Anantpinijwatna, Amata

Preferred name

Alternative Name

Anantpinijwatna, A.

Main Affiliation

School of Engineering

amata.an@kmitl.ac.th

Now showing 1 - 10 of 18

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Process Improvement and Economic and Environmental Evaluation of Bio-Hydrogenated Diesel Production from Refined Bleached Deodorized Palm Oil
(2025-01-01)
Anantpinijwatna, Amata
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Simasatitkul, Lida
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Yooyen, Kanokporn
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Amornraksa, Suksun
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Assabumrungrat, Suttichai
The co-production of BHD with other renewable fuels (i.e., using a novel process involving carbon dioxide utilization to achieve the global sustainability goal) is presented. The three configurations of BHD production from refined bleached deodorized palm oil (RBDPO), including (1) the conventional BHD process with hydrogen recovery (BHD process), (2) the BHD process coupled with the Fischer–Tropsch process (BHD-FT process), and (3) the BHD process coupled with the bio-jet fuel and methanol processes (BHD-BIOJET-MEOH process) are investigated using the process model developed in Aspen Plus. The effect of the operating parameters is studied, and the condition of each process offering the highest BHD yield is proposed. Then, the pinch analysis and heat exchanger network (HEN) design of each proposed process are performed to find the highest energy-efficient configuration. The economic and environmental analysis is later performed to investigate the sustainability performance of each configuration. The conventional BHD process requires less hydrogen and consumes less energy than the others. The BHD-BIOJET-MEOH process is the most economically feasible, offering the highest net present value (NPV) of USD 7.93 million and the shortest payback period of 3 years and 1 month. However, it offers the highest carbon footprint of 0.820 kgCO<inf>2</inf> eq./kg of BHD, and it presented the highest potential environmental impact (PEI) in all categories.
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Water influence on the kinetics of transesterification using CaO catalyst to produce biodiesel
(2021-07-15)
Anantapinitwatna, Ajala
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Ngaosuwan, Kanokwan
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Kiatkittipong, Worapon
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Wongsawaeng, Doonyapong
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Anantpinijwatna, Amata
This research investigated the water influence on biodiesel production via transesterification, and especially on their kinetic parameters. The initial rate of transesterification was increased with increasing amount of water (0–5 wt%). On the contrary, the initial rate was significantly reduced for the water concentration of 8–15 wt%. Moreover, when the biodiesel yield reached the maximum value of 30–40%, saponification as a side reaction became more significant with the presence of the emulsion phase, resulting in a remarkable decrease in biodiesel yield. The simple kinetic model including the rate constant and apparent activation energy revealed that transesterification containing 5 wt% water gave the higher rate constant compared to the case with the absence of water. However, the simple model could not describe the case with high water content. The water effect should be accounted for in the reaction rate in the adsorption term. The modified Langmuir-Hinshelwood kinetic model including the effects of water contamination was originally proposed. Our finding suggested that despite the small amount of water content in transesterification using CaO catalyst giving rise in the initial rate, the water contamination in feedstocks for biodiesel production should be avoided because of the notable presence of saponification.
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A machine learning approach for predicting osmotic coefficients and deriving activity coefficients in alkyl ammonium salts
(2026-12-01)
Chawuthai, R.
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Murathathunyaluk, S.
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Saengsuradech, S.
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Nukaew, A.
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Simasatitkul, L.
Quaternary Ammonium Salts (Quats) have diverse applications across various domains. They are extensively used as phase-transfer catalysts (PTCs) in chemical reactions, facilitating the transfer of reactants between aqueous and organic phases. Their unique structure enables the formation of ion pairs, enhancing reaction rates at phase boundaries. This research develops a novel method for predicting Quats’ osmotic coefficients using Simplified Molecular Input Line Entry System (SMILES) notation and supervised machine learning. A comprehensive dataset of 1,654 data points from 52 distinct Quats was compiled. The structural characteristics were encoded using SMILES notation. The data was evaluated using random splitting and Leave-One-Group-Out (LOGO) validation to train seven machine learning algorithms. Gaussian Process (GP) emerged as the optimal algorithm. The GP model achieved a mean absolute percentage error (MAPE) of 5.29% and root mean square error (RMSE) of 0.034. Comparisons with Electrolyte-NRTL and Extended UNIQUAC models demonstrate that this data-driven approach offers competitive accuracy while enabling generalization to structurally similar compounds. This work marks a significant starting point for the machine learning-enhanced prediction of activity coefficients, with considerable potential for future refinement and application.
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Novel Soft Sensor for Measuring and Controlling Product Recovery in a High-Purity, Multicomponent, Side-Draw Distillation Column
(2019-10-30)
A. Udugama, Isuru
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Alvarez Camps, Martina
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Taube, Michael A.
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Thawita, Chatchayarat
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Anantpinijwatna, Amata
The use of soft sensors for monitoring purposes is an established practice in the process industry. In this study, the focus has been on developing a soft sensor that can be used to monitor the product recovery rate of double-ended, high-purity distillation columns with a side-draw. To this end, this study specifically focuses on developing a cost-effective and accurate soft sensor for an industrial methanol distillation unit where a side-draw is used to meet the parts per million level impurity specifications. The novel soft sensor is based on the unique characteristics of the mass balance in this type of column, and uses the density measurement at the side-draw together with the flow rates of the side-draw and product draw to calculate the product recovery rate. The developed soft sensor was validated against real plant data as well as on a process simulation of an industrial methanol distillation column. The soft sensor demonstrated the ability to predict product recovery to an accuracy of 0.05% and showed good dynamic performance. The proposed soft sensor was next used as a process variable in the development of a supervisory scheme and a model-predictive control scheme, which were able to operate the process at product recovery rates of 99.5% while honoring critical product and bottoms product specifications.
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Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning
(2025-12-01)
Chawuthai, Rathachai
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Promchan, Teeratat
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Rojsanga, Jularak
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Chandra-ambhorn, Somrerk
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Nilsonthi, Thanasak
Material degradation is one of the main problems in various high-temperature processes, directly resulting in the failure of the material. Crack and protective oxide film spallation caused either by mechanical stress development in the oxidation process or thermal stress due to a mismatch of the thermal expansions of the formed oxide and alloy are common forms of failure in high-temperature processes. Typically, the Pilling-Bedworth ratio (PBR) is employed to predict crack and spallation of the oxide by determining the volume changes of oxide and alloy because of its simplicity. However, this approach provides poor crack and spallation predictions. Hence, machine learning was adopted in the present work to predict oxide formation and spallation in the temperature range of 600-1,200 °C. The inputs for the present developed model were alloy compositions, oxide formed during oxidation, and oxidation conditions and periods. Furthermore, the predicted results of the present developed machine learning model were compared to those obtained by the PBR method. The present results revealed that the accuracy of the oxide spallation prediction of the present model was better than that of the PBR method. The random forest with 15 estimators was the best machine learning model. Finally, it can be concluded that the machine learning model is essential for accurate material failure prediction.
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Process development of sustainable biorefinery system integrated into the existing pulping process
(2020-05-10)
Mongkhonsiri, Ghochapon
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Charoensuppanimit, Pongtorn
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Anantpinijwatna, Amata
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Gani, Rafiqul
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Assabumrungrat, Suttichai
The change of paper consumption trend may jeopardize the future of pulp and paper industry. This work aims to design and develop the integrated network of biochemical and biofuel productions into existing pulp mills for sustainable purposes. The systematic methodology aided by computation tools is undertaken using the three-stage approach including process synthesis, design and innovation. Previously, the optimal technologies of the biorefinery-integrated pulping processes were successfully determined in the synthesis stage providing the highest cost-effective incorporation; 48 million USD/year of profit was estimated according to the integration of succinic acid and dimethyl ether productions into the soda pulping process. Herein, the process designs of the integrated processes were performed followed by evaluations of the process performances and identifications of the hot spots and targets for establishments of the innovations. In this work, the biomass gasification option is designed and implemented to enhance the material and energy utilizations in the previously determined biorefinery-integrated pulping processes. Electricity and biofuel are produced and sold, which contributes positively to the economic and environmental impacts of these processes. The hot spots and targets are subsequently identified prior to the innovation stage. According to this stage, the cleaner alternatives that implements the CO<inf>2</inf> utilization via methanol synthesis and solar cell installations are selected in order to minimize the CO<inf>2</inf> emission. A net CO<inf>2</inf> reduction of 42% is achieved when the cleaner alternatives are applied. Accordingly, these biorefinery-integrated innovations are not only conducive to the enhanced sustainability of existing pulp mills but also adaptive in response to the change of paper consumption trend.
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Mathematical optimization of the anti-corrosive rice husk ash enhanced concrete under marine environment
(2018-01-01)
Wongpromrat, Patthranit
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Anantpinijwatna, Amata
Chloride induced steel corrosion causes safety and stability problems to the reinforced concrete structure located closed to or under marine environment. The corrosive steel rust could potentially lead to surface swelling altering the external appearance, generating the concrete cracking, lowering the elasticity, reducing tensile strength, and thus leading to the deterioration of the concrete structure. Recent studies present an innovative method for inhibiting chloride corrosion by the addition of fibres into concrete to improve its toughness and tensile properties. By the addition of high fineness rice husk ash (RHA), the RHA would function as chloride adsorbent, preventing the chloride penetration through the concrete into the steel foundation. However, the addition of the RHA also affects the compressive strength, the workability, the consistency, and the slump of the concrete structure, limiting the mixed amount of the RHA that could be added in the concrete. A linear optimization model of the anti-corrosive RHA enhanced concrete has been formulated with the objective to minimize the effect of the chloride corrosion of the steel; whereas, the amount of the mixed RHA is limited by the critical concrete strength in term of modulus elasticity. In this study, the mass transfer coefficient, adsorption coefficient, and the Langmuir equilibrium isotherm are taken from the literatures. The chloride concentration is assumed to be 3.5 % w/v of the total chloride ion in salt water. The model has been validated with the measured data collected from open literatures. The optimum ratio between the RHA and cement mixture is discovered to be based on the void fraction of the concrete mixture. The optimum ratio is found to be around 10 % at the void fraction 0.8 and increasing to 25 % at the void fraction 1.2.
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Integration of Genetic Algorithm with Machine Learning for Properties Prediction
(2025-01-01)
Chawuthai, Rathachai
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Murathathunyaluk, Siripan
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Amornratthamrong, Nalin
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Arunchaipong, Run
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Anantpinijwatna, Amata
Numerous studies have demonstrated that machine learning (ML) provides more accurate estimations of properties for oxygenated organic derivatives compared to the conventional Quantitative Structure-Property Relationship (QSPR) method. Consequently, ML’s predictive capabilities have been extended to encompass a broader range of properties, including Partition Coefficient, Boiling Point, and Solubility, among others, for oxygenated hydrocarbon derivatives. Algorithms such as Linear Regression, Support Vector Machine, Random Forest, and Gaussian Process are selected through trial-and-error to identify the most suitable approach. The models are trained and validated using experimental data from published literature. Despite the accuracy of these property predictions, they have limited practical utility in industry, where specific property ranges are essential for processes. To address this, Genetic Algorithms (GA) are employed to design chemical compounds that meet industrial requirements. Integrating GA with ML could yield alternative chemical compounds, enhancing overall production processes by increasing economic potential, sustainability, and reducing environmental impact.
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Comparative CFD modeling of foam and conventional pellet catalysts in glycerol steam reforming
(2025-07-15)
Simasatitkul, Lida
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Phitchayakorn, Chattharika
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Amornraksa, Suksun
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Anantpinijwatna, Amata
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Assabumrungrat, Suttichai
A novel approach to glycerol valorization via steam reforming was investigated through computational fluid dynamics (CFD) modelling. The performance characteristics of conventional pellet catalysts were compared with foam catalysts in a 6-inch diameter packed bed reactor. A two-dimensional pseudo-homogeneous steady-state model was employed to evaluate catalyst configurations ranging from 10 to 30 pores per inch (PPI). The foam catalyst structures exhibited superior performance across key metrics, achieving maximum hydrogen yield (60 %) at one-third of the reactor length whilst reducing pressure drop by 95 % compared to conventional pellets. Within the foam configurations, the 10PPI variant demonstrated optimal performance characteristics, with an 80 % reduction in normalized pressure drop compared to 30PPI, whilst maintaining comparable product yields. The enhanced performance was attributed to the open-cell architecture, which facilitated improved mass transfer and reduced diffusion limitations. These findings suggest that foam catalysts represent a promising alternative to conventional pellet configurations for glycerol steam reforming processes.
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Effects of gallery-walk in process simulation course
(2026-01-01)
Anantpinijwatna, A.
This study examines the integration of the gallery-walk active learning strategy into a third-year Process Simulation course. Using a quasi-experimental design, student performance was compared across two traditional lecture cohorts (n = 157) and two gallery-walk cohorts (n = 158). Direct analysis revealed the gallery-walk was associated with improved performance on simple simulation problems, with the average percentage of correct answers rising from 63 % to 70 %. However, a performance decline on complex problems was observed in the initial implementation; this was mitigated in the second iteration by adding an instructor-led summarization phase. Notably, the intervention did not increase the number of top-performing students (scores >90 %), a finding potentially explained by the expertise reversal effect. Anonymous surveys indicated a significant increase in student satisfaction and engagement. This study demonstrates the gallery-walk's potential in engineering education but underscores the necessity of iterative refinement to address complex problem-solving and support learners at all proficiency levels.

Anantpinijwatna, Amata

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