Thailand is located in an equatorial belt that receives abundant solar energy. In order to achieve the optimum utilization of solar energy available, it is necessary to evaluate the incident solar radiation over the region of interest. Solar radiation can be assessed by means of measurements or mathematical modeling. Accurate measurements, using sophisticated and costly equipment, is available and has indeed been used extensively to assess solar radiation. This paper concentrates on an alternative approach to assess global solar radiation (GSR) by using Artificial Neural Networks (ANNs) together with classical observed meteorological data. The model is applied to the region of Bangkok, Thailand, using meteorological data, along with solar radiation measurements, for the period 2001-2010 from the Thai Meteorological Department (TMD). More precisely, three combinations of observed monthly mean meteorological data, i.e. maximum, minimum, and mean temperatures; relative humidity; rainfall amount; and sunshine hours were used with 3, 5 and 6 parameters as the model input for the ANN training to predict the solar radiation over the territory. A feed-forward back-propagation ANNs were trained based on three algorithms, i.e. the Quasi-Newton, the conjugate gradient with Polak-Ribiére updates and the Bayesian regularization. The root mean square error (RMSE) and the mean bias error (MBE) between the observed and the predicted solar radiations in 2011-2012 were computed in order to investigate the performance of the ANNs. Results showed that, for monthly mean number of sunshine hours in the range of 3.58 to 9.55 hr/day, the monthly mean GSR above the atmosphere of Bangkok was in the range of 5.64 to 22.53 MJ/m 2 /day. The RMSE and the MBE were 0.0031 -0.3632 and -0.0203 -0.003, respectively, thus indicating that the ANN modeling has sufficient performance to predict the monthly mean GSR over an area where classical meteorological data are measured.

The main purpose of this paper is to present a biomass gas engine system for power generation of OTOP building in southern Thailand. It consists of an electrical steam generator, an Imbert-type fixed bed downdraft gasifier of 1 m 3 capacity, a cyclone, a water scrubber, a gas cooling system and a packed bed filter, a gas engine of 1,425 cc and a 10 kWe generator. Rice husk and sawdust were used as biomass feedstock and steam was used as an agent in a gasification process. A preliminary result from the stationary model under the condition of 15% fuel moisture content, 20 kg/hr fuel feed rate and 10 kg/hr steam feed rate, indicated that the composition of product gas was CO of 14%, CO 2 of 16.2%, CH 4 of 2% and H 2 of 26.9%. Lower calorific of product gas was about 5,382 kJ/Nm 3 . The lcv of product gas against fuel was about 77%.

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The research aims to develop the experimental set of the temperature measurement in liquid by Arduino program displaying data on a smartphone via the Blynk application. The experimental set is composed of 1) 2 liquid temperature sensors (DS18B20 model), 2) Arduino program, and 3) LED screen for showing the temperature value in unit of °C and connect to a smartphone. The Arduino temperature sensor 1 and sensor 2 of the experimental set have 0.57% and 0.51% errors, respectively, compared with the temperature sensor of the B Smart Science Co., Ltd. company. The instrument is applied to the physics laboratory on Newton’s law of cooling to find the cooling rate of water and coffee. This low-cost instrument revealed high accuracy results and easy to connect with other devices.

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