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Adsorption refrigeration, as a renewable cooling method, has received more attention in the last few years. The interest in this technology comes especially from developing and tropical countries, where the demand for cooling increases every year due to economy and population growth. Based on this scenario, this work aims to develop a numerical model of an adsorption chiller driven with solar energy, which can be used to optimize the cooling system operation of the building where the device is situated and compare it with the current cooling methods in use. The numerical study here presented was created using Matlab/Simulink™, it is based on a lumped parameter model that relies on physical properties and represents a cooling system using a pair of silica gel-water in a two-bed chiller. In this study, the authors proposed a simplified version of the system and the numerical model, which aims to reduce the simulation time and provide faster results. Besides the temperatures in the system, which range from 52 °C to 72 °C in the hot cycle and 12 °C to 23 °C in the chilled water cycle, the results also include the variation of water uptake in the two adsorbent beds. In general, the simulated temperature, cooling and heating power and coefficient of performance (COP) are in fair agreement with the literature data, nevertheless, the final results show that improvements still have to be performed.

Adsorption refrigeration, as a renewable cooling method, has received more attention in the last few years. The interest in this technology comes especially from developing and tropical countries, where the demand for cooling increases every year due to economy and population growth. Based on this scenario, this work aims to develop a numerical model of an adsorption chiller driven with solar energy, which can be used to optimize the cooling system operation of the building where the device is situated and compare it with the current cooling methods in use. The numerical study here presented was created using Matlab/Simulink™, it is based on a lumped parameter model that relies on physical properties and represents a cooling system using a pair of silica gel-water in a two-bed chiller. In this study, the authors proposed a simplified version of the system and the numerical model, which aims to reduce the simulation time and provide faster results. Besides the temperatures in the system, which range from 52 °C to 72 °C in the hot cycle and 12 °C to 23 °C in the chilled water cycle, the results also include the variation of water uptake in the two adsorbent beds. In general, the simulated temperature, cooling and heating power and coefficient of performance (COP) are in fair agreement with the literature data, nevertheless, the final results show that improvements still have to be performed.

This paper is devoted to study the electrochemical behavior of Pt catalyst in a polymer electrolyte fuel cell at various operating conditions and at different electric potential difference (also known as voltage) cycling applied in accelerated stress tests. The degradation of platinum is considered with respect to the Pt ion dissolution and the Pt oxide coverage of catalyst described by a one-dimensional model. In the model, degradation rate increases with temperature and decreasing particle diameter of Pt nano-particles. The theoretical study of the underlying diffusion system with the nonlinear reactions is presented by analytical methods and gives explicit solutions through a first integral of the ODE system. Numerical tests are obtained using a second order implicit-explicit scheme. The computer simulation shows that the lifetime of the catalyst depends on the voltage profile and the upper potential level. By this Pt mass loss is more significant at the membrane surface than at the gas diffusion layer.

This paper is devoted to study the electrochemical behavior of Pt catalyst in a polymer electrolyte fuel cell at various operating conditions and at different electric potential difference (also known as voltage) cycling applied in accelerated stress tests. The degradation of platinum is considered with respect to the Pt ion dissolution and the Pt oxide coverage of catalyst described by a one-dimensional model. In the model, degradation rate increases with temperature and decreasing particle diameter of Pt nano-particles. The theoretical study of the underlying diffusion system with the nonlinear reactions is presented by analytical methods and gives explicit solutions through a first integral of the ODE system. Numerical tests are obtained using a second order implicit-explicit scheme. The computer simulation shows that the lifetime of the catalyst depends on the voltage profile and the upper potential level. By this Pt mass loss is more significant at the membrane surface than at the gas diffusion layer.

Problem statement: In general, there are a number of rural dialects i n Thai. However, four dialects are mainly spoken by Thai people residing in four core region including central, north, northeast and south regions. Recognizing and synthe sizing Thai speech with different dialects are consequently difficult. Approach: Prosody is an important factor that must be taken into account, since the prosody effects on not only the naturalne ss but also the intelligibility of speech. To treat the problem, the speech prosody is carefully preserved through modeling the fundamental frequency (F0) contours. The differences among the model parameters of four Thai dialects have been summarized. This study proposed an analysis of model parameters for Thai speech prosody with four regional dialects and two genders which is a preliminary wor k for speech recognition and synthesis. Fujisaki's modeling; a powerful tool to model the F0 contour h as been adopted. Seven derived parameters from the Fujisaki's model are as follows. The first para meter is baseline frequency which is the lowest lev el of F0 contour. The second and third parameters are the numbers of phrase commands and tone commands which reflect the frequencies of surges of the utterance in global and local levels, respectively. The fourth and fifth parameters are p hrase command and tone command durations which reflect the speed of speaking and the length of a s yllable, respectively. The sixth and seventh parameters are amplitudes of phrase command and tone command which reflect the energy of the global speech and the energy of local syllable. Results: In the experiments, each regional dialect includes 200 samples of one sentence with male and female speech. Therefore our speech database contains 1600 utterances in total. The results show ed that most of the proposed parameters can distinguish four kinds of regional dialects explici tly. Conclusion: By using the Fujisaki's model, the results confirm that the proposed parameters can di stinguish the regional dialects efficiently. In the future research, they were expected to be applied i n the speech recognition and synthesis with various regional dialect characteristics.

Problem statement: In general, there are a number of rural dialects i n Thai. However, four dialects are mainly spoken by Thai people residing in four core region including central, north, northeast and south regions. Recognizing and synthe sizing Thai speech with different dialects are consequently difficult. Approach: Prosody is an important factor that must be taken into account, since the prosody effects on not only the naturalne ss but also the intelligibility of speech. To treat the problem, the speech prosody is carefully preserved through modeling the fundamental frequency (F0) contours. The differences among the model parameters of four Thai dialects have been summarized. This study proposed an analysis of model parameters for Thai speech prosody with four regional dialects and two genders which is a preliminary wor k for speech recognition and synthesis. Fujisaki's modeling; a powerful tool to model the F0 contour h as been adopted. Seven derived parameters from the Fujisaki's model are as follows. The first para meter is baseline frequency which is the lowest lev el of F0 contour. The second and third parameters are the numbers of phrase commands and tone commands which reflect the frequencies of surges of the utterance in global and local levels, respectively. The fourth and fifth parameters are p hrase command and tone command durations which reflect the speed of speaking and the length of a s yllable, respectively. The sixth and seventh parameters are amplitudes of phrase command and tone command which reflect the energy of the global speech and the energy of local syllable. Results: In the experiments, each regional dialect includes 200 samples of one sentence with male and female speech. Therefore our speech database contains 1600 utterances in total. The results show ed that most of the proposed parameters can distinguish four kinds of regional dialects explici tly. Conclusion: By using the Fujisaki's model, the results confirm that the proposed parameters can di stinguish the regional dialects efficiently. In the future research, they were expected to be applied i n the speech recognition and synthesis with various regional dialect characteristics.

Abstract 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.

Abstract 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.