Control of a Reactive Distillation Process Using Model Predictive Control Toolbox of MATLAB

2017 ◽  
Vol 30 ◽  
pp. 167-180 ◽  
Author(s):  
Abdulwahab Giwa ◽  
Abel Adekanmi Adeyi ◽  
Saidat Olanipekun Giwa
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Reactive Distillation ◽  
Research Work ◽  
Variable Rate ◽  
Control Parameters ◽  
Distillation Process ◽  
Prediction Horizon ◽  
Better Than

This research work has been carried out to investigate the application of the Model Predictive Control Toolbox contained in MATLAB in controlling a reactive distillation process used for the production of a biodiesel, the model of which was obtained from the work of Giwa et al.1. The optimum values of the model predictive control parameters were obtained by running the mfile program written for the implementation of the control simulation varying the model predictive control parameters (control horizon and prediction horizon) and recording the corresponding integral squared error (ISE). Thereafter, using the obtain optimum value of 5 and 15 for control horizon and prediction horizon respectively as well as a manipulated variable rate weight of 0.025 and an output variable rate weight of 1.10, various steps were applied to the setpoint of the controlled variable and the responses plotted. The results given by the simulations carried out by varying the model predictive control parameters (control horizon and prediction horizon) for the control of the system revealed that optimizing the control parameters is better than arbitrary choosing. Also, the simulation of the developed model predictive control system of the process showed that its performance was better than those used to control the same process using a proportional-integral-derivative (PID) controller tuned with Cohen-Coon and Ziegler-Nichols techniques. It has, thus, been discovered that the Model Predictive Control Toolbox of MATLAB can be applied successfully to control a reactive distillation process in order to obtain better performance than that obtained from a PID controller tuned with Cohen-Coon and Ziegler-Nichols methods.

Classical and Neural Network–Based Approach of Model Predictive Control for Binary Continuous Distillation Column

2014 ◽  
Vol 9 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Amit Kumar Singh ◽  
Barjeev Tyagi ◽  
Vishal Kumar
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Distillation Column ◽  
Chemical Engineering ◽  
Research Work ◽  
Settling Time ◽  
Training Data ◽  
Main Process

Abstract The objective of present research work is to develop a neural network–based model predictive control scheme (NN-MPC) for distillation column. To fulfill this objective, an existing laboratory setup of continuous binary-type distillation column (BDC) is used. An equation-based model that uses the fundamental physical and chemical laws along with valid normal assumptions is validated for this experimental setup. Model predictive control (MPC) is one of the main process control techniques explored in the recent past for various chemical engineering applications; therefore, the conventional MPC scheme and the proposed NN-MPC scheme are applied on the equation-based model to control the methanol composition. In NN-MPC scheme, a three-layer feedforward neural network model has been developed and is used to predict the methanol composition over a prediction horizon using the MPC algorithm for searching the optimal control moves. The training data is acquired by the simulation of the equation-based model under the variation of manipulated variables in the defined range. Two cases have been considered, one is for set point tracking and another is for feed flow disturbance rejection. The performance of the control schemes is compared on the basis of performance parameters namely overshoot and settling time. NN-MPC and MPC schemes are also compared with conventional PID controller. The results show the improvement in settling time with NN-MPC scheme as compared to MPC and conventional PID controller for both the cases.

scholarly journals Application of Model Predictive Control to Renewable Energy Development via Reactive Distillation Process

2016 ◽  
Vol 27 ◽  
pp. 95-110 ◽  
Author(s):  
Saidat Olanipekun Giwa ◽  
Abel Adekanmi Adeyi ◽  
Abdulwahab Giwa
Keyword(s):  
Renewable Energy ◽  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Reactive Distillation ◽  
Regulatory Control ◽  
Distillation Process ◽  
Closed Loop System ◽  
Tuning Parameters

Reactive distillation is a process that combines chemical reaction and separation in a single piece of equipment (distillation column). The process has a lot of benefits especially for those reactions occurring at conditions suitable for the distillation of the process components, and these result in significant economic advantages. However, owing to the complexities resulting from the integration of reaction and separation, its control is still a challenge to process engineers because it requires a control method that is robust enough to handle its complexities. Therefore, in this work, model predictive control (MPC) has been applied to a reactive distillation process used for developing a renewable energy known as biodiesel. The control algorithm of the MPC was formulated with the aid of MPC toolbox of MATLAB/Simulink in which the closed-loop models of the process were developed and simulated. The analysis of the results obtained from the simulations carried out for the optimization of the tuning parameters revealed that, among the tuning parameters considered, integral absolute error of the control system was less affected by the control horizon because its p-value was greater than 0.05 based on 95% confidence level. Furthermore, the simulation of the closed-loop system of the process using model predictive control tuned with control horizon of 11, prediction horizon of 18, weight on manipulated variable rate of 0.05 and weight on output variable of 2.17, which were the optimum parameters obtained using Excel Solver, showed that the system was well handled by the controller under servo control because it was able to get settled at desired mole fractions within 60 min. However, the settling time recorded in the case of regulatory control system of the process with the same controller was found not to be encouraging. Therefore, it is recommended that further work should be carried out on this subject matter in an attempt to obtain tuning parameters that will make the settling time of the closed-loop system of the process under regulatory control simulation very reasonable.

scholarly journals Reinforcement Learning of the Prediction Horizon in Model Predictive Control

2021 ◽  
Vol 54 (6) ◽  
pp. 314-320
Author(s):  
Eivind Bøhn ◽  
Sebastien Gros ◽  
Signe Moe ◽  
Tor Arne Johansen
Keyword(s):  
Reinforcement Learning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Horizon

scholarly journals Stable Model Predictive Control Design: Sequential Approach

2011 ◽  
Vol 62 (2) ◽  
pp. 99-103
Author(s):  
Vojtech Veselý
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Design Method ◽  
Control Design ◽  
Sequential Design ◽  
Stable Model ◽  
Sequential Approach ◽  
Prediction Horizon ◽  
Guaranteed Cost ◽  
One Step

Stable Model Predictive Control Design: Sequential Approach The paper addresses the problem of output feedback stable model predictive control design with guaranteed cost. The proposed design method pursues the idea of sequential design for N prediction horizon using one-step ahead model predictive control design approach. Numerical examples are given to illustrate the effectiveness of the proposed method.

Model Predictive Control for DC–DC Boost Converters With Reduced-Prediction Horizon and Constant Switching Frequency

2018 ◽  
Vol 33 (10) ◽  
pp. 9064-9075 ◽  
Author(s):  
Long Cheng ◽  
Pablo Acuna ◽  
Ricardo P. Aguilera ◽  
Jiuchun Jiang ◽  
Shaoyuan Wei ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Switching Frequency ◽  
Boost Converters ◽  
Prediction Horizon

scholarly journals MODEL PREDICTIVE CONTROL TOOLBOX DESIGN FOR NONSTATIONARY PROCESS

2021 ◽  
Author(s):  
Oleksandr V. Stepanets ◽  
Yurii I. Mariiash
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Complex Structure ◽  
Nonstationary Process ◽  
Actual State ◽  
Quadratic Functional ◽  
Linear Quadratic ◽  
Prediction Horizon

Background. Model predictive control (MPC) approach is the basic feedback scheme, combined with high adaptive properties, which determines its successful use in the practice of design and operation of control systems. These advantages allow managing multidimensional objects with a complex structure, including nonlinearity, optimizing processes in real time within the constraints on controlled and managed variables, taking into account uncertainties in the task of objects and perturbations. Objective. The purpose of the paper is to design and analyse control system of carbon monoxide oxidation in the convector cavity based on MPC with linear-quadratic cost functional with constraint. Methods. The design of MPC is based on mathematical model of an object (relatively simple). At the current step, the prediction of object dynamic response on some final period of time (prediction horizon) is carried out; control optimization is performed, the purpose of which is to approximate the control variables of the prediction model to the corresponding setpoint on the predict horizon. The found optimal control is applied and measurement of an actual state of object at the end of a step is carried out. The prediction horizon is shifted one step further, and this algorithm are repeated. Results. The results of modeling the automatic control system show that the MPC approach provides maintenance of carbon dioxide content when changing oxygen consumption and overshoot caused by introduction bulk does not exceed 0.6 % that meets the technological requirements of the process. Conclusions. A fuse of the MPC and the quadratic functional given the constraints on the input signals is proposed. The problems of control degree of carbon oxidation in the convector cavity include non-stationarity, so the use of classical control methods is difficult. The MPC approach minimizes the cost function that characterizes the quality of the process. The predicted behaviour of a dynamic system will usually differ from its actual motion. The obtained quadratic functional is optimized to find the optimal control of degree of CO oxidation to CO2.

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