Seaborn)
- Interactive visualization (Plotly)
- Code examples: 5 executable samples
Learning Objectives
- ✅ Explain the definition of PI and its role in process industries
- ✅ Classify major types of process data
- ✅ Describe advantages of data-driven approaches with concrete examples
- ✅ Create basic process data visualizations using Python
Chapter 2: Process Data Preprocessing and Visualization
Difficulty: Beginner to Intermediate
Reading Time: 20-25 minutes
Learning Content
-
Handling Time Series Data
- Utilizing Pandas DatetimeIndex
- Resampling: Downsampling and upsampling
- Rolling statistics (moving average, moving variance)
- Trend analysis and seasonality detection -
Missing Value Treatment and Outlier Detection
- Types of missing values (MCAR, MAR, MNAR)
- Imputation methods: Forward fill, linear interpolation, spline interpolation
- Outlier detection: Z-score method, IQR method, Isolation Forest
- Practical example: Sensor data cleaning -
Data Scaling and Normalization
- Min-Max scaling
- Standardization (Z-score normalization)
- RobustScaler (robust to outliers)
- When to use which method -
Visualization with Pandas/Matplotlib/Seaborn
- Time series plots: Simultaneous display of multiple variables
- Correlation matrix: Heatmaps
- Scatter plot matrix: Relationships between variables
- Box plots: Distribution comparison
- Code examples: 10 practical samples -
Process Data Specific Challenges
- Handling time delays (time lags)
- Non-uniform sampling rates
- Multi-rate problems (different measurement frequencies)
- Process stationarity and non-stationarity
Learning Objectives
- ✅ Efficiently process time series data with Pandas
- ✅ Select appropriate treatment methods for missing values and outliers
- ✅ Understand the necessity and methods of data scaling
- ✅ Create diverse visualizations with Matplotlib/Seaborn
- ✅ Recognize and address challenges specific to process data
Chapter 3: Fundamentals of Process Modeling
Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 12 (all executable)
Learning Content
-
Process Model Construction with Linear Regression
- Simple regression analysis: 1-input-1-output model
- Multiple regression analysis: Multi-input-1-output model
- Model evaluation: R², RMSE, MAE
- Residual analysis: Assumption verification
- Code example: Implementation with Scikit-learn -
Multivariate Regression and PLS (Partial Least Squares)
- Principles and characteristics of PLS
- Multicollinearity problem and effectiveness of PLS
- PLS implementation (scikit-learn)
- Determining number of components
- Case study: Modeling chemical reaction processes -
Soft Sensor Concept and Implementation
- What is a soft sensor?
- Difference from hard sensors
- Real-time estimation of quality variables
- Soft sensor design procedure
- Implementation example: Predicting product purity -
Model Evaluation Metrics
- Interpretation of coefficient of determination (R²)
- RMSE (Root Mean Square Error)
- MAE (Mean Absolute Error)
- Cross-validation: K-fold CV
- Training data vs Test data -
Extension to Nonlinear Models
- Polynomial regression
- Random Forest regression
- Support Vector Regression (SVR)
- Model selection guidelines
- Comparison table: Linear vs Nonlinear models
Learning Objectives
- ✅ Build and evaluate linear regression models
- ✅ Understand PLS principles and application scenarios
- ✅ Design and implement soft sensors
- ✅ Select and interpret appropriate model evaluation metrics
- ✅ Distinguish between linear and nonlinear models
Chapter 4: Practical Exercises with Real Process Data
Difficulty: Intermediate
Reading Time: 25-35 minutes
Code Examples: 8 (integrated project)
Learning Content
-
Case Study: Chemical Plant Operation Data Analysis
- Dataset introduction: Distillation column operation data
- Variables: Temperature (5 points), pressure, reflux ratio, product purity
- Exploratory data analysis (EDA)
- Data cleaning and preprocessing
- Feature engineering -
Quality Prediction Model Construction
- Objective: Predicting product purity (soft sensor construction)
- Data split: Training, validation, test
- Model selection: Linear regression, PLS, Random Forest
- Hyperparameter tuning
- Model performance comparison and final selection
- Implementation code: Step-by-step -
Fundamentals of Process Condition Optimization
- Objective: Energy consumption minimization
- Constraints: Maintaining product quality specifications
- Optimization by grid search
- Finding optimal operating conditions
- Result visualization and interpretation -
Complete Implementation Project Workflow
- Step 1: Data loading and understanding
- Step 2: Preprocessing pipeline construction
- Step 3: Model training and evaluation
- Step 4: Optimization and result analysis
- Step 5: Report creation
- Complete integrated code (Jupyter Notebook format) -
Summary and Next Steps
- PI learning summary
- Topics for further study:
- Process monitoring (Statistical process control)
- Process control (MPC, PID)
- Design of Experiments (DOE)
- Digital twins
- Recommended resources: Books, online courses, papers
- Introduction to other series in Process Informatics Dojo
Learning Objectives
- ✅ Execute complete projects using real process data
- ✅ Build quality prediction soft sensors
- ✅ Apply basic approaches to process optimization
- ✅ Understand workflow from preprocessing to modeling and optimization
- ✅ Plan next steps in PI learning
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain the definition of PI and its role in process industries
- ✅ Understand types and characteristics of process data
- ✅ Know methods for data-driven process improvement
- ✅ Understand basic theory of process modeling
Practical Skills (Doing)
- ✅ Process time series process data with Pandas
- ✅ Perform appropriate data preprocessing (missing values, outliers, scaling)
- ✅ Visualize process data with Matplotlib/Seaborn
- ✅ Build process models with linear regression, PLS, Random Forest
- ✅ Design and implement soft sensors
- ✅ Properly evaluate model performance
Application Ability (Applying)
- ✅ Execute complete projects using real process data
- ✅ Practice quality prediction and condition optimization
- ✅ Plan next learning steps (control, optimization, DOE)
- ✅ Handle data analysis tasks in process industries
Recommended Learning Patterns
Pattern 1: Complete Mastery (For Beginners)
Target: First-time PI learners, those wanting systematic understanding
Duration: 1-2 weeks
Approach:
Week 1:
- Day 1-2: Chapter 1 (PI Fundamentals)
- Day 3-4: Chapter 2 (Data Preprocessing & Visualization)
- Day 5-7: Chapter 2 exercises, review
Week 2:
- Day 1-2: Chapter 3 (Process Modeling)
- Day 3-4: Chapter 3 exercises
- Day 5-7: Chapter 4 (Practical Exercise Project)
Deliverables:
- Chemical plant quality prediction soft sensor (R² > 0.80)
- Process optimization report
Pattern 2: Fast Track (For Python/Data Analysis Experienced)
Target: Those with Python and Pandas fundamentals
Duration: 3-5 days
Approach:
Day 1: Chapter 1 + Chapter 2 (Concept understanding)
Day 2: Chapter 2 (Code practice)
Day 3: Chapter 3 (Modeling implementation)
Day 4-5: Chapter 4 (Integrated project)
Deliverables:
- Complete preprocessing-modeling-optimization pipeline
- GitHub-ready project
Pattern 3: Targeted Learning (Specific Topic Focus)
Target: Those wanting to strengthen specific skills
Duration: Flexible
Examples:
- Master time series data processing → Chapter 2 (Section 2.1-2.2) intensive
- Learn soft sensor construction → Chapter 3 (Section 3.3) + Chapter 4
- Practice process optimization → Chapter 4 (Section 4.3)
- Improve data visualization skills → Chapter 1 (Section 1.5) + Chapter 2 (Section 2.4)
FAQ (Frequently Asked Questions)
Q1: What's the difference between PI and MI?
A: Materials Informatics (MI) focuses on material property prediction and new material design, whereas Process Informatics (PI) focuses on operation data analysis, quality prediction, and condition optimization in process industries. PI is characterized by time series data, process control, and real-time requirements.
Q2: Can I understand without process industry experience?
A: Yes. Chapter 1 explains process industry fundamentals. Chemical engineering expertise is not required, but basic understanding of data analysis and machine learning will facilitate smooth learning.
Q3: What level of Python skills is required?
A: It's desirable to understand basic Python syntax (variables, functions, control structures) and fundamental use of Pandas/NumPy. Machine learning experience is not required.
Q4: Where can I obtain real process data?
A: Chapter 4 uses public datasets (UCI Machine Learning Repository, etc.). Actual corporate data has high confidentiality, so we use public data or simulation data for learning.
Q5: What should I learn next after this series?
A: We recommend other series from Process Informatics Dojo:
- Process Monitoring & Control Introduction: Learn SPC, MPC
- Process Optimization Introduction: Mathematical optimization, Bayesian optimization
- Design of Experiments (DOE) Introduction: Efficient experimental design
- Digital Twin Construction Introduction: Virtual process models
Q6: How is it utilized in industry?
A: Applications span widely: chemical plants (yield improvement), pharmaceuticals (quality control), semiconductors (process control), food (batch optimization), etc. Specific cases are introduced in Chapters 1 and 4.
Next Steps
Recommended Actions After Series Completion
Immediate (Within 1 week):
1. ✅ Publish Chapter 4 project on GitHub
2. ✅ Practice with other public datasets (Kaggle, etc.)
3. ✅ Add "Process Informatics" skill to LinkedIn profile
Short-term (1-3 months):
1. ✅ Study next series from Process Informatics Dojo
2. ✅ Apply to data analysis projects in actual work
3. ✅ Learn process control and experimental design
4. ✅ Read related papers (Journal of Process Control, etc.)
Long-term (6+ months):
1. ✅ Master advanced process modeling methods
2. ✅ Learn digital twins and AI utilization
3. ✅ Build career as process engineer
4. ✅ Conference presentations and paper writing
Feedback and Support
About This Series
This series was created under Dr. Yusuke Hashimoto, Tohoku University, as part of the PI Knowledge Hub project.
Created: October 25, 2025
Version: 1.0
We Welcome Your Feedback
We welcome your feedback to improve this series:
- Typos, errors, technical mistakes: Report via GitHub repository Issues
- Improvement suggestions: New topics, code examples to add, etc.
- Questions: Parts difficult to understand, sections needing additional explanation
- Success stories: Projects using what you learned from this series
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.
Permitted:
- ✅ Free viewing and downloading
- ✅ Use for educational purposes (classes, study groups, etc.)
- ✅ Modification and derivative works (translation, summarization, etc.)
Conditions:
- 📌 Author credit attribution required
- 📌 Indicate if modified
- 📌 Contact in advance for commercial use
Details: CC BY 4.0 License Full Text
Let's Get Started!
Are you ready? Start with Chapter 1 and begin your journey into the world of Process Informatics!
Chapter 1: PI Fundamentals and Data Utilization in Process Industries →
Update History
- 2025-10-25: v1.0 First release
Your PI learning journey starts here!