By the end of this course delegates will be able to:

• Understand the essentials of Advanced Process Control (APC)
• Grasp the key differences between the various technologies
• Perform simple APC design strategies and implementations
• Be able to perform PID control
• Troubleshoot simple APC problems
• Identify processes suited to APC

Electrical engineers, instrumentation and control engineers, process control engineers, process engineers, senior technicians, automation engineers, chemical engineers, chemical plant technologists, system integrators

Fundamentals of Process Control

• Processes, controllers and tuning
• PID controllers – P, I and D modes off operation
• Load disturbances and offset, Speed, stability and robustness
• Gain, dead time and time constants, Process noise, Feedback controllers

 Fundamentals of Tuning PID Loops

• Open and closed loop tuning, Ziegler Nichols
• Fine tuning for different process types, Lambda tuning
• Ten different rules compared, Cascade systems, Feedforward control
• Dead time, Models and disturbances

Internal Model Control (IMC)

• Open loop model of the process in parallel with the process
• Control system in two blocks, Equivalence with a classical controller
• Disturbances rejection and control, IMC and delays
• IMC and feed forward (measured disturbances rejection)

 Model Predictive Control (MPC)

• Single input / output versus multivariable control
• Example on a binary column causality graph
• Constraints and planning ahead before acting
• Different notions of models, Action model - measured disturbances model
• Unmeasured disturbance models, Reference trajectories

 MPC: Model Representations

• State space representation, Transfer function representation
• Impulse response representation, Various mathematical formulations

 MPC: Model Identification

• Identification require a good knowledge of the unit
• Black box models / grey box models
• Causality graph of the unit, What to identify?
• How? Step responses - pseudo random binary signals
• MPC: OBSERVERS, Overall formulation
• Purpose of observers in control algorithm based on state / space representation
• Innovation on measured output - estimation of the state, Study of Kalman algorithm

 MPC: Control

• Overall formulation, Hard constraints on manipulated variables
• Set values and soft constraints on control variables, The notion of horizon

 Reference Models

• Handling setpoints on controlled variables, Measured disturbances rejection
• Unmeasured disturbances rejection
• Handling soft constraints on controlled variables, Rejection of disturbances

 Control Formulation Problem

• Quadratic criterion versus geometric control, Importance of the horizon length
• Use of the weight matrix, Handling output constraints along the horizon
• Projection of measured and unmeasured disturbances along the horizon
• Final quadratic problem formulation and resolution

 MPC Steady State Optimisation

• Degrees of freedom and rationale for optimization
• Economic output submitted to setpoint, Slogans to maximise or minimise
• Bridge from optimisation to control, Reachable targets for economic variables
• Interpretation of the horizon for economic variables
• Change of the control formulation problem

CDGA attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration