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Author: search.filters.author.Triampo, Darapond

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    Effect of Pre-Vulcanization Time on Structure and Thermal Insulation of Natural Rubber Latex/Silica Aerogel Composites
    (2026-07-01)
    Boonrawd, Chayanan
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    Vittayakorn, Wanwilai
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    Triampo, Darapond
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    Yodyingyong, Supan
    Polymer/Silica aerogel (SA) composites improve mechanical properties strategically, but the mixing process disrupts the aerogel’s structure, reducing its efficiency due to polymer chains filling the pores. Pre-vulcanized natural rubber latex (PVNRL) with a higher crosslink density can strain the moving chains, thereby preserving the SA-porous structure in the bulk composite for thermal insulation materials. This study aimed to investigate the effects of PVNRL pre-vulcanization time and SA-immersion time in PVNRL. For PVNRL/SA composite preparation, various PVNRL, from 0 days to 8 days of pre-vulcanization time, were mixed with a fixed SA content of 20 parts per hundred of rubber (phr) using a latex compounding method. Subsequently, the PVNRL/SA slurries were cast on glass plates with 0, 3, and 6 days to obtain the PVNRL/SA composite. Considering the effect of pre-vulcanization time, the crosslink density of the composite increased and revealed a peak at PVNRL/SA with 8-day PVNRL by 7.277 ± 0.881 μmol (Formula presented.), corresponding to the closest percentage of pore area in the SA’s structure to the pristine SA, and eventually a 42.41% lower thermal conductivity than the PVNRL/SA with 0-day PVNRL exhibited. In addition, the thermal conductivity increased more slowly over immersion time with the presence of 8-day PVNRL. The proposed correlation states that increasing the pre-vulcanization improves the thermal insulation performance of PVNRL/SA composites, emphasizing the reduction of filled SA’s pore with unvulcanized NR chains. Furthermore, the PVNRL/SA composite with 8-day PVNRL maintains thermal stability at 387.3 °C, and can be flexed at room temperature. These fascinating discoveries may be advantageous for further applications related to thin-film and flexible thermal insulation materials.
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    Estimation of algae growth model parameters by a double layer genetic algorithm
    (2012-11-01)
    Nokkaew, Artorn
    ;
    Pimpunchat, Busayamas
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    Modchang, Charin
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    Amornsamankul, Somkid
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    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.
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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
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    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.