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Intelligent model and optimization of ultrasound-assisted extraction of antioxidants and amylase enzyme from Gnaphalium affine D. Don
(2025-01-01)
Luangsakul, Naphatrapi
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Kunyanee, Kannika
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Kusumawardani, Sandra
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Ngo, Tai Van
The study uses ultrasound-assisted extraction to recovery the antioxidant and amylase enzyme from Gnaphalium affine D. Don, namely “chewcut” in Thailand. The study involves two statistical methods: artificial neural networks (ANN) and response surface methodology (RSM) to model and optimize extraction procedure for improving the yield of antioxidant and amylase enzyme activity (AEA). Both RSM and ANN showed the potential to predict and find the optimal extraction conditions. However, ANN model could give more accurate values compared with validation test. ANN model found that under optimal conditions (temperature: 65.92 °C, ultrasonic power: 58.22 %, extraction time: 37.95 min), the total phenolic compounds, total flavonoid compounds, antioxidant activity and AEA were 218.35 ± 0.34 mgGAE/g, 0.554 ± 0.045 mgQE/g, 84.2 ± 0.2 %, 364.14 ± 1.35 mg-maltose/g. This is the first report on amylase potential of chewcut, which could be further served as the natural enzyme source. Moreover, by adding its bioactive compounds, it may be possible to improve nutraceutical properties and quality of products.
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Prediction of the germination rate and antioxidant properties of VD20 Rice by utilizing Artificial neural network-coupled response surface methodology and product characterization
(2024-10-01)
Loan, Le Thi Kim
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Tat, Truong Quoc
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Minh, Pham Do Trang
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Thao, Vo Thi Thu
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Hoang, Pham Thi Minh
The current research aims to predict and optimize process conditions to produce germinated VD20 with a high rate of germination and antioxidant properties. Box-Behnken design (BBD) was used to build models with three factors [soaking time (ST: 4–6 h), germination time (GT: 18–22 h), and germination temperature (33–37 °C)] and three replications. The data set from the BBD experiment was used to create an artificial neural network (ANN) model that estimated the change in responses by variable factors. The ANN model was extremely accurate, with an overall correlation coefficient (R) of 0.9997 and showed the best fit with actual and predicted data. The germination conditions were further optimized using multi-objective RSM, which revealed that the optimal conditions were ST of 5.34 h, GT of 20.78 h, and germination temperature of 35.6 °C. The experimental validation revealed a high level of agreement between the results of the BBD models forecasted and the actual experimental values. The ANN-coupled BBD methodology is a promising hybrid method for modeling, forecasting, and optimizing the impact of process conditions on the quality of germinated grain. In addition, the raw and germinated VD20 rice were analyzed for their techno-functional properties, estimated glycemic index (eGI), and FTIR. Lower peak viscosity, values of breakdown and setback, and phytic acid were found after rice was germinated. The result revealed that high antioxidant content and activity, which were confirmed by the FTIR pattern, and low digestion behaviors (eGI = 64.23) were the attributes of the germinated product. Furthermore, the results of pasting, thermal, swelling power, and solubility showed the wide range of further application of this material, which should receive more consideration in future research.
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Drying modelling of amylose fatty acid complex formation for reducing rapidly available glucose of geographical indication rice during high-temperature fluidisation
(2022-04-01)
Junka, Nittaya
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Rattanamechaiskul, Chaiwat
This study analysed changes in the available glucose of geographical indication rice, Sang Yod variety, from Thailand after drying through the fluidisation technique in the temperature range of 85–115 °C. When the rice with an initial moisture content of 33.3% dry basis was dried at temperature over 85 °C, starch gelatinisation occurred. As the starch underwent gelatinisation, the orderly structure of the starch granules was partially disrupted. Thus, amylose leached out of the granules and simultaneously formed fatty acids in the rice. This created amylose fatty acid complexes (AFCs). This change was detected from the A and V type crystallinity patterns of the starch and complexes using X-ray diffractometry. When drying the rice at 115 °C, the formation of AFCs resulted in rapidly available glucose of dehulled rice decreasing from a medium-high level to a medium-low level; the slowly available glucose value also increased. The drying modelling was developed based on experimental data to explain the observed changes. The developed mathematical model could predict changes in A type crystallinity with the R<sup>2</sup> value and average of relative root mean square error of 0.9855 and 6.34%, while those of the V type changes were 0.9879 and 4.64%, respectively. This data can explain the mechanism underlying the available glucose response observed during drying.
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Theoretical study on a novel temperature breakpoint cyclic operation to enhance desiccant packed bed performance
(2021-08-01)
Murathathunyaluk, Siripan
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Kitchaiya, Prakob
In a conventional desiccant packed bed dehumidification, the adsorption and desorption operations switched at a constant cycle. However, this Conventional Steady Cyclic (CSC) operation was not performed well under disturbances. Therefore, a Temperature Breakpoint Cyclic (TBC) operation is proposed. A numerical model of the desiccant packed bed dehumidification system has been constructed and validated. The model was then used to assess the desiccant packed bed dehumidification performances in term of moisture removal capacity (MRC) and dehumidification coefficient of performance (DCOP) under various cycle times and temperature factors. The calculation results showed that under CSC, larger amounts of energy were required in the desorption operation, while the TBC exhibited higher performances in term of both MRC and DCOP. Notably, at a high regeneration temperature, the MRC of the TBC was 10% higher than the CSC's; moreover, the DCOP of the TBC was twice higher than the CSC's. In other words, at high recovery temperature, both operations exhibited comparable capacities while the energy cost was halved under TBC operation.
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Drying and mathematical modelling for the decelerated rancidity of treated jasmine brown rice using different drying media
(2021-01-01)
Junka, Nittaya
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Rattanamechaiskul, Chaiwat
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Wongs-Aree, Chalermchai
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Soponronnarit, Somchart
Jasmine brown rice (JBR) attains a rancid odour because of lipid deterioration, which reduces its shelf life. This study focused on preserving JBR's stability through drying in a fluidised bed at 100–150 °C using different drying media: hot air (HA), humidified hot air (HHA), and superheated steam (SHS). The JBR and the rancid odour caused by thiobarbituric acid (TBA) were analysed. A mathematical model was developed for the drying process, and associated equations were formulated to predict the mechanism underlying changes in TBA during 180 days of storage. The dried samples became harder owing to gelatinisation of starch and decrease in 2-acetyl-1-pyrroline volatility. Changes in JBR quality were correlated to the drying temperature and drying media used. SHS drying at 150 °C decelerated TBA formation the most. The proposed mathematical model and associated equations can predict changes in TBA during storage accurately and be applied to JBR under most drying conditions.
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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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