Introduction

As industries shift towards Industry 4.0 and smart automation, the demand for advanced process control (APC) strategies has never been greater. Traditional PID control is no longer sufficient to handle the complexities of multi-variable, nonlinear, and dynamic industrial processes. This course provides a deep dive into modern APC techniques, covering model predictive control (MPC), adaptive control, fuzzy logic, and AI-driven process optimization. Participants will gain hands-on experience with simulation tools, industrial applications, and real-time process control strategies to optimize plant performance, reduce variability, and enhance efficiency.

Course Objectives

• Understand modern APC techniques and their advantages over conventional control.
• Implement model predictive control (MPC) for complex process optimization.
• Apply adaptive, nonlinear, and AI-driven control strategies.
• Improve PID tuning and multi-variable process interactions.
• Optimize control systems using real-time data analytics, machine learning, and digital twins.
• Learn advanced troubleshooting and fault detection techniques in process control.

Who Should Attend?

• Process Control Engineers and Automation Specialists
• Instrumentation and Control Engineers
• Industrial AI and Machine Learning Engineers
• System Integrators and Process Optimization Professionals
• Plant Managers and Operations Engineers
• Professionals in Oil & Gas, Power, Chemical, Pharmaceutical, and Manufacturing Industries

Day 1: Fundamentals of Advanced Process Control

Module 1: Introduction to Advanced Process Control (APC)

• The evolution of process control: From PID to AI-based APC
• Benefits and applications of APC in modern industries
• Case studies: APC-driven efficiency improvements

Module 2: Advanced PID Control and Loop Tuning

• Common PID tuning techniques and limitations
• Methods for improving PID performance in complex processes
• Auto-tuning and self-correcting PID controllers

Module 3: Multi-Variable Process Interactions and Decoupling

• Understanding multi-loop control challenges
• Decoupling strategies for interacting control loops
• Real-world case study: Decoupling a complex chemical process

Day 2: Model Predictive Control (MPC) and Optimization

Module 4: Introduction to Model Predictive Control (MPC)

• How MPC predicts future process behavior
• Comparison of MPC vs. PID in dynamic systems
• Industrial applications: Refining, chemical, power, and pharma

Module 5: Implementing Model Predictive Control in Industrial Applications

• Key elements: Dynamic models, constraints, and optimization
• Real-time MPC tuning and troubleshooting
• Hands-on MPC simulation for distillation column control

Module 6: MPC-Based Process Optimization

• Using MPC for energy efficiency and cost reduction
• Adaptive MPC: Handling model inaccuracies in real-time
• Case study: MPC-based optimization in Oil & Gas processing

Day 3: Adaptive, Nonlinear, and AI-Based Control

Module 7: Adaptive and Self-Tuning Controllers

• Overview of adaptive control strategies
• Handling time-varying and nonlinear processes
• Applications of self-tuning regulators (STRs) in automation

Module 8: Nonlinear and Fuzzy Logic Control

• Introduction to nonlinear process control techniques
• Implementing fuzzy logic control in industrial systems
• Hands-on simulation: Fuzzy logic vs. traditional control

Module 9: AI and Machine Learning in Process Control

• The role of AI in optimizing control loops
• AI-based fault detection and predictive control
• Real-time case study: Using machine learning for process stability

Day 4: Process Data Analytics and Fault Detection

Module 10: Process Data Analytics and Digital Twins

• Introduction to big data analytics in process control
• Creating digital twins for predictive and prescriptive control
• Case study: AI-powered predictive maintenance in manufacturing

Module 11: Advanced Process Fault Detection and Alarm Management

• Fault detection and isolation (FDI) techniques
• Alarm rationalization and real-time anomaly detection
• Hands-on exercise: Implementing AI-driven fault prediction

Module 12: Cybersecurity in Process Control Systems

• Cyber threats in process automation and industrial networks
• Secure remote monitoring and cloud-based APC
• Best practices for SCADA, DCS, and PLC security

Day 5: Future Trends, Industry Applications, and Certification

Module 13: The Future of Process Control Optimization

• Emerging trends: Edge computing, 5G, and blockchain in process control
• Autonomous control systems and self-learning AI controllers
• Sustainability and green process control innovations

Module 14: Certification Preparation and Exam Readiness

• Reviewing APC concepts, frameworks, and methodologies
• Sample certification exam questions and practice tests
• Best practices for continuous process control improvement

Module 15: Industry Case Studies and Expert Panel Discussion

• Real-world APC implementations in Oil & Gas, Chemical, and Power industries
• Lessons learned from process control failures and their solutions
• Open Q&A with industry experts on APC future trends