Refinery Digitalization and Advanced Process Control Training Course

Refinery Digitalization and Advanced Process Control Training Course

Introduction

Refinery Digitalization and Advanced Process Control Training Course is designed to equip oil and gas professionals with the technical knowledge and practical skills required to accelerate digital transformation in refinery operations, optimize process performance, improve operational efficiency, and enhance plant reliability. This training course explores the application of advanced process control (APC), model predictive control (MPC), digital twins, smart sensors, distributed control systems (DCS), supervisory control and data acquisition (SCADA), and manufacturing execution systems (MES) to improve refinery productivity, energy efficiency, product quality, and process stability. Participants will learn how digital technologies integrate with existing refinery infrastructure to support data-driven decision-making, predictive maintenance, process optimization, and operational excellence.

Through practical exercises, technical demonstrations, industry scenarios, and case studies, participants will develop the ability to evaluate digital readiness, configure advanced control strategies, interpret real-time operational data, and identify opportunities for continuous improvement. Particular attention is given to AI-driven process optimization, real-time performance monitoring, predictive analytics, cybersecurity for industrial control systems, and digital integration across refinery assets. By the end of the training, participants will be better prepared to support smart refinery initiatives, improve process control performance, strengthen operational resilience, reduce operating costs, and contribute to sustainable, data-driven refining operations.

Course Duration

5 days

Course Objectives

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

1. Explain the principles of refinery digitalization, Industry 4.0, and smart manufacturing in modern petroleum refining operations.
2. Evaluate refinery digital maturity and identify opportunities for digital transformation and operational excellence.
3. Apply advanced process control (APC) techniques to improve process stability, throughput, and product quality.
4. Understand and assess model predictive control (MPC) strategies for complex refinery processes.
5. Integrate real-time operational data from DCS, SCADA, sensors, and industrial data historians.
6. Utilize artificial intelligence and machine learning to identify process inefficiencies and optimization opportunities.
7. Interpret process data using real-time analytics, performance dashboards, and industrial visualization tools.
8. Apply digital twin technology to simulate refinery processes, evaluate operating scenarios, and support decision-making.
9. Optimize energy consumption through advanced control strategies and energy-efficiency analytics.
10. Develop predictive maintenance approaches using equipment condition monitoring and predictive analytics.
11. Identify process bottlenecks and improve production performance through constraint-based optimization.
12. Strengthen industrial cybersecurity, data integrity, and control-system resilience in digital refinery environments.
13. Develop practical roadmaps for implementing and sustaining digital refinery optimization and continuous improvement initiatives.

Target Audience

1. Refinery managers and operations managers.
2. Process engineers and chemical engineers.
3. Instrumentation and control engineers.
4. Automation engineers and DCS/SCADA specialists.
5. Digital transformation managers and Industry 4.0 specialists.
6. Production optimization and performance improvement engineers.
7. Reliability, maintenance, and asset management professionals.
8. IT/OT integration specialists, data analysts, and industrial cybersecurity professionals.

Course Modules

Module 1: Fundamentals of Refinery Digitalization

• Introduction to digital transformation in the oil refining industry.
• Industry 4.0 technologies and smart refinery operating models.
• Digital maturity assessments and refinery technology roadmaps.
• Integration of operational technology (OT) and information technology (IT).
• Business cases for refinery digitalization, including productivity, reliability, and cost reduction.
• Case study: Assessing the digital maturity of a conventional refinery and developing a phased roadmap for implementing connected operations, real-time monitoring, and integrated performance reporting.

Module 2: Advanced Process Control Principles and Applications

• Fundamentals of regulatory control and multivariable process control.
• Principles and applications of advanced process control (APC).
• Model predictive control (MPC) architecture and optimization objectives.
• Control-loop performance assessment, tuning, and troubleshooting.
• APC implementation challenges, operator engagement, and performance monitoring.
• Case study: Improving crude distillation unit stability using multivariable control to reduce process variability, maintain product specifications, and increase throughput within operating constraints.

Module 3: Industrial Data Integration and Real-Time Monitoring

• Data acquisition through DCS, SCADA, PLCs, and smart instrumentation.
• Industrial data historians and time-series data management.
• Real-time data integration across refinery process units.
• Data quality assurance, validation, contextualization, and reconciliation.
• Operational dashboards, alarm analytics, and key performance indicators (KPIs).
• Case study: Integrating data from refinery control systems and laboratory information systems to improve visibility into product quality, process deviations, and operating performance.

Module 4: Artificial Intelligence and Machine Learning for Refinery Optimization

• Applications of AI and machine learning in refining operations.
• Predictive analytics for process performance and equipment behavior.
• Anomaly detection and early identification of abnormal operating conditions.
• Data-driven optimization of yields, energy consumption, and operating parameters.
• Model validation, explainability, and human oversight in industrial AI.
• Case study: Developing an illustrative machine-learning model to identify operating conditions associated with excessive energy consumption in a refinery heating system.

Module 5: Digital Twins and Process Simulation

• Digital twin architecture and applications in refinery environments.
• Integration of process simulators with real-time plant data.
• Dynamic process modeling and virtual commissioning.
• Scenario analysis, control strategy testing, and operational decision support.
• Digital twin lifecycle management, calibration, and model validation.
• Case study: Using a refinery process digital twin to compare alternative operating scenarios for a distillation unit and assess their effects on energy use, throughput, and product quality.

Module 6: Energy Efficiency and Production Optimization

• Refinery energy management and process heat integration.
• Advanced control strategies for furnaces, boilers, and heat exchangers.
• Identification of production bottlenecks and process constraints.
• Optimization of feedstock processing, product yields, and blending operations.
• Energy performance indicators, emissions monitoring, and sustainability analytics.
• Case study: Evaluating advanced control opportunities in a refinery furnace to reduce excess fuel consumption while maintaining safe operating limits and required process temperatures.

Module 7: Predictive Maintenance and Industrial Cybersecurity

• Predictive maintenance using condition monitoring and operational data.
• AI-enabled fault detection for pumps, compressors, turbines, and heat exchangers.
• Integration of maintenance management systems with refinery data platforms.
• Industrial cybersecurity risks in connected control systems.
• OT network segmentation, access control, incident response, and system resilience.
• Case study: Designing a predictive maintenance workflow for a critical refinery pump using vibration, temperature, and operating data, alongside appropriate industrial cybersecurity controls.

Module 8: Digital Implementation, Performance Measurement, and Continuous Improvement

• Digital project planning, prioritization, and implementation governance.
• Integration of APC solutions with existing refinery control infrastructure.
• Change management, workforce skills, and operator adoption.
• Measurement of digital project performance using operational and financial KPIs.
• Continuous improvement, scalability, and long-term digital strategy.
• Case study: Preparing an implementation roadmap for a refinery digitalization program covering APC deployment, real-time dashboards, predictive maintenance, workforce training, and benefits realization.

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.