Formaldehyde production process control performance improvement using model predictive control

2021 ◽  
Author(s):  
Abdul Wahid ◽  
Sultan Shiddiqi Salman
Keyword(s):  
Process Control ◽  
Model Predictive Control ◽  
Production Process ◽  
Performance Improvement ◽  
Predictive Control ◽  
Control Performance ◽  
Formaldehyde Production

scholarly journals Control of Gas Dehydration Unit Using Multivariable Model Predictive Control (MMPC) to Obtain More Optimal Control Performance

2018 ◽  
Vol 67 ◽  
pp. 03013 ◽  
Author(s):  
Abdul Wahid ◽  
Rickson Mauricio ◽  
Naufal Syafiq Maro
Keyword(s):  
Model Predictive Control ◽  
Performance Improvement ◽  
Predictive Control ◽  
Gas Flow ◽  
Pressure Control ◽  
Control Performance ◽  
Multivariable Model ◽  
Feed Pressure ◽  
Integral Controller ◽  
Proportional Integral Controller

A multivariable model predictive control (MMPC) is proposed to improve a control performance in Gas dehydration process. The FOPDT models are used to build an MMPC derived from the selected controlled variables (CV) and manipulated variables (MV). A set point (SP) tracking is used to test the control performance, with proportional-integral controller (PI) as a comparison. As an indicator of the control performance is the integral of square error (ISE). The result is a TITO (two-inputs two-outputs) MMPC, with sweet gas flow rate and heat duty of heater as MVs, and feed pressure and heater temperature as CVs, respectively. In the SP tracking test, MMPC showed better control performance than the PI controller with 11.29% performance improvement (pressure control) and 16.39% (temperature control).

scholarly journals A Reverse Model Predictive Control Strategy for a Modular Multilevel Converter

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 297 ◽  
Author(s):  
Weide Guan ◽  
Shoudao Huang ◽  
Derong Luo ◽  
Fei Rong
Keyword(s):  
Computational Complexity ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Performance ◽  
Multilevel Converter ◽  
Multilevel Converters ◽  
Computational Burden ◽  
Modular Multilevel Converters ◽  
The Cost ◽  
Fast Dynamic

In recent years, modular multilevel converters (MMCs) have developed rapidly, and are widely used in medium and high voltage applications. Model predictive control (MPC) has attracted wide attention recently, and its advantages include straightforward implementation, fast dynamic response, simple system design, and easy handling of multiple objectives. The main technical challenge of the conventional MPC for MMC is the reduction of computational complexity of the cost function without the reduction of control performance of the system. Some modified MPC scan decrease the computational complexity by evaluating the number of on-state sub-modules (SMs) rather than the number of switching states. However, the computational complexity is still too high for an MMC with a huge number of SMs. A reverse MPC (R-MPC) strategy for MMC was proposed in this paper to further reduce the computational burden by calculating the number of inserted SMs directly, based on the reverse prediction of arm voltages. Thus, the computational burden was independent of the number of SMs in the arm. The control performance of the proposed R-MPC strategy was validated by Matlab/Simulink software and a down-scaled experimental prototype.

scholarly journals Multi Input Multi Output (MIMO) Control 2x2 at Vacuum Distillation Unit for LVGO, MVGO, and HVGO Production

2018 ◽  
Vol 67 ◽  
pp. 03012
Author(s):  
Abdul Wahid ◽  
Naufal Syafiq Maro
Keyword(s):  
Performance Improvement ◽  
Predictive Control ◽  
Vacuum Distillation ◽  
Pi Controller ◽  
Oil Refinery ◽  
Distillation Unit ◽  
Control Performance ◽  
Multivariable Model ◽  
Atmospheric Residue ◽  
Better Than

Currently, Indonesia is still experiencing a fuel deficit, so it is necessary to build a new oil refinery and a process optimization at an existing refinery. A vacuum distillation unit (VDU) is used to process the atmospheric residue products from crude distillation unit (CDU). A multivariable model predictive control (MMPC) is proposed to improve a control performance in VDU because of the interaction between variables in the unit. Therefore, it is necessary to find the variables that interact with each other. In this study only two variables are discussed. Set point (SP) and disturbance changes are used to test the control performance with integral of square error (ISE) as the indicator. The results are compared with the control performance of the PI controller and a single MPC. As a result, the feed flow rate and bottom-stage temperature are strongest interactions so that both are determined as controlled variables in MMPC. The control performance of MMPC is better than the PI controller and the single MPC with control performance improvement of 48% to the PI controller and 21% to MPC on for Feed Flow Rates, and 98% to the PI controller and 27% to MPC on Bottom Stage Temperature. While on disturbance changes the enhancement is 35% for the Bottom Stage Temperature.

Explicit model predictive control of semi-active suspension systems with magneto-rheological dampers subject to input constraints

2020 ◽  
Vol 31 (9) ◽  
pp. 1157-1170 ◽  
Author(s):  
Van Ngoc Mai ◽  
Dal-Seong Yoon ◽  
Seung-Bok Choi ◽  
Gi-Woo Kim
Keyword(s):  
Model Predictive Control ◽  
Vibration Control ◽  
Predictive Control ◽  
Active Suspension ◽  
Suspension System ◽  
Computational Time ◽  
Control Input ◽  
Control Performance ◽  
Damping Force ◽  
Magneto Rheological

This article presents vibration control of a semi-active quarter-car suspension system equipped with a magneto-rheological damper that provides the physical constraint of a damping force. In this study, model predictive control was designed to handle the constraints of control input (i.e. the limited damping force). The explicit solution of model predictive control was computed using multi-parametric programming to reduce the computational time for real-time implementation and then adopted in the semi-active suspension system. The control performance of model predictive control was compared with that of a clipped linear-quadratic optimal controller, where the damping force was bound using a standard saturation function. Two types of road conditions (bump and random excitation) were applied to the suspension system, and the vibration control performance was evaluated through both simulations and experiments.

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