http://mypcs.com.my/journal/index.php/ajpc/issue/feed ASEAN Journal of Process Control 2024-02-05T22:15:20+00:00 Associate Prof. Dr. Jobrun Nandong jobrun.n@curtin.edu.my Open Journal Systems <p>The Asean Journal of Process Control (AJPC), a journal affiliated with the Malaysian Process Control Society (<strong>MyPCS</strong>) is the first Journal in Process Control originating from the Asean Region. It is considered an important milestone of MyPCS, officially launched in 2019, for promoting the development of the process control discipline in Malaysia and the Asean region. With the advent of IR 4.0 in the manufacturing sector and the rapid development of Artificial Intelligence and machine learning techniques in engineering applications, there has been a resurgence of research interests in various research topics related to process control in this region, as well as globally at large.. [<a href="http://mypcs.com.my/journal/index.php/ajpc/about">more</a>]</p> http://mypcs.com.my/journal/index.php/ajpc/article/view/23 Using Apps for Teaching Process Control Classes 2024-01-08T21:43:17+00:00 A.W. Hermansson a.hermansson@hw.ac.uk <p>Process Control classes focus on theory, with some education laboratories included where the student got some direct steps to work through. Thus, limiting the learning experience and the involvement of all students as the laboratories are run in groups. The temperature control labs produced by companies such as IJ Instruments and APMonitor offer the possibility of students working individually on process control experiments and gaining a more conceptual understanding of process control. However, considering that both control labs are using a GUI to control a small unit connected through a USB cable, the students are not gaining psychomotor skills anyway. This paper demonstrates that app-based control simulators can be an effective alternative to traditional laboratory experiments for teaching process control. App-based simulators can provide students with the same learning outcomes as traditional experiments, such as modeling systems, analyzing PID controllers, and designing feedback controllers. Additionally, app-based simulators offer several advantages over traditional experiments, including, Individualized learning, Flexible experiments: Students can easily change the parameters of the system and experiment with different control strategies, Varied experiments: A wide range of experiments can be created using app-based simulators, Cost-effective: App-based simulators are inexpensive to develop and use. The paper concludes by discussing the potential of app-based control simulators to revolutionize process control education. App-based simulators can make process control education more accessible, affordable, and effective. They can also help to prepare students for the challenges of working in the process control industry. </p> 2023-12-15T00:00:00+00:00 Copyright (c) 2024 ASEAN Journal of Process Control http://mypcs.com.my/journal/index.php/ajpc/article/view/24 Modeling and Control of Steam Methane Reforming Process Using Model Predictive Control 2023-12-20T21:57:48+00:00 Tan Li Ting dinie.muhammad@utm.my Nabilla Wahyu Hasanah dinie.muhammad@utm.my Fakhrony Sholahudin Rohman dinie.muhammad@utm.my Dinie Muhammad dinie.muhammad@utm.my <p>This study focuses on modeling and controlling steam methane reforming (SMR) using model predictive control (MPC). The problem in the SMR plant is the limitation of the conventional control in controlling a wide range of operating conditions. The nonlinear dynamic behavior of the SMR process has further complicated the situation, as existing methods lack the adaptability to the changing operating conditions and disturbances. To address these challenges, MPC control scheme is proposed. The development of MPC involves developing a simulation process for Steam Methane Reforming (SMR) using Aspen Plus, creating the process model of SMR using the system identification technique, and designing the MPC control scheme. The development of a simulation plant includes constructing the process flowsheet, determining kinetics parameters, and creating a dynamic model. Model input-output selection and data generation are performed to facilitate the development of the process model. Based on the state space identification technique, the process model is developed with a normalized root mean square error (NRMSE) of 0.8567 for CV1 and 0.3005 for CV2. Then, the core focus lies in designing the MPC control structure and tuning the MPC for enhanced performance. During set point tracking, starting from steady-state hydrogen production and increasing by 20% at 2 minutes, the state-space MPC outperformed the PID controllers, displaying more aggressive capabilities in reaching the desired set point. For the disturbance rejection test, the state-space MPC is able to control the reactor outlet temperature with minimal overshoot, as seen in the CV2 profile. This behavior is due to the predictive capabilities of MPC, enabling quicker controller actions than PID. By addressing the traditional control limitations, the proposed MPC aims to enhance the operation and control of SMR plants while optimizing hydrogen production through advanced control strategies. </p> 2023-12-15T00:00:00+00:00 Copyright (c) 2024 ASEAN Journal of Process Control http://mypcs.com.my/journal/index.php/ajpc/article/view/21 Model Predictive Control for Efficient Process Control: A Case Study for Absorber-Stripper System with MEA in Hydrogen Plant 2023-12-07T23:05:04+00:00 Rendra Panca Anugraha renanto@chem-eng.its.ac.id Renanto renanto@chem-eng.its.ac.id Juwari renanto@chem-eng.its.ac.id Hugo renanto@chem-eng.its.ac.id Bernardus Krisna Brata renanto@chem-eng.its.ac.id <p>Control mechanisms are essential for all operations in chemical processing facilities, including the absorber-stripper system. This system, which is utilized for CO2 capture, is a common feature in hydrogen plants that use steam-methane reforming technology to purify their flue gas (post- combustion). The primary objective of this system is to maximize CO2 recovery in a cost-effective manner. Given the dynamic nature of plant operations, with frequent changes in production capacity, a robust controller is crucial for maintaining system stability. Model Predictive Control (MPC) is a widely used control method. This study focuses on developing a control simulation for an absorber- stripper system using monoethanolamine solution (MEA) and implementing it with MPC. The steady- state system is designed using Aspen Plus and integrated with MATLAB/Simulink to construct a multi- input-multi-output controller with MPC. The simulation aims to regulate the CO2 recovery within a specific range of input disturbances while ensuring that the system operates within acceptable operating conditions. This is achieved by controlling all potential manipulated variables, such as flow of MEA solution to both absorber and stripper, flow of cooling and heating agent, and make up water. A comparison with the traditional PID control approach is also presented as part of this research. It is concluded from the result that MPC Controller is superior than PID. The MPC controller exhibits superior performance by having less settling time. This is accomplished by effectively minimizing transient deviations from the desired output.</p> 2023-12-15T00:00:00+00:00 Copyright (c) 2024 ASEAN Journal of Process Control http://mypcs.com.my/journal/index.php/ajpc/article/view/18 Bayesian-ANN controller for pH-control 2023-12-07T22:59:32+00:00 A.W. Hermansson a.hermansson@hw.ac.uk S. Syafiie a.hermansson@hw.ac.uk <p>The major problems using artificial neural networks (ANN) for the controller in process control are overfitting and extrapolation. The former commonly occur when unmeasured disturbances affect the process and the ANN will average out the controller output, between the disturbance case and the case where there are no disturbances. Extrapolation problem stems from the ANN suggesting controller outputs in areas where the ANN has not been trained for. This paper proposes a Bayesian weighting approach to improve the generalization performance of ANN controllers in pH control where a feedforward ANN was trained to mimic the behavior of a Robust Model Predictive Controller (RMPC). The proposed approach relies on separating the training data based on the presence and size of disturbances, as well as a Bayesian weighting scheme. The training data was generated from running multiple tests on the RMPC for different requirements and cases of pH control. The training algorithm used was the Levenberg-Marquardt algorithm. The proposed approach was applied to a MATLAB® simulated pH control system. Bayesian-ANN is a novel approach for enhancing the generalization performance of ANNs in pH control. It is straightforward to implement and can be utilized with any ANN architecture. The proposed Bayesian weighted ANN (ANN-MPC) method in controlling pH process is found to be superior to a single ANN and multiple model predictive control (MMPC). This is particularly true with regard to reducing overshoots and oscillations.</p> 2023-12-15T00:00:00+00:00 Copyright (c) 2024 ASEAN Journal of Process Control http://mypcs.com.my/journal/index.php/ajpc/article/view/19 Dynamic simulation and optimization of Chemical Looping Hydrogen Production in inter-connected moving bed reactors 2023-12-07T23:01:51+00:00 Priyam Kataria jobrun.n@curtin.edu.my Jobrun Nandong jobrun.n@curtin.edu.my Christine Yeo jobrun.n@curtin.edu.my <p>The current study investigates the dynamics of the chemical looping hydrogen production (CLHP) process, which consists of three interconnected moving-bed reactors (MBRs) that circulate a solid oxygen carrier (OC). The OC undergoes redox reactions in each unit to generate various product gases, including the target hydrogen (H<sub>2</sub>) gas, which is a highly efficient fuel source. The first two reactors known as the reducer and oxidizer respectively, were simulated in the MATLAB environment. The reactions are assumed to be conducted within a multi-tubular vessel while the pellet-grain model (PGM) was employed to represent the gas-solid interaction within a single particle, due to its reliability proven through several studies. The initial test data for reducer was retrieved from previous investigations, involving experimental verification and evaluation of the flow rates. Solid achieved the target phase for the oxidizer process, though a significant amount of unconverted gas (~ 27.5 %) was present in the outlet stream due to flow restrictions. The analysis was extended in this study by testing under reduced pellet sizes, where impurity concentrations as low as 6.5 mol% were achieved. The system was further investigated by varying the reaction temperature, which exhibited sufficient conversions (&gt; 95%) for implementing a carbon capture strategy. A critical trade-off point was observed at 1070 K, which can be utilized for developing a temperature control system within the reactor. The oxidizer was simulated using reducer output flows to represent inter-connectivity, aimed at describing continuous production characteristics. Co-current and counter-current flows were compared for the reactor design, where the former regime was considered suitable due to a larger effective reaction zone. Due to several restrictions from the reducer, the process within the oxidizer could only be tested under the effect of reaction temperature. The optimum H<sub>2</sub> concentration (~ 34 mol%) recorded at 950 K, which must be controlled within a narrow range (± 5K) to ensure consistent quality. These findings indicated the importance of maintaining isothermal operation, which could be potentially solved through an effective tubular reactor design. The third unit called combustor was not included due to unavailability of the reaction kinetics, serving as prospects for further research in the field. Control system and scale-up design implications of the two reactors were further discussed, serving as essential information for future investigations. The findings presented within this study are vital contributions to the presently available research on CLHP, which is an important process for producing affordable green hydrogen fuel. </p> 2023-12-15T00:00:00+00:00 Copyright (c) 2024 ASEAN Journal of Process Control http://mypcs.com.my/journal/index.php/ajpc/article/view/16 Optimization of Coagulation Tank Processes through Interval Fuzzy Type 2 Logic System: A Study of Turbidity Reduction 2023-09-05T19:36:43+00:00 Mohd Fauzi Bin Zanil fauzi.zanil@apu.edu.my Lorna Ahlaami Binti Ramzan mohdfauzi@ucsiuniversity.edu.my <p>This research optimizes wastewater treatment's coagulation process through a Genetic Algorithm<br>and an Interval Type-2 Fuzzy Logic System (IT2FLS). It focuses on enhancing key parameters such<br>as coagulant dosage, mixing speed and time, pH, and temperature. Comparison with traditional jar<br>test results under specific conditions validates the effectiveness of these innovative approaches.<br>Although the final turbidity was marginally higher using the Genetic Algorithm, the IT2FLS closely<br>mirrored the trend of the jar test results, showing remarkable accuracy in predicting final turbidity.<br>This predictive accuracy was quantified using Mean Absolute Error, Mean Squared Error, and Root<br>Mean Squared Error measurements. The research has significant implications for public health,<br>safety, environmental sustainability, and economic concerns by improving wastewater treatment<br>efficiency. It recommends further studies and validations with varied datasets for robust real-world application of the IT2FLS model. This work's novelty lies by focusing on optimization techniques<br>for the wastewater coagulation process. Through the application of Genetic Algorithm and IT2FLS<br>to observe key parameters, outcome and comparison with traditional methods, the study contributes to a more sustainable and efficient future in wastewater treatment.</p> 2023-12-15T00:00:00+00:00 Copyright (c) 2024 ASEAN Journal of Process Control http://mypcs.com.my/journal/index.php/ajpc/article/view/27 Special Issue Part 1: Process Control Nexus Bridging, Technology, Sustainability and Industry 2024-02-05T22:15:20+00:00 Mohd Azlan Hussain mohd_azlan@um.edu.my 2023-12-15T00:00:00+00:00 Copyright (c) 2024 ASEAN Journal of Process Control