Anantpinijwatna, Amata

Preferred name

Alternative Name

Anantpinijwatna, A.

Main Affiliation

School of Engineering

amata.an@kmitl.ac.th

Now showing 1 - 10 of 30

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Modeling of Non-Isothermal Adsorption Process in a Silica Gel Desiccant Packed Bed
(2018-01-01)
Murathatunyaluk, Siripan
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Srichanvichit, Koranut
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Anantpinijwatna, Amata
;
Kitchaiya, Prakob
This study investigated a numerical simulation of a column packed bed using silica gel. This bed is either stationary or steadily rotating, leading to different operation-regeneration schemes. Although models of these systems have already been developed and fitted with available data, they require large number of costly and time-consuming experiment to be applicable. Therefore, the development of a fundamental predictive mathematical model is necessary. The present model is a one-dimensional numerical solution of the conservation equations for heat, water vapor, and adsorbed water inside the silica gel desiccant under the constraint of local equilibrium between the two phases, which is characterized by fundamental sorption isotherms. The system of a non-isothermal adiabatic under constant pressure containing heat and mass transfer phenomena between vapor and solid phases are considered. The numerical results show good agreement with a maximum root of mean square of errors of 6.6% and 9.6% for exit air temperature and humidity, respectively
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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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Complete design case study for pulp and paper industry
(2022-01-01)
Anantpinijwatna, Amata
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Charoensuppanimit, Pongtorn
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Mongkhonsiri, Ghochapon
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Gani, Rafiqul
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Assabumrungrat, Suttichai
Pulp and paper industry is a traditional biorefinery system that produces low margin paper products at low innovation development. It needs business transformation to enhance profitability along with efficient material and energy consumption through process development of high-value bioproducts. In response to climate change concerns and declining petroleum resources, the concept of biorefinery has developed using biochemical and thermochemical technologies. To develop biorefinery together with the conventional pulp and paper industry, integrated biorefinery in the existing pulp mill has been designed as a long-term sustainable solution. A systematic framework is needed to synthesize and design promising integrated systems from numerous alternatives. A three-level methodology, involving Level-1 Base Case Design, Level-2 Optimization and Analysis, and Level-3 Innovation, is proposed as an effective approach to determine optimal technologies suitable for the transformation of the traditional system through superstructure optimization, process analysis, and process improvement in terms of economic and environmental issues. To achieve a sustainable development of the integrated biorefinery system, innovative alternatives are discovered to satisfy improvement targets. Computer-aided tools are employed to support systematic data collection, mathematical model formulation, and complicated problem solving. Case studies of synthesis, design and innovation tasks illustrating the application of the framework to obtain promising integrated pulp mill-biorefinery alternatives are presented.
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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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Coke Formation Model in Crude Oil Furnace for Maintenance Scheduling
(2018-01-01)
Anantpinijwatna, Amata
A crude oil distillation unit (CDU) is one of the most important unit in petroleum industry. Its main function is to separate the crude oil into many kinds of petroleum products. Generally, the CDU's design includes the crude oil preheater, which are either cabin or vertical cylindrical furnace, for adjusting the crude feed properties and increasing feed temperature. Carbon coking inside the furnace during the preheating process leads to accumulating of the coke, deteriorating of the product quality, increasing of the pressure drop across the furnace, and increasing of the energy consumption. The de-coking process is normally executed on demand based on the measured heat loss or performed every fixed period of time. However, due to the different rate of coke formation of various crude oil grades, as well as the difference in cost of product, process operation, and maintenance operation; both on-demand and fixed maintenance practices are not optimal method for de-coking. Model of the coking rate and accumulation inside of the furnace could be a useful tool for scheduling the decoking. The model includes the balance equations for the heat generated, the heat transfer in forms of convection and radiation, the changes of the temperature and the amount of crude oil and coke, the constitutive equations for the coke formation and accumulation, and the conditional equations for optimization of the de-coking schedule. The model parameters are fitted to the data provided by the refinery in Thailand with absolute average deviation below 3%; the operation and maintenance costs are also estimated from the financial activity report of the similar sources. It is found that with different sources of crude oil, the optimal furnace maintenance schedules are different. The improvements, in term of cost per maintenance, are found to be 15 - 34% depending on the operation scenario. Although, the initial results look promising and the initial goal is accomplished, the application of the model toward multiple crude oil feed for better operation is under development.
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Mathematical optimization of the anti-corrosive rice husk ash enhanced reinforced concrete under marine environment
(2018-01-01)
Wongpromrat, W.
;
Anantpinijwatna, A.
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The kinetic model and temperature effect of Caulerpa Lentillifera drying process
(2018-10-29)
Anantpinijwatna, Amata
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Nuntamongkol, Sitanan
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Tudkesorn, Benjamaporn
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Sukchoy, Orawan
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Deetae, Pawinee
The drying mechanism of the seaweed, Caulerpa Lentillifera, at different temperature were studied. This involved the modelling of the drying kinetic and the studied of the effect of the relative humidity. Five empirical drying kinetic models of Newton, Page, Modified Page, Logarithmic, and Henderson-Pabis were fitted to the experimental data with the kinetic parameters following the modified Arrhenius equation. The decency of the fit of different model was statistically evaluated. Moreover, the milestones for optimization of the drying procedure towards the energy preservation and the valuable constituent saving have been set through the modelling of the drying process energy consumption and the studied of the effect of drying temperature to the seaweed physical appearance. This work should be an interesting starting point for the further study, analysis, and improvement of the Caulerpa Lentillifera drying process.

Anantpinijwatna, Amata

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