Process Informatics Dojo

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📚 Introduction Series (5 Series)

📘

Process Informatics (PI) Introduction ⭐ Required

Fundamental concepts of PI and practical data utilization in process industries

Beginner to Intermediate 90-120 min 4 Chapters, 35 Examples

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📗

Process Monitoring and Control Introduction

Sensor data acquisition, anomaly detection, statistical process management, PID control

Beginner to Intermediate 120-150 min 5 Chapters, 40 Examples

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📗

Process Optimization Introduction

Optimization problem formulation, linear and nonlinear programming, multi-objective optimization, constrained optimization

Beginner to Intermediate 130-160 min 5 Chapters, 45 Examples

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📗

Design of Experiments (DOE) Introduction

Orthogonal arrays, factorial design, response surface methodology, Taguchi method, Python automation

Beginner to Intermediate 120-150 min 5 Chapters, 40 Examples

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📗

Quality Management and Quality Assurance Introduction

Seven QC tools, statistical quality management, process capability analysis, quality prediction models

Beginner to Intermediate 120-150 min 5 Chapters, 40 Examples

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🛠️ Practical Technology Series (5 Series)

📗

Process Data Analysis Practice

Large-scale data handling, PCA, PLS regression, multivariate statistical process management, soft sensor development

Intermediate to Advanced 150-180 min 5 Chapters, 50 Examples

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📗

Scale-Up and Scale-Down Introduction

Scale-up fundamentals, similarity rules, reaction engineering, mass and heat transfer, machine learning prediction

Intermediate to Advanced 140-170 min 5 Chapters, 35 Examples

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📗

Process Simulation Introduction

Process simulation fundamentals, mass and energy balance, distillation, extraction, reactor design, Aspen Plus/DWSIM integration

Intermediate to Advanced 150-180 min 5 Chapters, 40 Examples

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📕

Digital Twin Construction Introduction

Digital twin concepts, real-time data integration, hybrid modeling, virtual process optimization

Advanced 130-160 min 5 Chapters, 35 Examples

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📗

Process Safety Assessment Introduction

HAZOP, FMEA, risk assessment, safety improvement through anomaly detection, accident prediction

Intermediate 100-120 min 4 Chapters, 30 Examples

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🚀 Advanced Series - Latest AI Technology (4 Series)

📕

Process Optimization using Bayesian Optimization

Bayesian optimization theory, acquisition functions considering experimental cost, constrained and multi-objective Bayesian optimization

Advanced 140-170 min 5 Chapters, 35 Examples

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📕

Process Modeling using Deep Learning

RNN/LSTM/GRU time-series forecasting, Transformer, CNN image analysis, reinforcement learning control optimization

Advanced 150-180 min 5 Chapters, 40 Examples

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📕

Autonomous Process Operation using AI Agents

AI agent fundamentals, process environment modeling, reward design, multi-agent collaborative control

Advanced 130-160 min 5 Chapters, 35 Examples

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📕

Process Ontology and Knowledge Graph

Ontology design, knowledge graph construction (RDF/OWL), semantic search, LLM integration

Advanced 140-170 min 5 Chapters, 35 Examples

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🏭 Industrial Application Series (4 Series)

📙

AI Application to Chemical Plants

Process monitoring, predictive maintenance, real-time optimization, supply chain management, implementation strategy

Intermediate to Advanced 150-180 min 5 Chapters, 40 Examples

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📙

AI Application to Pharmaceutical Manufacturing

GMP-compliant quality management, batch record analysis, PAT, continuous production optimization, regulatory compliance

Intermediate to Advanced 150-180 min 5 Chapters, 40 Examples

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📙

AI Application to Food Processes

Quality prediction, flavor optimization, fermentation control, traceability, shelf-life prediction

Intermediate 150-180 min 5 Chapters, 40 Examples

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📙

AI Application to Semiconductor Manufacturing

Wafer process control, defect inspection, yield improvement, APC, FDC

Advanced 150-180 min 5 Chapters, 40 Examples

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References

Montgomery, D. C. (2019). Design and Analysis of Experiments (9th ed.). Wiley.
Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.
Seborg, D. E., Edgar, T. F., Mellichamp, D. A., & Doyle III, F. J. (2016). Process Dynamics and Control (4th ed.). Wiley.
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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