Crude Oil Refinery Process Optimization Training Course

Crude Oil Refinery Process Optimization Training Course

Introduction

Crude Oil Refinery Process Optimization Training Course is designed to equip oil and gas professionals with advanced technical knowledge and practical skills for improving refinery performance, maximizing product yields, reducing operating costs, and enhancing energy efficiency. The course explores crude oil refining operations, process engineering, refinery optimization, crude assay evaluation, atmospheric and vacuum distillation, catalytic cracking, hydroprocessing, reforming, and process control. Participants will learn how crude oil characteristics, operating conditions, equipment performance, and feedstock variability influence refinery profitability and product quality. The training also introduces digital refinery technologies, advanced process control (APC), real-time process monitoring, predictive analytics, process simulation, and data-driven decision-making to support modern refinery operations and operational excellence.

Through practical exercises, technical discussions, and industry-based case studies, participants will develop the ability to identify process bottlenecks, troubleshoot operational inefficiencies, optimize heat integration, improve energy utilization, and minimize product losses. The course emphasizes refinery process safety, environmental compliance, emissions reduction, asset reliability, sustainable refining, and continuous improvement while balancing throughput, product specifications, equipment limitations, and economic objectives. By applying process optimization methodologies, performance benchmarking, mass and energy balance analysis, and refinery economics, participants will be better prepared to improve plant availability, increase operational flexibility, and support profitable, safe, and environmentally responsible refinery operations in a rapidly evolving petroleum industry.

Course Duration

5 days

Course Objectives

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

1. Understand the fundamentals of crude oil refining processes and integrated refinery operations.
2. Evaluate crude oil properties using crude assays, API gravity, sulfur content, and distillation curves.
3. Optimize atmospheric and vacuum distillation units to improve separation efficiency and product recovery.
4. Apply mass balance and energy balance analysis to identify process inefficiencies.
5. Improve refinery energy efficiency through heat integration, heat exchanger optimization, and utility management.
6. Optimize catalytic cracking, catalytic reforming, and hydroprocessing operations.
7. Use advanced process control (APC) to improve stability, throughput, and product consistency.
8. Identify refinery bottlenecks through process simulation, performance monitoring, and constraint analysis.
9. Apply predictive analytics and artificial intelligence (AI) to support process optimization and equipment performance monitoring.
10. Reduce operating expenditure through yield optimization, energy savings, and loss prevention.
11. Strengthen process safety through hazard identification, operating-envelope management, and risk-based decision-making.
12. Improve environmental performance through emissions monitoring, waste reduction, and energy-efficient operations.
13. Develop data-driven continuous improvement strategies using key performance indicators (KPIs), benchmarking, and refinery economics.

Target Audience

1. Refinery process engineers and chemical engineers.
2. Petroleum engineers and oil and gas production specialists.
3. Refinery operations managers and plant supervisors.
4. Process control and instrumentation engineers.
5. Production optimization and performance improvement specialists.
6. Maintenance, reliability, and asset management professionals.
7. Energy management and environmental compliance specialists.
8. Technical consultants, project engineers, and refinery optimization analysts.

Course Modules

Module 1: Fundamentals of Crude Oil Refining and Process Optimization

• Overview of refinery configurations and integrated process units.
• Crude oil composition, hydrocarbon chemistry, and feedstock characteristics.
• Refinery flow diagrams, process flow diagrams (PFDs), and piping and instrumentation diagrams (P&IDs).
• Refinery throughput, conversion, recovery, and product quality indicators.
• Introduction to process optimization frameworks and refinery performance benchmarking.
• Case Study: Evaluate a refinery experiencing declining throughput and identify potential process constraints across crude distillation, conversion, and product blending operations.

Module 2: Crude Oil Characterization and Feedstock Optimization

• Crude assay interpretation and feedstock quality assessment.
• API gravity, sulfur content, viscosity, acidity, and metals analysis.
• TBP and ASTM distillation curves and their operational significance.
• Crude compatibility, blending strategies, and feedstock selection.
• Impact of crude variability on refinery yields, fouling, corrosion, and operating costs.
• Case Study: Assess two crude oil blends and determine which feedstock combination offers a better balance of product yield, processing requirements, and expected refinery margin.

Module 3: Atmospheric and Vacuum Distillation Optimization

• Principles of atmospheric and vacuum distillation operations.
• Optimization of column temperature profiles, pressure, and reflux ratios.
• Side-draw quality, stripping steam, and product recovery management.
• Furnace efficiency, heat exchanger performance, and crude preheat train optimization.
• Troubleshooting flooding, entrainment, fouling, and separation inefficiencies.
• Case Study: Investigate excessive furnace fuel consumption and off-specification distillate products, then recommend operating changes to improve separation and energy performance.

Module 4: Conversion Processes and Product Yield Optimization

• Fluid catalytic cracking (FCC) process fundamentals and operating variables.
• Catalytic reforming, octane improvement, and gasoline production.
• Hydrocracking and hydrotreating process optimization.
• Hydrogen consumption, catalyst activity, and reaction severity management.
• Product yield improvement through integrated refinery process coordination.
• Case Study: Analyze declining gasoline production in a refinery and evaluate possible adjustments to FCC operating conditions, feed quality, and conversion-unit coordination.

Module 5: Energy Efficiency and Heat Integration

• Refinery energy consumption patterns and energy performance indicators.
• Heat exchanger network analysis and pinch analysis fundamentals.
• Furnace combustion efficiency and excess-air management.
• Steam systems, cooling water, and utility consumption optimization.
• Waste heat recovery, energy benchmarking, and carbon intensity reduction.
• Case Study: Review a refinery with rising fuel and steam costs and develop an energy optimization plan targeting heat recovery, furnace performance, and utility losses.

Module 6: Advanced Process Control, Automation, and Digital Optimization

• Distributed control systems (DCS) and supervisory control fundamentals.
• Advanced process control (APC) and model predictive control (MPC).
• Real-time process monitoring, alarm management, and operating-envelope control.
• Process simulation, digital twins, and online optimization.
• AI-driven analytics, anomaly detection, and predictive performance monitoring.
• Case Study: Develop an optimization strategy for a distillation column experiencing frequent temperature fluctuations and inconsistent product specifications using process data and advanced control concepts.

Module 7: Process Safety, Reliability, and Environmental Performance

• Process hazard analysis (PHA), HAZOP, and risk-based operational reviews.
• Equipment reliability, fouling prevention, and corrosion management.
• Safe operating limits, process deviations, and management of change.
• Air emissions, wastewater management, and refinery waste minimization.
• Energy transition, carbon emissions reduction, and sustainable refinery practices.
• Case Study: Examine a refinery heat exchanger experiencing repeated fouling and corrosion, and recommend a combined reliability, process safety, and environmental improvement strategy.

Module 8: Refinery Economics, Performance Benchmarking, and Continuous Improvement

• Refinery margin calculations and crude selection economics.
• Product yield valuation, operating costs, and profitability analysis.
• Key performance indicators (KPIs) for throughput, energy, reliability, and yield.
• Optimization under feedstock, equipment, and product specification constraints.
• Continuous improvement plans, performance dashboards, and operational decision-making.
• Case Study: Compare alternative refinery operating scenarios using crude costs, product prices, energy consumption, and unit constraints to identify the most economically attractive operating strategy.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.