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📕 AI Applications in Chemical Plants

Chemical Plant AI Applications - Process Informatics Dojo Industrial Application Series

⏱️ 150-180 min 📚 5 Chapters 💻 40 Code Examples 📊 Practical/Application Level

📖 Series Overview

This series teaches practical applications of AI technology in chemical plants. You will master implementation-level solutions using machine learning, deep learning, and reinforcement learning for chemical industry-specific challenges such as continuous processes, batch processes, distillation, and reactor control.

From process monitoring, predictive maintenance, real-time optimization, and supply chain management to implementation strategies, we systematically cover practical knowledge that can be immediately applied in chemical plant operations.

Each chapter provides abundant code examples based on actual plant data scenarios, allowing you to master the application of AI technology to chemical plants through Python implementation.

🎯 Learning Objectives

📚 Prerequisites

📚 Chapter Structure

1

Process Monitoring and Soft Sensors

Learn AI-based process monitoring technologies for chemical plants. Implement anomaly detection (statistical methods, deep learning), quality prediction, and soft sensor design.

⏱️ 30-35 min 💻 8 Code Examples
2

Predictive Maintenance and RUL Estimation

Learn equipment degradation prediction and Remaining Useful Life (RUL) estimation. Implement vibration data analysis, LSTM/TCN-based time series prediction, and failure mode classification.

⏱️ 30-35 min 💻 8 Code Examples
3

Real-time Optimization and APC

Learn real-time process optimization and Advanced Process Control (APC). Implement online optimization, MPC, reinforcement learning control, and economic optimization.

⏱️ 35-40 min 💻 8 Code Examples
4

Supply Chain and Production Optimization

Learn supply chain optimization for chemical plants. Implement demand forecasting, production scheduling, inventory optimization, and distribution planning.

⏱️ 30-35 min 💻 8 Code Examples
5

Implementation Strategy and Case Studies

Learn actual plant deployment strategies and integration case studies of AI technology. Practice data integration, model updating, operational maintenance, and ROI evaluation.

⏱️ 30-35 min 💻 8 Code Examples

🔄 Learning Flow

graph TD A[Chapter 1: Process Monitoring] --> B[Chapter 2: Predictive Maintenance] B --> C[Chapter 3: Real-time Optimization] C --> D[Chapter 4: Supply Chain Optimization] D --> E[Chapter 5: Implementation Strategy] A --> A1[Anomaly Detection] A --> A2[Quality Prediction] A --> A3[Soft Sensors] B --> B1[Degradation Prediction] B --> B2[RUL Estimation] B --> B3[Failure Mode Classification] C --> C1[Online Optimization] C --> C2[MPC Implementation] C --> C3[Reinforcement Learning Control] D --> D1[Demand Forecasting] D --> D2[Production Scheduling] D --> D3[Inventory Optimization] E --> E1[Data Integration] E --> E2[Model Updating] E --> E3[Operational Maintenance] style A fill:#11998e,stroke:#0d7a6f,color:#fff style B fill:#15a896,stroke:#10856f,color:#fff style C fill:#1fb89e,stroke:#178c76,color:#fff style D fill:#28c7a6,stroke:#1e9b80,color:#fff style E fill:#38ef7d,stroke:#2bc766,color:#fff

❓ Frequently Asked Questions

Q1: Who is the target audience for this series?

Chemical plant field engineers, process engineers, data scientists, and graduate students majoring in chemical engineering. You can understand the content with basic Python and chemical engineering knowledge.

Q2: How does this differ from other Process Informatics Dojo series?

This series focuses on challenges specific to chemical plants. It covers practical challenges in the chemical industry such as continuous processes, batch processes, distillation, and reactors. Foundational techniques can be learned in the "Introduction to Process Monitoring" and "Introduction to Process Optimization" series.

Q3: Can this be applied to actual plants?

The code examples in this series are designed assuming application to actual plants. Chapter 5 provides practical coverage of implementation strategies, data integration, model updating, and operational maintenance. However, safety evaluations and plant-specific constraints must be considered individually.

Q4: What programming environment is required?

Python 3.8 or higher, major libraries (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow), and optimization libraries (SciPy, Pyomo) are required. GPU is recommended but not essential for deep learning sections.

Q5: How long does it take to complete the learning?

All 5 chapters are expected to take approximately 150-180 minutes of learning time. Including code implementation, thorough engagement may take 2-3 days. Each chapter is independent, so you can start learning from any chapter of interest.

References

  1. Montgomery, D. C. (2019). Design and Analysis of Experiments (9th ed.). Wiley.
  2. Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.
  3. Seborg, D. E., Edgar, T. F., Mellichamp, D. A., & Doyle III, F. J. (2016). Process Dynamics and Control (4th ed.). Wiley.
  4. McKay, M. D., Beckman, R. J., & Conover, W. J. (2000). "A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code." Technometrics, 42(1), 55-61.

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