Process Control: How to Learn from Thomas E. Marlin's Solution Manual
Process control is the discipline of designing and operating processes that produce desired outputs in a safe and efficient manner. Process control involves monitoring and manipulating variables such as temperature, pressure, flow rate, concentration, pH, and composition that affect the performance of a process. Process control is essential for industries such as chemical, petrochemical, pharmaceutical, biotechnology, food and beverage, pulp and paper, and power generation.
thomas e marlin solution manual process control.rargolkes
One of the best resources for learning process control is the book Process Control: Designing Processes and Control Systems for Dynamic Performance by Thomas E. Marlin. This book covers the fundamentals of process control as well as advanced topics such as multivariable control, model predictive control, adaptive control, and nonlinear control. The book also provides practical examples and case studies that illustrate the application of process control techniques to real-world problems.
The book comes with a solutions manual that contains detailed answers to all the exercises and problems in the book. The solutions manual is a valuable tool for students and instructors who want to check their understanding of the concepts and methods presented in the book. The solutions manual also helps students to develop their problem-solving skills and learn how to apply process control theory to practice.
Who is Thomas E. Marlin and Why You Should Read His Book
Thomas E. Marlin is a professor emeritus of chemical engineering at McMaster University in Canada. He has over 40 years of experience in teaching and research in process control, process design, and process safety. He has also worked as a consultant for ExxonMobil and other companies in the oil and gas industry.
Thomas E. Marlin is the author of Process Control: Designing Processes and Control Systems for Dynamic Performance, a widely used textbook that covers both the theory and practice of process control. The book is suitable for undergraduate and graduate students, as well as practicing engineers who want to update their knowledge and skills in process control.
The book provides a comprehensive and systematic approach to designing processes and control systems that achieve dynamic performance objectives such as safety, profitability, quality, and environmental protection. The book covers topics such as:
The principles of feedback, feedforward, cascade, ratio, and override control
The design and tuning of PID controllers
The analysis and design of multivariable control systems
The use of model predictive control, adaptive control, and nonlinear control techniques
The integration of process design and control design
The assessment and improvement of process operability
The application of process control to batch processes, distillation columns, reactors, heat exchangers, and other unit operations
The book also includes numerous examples, exercises, problems, case studies, and software tools that help the reader to apply the concepts and methods learned to real-world situations.
What is Model Predictive Control and How Does It Work
Model predictive control (MPC) is a type of advanced process control that uses a mathematical model of the process to predict its future behavior and optimize the control actions accordingly. MPC can handle complex processes with multiple inputs and outputs, time delays, nonlinearities, and constraints on both the manipulated variables and the controlled variables.
MPC works by solving an optimization problem at each time step, where the objective is to minimize a cost function that reflects the performance goals of the process, such as setpoint tracking, disturbance rejection, or energy efficiency. The cost function is usually a weighted sum of the deviations of the controlled variables from their desired values and the changes in the manipulated variables over a finite prediction horizon. The prediction horizon is the time window over which the MPC controller forecasts the future behavior of the process using the model and the current measurements.
The optimization problem also includes constraints on the manipulated variables and the controlled variables, such as physical limits, safety requirements, or quality specifications. These constraints ensure that the MPC controller respects the operational boundaries of the process and avoids undesirable or infeasible situations.
The solution of the optimization problem is a sequence of optimal control actions that minimize the cost function and satisfy the constraints over the prediction horizon. However, the MPC controller only implements the first control action in this sequence and discards the rest. At the next time step, the MPC controller repeats the optimization problem with updated measurements and a shifted prediction horizon. This way, MPC adapts to changes in the process dynamics, disturbances, or setpoints.
How to Design an MPC Controller for Your Process
If you want to design an MPC controller for your process, you need to follow some basic steps that involve specifying the plant model, the controller structure, the cost function, and the constraints. You also need to test and validate your controller design before implementing it on the real process.
One way to design an MPC controller is to use the MPC Designer app that comes with Model Predictive Control Toolbox. This app allows you to interactively design and simulate MPC controllers in MATLAB or Simulink. You can also use the app to linearize a nonlinear plant model, specify the controller structure and parameters, tune the controller performance, and generate code for deployment.
To use the MPC Designer app, you need to follow these steps:
Open the app by typing mpcDesigner at the MATLAB command prompt or by clicking Design in the MPC Controller block parameters dialog box in Simulink.
Specify the plant model by importing an LTI object from the MATLAB workspace or by linearizing a Simulink model. You can also identify a plant model from data using System Identification Toolbox.
Define the MPC structure by selecting the controller sample time, prediction horizon, control horizon, input and output signal types, and signal names and units.
Specify the cost function by assigning weights to the output variables and input rate of change variables. You can also specify custom output and input rate of change weights as functions of time.
Specify the constraints by setting upper and lower bounds on the input variables and output variables. You can also specify custom constraints as functions of time.
Tune the controller performance by adjusting the weights and constraints, observing the closed-loop response plots, and using the tuning tools such as response time slider and disturbance rejection slider.
Validate the controller design by simulating different scenarios such as setpoint changes, reference signals, measured disturbances, unmeasured disturbances, and plant-model mismatch.
Generate code for deployment by exporting an mpc object to the MATLAB workspace or by generating a Simulink block or a standalone C code.
For more information on how to use the MPC Designer app, see Design Controller Using MPC Designer.
How to Generate Code and Deploy Your MPC Controller
Once you have designed and validated your MPC controller in MATLAB or Simulink, you may want to generate code and deploy it to a real-time target for practical applications. Model Predictive Control Toolbox provides several options for code generation and deployment depending on your controller type and target platform.
In MATLAB, you can use MATLAB Coder to generate C code for computing optimal control moves for linear or nonlinear MPC controllers. You can then use the generated code in your own applications or integrate it with other code generation tools. To generate code in MATLAB, you need to follow these steps:
Create an MPC controller or an explicit MPC controller object using the mpc or mpcmoveopt commands.
Generate data structures from the controller object using the getCodeGenerationData function.
Simulate your controller using the mpcmoveCodeGeneration function instead of mpcmove. This function supports code generation and has the same syntax and behavior as mpcmove.
Generate code for mpcmoveCodeGeneration using the codegen command. This command requires MATLAB Coder software.
If your controller is a nonlinear MPC controller that uses the default fmincon solver with the SQP algorithm, you can use the same steps but replace mpcmoveCodeGeneration with nlmpcmoveCodeGeneration.
In Simulink, you can use any of the Model Predictive Control Toolbox blocks to design and simulate your controller. You can then use Simulink Coder, Embedded Coder, Simulink PLC Coder, or Simulink Real-Time to generate code and deploy it to various targets. To generate code in Simulink, you need to follow these steps:
Add an MPC Controller block, an Explicit MPC Controller block, an Adaptive MPC Controller block, a Nonlinear MPC Controller block, or a Multiple MPC Controllers block to your model.
Specify the controller object or parameters in the block dialog box.
Simulate your model and verify the controller performance.
Select a code generation tool and configure the model settings accordingly.
Generate code and deploy it to your target platform.
You can also use some of the predefined blocks for automotive applications, such as Adaptive Cruise Control System, Lane Keeping Assist System, and Path Following Control System. These blocks are based on adaptive MPC controllers and include additional features such as reference signals and sensor models.
Conclusion
MPC is a powerful and versatile technique for process control that can handle complex dynamics, multiple objectives, and various constraints. MPC uses a model of the process to predict its future behavior and optimize the control actions accordingly. MPC can be applied to linear or nonlinear processes, with implicit or explicit solutions, and with adaptive or gain-scheduled features.
To design an MPC controller for your process, you can use Model Predictive Control Toolbox and its graphical and command-line tools. You can also generate code and deploy your controller to real-time targets using MATLAB Coder, Simulink Coder, Embedded Coder, Simulink PLC Coder, or Simulink Real-Time. By using MPC, you can improve the performance, safety, and efficiency of your process control system. d282676c82
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