ASEAN Journal of Process Control <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 is a resurgence of research interests recently in various research topics related to process control in this region, as well as globally at large.. [<a href="">more</a>]</p> en-US (Associate Prof. Dr. Jobrun Nandong) (ICT Team) Thu, 01 Sep 2022 00:00:00 +0800 OJS 60 Plantwide Design and Control of Sulfur-Iodine Thermochemical Cycle Plant for Hydrogen Production <p>Through several credible studies, some researchers have identified the Sulfur-Iodine Thermochemical Cycle (SITC) process as the most promising one among over 350 different types of thermochemical cycles for large-scale hydrogen production. Detailed complete design and control study of the SITC plant at an industrial scale so far remains scarce. This paper presents the plantwide design and control study based on a pre-defined SITC flowsheet. In the flowsheet, a multi-bayonet reactor configuration is adopted in the sulfuric acid decomposition section to improve the plant's thermal efficiency. A fundamental model of the complete SITC plant enables process scale-up, optimization, and plantwide simulation. The Self-Optimizing Control Structure (SOCS) approach is adopted to construct a complete control (PWC) strategy for the SITC plant. The plantwide SOCS strategy enables robust and flexible operation of the SITC plant, which allows the production rate to vary over a wide range, from 24 tons/day to 57.6 tons/day of hydrogen without leading to unstable operation. At the maximum production capacity, the plant thermal efficiency reaches 68.6% and gross profit of USD 35 million per annum. The extensive simulation study shows that it is vital to control the Bunsen reactor well within a narrow range of conditions. Poor control of the Bunsen reactor can lead to severe challenges to achieving smooth plant operation overall. Detailed analyses and simulations show that the industrial-scale SITC plant is viable in terms of economic and controllability.</p> Noraini Mohd, Jobrun Nandong Copyright (c) 2022 ASEAN Journal of Process Control Thu, 01 Sep 2022 00:00:00 +0800 Modeling and parameter analysis of Solid oxide fuel cell for power and heat generation based on different fuel operating modes <p>This paper presents a comprehensive mathematical model and simulation of Solid Oxide Fuel Cell (SOFC) in a single cell model. Steady-state responses of SOFC are analyzed based on operating condition points for each operating mode i.e. constant fuel flow (CFF) and constant fuel utilization (CFU). For the CFF mode, the six operating conditions are fuel flow, air flow, fuel utilization, pressure, fuel temperature and air temperature, whereas, in CFU, the operating conditions are fuel utilization, air to fuel ratio, and current limiter, pressure, fuel temperature and air temperature. These changed operating points are directly analyzed for changes in the output voltage, power, electrical efficiency, temperature, heat power and heat efficiency of a single tubular SOFC. From the analysis, it is observed that those six parameters have a significant impact on the generation of maximum power and efficiency for electricity and heat generations. The proposed model can be used to find the optimal parameters of SOFC that will produce the maximum electrical and heat power as well as their efficiencies. It also can be extended into the stack model for larger systems.</p> Farah Ramadhani, Mohd. Azlan Hussain, Hazlie Mokhlis, Oon Erixno Copyright (c) 2022 ASEAN Journal of Process Control Thu, 01 Sep 2022 00:00:00 +0800 Locally weighted kernel partial least square model for nonlinear processes: A case study <p>A soft sensor, namely locally weighted partial least squares (LW-PLS) cannot cope with the nonlinearity of process data. To address this limitation, Kernel functions are integrated into LW-PLS to form locally weighted Kernel partial least squares (LW-KPLS). In this study, the different Kernel functions including Linear Kernel, Polynomial Kernel, Exponential Kernel, Gaussian Kernel and Multiquadric Kernel were used in the LW-KPLS model. Then, the predictive performance of these Kernel functions in LW-KPLS was accessed by employing a nonlinear case study and the analysis of the obtained results was then compared. In this study, it was found that the predictive performance of using Exponential Kernel in LW-KPLS is better than other Kernel functions. The values of root-mean-square errors (RMSE) for the training and testing dataset by utilizing this Kernel function are the lowest in the case study, which is 44.54% lower RMSE values as compared to other Kernel functions.</p> Joyce Ngu Chen Yen, Wan Sieng Yeo Copyright (c) 2022 ASEAN Journal of Process Control Thu, 01 Sep 2022 00:00:00 +0800 Enhanced Production Of Levulinic Acid Using Carbon Source From Oil Palm Biomass And Matured Coconut Water <p><strong>Abstract </strong><strong>Levulinic acid (LA), a powerful platform chemical that can only be synthesised from biomass. LA have the capability to act as a substitute intermediate chemical in the production of hydrocarbon-based products. The initial purpose of this research project was to investigate different types of metal chloride catalyst along with determining the optimum process conditions to synthesis LA from oil palm biomass such as oil palm sap (OPS) and matured coconut water. However, the project was disrupted due to the COVID-19 pandemic. Thus, the completion to this project is via artificial intelligence approach. Artificial neural network (ANN) was employed to create a shallow neural network to predict the LA yield using experimentation data sourced from a relatable journal, whereby the inputs were amount of sugar (g/L), catalyst loading (g), temperature (</strong><strong>) and reaction time (hr) and the output was LA yield &nbsp;(%). Database for sugar composition in both oil palm biomass and matured coconut water was developed to illustrate that it has the capability to produce LA.</strong><strong>&nbsp;</strong><strong>Second, a performance comparison and evaluation was carried out to analyse and validate the Levenberg-Marquardt (LM) &amp; 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 below </strong><strong>&nbsp;and </strong><strong>&nbsp;values were more than 0.88. &nbsp;It was determined that LMANN with 21 hidden neurons is suitable for the experimental database used to build the ANN models.</strong></p> <p><strong><em>&nbsp;</em></strong></p> Norliza Abd.Rahman, Sharmela Raj, Jarinah Mohd Ali Copyright (c) 2022 ASEAN Journal of Process Control Thu, 01 Sep 2022 00:00:00 +0800 Status of Process Control in ASEAN Countries <p>This is a short review paper on the status of process control in ASEAN countries. It begins with an overall perspective of industries involved in process control and then reviews the status of research in universities in ASEAN with examples of activities in some of their universities. A future outlook is given in the last section.</p> Mohd Azlan Hussain; Syamsul Rizal Abd Syukor, Zainal Ahmad Copyright (c) 2022 ASEAN Journal of Process Control Thu, 01 Sep 2022 00:00:00 +0800 Feedforward Artificial Neural Networks-Model Prediction Control (FANN-MPC) for Semi-Simultaneous Saccharification and Fermentation (SSSF) Bioethanol Process <p>The main focus of this work is on the development of nonlinear model-based control for the production of bioethanol from lignocellulosic materials following the semi-simultaneous saccharification and fermentation (SSSF) process. It is observed from the current study that the SSSF process is able to give higher yield and higher ethanol concentration in comparison with the simultaneous saccharification and fermentation (SSF) process, and the separate hydrolysis and fermentation (SHF) process. The SSSF process in this study, which is referred to as the SSSF 24, includes 24 hours of pre-hydrolytic phase, and 48 hours of SSF phase. Mechanistic models for the fed-batch operating mode of SSSF 24 were initially developed to represent the actual dynamics of this fermentation process. Feedforward Artificial Neural Network (FANN) model was developed in nonlinear model predictive control (NMPC) to improve the performance of the controller. Data for the training, testing and validation procedure to construct the FANN were generated through randomization of the input cellulose concentration and dilution rate using the mechanistic models. The total sum of squared errors (SSE) for training and testing, and coefficient of determination, <em>R<sup>2</sup></em>, value for fed-batch operating mode are 0.0508 and 0.9998, respectively, for their best FANN architecture of&nbsp; 2 – 15 – 1 with 15 hidden neurons. The FANN-model predictive control (NMPC) strategy developed for the fed-batch operating mode of SSSF 24 has good setpoint tracking ability and is robust</p> Nor Irwin, Clara Xiang Xiang, Zainal Ahmad, Syamsul Rizal Copyright (c) 2022 ASEAN Journal of Process Control Thu, 01 Sep 2022 00:00:00 +0800