Pimpunchat, Busayamas

Preferred name

Alternative Name

Pimpunchat, B.

Main Affiliation

School of Science

busayamas.pi@kmitl.ac.th

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Applying genetic algorithm and fourier series to WQI of Tha Chin river in Thailand
(2012-12-01)
Amornsamankul, Somkid
;
Pimpunchat, Busayamas
;
Duangchai-Yoosook, Sartja
;
Triampo, Wannapong
Water pollution is the main problem that effects on the community in Thailand. The Pollution Control Department in Thailand has indexed the water quality using eight parameters. In this paper, the main propose is to reduce eight parameters of water quality index(WQI) to four parameters which are dissolved oxygen (DO), total solid (TS), biological oxygen demand (BOD), and suspended solid (SS). The factor analysis, correlation analysis and fourier series are used to simulate the data. The data obtained from Tha Chin river during 2002 - 2007 is used in this model. The genetic algorithm is applied to find the weight of each parameter. The result shows that the modified WQI using four parameters provides the same result obtained from the model using eight parameters.
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Using ANOVA to evaluate the effects of swine slaughterhouse wastewater conditions on algae growth
(2018-01-01)
Sornnery, Achara
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Pimpunchat, Busayamas
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Tuntiwarasakul, Daranporn
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Kitrungloadjanaporn, Pongpatai
;
Amornsamankul, Somkid
Wastewater is a major environmental problem. Swine slaughterhouses generate a large volume of wastewater with high an organic load and nutrients. It therefore has the potential to cause environmental problems. The effects of swine slaughterhouse wastewater conditions on algae growth are evaluated. Microalgae, Chlorella vulgaris TIST8580 in water mixtures Tris acetate phosphate medium (control) and in diluted sewage from a slaughter house with the ratio of 25:75 and 50:50 (sewage: water) were experimentally studied. We used One-way ANalysis Of Variance (One-way ANOVA) and Two-ways analysis of variance (Two-way ANOVA) techniques for testing the differences between the 3 cases of the culture conditions used. It was found that from the One-Way ANOVA, the average number of cells of algae in wastewater in the 25% and 50% groups is not significantly different but both of these groups are significantly different from that of the control group. In addition, from the Two-Way ANOVA, we found that unlike the control group, the same kind of data in both 25% and 50% groups had no significant difference. This implies that the characteristic growth of algae in these two group do not significantly change over the culture period. We believe that our finding could benefit the researchers to properly design experiments especially for the case of resource limitation.
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Mathematical modeling of infectious disease transmission in macroalgae
(2017-12-01)
Nokkaew, Artorn
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Modchang, Charin
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Amornsamankul, Somkid
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Lenbury, Yongwimon
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Pimpunchat, Busayamas
Understanding the infectious diseases outbreak of algae can provide significant knowledge for disease control intervention and/or prevention. We consider here a disease caused by highly pathogenic organisms that can result in the death of algae. Even though a great deal of understanding about diseases of algae has been reached, studies concerning effects of the outbreak at the population level are still rare. For this reason, we computationally model an outbreak in the algae reservoir or container systems consisting of several patches or clusters of algae being infected with a contagious infectious disease. We computationally investigate the systems as well as make some predictions via the deterministic SEIR epidemic model. We consider the factors that could affect the spread of the disease including the number of patches, the size of initial infected population, the distance between patches or spatial range, and the basic reproduction number (R<inf>0</inf>). The results provide some information that may be beneficial to algae disease control, intervention or prevention.
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Modelling greenhouse gas generation for landfill
(2018-01-01)
Sirimangkhala, Khwansiri
;
Pimpunchat, Busayamas
;
Amornsamankul, Somkid
;
Triampo, Wannapong
Landfill has been widely used in many countries for the final disposal of solid waste material due to its economic advantages. Landfills emit mostly methane and carbon dioxide. In this work, the dynamic characteristics of greenhouse gases generated from the closed landfill system were studied. The chemical reaction processes involved are considered and a mathematical model is formulated and analyzed. The behaviors of the key greenhouse gases, namely, carbon dioxide and methane, are carefully studied. It was found that our proposed model is qualitatively in good agreement with the real world phenomena. The characteristics of greenhouse gases of interest namely carbon dioxide and methane were found to depend on the reaction rate in a complex manner. Since the system is closed, each gas species becomes a constraint to one another. We can utilize these findings to control the landfill system and to take benefit from the useful products. Moreover, it suggests that methane and carbon dioxide could be used for electricity generation which helps reduce greenhouse gases in atmosphere.
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Modeling and optimization of G-protein coupled receptor signal transduction
(2011-06-01)
Modchang, Charin
;
Pimpunchat, Busayamas
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Triampo, Wannapong
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Triampo, Darapond
;
Lenbury, Yongwimon
Signal transduction is the process by which a cell converts one kind of signal or stimulus into another. In this process, G-protein coupled receptors (GPCRs) are considered a major class of membrane protein receptors. GPCRs play a critical role in signal transduction, and they are important pharmacological drug targets. Motivated by some specific experimental data, we construct a mathematical model to investigate a signaling system of interest. The model is composed of mass-action ordinary differential equations that describe ligand-receptor and receptor- G-protein interactions. Because the kinetic reaction rates in the signaling processes previously gathered in reliable in vivo and in vitro experiments are limited to a small number of known values, we apply a genetic algorithm (GA) to estimate the parameter values in our model. In order to carry out the parameter estimation, we use the Augmented Lagrangian Genetic Algorithm (ALGA) with help from the mathematical theorem of infinite norm. This method ensures a faster parameter estimation speed in the modeled system. In addition, mathematical analyses are also performed. Some good agreement between analytic, numerical and experimental data was found. The simulation results of the model are extensively discussed and compared with the experimental data. © 2011 Pushpa Publishing House.
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Size distribution, characterization and risk assessment of particle-bound polycyclic aromatic hydrocarbons during haze periods in Phayao Province, northern Thailand
(2017-11-01)
Pooltawee, Jaturaporn
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Pimpunchat, Busayamas
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Junyapoon, Suwannee
This study aims to investigate the concentrations and distributions of particulate matters and particle-bound polycyclic aromatic hydrocarbons (p-PAHs) in different inhalable fractions during haze periods in Phayao Province, northern Thailand and identify their possible emission sources through the characteristic ratios of selected PAHs, then determine an estimate of the potential lung cancer risk. The particulate samples were collected during haze periods between February 18 and April 8, 2014 and non-haze period from June 17 to 22, 2014 using an eight-stage cascade impactor. Each filter sample was weighed, ultrasonically extracted with acetonitrile, and then analyzed by SIM-GC-MS to identify 16 PAHs. The experimental results show that the concentrations of ultrafine (d<inf>ae</inf> < 0.4 μm), fine (0.4 μm < d<inf>ae</inf> < 2.1 μm), and coarse (d<inf>ae</inf> > 2.1 μm) particles during haze periods were in the range of 14.98–26.52 μg m−3, 62.57–101.52 μg m−3, and 55.27–89.68 μg m−3, respectively; whereas, their concentrations of 16 PAHs were in the range of 7.82–36.06 ng m−3, 26.66–61.89 ng m−3, and 9.35–30.93 ng m−3, respectively. The p-PAH distribution profiles during the haze periods were bimodal in coarse and accumulation modes, which are closely related to their particle size distributions. The characteristic ratios of BaP/BgP and INP/(BgP+InP) adsorbed on ultrafine and fine particles were 0.81–0.88 and 0.38–0.86 and 0.54–0.57 and 0.44–0.52, respectively. The values of B[a]P<inf>eq</inf> observed during haze periods varied from 9.57 to 29.05 ng m−3. Estimated lifetime cancer risks during haze periods ranged from 8.324 to 25.27 additional cases per 10,000 people exposed, which is about 10 times higher than that during non-haze period.
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Simple stochastic model for random waste absorption of an algae cell: Analytic approach
(2013-11-04)
Nokkaew, Artorn
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Amornsamankul, Somkid
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Pimpunchat, Busayamas
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Saengpayab, Yaowapa
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Triampo, Wannapong
Over the past few decades, research on water quality using biological treatment has been considerably done. The problem of how the algae dynamically absorb waste is of great important both environmental and biological science particularly concerning a problem of waste water treatment. With this regards, we have applied a model in which a Brownian agent interacts with a spin or clock in 1D to describe and predict the system of waste absorption by algae. We assumed that the waste particles executing Brownian like motion and occasionally absorbed by algae. Analytic results are presented and discussed in connection with the waste absorption by algae. It was found that the absorption nature is dominated by the exponential like nature. How this model can be improved to better understanding or match with the real system is also elaborated.
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Modeling haze problems in the North of Thailand using logistic regression
(2014-01-01)
Pimpunchat, Busayamas
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Sirimangkhala, Khwansiri
;
Junyapoon, Suwannee
At present, air pollution is a major problem in the upper northern region of Thailand. Air pollutants have an effect on human health, the economy and the traveling industry. The severity of this problem clearly appears every year during the dry season, from February to April. In particular it becomes very serious in March, especially in Chiang Mai province where smoke haze is a major issue. This study looked into related data from 2005-2010 covering eight principal parameters: PM10 (particulate matter with a diameter smaller than 10 micrometer), CO (carbon monoxide), NO<inf>2</inf> (nitrogen dioxide), SO<inf>2</inf> (sulphur dioxide), RH (relative humidity), NO (nitrogen oxide), pressure, and rainfall. Overall haze problem occurrence was calculated from a logistic regression model. Its dependence on the eight parameters stated above was determined for design conditions using the correlation coefficients with PM10. The proposed overall haze problem modeling can be used as a quantitative assessment criterion for supporting decision making to protect human health. This study proposed to predict haze problem occurrence in 2011. The agreement of the results from the mathematical model with actual measured PM10 concentration data from the Pollution Control Department was quite satisfactory.
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Approximate solutions and parameter estimations of G-protein coupled receptor signal transduction model
(2013-02-20)
Modchang, Charin
;
Pimpunchat, Busayamas
;
Lenbury, Yongwimon
;
Triampo, Wannapong
We find approximate analytical solutions of a model for the signal transduction mediated by the G-protein coupled receptor proposed earlier by Modchang et al. The time evolution of the approximate solutions will be explored and compared with numerical solutions of the model. Moreover, an alternative procedure for estimating unknown parameters in the model has been proposed. This alternative parameter estimation method directly uses the exact equilibrium solutions for fitting with the experimental data. We found that this new parameter estimation method can estimate unknown parameter values faster than the method used in previous works. © 2013 Pushpa Publishing House.
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Estimation of algae growth model parameters by a double layer genetic algorithm
(2012-11-01)
Nokkaew, Artorn
;
Pimpunchat, Busayamas
;
Modchang, Charin
;
Amornsamankul, Somkid
;
Triampo, Wannapong
This paper presents a double layer genetic algorithm (DLGA) to improve performance of the information-constrained parameter estimations. When a simple genetic algorithm (SGA) fails, a DLGA is applied to the optimization problem in which the initial condition is missing. In this study, a DLGA is specifically designed. The two layers of the SGA serve different purposes. The two optimizations are applied separately but sequentially. The first layer determines the average value of a state variable as its derivative is zero. The knowledge from the first layer is utilized to guide search in the second layer. The second layer uses the obtained average to optimize model parameters. To construct a fitness function for the second layer, the relative derivative function of the average is combined into the fitness function of the ordinary least square problem as a value control. The result shows that the DLGA has better performance. When missing an initial condition, the DLGA provides more consistent numerical values for model parameters. Also, simulation produced by DLGA is more reasonable values than those produced by the SGA.

Pimpunchat, Busayamas

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