The Asean Journal of Process Control (AJPC), a journal affiliated with the Malaysian Process Control Society (MyPCS) 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.. [more]
Multi-Objective Optimization of Catalytic Reforming Using Deep Neural Network Surrogate Modeling
Process optimization in catalytic reforming is essential to maximize high-octane reformate yield and reduce energy consumption while maintaining reformate Research Octane Number (RON) as a constraint. However, process optimization initiative for this unit encounters several challenges, such as complex reaction mechanism, large option for decision variables, and conflicting objective functions. Moreover, characteristics of naphtha are very variative from different oil fields and different operating parameters of crude distillation unit (CDU), so they are difficult to generalize. This study intends to optimizes the energy costs and the reformate yield for the reactor and purification section by considering different feed characteristics of naphtha. A detailed first-principles model of the reforming and separation sections was constructed using UniSim Design, and 200 simulated operating points were generated for training and testing the surrogate. The DNN achieved strong predictive accuracy (MSE < 0.354, MAE < 0.0467, R² > 0.93), successfully replicating the behavior of the reactor and downstream separation units. The framework simultaneously minimized utility cost and maximized reformate yield, subject to the constraint of RON ≥ 92. SPEA2 produced 11–19 Pareto-optimal solutions across three distinct naphtha feeds. Visualization through parallel-coordinate plots and correlation heatmaps revealed key operational trends, including the positive influence of H₂/HC ratio, flash pressure, and boil-up ratio on yield, and the negative effect of high reactor and flash temperatures. The results demonstrate that combining surrogate modelling with evolutionary optimization provides a robust, computationally efficient approach for identifying optimal operating windows and understanding trade-offs in catalytic reforming units.
Copyright (c) 2026 ASEAN Journal of Process Control https://creativecommons.org/licenses/by/4.0
Modeling and Control of CO₂ Capture Plant using Model Predictive Control
The increasing concentration of atmospheric carbon dioxide (CO₂) necessitates the development of efficient carbon capture technologies to mitigate climate change. Post-combustion capture using Monoethanolamine (MEA) is a mature technology; however, its operational efficiency is hindered by process nonlinearities and multivariable interactions. This research focuses on enhancing the control of a CO₂ absorption column by implementing a Model Predictive Control (MPC) strategy. A dynamic process model was first developed and validated using Aspen Plus Dynamics. Subsequently, a linear state-space model was derived from this high-fidelity model through system identification techniques. The performance of the MPC was then benchmarked against a conventional Proportional-Integral (PI) control scheme in both setpoint tracking and disturbance rejection scenarios. The results demonstrate that the MPC provides superior control, exhibiting faster settling times, minimal overshoot, and more effective disturbance rejection compared to the PI controller. The enhanced performance, quantified by a lower Integral Squared Error (ISE), highlights the capability of MPC to handle complex process dynamics and improve the stability and efficiency of CO₂ capture plants.
Process Simulation and Optimization of a Crude Distillation Unit Using Aspen Plus and MATLAB
The crude distillation unit (CDU) is a primary refinery process where operating decisions impact both yield and energy requirements. This work presents an integrated Aspen Plus-MATLAB framework for CDU simulation and optimization. An Aspen Plus model was first established to simulate the base case, focusing on kerosene yield and energy consumption. Two surrogate modelling approaches, namely an autoencoder (AENN) and feed-forward (FFNN) neural networks were trained to predict CDU performance. FFNN outperformed AENN in predicting kerosene yield (RFFNN2 = 0.8467 vs. RAENN2 = 0.7834) and energy consumption (RFFNN2 = 0.9132 vs. RAENN2 = 0.8639), confirming its suitability as a reliable surrogate. The FFNN model was subsequently coupled with a genetic algorithm (GA) to identify optimal operating parameters for minimizing energy use. The FFNN-GA converged smoothly, recommending a CDU pressure of 2.58 bar and feed rate of 400 ton/h. Despite acceptable prediction error between FFNN/GA and Aspen Plus simulation below 20%, the optimized case yielded no net energy savings due to yield-energy trade-offs. The results demonstrate the viability of surrogate-based CDU optimization while emphasizing the need for multi-objective formulations and model refinement.
Adaptive Neuro-Fuzzy Control of pH and its Derivative in Neutralization Processes: A Comparative Study
Models for pH and its derivative with respect to base flow rate, dpH/dFb, were derived for the neutralization of a strong base-weak acid systems. The systems are then controlled and compared through simulation using PI Control, Fuzzy Logic Control (FLC) and Adaptive Neuro-Fuzzy Control (ANFC). Control of dpH/dFb is an effort made to utilize other phenomenological information of the pH process in a way to improve its control. The dynamics of pH and dpH/dFb were first compared for various acid/base concentrations and flow rates ratios. The comparison reveals that the dpH/dFb curve is highly non-linear and sensitive than the pH neutralization one. The dpH/dFb curve is characterized by the singular equivalence point in a two sensitivity regions which makes it a good candidate as bench mark test to check the effectiveness of non-linear regulatory control strategies of pH. The control of pH and of dpH/dFb was then compared for the system with and without time delay. The comparison showed that FLC is superior to PI controller for the system with time delay for both pH and dpH/dFb control for a variety of load changes. Because of the multi-sensitivity-region characteristics of the strong base-weak acid system, adaptation of the FLC could enhance the controllability of both pH and dpH/dFb. An adaptive method for fuzzy logic control of pH and dpH/dFb was designed, incorporating neural network in identifying and positioning of the membership functions in the FLC and for determining membership functions' position in the fuzzy logic adapter (FLA) of the scaling factors. The results satisfactorily show that the flexible learning ability of neural network improves adaptation and control performance for both pH and dpH/dFb.
neuro-fuzzy control, pH derivative, neutralization process
Artificial Pancreas Control Algorithms Using the Hovorka Equations for Personalized Insulin Delivery in Children with T1DM
Type 1 Diabetes Mellitus (T1DM) requires precise insulin regulation to prevent complications such as hyperglycemia and hypoglycemia. This study compares the control algorithms of an artificial pancreas by integrating both the Hovorka equation and its improved version to enhance personalized insulin delivery for adolescents with T1DM. A mathematical model was developed in MATLAB to simulate glucose-insulin dynamics and assess performance under real-world conditions, including meal intake and insulin sensitivity. The Ordinary Differential Equation Solver 45 (ODE45) was used to model the dynamic interaction between glucose levels and insulin infusion over 24 hours. The standard Hovorka equation established baseline insulin delivery, while the improved version introduced a trial-and-error approach for bolus insulin adjustments and refined carbohydrate intake calculations. Performance was evaluated based on the ability to maintain blood glucose within the normoglycemic range (4.0–7.0 mmol/L). Simulation results showed that both models effectively regulated glucose levels, but the improved equation provided better glycemic stability by reducing postprandial spikes more efficiently. Blood glucose, which initially rose to 10–12 mmol/L after meals, returned to normal faster with the improved model. This enhanced adaptability suggests its potential for real-time continuous subcutaneous insulin infusion (CSII) systems. The findings emphasize the importance of refining control algorithms for personalized insulin delivery. Future research should incorporate additional physiological factors such as stress and exercise to improve real-world applicability and optimize automated diabetes management.
This is preface for 2nd issue of the Asean Journal of process Control
Copyright (c) 2025 ASEAN Journal of Process Control https://creativecommons.org/licenses/by/4.0
To Control, Or Not to Control, That Is the Question: Why and How?
Highlights of Keynote Speech at PCVS2024
Copyright (c) 2024 ASEAN Journal of Process Control https://creativecommons.org/licenses/by/4.0
Enhancing PID Tuning with Routh Stability and Empirical Correlations: Performance Comparison and Applications
This paper presents a new rule-based method called the Routh Stability with Empirical Correlation (RSEC) for tuning a PID controller under model/process mismatch. The RSEC method employs a two-stage calibration procedure to accommodate the FOPDT modeling error in the PID tuning. The RSEC calibration utilizes the deadtime to time constant ratio to adjust the dimensionless tuning parameters subject to specified gain and phase margins. It results in empirical correlations relating the dimensionless tuning parameters to the deadtime to time constant ratio. The second calibration establishes correcting factor correlations for the modeling error. Servo and regulatory simulations demonstrated that RSEC produces the smallest ITAE of 0.396 and 0.151 when tested in a cascade process, outperforming the other control tuning methods, including Skogestad IMC (SIMC). Further application of the developed RSEC method to several examples of SISO and MIMO processes shows the efficacy of the new tuning method compared to the well-established Robust Response Time, AMIGO, and SIMC.
Fault Detection and Classification in Steam Methane Reforming Process Using Deep Learning
Fault detection and classification in steam methane reforming (SMR) processes using LSTM (Long Short-Term Memory) models is a sophisticated and effective method for SMR system reliability and efficiency. Detecting and identifying SMR problems is crucial for reducing disruptions and maximizing productivity in hydrogen generation. SMR processes are dynamic, making LSTM models ideal for evaluating them. LSTM models can identify fault types by continually monitoring process variables, including temperature, pressure, and gas composition. LSTM fault detection and classification in SMR processes improves system reliability, safety, and operational efficiency, increasing productivity and cost savings. This work focuses on the development of a fault detection and classification framework for steam methane reforming based on the LSTM model. This framework uses historical data to train LSTM models, which allows them to learn the normal operating behavior of SMR processes. Real-time data from sensors and process variables is continuously fed into the trained model, which detects and classifies errors by identifying deviations from the learned pattern. The effectiveness of the proposed framework was demonstrated through extensive simulations and experiments, demonstrating its ability to accurately detect and classify various types of errors in the SMR process. The results show that the LSTM-based approach offers superior performance compared to normal conditions, with high accuracy and efficiency in fault detection and classification. The proposed framework provides valuable insights into fault characterization, enabling operators to implement timely and targeted maintenance strategies, optimize system performance, and ensure reliable and efficient steam methane reforming process operation.
A Graphical User Interface for Determining the Safe Consumption of Food based on the Concentration of SD, BA, and SA
The increasing demand for canned foods has led to a higher reliance on preservatives to extend shelf life, maintain color, taste, texture, and overall quality. However, excessive use of food preservatives such as sulphur dioxide (SD), benzoic acid (BA), and sorbic acid (SA) can cause significant health risks, including cancer, allergies, and heart palpitations. Therefore, monitoring and controlling preservative levels is crucial to ensure food safety. This paper introduces a graphical user interface (GUI) based on a fuzzy logic framework designed to assess the concentrations of SD, BA, and SA in five food categories, in compliance with Malaysia’s Food Act 1983 and Food Regulations 1985. The developed GUI allows users to input preservative concentrations and receive real-time feedback on whether the levels are within safe consumption limits. Validation of the system was conducted using both published journal data and laboratory results from industrial food samples. The outcomes demonstrate that the GUI can accurately predict preservative levels, ensuring they remain within legal thresholds and promoting safe consumption. This tool offers a practical solution for food industries and regulatory authorities to monitor preservative usage effectively.
Tracking-error passivity-based control approach for the stabilization of chemical reaction systems using partially decoupled dynamics
This work aims to develop a passivity-based control method for a non-isothermal homogeneous reaction system by integrating its partially decoupled dynamics based on the concept of reaction variants/invariants with the tracking-error-based strategy in the framework of port-Hamiltonian (PH) representation. More precisely, the original reactor dynamics is transformed into an alternative model, expressed in terms of enthalpy, reaction-variant and reaction-invariant states, using a linear transformation, associated with stoichiometric matrix. From this, it is shown that without using any state or input coordinate transformations, the transformed reactor model can be formulated into an extended class of PH systems with a unified quadratic storage function usable for control design. An irreversible first-order reaction system having multiple steady states and occurring in a continuous stirred tank reactor is utilized to illustrate the proposed control method. Simulations show that the closed-loop system is globally exponentially stabilized at the desired equilibrium point.
Simulation and Performance Assessment of a Pure Acetone Production Facility via Isopropanol Dehydrogenation in a Heterogeneous Catalytic Reactor
Nowadays, considering the country's extensive need for acetone as a strategic product, innovative processes for its production can be explored. Up to 75% of the formulation and production of chemical and healthcare products are influenced by the presence of this valuable product in their structure. Among the older processes for acetone production, direct hydroperoxide conversion of cumene according to the Wacker-Hoechst method and direct acetone production from phenol according to the Hock method can be mentioned. In this article, an acetone production process using hydrogenation of inexpensive 2-propanol feedstock is simulated with the help of Aspen HYSYS and Aspen Plus software, utilizing first-order kinetics and industrial data. Subsequently, necessary optimizations were made to prevent water wastage and achieve 98-99% pure acetone production by reclaiming two additional water streams and part of the unused reaction feed. The acetone production process, based on hydrogenation, has led to increased purity in the production unit compared to common and old methods, along with a 32% reduction in energy consumption due to the utilization of a novel process. This method lacks explosion risks associated with high pressure and temperature in the reactor, typically accompanied by oxygenated water in conventional acetone production methods.
Simulation, Acetone, Isopropyl alcohol, Aspen.
Metaheuristic Inspired Algorithm for Supervisory Fuzzy Controller for Plant wide
Plantwide faces challenges to obtain controllers that are fit to correct errors from actual and desired values. This makes industries such as methanol production industry to have nonlinear process in which the process conditions such as temperature, pressure, and level are interacting each other and causing to reduce product quality, equipment damage, explosion, and flooding. Therefore, the goal of this research is to investigate tuning algorithm performance for multiple loop control system by developing metaheuristic tuning algorithm performance for fuzzy logic controller and by developing dynamic process models for plantwide application. However, plantwide process dynamic is simulated using UniSim software for controlling temperature of cooler, pressure and level of flash drum. Mathematical modelling is performed to derive transfer function of the respective process while taking the values of variables from the process simulated in UniSim. Fuzzy Logic Controller (FLC) is designed as there are three inputs and outputs as each one have gaussian type membership functions in which finally rules were defined. Since manually configuration control system is time consuming and abundant trial and errors to get the optimal parameters Particle Swarm Optimization (PSO) is implemented to use as metaheuristic algorithm to optimise control performance by finding particle best to update global best through successive iterations and swarm size. Thus, it is found that control performance increased as the PSO used to optimize control system for Proportional-Integral-Gain (PID) and FLC. The result got shows significant decrease of overshoot and undershot and responding faster with less rise time and settling time after using PSO to optimise conventional controller that combined with FLC. For example, pressure is controlled quickly to reach stability within 1 second after optimised FLC and PID as it has rise time of 2.8704 x 10-11 sec for the PSO-PID and 3.3242 x 10-12 sec for Optimised FLC.
Fuzzy Logic Controller; Particle Swarm Optimization; Plantwide Dynamic Process; Interacting Process; Dynamic Process Model
Concentrating and Rejecting Lithium Ion Using Forward and Reverse Electro-nanofiltration
The increasing use of electric vehicles (EVs) and hybrid electric vehicles (HEVs) to reduce CO2 emissions has raised the demand for lithium for lithium-ion batteries (LIB). LIBs are not solely confined to applications within EVs and HEVs. LIBs are crucial in capturing and storing energy generated from renewable sources, including solar and wind, highlighting their importance in advancing sustainable energy technologies. Therefore, effectively concentrating lithium (Li+) containing lithium-ion is crucial for recovering Li+ from brine or repurposing battery effluent to meet the demands for producing LIB. Electro-nanofiltration is one of the convincing methods because of its low energy consumption and cost compared to other filtration methods. In this paper, a low concentration of Li+ (78 ppm) and a high concentration of Na+ (2200 ppm) solution will be fed into the nanofiltration module. The nanofiltration membranes used are NF 1 and NF 2 from Membrane Technology (Beijing) Co., Ltd., with a forward electric field (EFs), reverse (EFs), and no electric field conditions. The experiment with 5 bar pressure findings shows that a high concentration of Na+ solution has a high rejection rate with NF 1 membrane without EF 40%, compared with a low concentration of Li+ solution with NF 1 10% rejection. The presence of EFs significantly influences rejection rates, with the NF 2 membrane in the Forward EF configuration Na+ solution having a -327% rejection rate, which is a dramatic decrease from the standard filtration of 40% rejection.
Aqueous Mineral Carbonation of Red Mud for CO2 Permanent Sequester
Red mud is a solid waste (slurry) produced during the extraction of alumina, containing minerals such as cancrinite, sodalite, tricalcium aluminate (TCA), and katoite. Due to the use of NaOH in alumina extraction, red mud has a high alkalinity (pH 12–13). Currently, the storage and management of red mud are suboptimal, as it is directly disposed of in landfills without treatment, leading to soil and water contamination and posing environmental risks. Indonesia produces a significant volume of red mud, amounting to 4.4 million tons per year, necessitating effective treatment methods to reduce its danger. One of the solution is mineral carbonation, which neutralizes red mud and serves as permanent CO2 sequestration by injecting CO2 into the red mud slurry. This process dissolves the minerals in red mud into the liquid phase, where they react with CO2 to form mineral carbonates, especially calcium carbonate. The carbonation product of red mud can subsequently be used in cement, brick, tile, and fertilizer production.The red mud used in this research was sourced from Tayan mining in West Kalimantan, Indonesia. Initial characterization of the red mud using XRD and XRF revealed a CaO content 2.936%. The sequestration capacity of red mud, with various liquid/solid (L/S) ratios (1:1–5:1) at ambience temperature of 30°C, was analyzed using TIC (total inorganic carbon) over an observation period of 80 minutes. The optimal L/S ratio was determined to be 1:1, with a conversion rate of 23.886%, while the best CO2 capture capacity is also at a 1:1 ratio with a capacity of 0.551 kg CO2/kg red mud, with neutralization occurring at the 15-minutes. This method neutralizes red mud and provides sustainable CO2 sequestration
Abstracts of the presentations made at PCVS2023 without full papers
This paper presents the abstracts of the presentations made at PCVS2023 without full papers.
Special Issue Part 1: Process Control Nexus Bridging, Technology, Sustainability and Industry
Simulation of Multi-Loop PI Control Strategies for Optimizing Microalgae Cultivation and CO2 Capture in 3-Stage Bubble Column Photobioreactors
Microalgae holds several promising applications, such as renewable sources for biofuel production and CO2 capture from industrial flue gases. There are limited studies so far focusing on the challenges of optimizing microalgae cultivation productivity and CO2 capture efficiency. The present work shows that one of the challenges to aim at high microalgae cultivation and CO2 capture efficiency is maintaining the oxygen level in water below its maximum threshold value, beyond which microalgae photosynthesis switches to respiration. The current work explores various control strategies, such as directly maintaining the oxygen level, microalgae concentration and CO2 capture efficiency. One of the findings revealed that the conditions that increase the microalgae concentration tend to reduce CO2 capture and vice versa. The results emphasize the need to improve the mass transfer rates of CO2 and O2 across the liquid-gas phases by modification of the existing PBR design to improve microalgae productivity and CO2 capture efficiency simultaneously.
Production of Monoethylene Glycol via Reactive Distillation Column
Monoethylene Glycol is a widely used chemical and is a crucial component in the manufacturing of polyester film and fibers, Polyethylene Terephthalate (PET) resins and engine coolants. Monoethylene glycol application ranging from clothing and textiles, kitchenware, engine coolants, antifreeze, and packaging. In 2011, the global usage and production of monoethylene glycol reach 20 million metric tons and is expected to increase 5-10% each year. However, conventionally the production of Monoethylene Glycol is not considered as a sustainable process since it is known as an energy intensive process. Production of Monoethylene Glycol usually involves excess water and the ratio of water to ethylene oxide is 20:1. Besides that the conversion of this process also did not reach maximum conversion. By applying reactive distillation column to replace the conventional process the problems stated above can be solved. Analysis for the selectivity, conversion, process mass intensity, heat and pressure will result in higher selectivity toward product, maximum conversion, lesser capital investment and lower energy usage. The simulation and modelling for this process will be carried out in ASPEN Plus TM. The result from this simulation is by using lesser molar ratio of water and ethylene oxide the conversion is bigger than 99% and the process mass intensity of reactive distillation column is 1, where all the raw material is converted to monoethylene glycol.
Advanced Process Control for Production of Influenza Vaccine Shariah Compliance using Acutase in Batch Reactor
Influenza viruses cause significant worldwide morbidity and mortality every year. Muslims all over the world are debating whether the vaccine qualifies as halal. When this topic is brought up again, some of them even took the chance of forgoing the vaccine. Muslim reluctance and rejection of vaccination is founded on presumptions and convictions that such a procedure and the substances employed are "doubtful (mashbooh)". Process control for the production of shariah compliance vaccine mainly focused on bioreactor to produce hemagglutinin (HA) using Acutase. The process control is carried out via artificial intelligence approach. Artificial neural network (ANN) was employed to create a shallow neural network to predict the HA yield using experimentation data sourced from a relatable journal, whereby the inputs were pH, temperature (oC), pressure (bar), reaction time (day) and oxygen level. A performance comparison and evaluation was carried out to analyse and validate the Levenberg-Marquardt (LM) & Bayesian Regularisation (BR) based-ANN models by altering the number of neurons. Overall, LM generalised better than BR algorithm as all of LMANNs’ MSE values were kept low. It was determined that LMANN is suitable for the experimental database used to build the ANN models.
Using Apps for Teaching Process Control Classes
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.
Performance Evaluation of Unconventional Index for Controllability and Stability Analysis: Application on a Patented Reactor for Renewable Hydrogen Production
In a decentralised PID control, improper input-output pairings would reduce plant safety, productivity, and profitability. When designing a process control system, controllability evaluation is crucial since it helps to pinpoint the best input-output combinations. Current conventional indices, however, are unable to determine the highest possible closed-loop efficiency. Using MATLAB software, this study proposes an innovative index to simultaneously find control pairings and maximum control performance. The index, namely Loop Gain Controllability (LGC) index was evaluated on a distillation process and integrating tank before being used to study a patented industrial-scale reactor's controllability and stability. The LGC index provided values of 1.39, 1.45, 1.40, and 1.08 for four reactor schemes when applied to a decomposition reactor for hydrogen production, while RGA and NI proposed values of 1 for the first three schemes (direct pairing) and negative for the fourth plan (inverse pairing). The closed-loop control response of reactor scheme 2 (P2), which demonstrated that it was controllable and stable when simulated, proved the hypothesis that the larger the LGC value would lead to higher controllability and stability. This demonstrates that LGC
Modeling and Control of Steam Methane Reforming Process Using Model Predictive Control
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.
Model Predictive Control for Efficient Process Control: A Case Study for Absorber-Stripper System with MEA in Hydrogen Plant
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.
Dynamic simulation and optimization of Chemical Looping Hydrogen Production in inter-connected moving bed reactors
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 (H2) 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 (> 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 H2 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.
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.