Introduction to Process Monitoring and Control Series v1.0
From Statistical Process Control (SPC) to Real-time Monitoring Systems - Complete Practical Guide
Series Overview
This series is a comprehensive educational content consisting of 5 chapters that progressively covers process monitoring and control in process industries, from fundamentals to practical applications. It comprehensively addresses sensor data acquisition, Statistical Process Control (SPC), anomaly detection, PID control, and real-time monitoring system construction.
Features:
- ✅ Practice-oriented: 40 executable Python code examples
- ✅ Systematic structure: 5-chapter composition covering fundamentals to applications progressively
- ✅ Industrial applications: Rich real-world examples from chemical plants and manufacturing processes
- ✅ Latest technologies: Anomaly detection using machine learning, real-time dashboard construction
Total learning time: 120-150 minutes (including code execution and exercises)
How to Study
Recommended Learning Sequence
For beginners (first time learning process control):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Required time: 120-150 minutes
Python experts (with basic data analysis knowledge):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Required time: 90-120 minutes
Control engineers (with control theory knowledge):
- Chapter 1 (quick review) → Chapter 3 → Chapter 5
- Required time: 60-80 minutes
Chapter Details
Chapter 1: Fundamentals of Process Monitoring and Sensor Data Acquisition
Learning Content
- Fundamentals of Process Monitoring
- Purpose and importance of monitoring
- Role of sensors in process industries
- Overview of SCADA systems and DCS (Distributed Control System)
- Types of Sensors and Data Acquisition
- Characteristics of temperature, pressure, flow, and level sensors
- Sampling theory and Nyquist theorem
- A/D conversion and measurement accuracy
- Basic Processing of Time Series Data
- Time series data handling with Pandas
- Data logging and buffering
- Resampling and interpolation
- Data Quality Assessment
- Detection of missing values and noise
- Diagnosis of sensor drift
- Identification and processing of outliers
Learning Objectives
- ✅ Explain basic concepts of process monitoring
- ✅ Understand characteristics of sensor data and sampling theory
- ✅ Process time series sensor data with Python
- ✅ Apply basic data quality assessment methods
Chapter 2: Statistical Process Control (SPC)
Learning Content
- SPC Fundamental Theory
- Purpose and history of statistical process control
- Differences between common cause and special cause variation
- Distinction between control state and specification limits
- Shewhart Control Charts
- X̄-R chart (mean-range chart)
- I-MR chart (individual-moving range chart)
- Calculation of 3-sigma control limits
- Process Capability Analysis
- Calculation and interpretation of Cp, Cpk indices
- Practical process capability evaluation
- Relationship with specifications
- Advanced SPC Methods
- CUSUM (cumulative sum) control chart
- EWMA (exponentially weighted moving average) control chart
- Multivariate control chart (Hotelling's T²)
- SPC Abnormality Detection Rules
- Western Electric rules
- Run tests and trend detection
- Alarm generation system design
Learning Objectives
- ✅ Understand SPC basic theory and types of control charts
- ✅ Create control charts and calculate control limits
- ✅ Calculate and evaluate process capability indices (Cp, Cpk)
- ✅ Implement advanced control charts such as CUSUM and EWMA
- ✅ Apply abnormality detection rules and design alarm systems
Chapter 3: Anomaly Detection and Process Monitoring
Learning Content
- Fundamentals of Anomaly Detection
- Purpose and types of anomaly detection (point anomalies, contextual anomalies, collective anomalies)
- Supervised vs unsupervised anomaly detection
- Evaluation metrics (Precision, Recall, F1 score)
- Statistical Anomaly Detection Methods
- Threshold-based anomaly detection
- Z-score method and modified Z-score method
- Hotelling's T² statistic
- Anomaly Detection Using Machine Learning
- Isolation Forest
- One-Class SVM (normal operation modeling)
- Local Outlier Factor (LOF)
- Time Series Anomaly Detection Using Deep Learning
- Reconstruction error method using Autoencoder
- Time series anomaly detection using LSTM
- Anomaly score calculation and threshold setting
- Alarm Management
- Alarm prioritization
- False alarm reduction techniques
- Alarm flood prevention
- Root cause analysis
Learning Objectives
- ✅ Understand types of anomaly detection and evaluation metrics
- ✅ Detect anomalies using statistical methods
- ✅ Apply machine learning algorithms to anomaly detection
- ✅ Perform time series anomaly detection with LSTM and Autoencoder
- ✅ Design effective alarm management systems
Chapter 4: Feedback Control and PID Control
Learning Content
- Fundamentals of Feedback Control
- Basic configuration of control systems (setpoint, controlled variable, manipulated variable)
- Principles of feedback control
- Transfer functions and dynamic systems
- Step Response of First-Order Systems
- Characteristics of first-order lag systems
- Understanding time constant and gain
- Application examples to real processes
- PID Controller Design
- Principles of proportional (P) control and offset
- Elimination of steady-state error by integral (I) control
- Response improvement by derivative (D) control
- Implementation of PID controllers
- PID Controller Tuning
- Ziegler-Nichols method (ultimate sensitivity method, step response method)
- Adjustment of tuning parameters
- Evaluation of control performance (overshoot, settling time)
- Practical Control Problems
- Integral windup and anti-windup measures
- Manipulated variable saturation handling
- Fundamentals of cascade control
- Combination with feedforward control
Learning Objectives
- ✅ Explain basic principles of feedback control
- ✅ Understand roles of each element (P, I, D) of PID controllers
- ✅ Simulate first-order systems and PID controllers with Python
- ✅ Determine PID parameters using Ziegler-Nichols method
- ✅ Address practical control problems (windup, etc.)
Chapter 5: Practical Real-time Process Monitoring Systems
Learning Content
- Real-time Monitoring System Architecture
- Overall system design (data collection, processing, visualization)
- Data streaming and buffering
- Microservices architecture
- Dashboard Design with Plotly Dash
- Basic structure and components of Dash
- Implementation of real-time graphs
- Callbacks and interactive features
- Data Streaming with WebSocket
- Fundamentals of WebSocket and bidirectional communication
- Implementation of real-time data delivery
- Client-server coordination
- Multi-Chart Monitoring Interface
- Simultaneous display of multiple process variables
- Trend charts and gauge displays
- Alarm display and status indicators
- Construction of Integrated Monitoring Systems
- Historical database integration
- KPI calculation and reporting
- Notification systems (email, Slack integration)
- Case study: Chemical plant monitoring system
Learning Objectives
- ✅ Design real-time monitoring system architecture
- ✅ Build interactive dashboards with Plotly Dash
- ✅ Implement real-time data streaming with WebSocket
- ✅ Develop multi-chart monitoring interfaces
- ✅ Build complete integrated monitoring systems
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain basic principles of process monitoring and control
- ✅ Understand SPC theory and types of control charts
- ✅ Know anomaly detection methods (statistical, machine learning, deep learning)
- ✅ Understand PID control theory and tuning methods
- ✅ Understand real-time monitoring system architecture
Practical Skills (Doing)
- ✅ Acquire and preprocess sensor data
- ✅ Create and evaluate control charts (X̄-R, I-MR, CUSUM, EWMA)
- ✅ Build anomaly detection models with machine learning and deep learning
- ✅ Implement and tune PID controllers
- ✅ Build real-time dashboards with Plotly Dash
- ✅ Design alarm management systems
Application Ability (Applying)
- ✅ Design and implement complete process monitoring systems
- ✅ Select appropriate SPC and anomaly detection methods for real processes
- ✅ Evaluate and improve control system performance
- ✅ Respond to practical tasks as a process engineer
FAQ (Frequently Asked Questions)
Q1: Can I understand it without knowledge of control theory?
A: Yes. Chapter 4 explains control fundamentals from the basics. Advanced mathematics is minimized, and it is designed to be intuitively understood through Python simulations.
Q2: What is the difference between this series and the PI introduction series?
A: The PI introduction series focuses on "process modeling and optimization," while this series focuses on "real-time monitoring and control." They are complementary, and combining them allows you to acquire comprehensive PI knowledge.
Q3: Can it be applied to actual plants?
A: Yes. The code examples in this series are for educational purposes, but they are practical content assuming application to real processes. Chapter 5 covers a case study of an actual chemical plant monitoring system.
Q4: What level of Python skills is required?
A: It is desirable to have basic Python syntax, fundamentals of Pandas/NumPy, and experience with visualization using Matplotlib. Machine learning experience is helpful in Chapter 3 but is not mandatory.
Q5: What should I learn next?
A: The following topics are recommended:
- Model Predictive Control (MPC): Advanced methods for multivariable control
- Design of Experiments (DOE): Efficient process optimization
- Digital Twin: Construction of virtual process models
- Reinforcement Learning Control: Adaptive control using AI
Next Steps
Recommended Actions After Series Completion
Immediate (within 1 week):
1. ✅ Publish the integrated monitoring system from Chapter 5 on GitHub
2. ✅ Evaluate applicability to your company's processes
3. ✅ Create prototype SPC control charts with real data
Short-term (1-3 months):
1. ✅ Build soft sensors with real process data
2. ✅ Implement and tune anomaly detection models
3. ✅ Full-scale introduction of real-time dashboards
4. ✅ Learn Model Predictive Control (MPC)
Long-term (6 months or more):
1. ✅ Build integrated monitoring and control systems for entire processes
2. ✅ Develop digital twins
3. ✅ Conference presentations and paper writing
4. ✅ Career development as a process control 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 25, 2025
Version: 1.0
We Welcome Your Feedback
We look forward to your feedback to improve this series:
- Typos, errors, technical inaccuracies: Please report via GitHub repository Issues
- Improvement suggestions: New topics, code examples you would like added, etc.
- Questions: Parts that were difficult to understand, areas where additional explanation is needed
- Success stories: Projects where you used what you learned from 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.
Permitted:
- ✅ Free viewing and downloading
- ✅ Use for educational purposes (classes, study groups, etc.)
- ✅ Modifications and derivative works (translations, summaries, etc.)
Conditions:
- 📌 Author credit display required
- 📌 Indicate if modified
- 📌 Contact us in advance for commercial use
Details: CC BY 4.0 License Full Text
Let's Get Started!
Ready? Start with Chapter 1 and begin your journey into the world of process monitoring and control!
Chapter 1: Fundamentals of Process Monitoring and Sensor Data Acquisition →
Update History
- 2025-10-25: v1.0 First edition published
Your journey in learning process monitoring and control starts here!