This comprehensive 5-day course provides a deep dive into AI and ML principles tailored to refinery operations. It bridges the gap between theory and real-world application, focusing on how AI can solve pressing refinery challenges—from predictive maintenance and process optimization to emissions reduction and advanced control.

By the end of this course, participants will be able to:

• Understand core concepts of AI and Machine Learning and their relevance to refinery operations
• Identify key refinery challenges that can benefit from AI-based solutions
• Gain hands-on experience with ML modeling techniques for prediction, classification, and optimization
• Evaluate and compare different ML algorithms and deployment strategies
• Understand hybrid modeling approaches that combine first principles with data-driven models
• Explore case studies demonstrating AI applications in CDU, FCCU, blending, energy systems, and more
• Build a roadmap for deploying AI initiatives in their own organizations, considering ROI, change management, and system integration

This course is designed for professionals in the refining and downstream oil & gas sector who are interested in leveraging AI and ML to drive operational and business value. Ideal attendees include:

• Process Engineers seeking to enhance optimization and troubleshooting through data-driven methods
• Operations & Production Engineers aiming to increase reliability, reduce variability, and improve efficiency
• Digital Transformation & Innovation Leads responsible for adopting and scaling AI initiatives
• Data Scientists & Analysts working with refinery or industrial data
• Engineering Managers & Technical Consultants exploring new technologies to solve strategic refinery problems

Day 1: Introduction to AI & Digital Transformation in Refineries
Session 1: Introduction to Artificial Intelligence & Refinery 4.0
•What is AI? Definitions & evolution
•The digital transformation journey in oil & gas
•Overview of AI’s role in downstream operations
•Pros & cons of using AI in industrial environments
•Common misconceptions & pitfalls

Session 2: Machine Learning Theory & Concepts (Part 1)
•Supervised, unsupervised, and reinforcement learning
•Regression, classification, and clustering basics

Day 2: Machine Learning Concepts & Industrial Tools
Session 3: Machine Learning Theory & Concepts (Part 2)
•Overfitting, underfitting, bias-variance tradeoff
•Model evaluation: MAE, RMSE, ROC-AUC, confusion matrix

Session 4: Tools, Frameworks, and Industrial Readiness
•Tools: Python, Scikit-learn, TensorFlow, PyTorch
•Industrial-grade platforms: Aspen AI, SelexMB™, MATLAB
•Data types in refineries: time-series, event, lab data, simulations
•Data preprocessing, cleaning, and contextualization

Day 2 Use Case Spotlight:
AI for Predictive Maintenance of Heat Exchangers (Data wrangling, model building, maintenance planning)

Day 3: AI Methods & Data-Driven Modeling in Refinery Units
Session 5: Data-Driven Modeling Techniques
•Feature engineering for refinery datasets
•Time-series forecasting (LSTM, ARIMA, Prophet)
•Clustering process conditions (K-means, DBSCAN)
•Neural networks in chemical processes (ANN, CNN basics)

Session 6: Hybrid Modeling (1st Principles + AI)
•What is hybrid modeling?
•Bridging physics and data: use of SelexMB, HYSYS
•Case for hybrid digital twins
•Handling missing variables, soft sensing

Day 4: AI Deployment & Operational Integration
Session 7: AI Model Lifecycle in Operations
•Training, validation, and testing
•Model drift and re-training strategies
•Edge vs. cloud deployment
•Integration with existing DCS/SCADA/PI systems

Day 4 Use Case Spotlight:
Blending Optimization Using ML + ANN (Blending ratio prediction, quality control, economic optimization)

Day 5: Strategic Application, Business Impact & Capstone
Session 8: Key Refinery Challenges AI Can Solve
•Energy efficiency and emissions reduction
•Catalyst deactivation prediction
•Process optimization (CDU, VDU, HCU, FCCU)
•Real-time anomaly detection and root cause analysis

Session 9: Business Cases and ROI Evaluation
•Creating a business case for AI in refining
•Value quantification: $/bbl, OPEX reduction, MTBF increase
•Pitfalls in scaling AI solutions
•Skills, culture, and change management

Session 10: Capstone & Group Activity
•Group project: Design an AI solution for a refinery challenge
•Presentations and feedback
•Q&A with an industry expert panel (optional if live session)
Day 5 Use Case Spotlight:
Real-time Fouling Prediction in Preheat Trains (Machine learning models on historical OLT and process data)

CDGA attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration.