Lerkkasemsan, Nuttapol
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Lerkkasemsan, Nuttapol
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nuttapol.le@kmitl.ac.th
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Item type:Publication, Study of ethanol fermentation reaction using Saccharomyces diastaticus in a two-tank fermentation system with cell recycling(2018-10-01); Lee, Wen ChienExperimental data of ethanol fermentation in a two-tank system with cell recycling using sucrose as a substrate were investigated to establish a kinetic model that described the reaction. Flocculent yeast Saccharomyces diastaticus LORRE-316 was used as the fermenting yeast, and the fermentation medium comprised 80 g/L sucrose, 10 g/L yeast extract, 10 g/L peptone, 2 g/L potassium phosphate monobasic (KH<inf>2</inf>PO<inf>4</inf>), and 0.5 g/L magnesium sulfate heptahydrate (MgSO<inf>4</inf>·7H<inf>2</inf>O). Three models, the Monod model, the modified Monod model, and the extended-modified Monod model, were used to describe fermentation, and the extended-modified Monod model described the reaction more accurately than the other two models. The model took substrate limitations plus substrate and ethanol inhibitive effects into consideration, and was modified to include assumptions that included terms for substrates (sucrose, glucose and fructose) and product (ethanol). In addition, the ethanol concentration had a significant effect on cell growth. The results of Lineweaver–Burk plots showed that the maximum specific growth rate (μ<inf>MAX</inf>) and the Monod constant (K<inf>S</inf>) were 0.72/h and 26.77 g/L, respectively. The models were suitable for describing the experimental data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Outer-tubes Falling Film Evaporator with Well-Mixed Surface Renewal(2017-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Kinetic modeling of CO2 gasification reactivity of Palm Kernel Shell (PSK)(2017-01-01)This research demonstrates the investigation of gasification reactivity behavior of palm kernel shell bio char using thermogravimetric analysis (TGA) at 850, 900 or 950°C under CO<inf>2</inf>. There are three fluid-solid kinetic models used to describe the reaction behavior of palm kernel shell bio char. The three models are volumetric model (VM), grain model (GM), and random pore model (RPM). From model results, the GM model and RPM model describe the reaction quite well. However, the GM model is considered as the best model in all three models to describe the reactivity of palm kernel shell bio char gasification reaction. From the GM model, the reaction starts from the surface and it moves to the core. As time go on, the gasify agent will defuse through the core and it keep the reaction go into the core. The activation energy of gasification reactivity of palm kernel shell bio char are 150kJ/mol for GM model. From the results, the coefficient of determination of GM model are 0.989, 0.989, and 0.961 at 850, 900, 950°C respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Predicting Conversion from Pyrolysis of Pongmia(2015-01-01)This research demonstrates the technique of predicting pyrolysis of lignocellulosic biomass. Modeling of pyrolysis of biomass is complex and challenging because of short reaction times, temperatures as high as a thousand degrees Celsius, and biomass of varying or unknown chemical compositions. As such a deterministic model is not capable of representing the pyrolysis reaction system. To be able to predict a pyrolysis reaction of an unknown lignocellulosic biomass without an experimental data support or data fitting is an even more challenging work. In this research, we are trying to predict pyrolysis of Pongmia in Nitrogen to demonstrate that our technique is useful for predicting pyrolysis reaction of other biomass source. There are three main chemical compositions in lignocellulosic biomass which are cellulose, hemicellulose and lignin. We are considering that the total pyrolysis reaction is affected by the reaction of three main compositions. However, these three main chemical compositions of biomass is vary not only by type of biomass but also by other things such as where it is grown or even which part of biomass since the chemical compositions in the leaf can be different from the trunk. Our propose method is an extending study of our previous paper "pyrolysis of biomass-fuzzy modeling". Our model successfully gives a good predicting result. The result shows that our model can predict 91.82% of pyrolysis of Pongmia in Nitrogen correctly without any data from the experiment. Therefore, we could use this method to predict other lignocellulosic biomass before we perform an experiment. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fuzzy logic-based predictive model for biomass pyrolysis(2017-01-01)As pyrolysis reaction is one of an important reaction applied to lignocellulosic biomass in order to transform it to be user-friendly energy form recognized as prospective alternative energy source, the reaction has been widely investigated in order to understand the mechanisms and kinetics of the pyrolysis. However, modeling pyrolysis of biomass is full of complication. As lignocellulosic biomass is not a homogeneous chemical source, chemical compositions in biomass are also uncertain and they vary even in the same biomass. The reactions of imprecise chemical compositions in biomass affects the capability of deterministic model in modeling chemical reaction since available deterministic models are designed to model homogeneous and precise chemical compositions. With this problem, it raises the idea of using model which has ability to calculate something ambiguous. Since the fuzzy logic-based model which is adaptive network-based fuzzy inference system (ANFIS) is built to calculate uncertainty, the model should be suitable to handle uncertainty which is imprecise chemical compositions in the reaction. The proposed model is built with four input variables: the reaction time, amount of cellulose component, amount of hemicellulose component, and amount of lignin component in biomass. The model is trained with tuning datasets which are the pyrolysis datasets of lignin, cellulose and Madhuca before applying to predict the pyrolysis reactions of Pongamia pinnata and Jatropha curcas. The comparative results show that the proposed model can correctly predict 91.82% and 97.29%, respectively, of the pyrolysis reactions of P. pinnata and J. curcas. As the ANFIS model gives good prediction in modeling pyrolysis of two different biomasses, the model can be applied to predict the pyrolysis reaction of other lignocellulosic biomass products. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Life cycle assessment of diuron from cradle to grave: case study in agave farm(2019-11-01)