PiControl Solutions

CHE500C: Industrial Process Control - Primary and Advanced Process

Contact Us Today: [email protected], Tel: (832) 495 6436

Duration: Self-Paced Training Software
Audience: Process Control Engineers, DCS/ PLC Technicians, Operators, Process Engineers, Contact Engineers, Supervisors, Managers, Project Engineers
Prerequisites: None
Course Material: CBT (computer-based training) software

Course Description and Objectives: 
Process control courses in universities are still too academic and the training is not adequate for the industrial control room environment. This course covers the practical industrial aspects of process control like no other textbook or course available currently. What would take a typical engineer or technician numerous years of hands-on experience in the control room now can be mastered by this modern, powerful CBT in just an amazing span of a few hours. This course will benefit both new and experienced engineers, operators, supervisors, managers, professors and students. It covers modern industrial process control theory – both primary process control and advanced process control (APC) and can be used by both technicians and engineers without need for advanced math and other background or skills.

Learning Outcomes

After completion of the course, the students will understand the concept of DCS and PLC architectures and networks. They will understand process control schematics, basic process control theory, system identification, step testing, dynamics characterization. The course teaches PID control, cascade control, feedforward control, constraint control, override control, model-based control, model-predictive control, and other forms of APC (Advanced Process Control). The course teaches how to design and implement primary and advanced process control schemes inside a DCS or a PLC. It teaches skills on when to use PID and APC and when to use a DMC (dynamic matrix control) or other forms of multivariable model- predictive control. It teaches how to design and implement closed-loop controllers in the practical control room environment. It also teaches concepts typically not covered in universities and concepts that take a long time to learn on one’s own on-the-job time and effort. The course helps to convert both new and experienced personnel into skilled process control experts in a remarkably short time.

Course Chapters:

Part I: Primary Process Control

Overview of Modern Industrial Process Control

  • Overview
  • Need for Process Control
  • Distributed Control System
  • Choice of DCS or PLC
  • Laboratory Information Management System (LIMS)
  • Safety Interlocks and Shutdowns
  • Permissives

Process Control Variable Definitions

  • Controlled Variables (CV)
  • Manipulated Variables (MV)
  • Process Variable (PV)
  • Setpoint (SP)
  • Process Dynamics
  • Transfer Function
  • Transfer Function Parameters
  • Linear and Nonlinear Processes
  • Identifying Process Dynamics
  • Dynamics Identification Procedure
  • Dynamics Identification with Multiple Inputs
  • Rules for Conducting Pulse Tests

Primary Control and The PID Algorithm

  • Manual Control
  • Automatic Control
  • The PID Algorithm
  • Sign of the three terms
  • Offset
  • Primary Control

PID Algorithm – Additional Options and Parameters

  • Process Noise
  • Filter Time Constant
  • Direction of Control Action
  • Direct and Reverse Action
  • Other Forms of The PID Algorithm
  • Nonlinear PID
  • Output Sponge PID
  • Split Range PID
  • PID Faceplate
  • PID Detailed Screens

Cascade PID Algorithm

  • Level-To-Flow Double Cascade
  • Temperature-To-Temperature Double Cascade
  • TC-FC Double Cascade
  • AC-TC-FC Triple Cascade
  • AC-TC-QC-FC Quadruple Cascade

Override Control Strategies

  • Dual Level Control
  • Dual Temperature Control
  • Low Level Override Constraint Control
  • Distillation Reflux Flow Override
  • Compressor Override Controls
  • High and Low Override Constraint Control
  • Maximization of Production Rates
  • Need for Constraint Override Control Strategies

PID Modes and PID Activation Procedure

  • PID Controller Modes
  • Summary of Different PID Modes and States
  • How to Change PID State
  • Ranges of A PID Controller
  • Setpoint Tracking and Output Initialization
  • The “Track” Flag
  • Bumpless Transfer
  • Cascade Chain Activation Sequence
  • Chain Activation Sequence for A Constraint Override Loop
  • PV Tracking
  • How To Enable PV Tracking
  • Benefits of PV Tracking
  • When to Use PV Tracking
  • PV Tracking in Case of Master PIDs

PID Tuning Procedures and Control Quality

  • Open-Loop and Closed-Loop Mode
  • Engineering Units of PID Tuning Parameters
  • PID Tuning Procedures
  • Effect of Range Change on PID Tuning Parameters
  • Advanced Control PID Control
  • PID Tuning and Control Quality
  • Comparison of The Criteria

Part II: Advanced Process Control

Disturbances, Feedforwards and Decouplers

  • Disturbance
  • Feedforward Control
  • Feedforward and Feedback Control Examples
  • Feedforward Strategy Implementation in DCS or PLC
  • LEAD and LAG Action
  • Final Steady State Value from Feedforward
  • Cases where Feedforward Control may not be effective
  • Distillation Column Feedforward

Process Signal Filtering and Control Valve Checkout

  • Signal Noise
  • Effect of excessive noise on control quality
  • Filter Constant
  • When to Use Filtering
  • Selecting Filter Constant
  • Optimal Filtering
  • Adding Filtering During PID Tuning
  • Impact of Noise Band on Open-Loop Test Procedure
  • Identifying Valve Problems
  • Effect of Noise on PID Control Action

Dead Time Compensation and Model-Based Control

  • Dead Time in Control Loops
  • Effect of Dead Time on Control Quality
  • When Dead Time Is Really Harmful In A Control Loop
  • Methods to combat dead time
  • Dead Time Compensation Implementation in DCS or PLC
  • Model-Based Control
  • Pure Transfer Function-Based Models
  • Rigorous Predictive Models
  • Steps in Implementing A Rigorous Model-Based Control Scheme

Control Schemes Using Discrete Signals

  • Continuous Signals
  • Discrete Signals
  • PV Sample Delay
  • Discrete Signals
  • Distillation Control with PV Sample Delay
  • Distillation Cascade Control with PV Sample Delay
  • Analyzer Multiplexing
  • Inferential Model-Based Control
  • Spike Rejection ii) Frozen Value Check
  • PID Scan Time

Model Predictive Control and Rule-Based Control

  • Types of Process Control Strategies
  • Characterizing Process Dynamics In MPC
  • Control Matrix
  • MPC Algorithm
  • Feedback Correction
  • Rate of Change
  • Multivariable MPC System
  • Priority of Controlled Variables
  • Local Optimization
  • When to Use MPC and When to Use TAC
  • When to Use MPC and When to Use TAC
  • Analyzing the criteria to select TAC or MPC
  • Benefits Due to Advanced Control
  • Pros and Cons of MPC Versus TAC
  • Operating Zones
  • Rule-Based Control and Fuzzy Control
  • Types of Advanced Control Tools

15) Handling Nonlinearities

  • Linearity
  • Valve-To-Flow Nonlinearity
  • Gain Scheduling
  • Valve Characterization
  • Constraint Control
  • Reflux-To-Product Impurity Nonlinearity
  • Average Temperature Control

Part III: Lab Session

Lab Sessions (Practical Exercises)

  • The various simulation exercises will be conducted with PITOPS industrial process control software. This software accompanies this training module and can be used in a variety of ways.
  • PITOPS software consists of two modules – PITOPS-PID and PITOPS-TFI. PID stands for PID Control Tuning and Design.
  • TFI stands for Transfer Function Identification.
  • PID module simulates PID controllers, cascade PIDs, feedforward loops and Dead Time Compensator. Various other features are provided for primary and advanced control tuning and design. TFI module identifies transfer functions using time-series plant data. The following nine lab sessions will be conducted using the PITOPS-PID module and tenth session using PITOPS- TFI module.
    • Configure a transfer function and study open-loop response.
    • Configure a PID loop, simulate a setpoint change, and tune the PID.
    • Add random noise to the previous simulation.
    • Configure external disturbances.
    • Tune a Temperature Control PID (TC).
    • Tune a Level Control PID (LC).
    • Tune a cascade PID.
    • Configure Disturbance and Feedforward transfer function.
    • Configure a Model-based Dead-time compensator.
    • Identify a transfer function using simulated plant data.
    • Guidelines and Recommendations