Advanced Process Control

Overview of the Course

The Advanced Process Control (APC) course at Pertecnica Engineering is designed to provide participants with a deep understanding of sophisticated techniques and strategies used to optimize and control complex industrial processes. This course covers advanced control methods, their implementation, and their applications to improve process performance, efficiency, and reliability.

Detailed Course Content

  1. Introduction to Advanced Process Control
    • Overview of Process Control: Basic Principles and Objectives
    • Importance of Advanced Process Control: Enhancing Process Efficiency, Stability, and Flexibility
    • Differences Between Basic and Advanced Control: Control Strategies, Techniques, and Technologies
    • APC in Industry: Applications in Chemical, Oil & Gas, Pharmaceutical, and Manufacturing Industries
  2. Advanced Control Strategies
    • Model Predictive Control (MPC):
      • Principles of MPC: Prediction Models, Control Objectives, and Constraints
      • Implementing MPC: Algorithms, Software Tools, and Case Studies
      • Benefits and Challenges of MPC: Performance, Complexity, and Computational Requirements
    • Adaptive Control:
      • Fundamentals of Adaptive Control: Parameter Estimation, Model Updating, and Adaptation Strategies
      • Implementing Adaptive Control Systems: Algorithms and Applications
      • Advantages and Disadvantages of Adaptive Control: Flexibility, Robustness, and Complexity
    • Inferential Control:
      • Introduction to Inferential Control: Estimating Process Variables from Indirect Measurements
      • Implementing Inferential Control: Models, Techniques, and Applications
      • Benefits and Challenges: Accuracy, Reliability, and Computational Effort
    • Fault Detection and Diagnosis (FDD):
      • Principles of FDD: Identifying and Diagnosing Process Faults
      • Techniques for FDD: Statistical Methods, Model-Based Approaches, and Hybrid Systems
      • Implementing FDD Systems: Tools, Algorithms, and Case Studies
  3. Control System Design and Tuning
    • Designing Advanced Control Systems:
      • System Requirements: Objectives, Constraints, and Specifications
      • Control System Architecture: Controllers, Sensors, Actuators, and Communication
      • Integration with Existing Systems: SCADA, DCS, and PLCs
    • Tuning Advanced Controllers:
      • Techniques for Tuning MPC, Adaptive Controllers, and Other Advanced Systems
      • Optimization Methods: Algorithms, Tools, and Best Practices
      • Performance Evaluation: Metrics, Benchmarks, and Continuous Improvement
  4. Data-Driven Control Approaches
    • Data-Driven Models:
      • Introduction to Data-Driven Models: Machine Learning, Data Mining, and Statistical Models
      • Implementing Data-Driven Control: Algorithms, Tools, and Applications
      • Benefits and Challenges: Data Quality, Model Accuracy, and Computational Requirements
    • Real-Time Data Analysis:
      • Techniques for Real-Time Data Analysis: Algorithms, Software Tools, and Applications
      • Integration with Control Systems: Data Acquisition, Processing, and Feedback
      • Use Cases and Case Studies: Examples from Various Industries
  5. Control System Integration and Interoperability
    • Integration with Other Systems:
      • Connecting APC Systems with PLCs, SCADA, and DCS
      • Communication Protocols: Ethernet/IP, Modbus, OPC, and Fieldbus
      • Interfacing with External Systems: Databases, ERP Systems, and MES
    • Interoperability Challenges:
      • Ensuring Compatibility: Standards, Protocols, and Interfaces
      • Addressing Common Issues: Data Consistency, System Reliability, and Integration Costs
  6. Case Studies and Practical Exercises
    • Real-World Applications: Examples of Advanced Process Control in Various Industries
    • Hands-On Practical Sessions: Designing, Implementing, and Tuning Advanced Control Systems
    • Simulation Exercises: Modeling and Testing Control Strategies in Virtual Environments
    • Capstone Project: Developing an Advanced Process Control Solution for a Specific Industrial Application
  7. Emerging Trends and Technologies
    • Advances in APC: AI Integration, Digital Twins, and Predictive Analytics
    • Future Trends: Industry 4.0, Smart Manufacturing, and IoT
    • Implications for Industry: Enhancing Performance, Flexibility, and Innovation

Who Should Attend?

  • Process Control Engineers and Technicians
  • Automation Engineers and Specialists
  • Manufacturing Engineers and Operations Managers
  • Systems Integrators and Technical Support Personnel
  • Data Scientists and Analysts
  • Students and Graduates in Engineering and Technology
  • Professionals interested in advanced control techniques and optimization

Our Training Methodology

Pertecnica Engineering’s Advanced Process Control course combines theoretical instruction with practical, hands-on experience. Participants will gain a deep understanding of advanced control strategies and their applications through interactive lectures, practical exercises, and real-world case studies. Our expert instructors provide detailed guidance on designing, implementing, and optimizing advanced control systems.

Why Choose Pertecnica Engineering?

  • Experienced Instructors: Learn from professionals with extensive experience in advanced process control and optimization.
  • Practical Training: Emphasizing hands-on learning, we provide direct experience with advanced control systems, programming, and integration.
  • Comprehensive Curriculum: The course covers a wide range of topics, from fundamental principles to advanced techniques and emerging trends.
  • Industry Recognition: Pertecnica Engineering is highly respected in the industry, enhancing your credentials and career prospects.
  • Flexible Learning Options: We offer both in-person and online courses to fit your schedule and learning preferences.

Enhance your expertise in advanced process control with our specialized course and learn to design, implement, and optimize sophisticated control systems for complex industrial processes.