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]
This is preface of papers in this issue
Copyright (c) 2026 ASEAN Journal of Process Control https://creativecommons.org/licenses/by/4.0
Comparative Study between Cascade-Forward Neural Network and Feedforward Neural Network Models for Enzymatic Polymerization Process
Enzymatic polymerization has gained attention as a more environmentally friendly alternative to conventional chemical methods, as it avoids the use of fossil fuel-derived monomers and requires milder operating conditions. However, the reaction kinetics and mechanism of the polymerization are complex. The process is also sensitive to the operating conditions, as harsh operating conditions would negatively impact the desired molecular weight of the resulting polymer. To address this, the process model must be developed accurately to predict the molecular weight under suitable conditions. This study compares the effectiveness of two types of artificial neural networks (ANN), namely cascade-forward neural network (CFNN) and feedforward neural network (FFNN) to determine the better-performing ANN type for the enzymatic polymerization process. A total of 84 experimental datasets obtained from laboratory batch study were used, which include the inputs of reaction time, reaction temperature and reactor impeller speed, as well as polymer molecular weight as the output. The effects of these operating inputs on the outputs were also investigated through sensitivity analysis. Twelve training algorithms for both CFNN and FFNN were compared and evaluated in terms of mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), regression (R), determinant coefficient (R2), and accuracy. CFNN model with the Levenberg-Marquardt backpropagation (LM) achieved the highest accuracy of 99.97%, yielding the least MSE, RMSE, MAE, MAPE and R of 2.36, 1.53, 0.80, 0.03%, 1, respectively. The sensitivity analysis showed that both temperature and speed have a significant negative impact on the molecular weight of polymer compared to the time input.
Attention-Based Gated Recurrent Unit Approach for Fault Diagnosis in the Tennessee Eastman Process
Fault diagnosis in chemical processing systems remains difficult because these operations are highly complex, nonlinear, and safety-critical; undetected abnormalities can escalate into major operational and economic losses. Traditional FDD techniques often have limited capability in handling high-dimensional sensor measurements and typically overlook time-dependent behavior, which reduces their diagnostic reliability. In this study, an attention-enhanced gated recurrent unit (Attention-GRU) model is introduced to improve process fault identification. The architecture couples GRU layers with an attention module that highlights the most relevant temporal segments, strengthening the learned feature representations. The derived context vector is then passed to a fully connected SoftMax classifier. Using the Tennessee Eastman process (TEP) as the evaluation benchmark, the method achieved average accuracy, precision, and F1-score values of about 90%, surpassing the standard GRU baseline, which recorded roughly 86%, 85%, and 85%, respectively. The findings indicate better diagnostic performance, adaptability, and earlier fault recognition, providing a more robust and reliable solution for industrial chemical process monitoring.
Tennessee Eastman process, fault diagnosis, gated recurrent unit, Attention mechanism, classification accuracy
MIMO Control of an Interacting Two-Tank System: A Comparative Study of PID and MPC via Chirp-Based Frequency-Domain System Identification
Effective control of interacting multiple-input-multiple-output (MIMO) systems is critical in many industrial applications since stable flow and level regulation affect process safety and efficiency. A commonly encountered challenge in such systems is the coupling between inputs and outputs, which complicates controller design and tuning. This study addresses the control of an interacting two-tank system by comparing the performance of conventional Proportional-Integral-Derivative (PID) controllers with Model Predictive Control (MPC). The main objective of the study is to evaluate the suitability and effectiveness of MPC in managing coupled dynamics, actuator constraints, and setpoint tracking, in contrast to classical PID controllers. The system was identified using frequency-domain techniques, where chirp signals were injected into each input, and the resulting output responses were processed using the Discrete Fourier Transform (DFT) to estimate transfer function models. PID controllers were designed using the root locus method, while MPC was implemented based on a discrete-time state-space model. Simulation results showed that while PID controllers performed acceptably in single-loop configurations, they suffered from excessive overshoot (up to 187%) and a prolonged settling time of 53 seconds when applied to the full MIMO system, despite exhibiting 0 steady-state error. More importantly, the actuator produced an impractically high flowrate of 2.7×104 L/min. In contrast, MPC achieved faster settling times (under 23 seconds) and eliminated overshoot entirely (0%). Also, it limited its actuator output to a feasible flowrate of 25 L/min. These findings suggest that MPC provides a more robust and practical solution for controlling interacting MIMO systems.
Multiple-Input-Multiple-Output (MIMO) Systems
Artificial Neural Network Modelling for Biodiesel Production Using Waste Camel Bone based Heterogenous Catalyst
In recent scientific findings, utilizing solid wastes, especially biowastes from animal bones, as heterogeneous catalysts for biofuel production has gained attention in shifting from first- to third-generation feedstocks for a more environmentally friendly and sustainable transition from petroleum diesel to biodiesel. However, commercialization is still far off, as challenges and limitations demand further technological advancements to achieve a holistic production process with bearable costs to support the economy. Modeling studies have always been essential to understanding the behaviour of process and reduce costs as they can predict experimental results for untested conditions. This study addresses the element of process modeling in biofuel production by targeting critical parameters such as catalyst loading rate, alcohol-to-oil molar ratio, reaction time, and temperature to maximize biodiesel yield from non-edible seed oil. Experimental study was carried out in which waste camel bone was utilized as a heterogeneous base catalyst for the conversion of non-edible Jatropha curcas seed oil to biodiesel. Three types of artificial neural networks were developed namely: feedforward (FFNN), cascade forward (CFNN), and patternet (PNN), applying various training algorithms. A comparison among them was made, and it is demonstrated that the FFNN performed better overall than the other networks in predicting oil yield. CFNN with Levenberg–Marquardt (LM) achieved perfect accuracy (R = 1.0) and outperformed FFNN under scaled conjugate gradient (0.9789 vs 0.9347), though its performance collapsed under Bayesian regularization (BR) (R = 0.04). FFNN demonstrated more stable performance across algorithms (R = 0.9961–1.0), while PNN also achieved near-perfect fits with LM (0.9999) and BR (1.0) but showed much weaker performance under gradient-based algorithms (R = 0.5664–0.6923). The results highlight the potential of integrating machine learning in biodiesel production to capture process patterns and improve yield prediction, offering a pathway to overcome current limitations and move closer to commercialization.
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.
Deep Neural Network-Model Predictive Control (DNN-MPC) for Temperature Control in Batch Reactor
A hybrid Deep Neural Network- Model Predictive Control (DNN-MPC) is developed regulate the temperature of batch reactor in the production of magnesium stearate. Thermal mathematical model of magnesium stearate production was employed to generate 1,000 simulation-based training and validation datasets, with a holdout ratio of 0.8. The DNN was trained using the Rectified Linear Unit (ReLU) activation function and the Adam optimizer. The trained DNN is integrated into a stable MPC with a prediction and control horizon of 60 seconds and 20 seconds respectively in MATLAB R2024a Simulink. Comparative test with standalone MPC in both servo and regulatory control system demonstrates that the proposed DNN-MPC exhibits fast settling time with zero overshoot and minimal error of 0.002794. DNN-MPC demonstrate great ability to reject disturbance where minimal error of 0.516% and small overshoot of 13% is observed. These results highlight the robustness, responsiveness, and efficiency of the DNN-MPC, establishing its potential as a highly effective temperature control strategy for batch reactor in the production of magnesium stearate.
Model Predictive Control for GPS based Eco-Driving
In the past century, the depletion of petroleum and its harmful effects on the environment has increasingly become a concern to the people. To help mitigate these issues, it is imperative that a proper optimisation system is devised to make vehicles more fuel-efficient. Model predictive control (MPC) is a process control method capable of of anticipating future events over a predictive horizon and adapting to them before they occur. The MPC controller can be applied to a vehicle and fuel consumption model to obtain the optimal vehicle velocity and acceleration at each timestep. This would in turn minimise the overall fuel consumption of the vehicle by avoiding any unnecessary engine power usage. A route on the Putrajaya-Dengkil-Sepang road is chosen as the road profile for its constantly varying altitudes, allowing the MPC controller's capability to be assessed. An adaptive cruise control system modelled as a proportional-integrative-derivative (PID) controller and a pulse-and-glide system were modelled to act as comparisons for the MPC controller. It was found that the MPC controller consumed 4.76 L of fuel at the end of the run, resulting in carbon dioxide emissions of 10.94 kg, 1.36% less than the PID controller and 7.06% less than the pulse-and-glide one. This means that the MPC controller is the most fuel-efficient of the three at adapting to a road with varying altitudes.
Machine Learning Surrogate-Assisted Genetic Algorithm for Energy Optimization in LDPE Tubular Reactors
This work introduces a machine learning-driven optimization framework that integrates Genetic Algorithm (GA) with Gaussian Process Regression (GPR) to enhance energy efficiency in low-density polyethylene (LDPE) production. The optimization targets minimizing energy consumption, maximizing conversion (), and improving profitability by tuning key process variables, including FM, , , and . Results indicate that FM and exert the most significant influence on energy consumption, whereas and jacket temperature contribute minimally. The optimization achieved a minimum energy consumption of 603.357 kWh. While adjustments in and led to higher profitability, the overall profit remained negative, underscoring the need for further refinement. The proposed framework offers valuable insights into LDPE process optimization and demonstrates the potential of advanced machine learning-based methods for improving both energy efficiency and economic performance. Future research will focus on refining the optimization strategy and exploring cost-reduction pathways.
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.