Statistical Process Control (SPC)

Course details

A Systematic studied approach to choose pertinent techniques and their integration into a cooperative management and process control system will significantly enhance plant operation and company’ profitably.

This course gives detail explanations about Process Models, Model Based Automatic Control, Statistical Process Control, and Higher Level Operations of Process Control such as Process Optimisation, Process Monitoring, and Process Supervision. 

Example cases such as Separator Process, Reactors, Power System, HVAC System are used to illustrate the application of Advanced Process Control.

Who should attend:

• Instrumentation Engineers and Technicians
• Telemetic and Control Systems Engineer
• Mechanical and Operation Engineers
• Production Supervisors, Asset Management Team Members who responsible of select, specify, design and operate process control

Course Outline:

INTRODUCTION:

PROCESS MODELS

• Mechanistic Models
• Black Box Models
• Quantitative Models
• Statistical Models

MODEL BASED AUTOMATIC CONTROL

• PID Control
• Predictive Constrained Control
• Multivariable Control
• Robust Control and Internal Model Principles

STATISTICAL PROCESS CONTROL (SPC)

• Conventional SPC
• Algorithmic SPC
• Active SPC

HIGHER LEVEL OPTIMISATION

• Process Optimization
• Process Monitoring, Fault Detection, Location and Diagnosis
• Process Supervision Via Artificial Intelligence Techniques

PRACTICAL WORK:

• Design and Practices in Process Control
• Conventional SPC
• Algorithmic SPC
• Active SPC

HIGHER LEVEL OPTIMISATION

• Process Optimisation
• Process Monitoring, Fault Detection, Location and Diagnosis
• Process Supervision Via Artificial Intelligence Techniques

PRACTICAL WORK: Design and Practices in Process Control

CONTROL EXAMPLES

• Reactor
• Separator Process
• Power System
• HVAC System

OPTIMISATION EXAMPLES 

SUMMARY