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The performance and emission of a small diesel engine fuelled by commercial diesel fuel in Indonesia are present in this paper. Various commercial diesel fuels in Indonesia are produced and marketed by Pertamina. As the largest oil company in Indonesia, Pertamina has developed various diesel fuels, namely Solar, Biosolar (B30), Dexlite, and Pertadex. This study explains in more detail the performance, fuel consumption, and emission produced by the four types of fuels, and they were investigated experimentally using a single-cylinder diesel engine at various engine speeds, from 1,000 rpm to 4,500 rpm. The result shows the engine fuelled by Pertadex generates the highest power and torque, while the lowest is generated by the Biosolar fuelled engine. The maximum torque and power generated by the Pertadex fuelled engine are about 25.5 Nm and 7200 W, respectively. The engine fuelled by Pertadex has the lowest brake specific fuel consumption (BSFC) of 0.3037 kg·kW·h, compared to the engines fuelled by the Dexlite, Solar, and Biosolar fuels, with values around 0.3127, 0.3215, and 0.3338 kg·kW·h, respectively. At the same time, the measurement of gas emissions, including CO2, CO, NOx, and HC was conducted simultaneously.

Regarding solar energy resource potential, Hungary has a daily total of around 3.2 to 3.6 kWh/m2 and an annual total of approximately 1168 to 1314 kWh/m2. It makes it a good position for solar thermal collectors in combination with an ORC system. In this study, the authors investigated solar thermal as a heat source to generate electricity, with ORC utilizing R245fa as a working fluid. Due to the limited time for utilizing solar thermal by ORC, while the highest electricity usage is at night, heat storage is carried out by integrating a TES-evaporator using organic PCM.

Since the agreement was signed officially in 2013, Institut Teknologi Nasional Bandung (ITENAS Bandung) – Indonesia and Hungarian University of Agriculture and Life Sciences (MATE Godollo) – Hungary have implemented a lot of scholarly activities, and one of them is research collaboration in the field of solar energy, especially in the thematic field of photovoltaic (PV). A set of experimental facilities has been developed and constructed to support related research. Basic facilities to understand the principle of electricity generated by PV, i.e., having the solar power plant (SPP) laboratory scale have been fulfilled by both universities. MATE Godollo has a 10 kWp SPP installation and uses polycrystalline and amorphous silicon PV modules, meanwhile, ITENAS Bandung has 1 kWp SPP and uses monocrystalline silicon PV modules. In this paper, a set of activities related to PV research collaboration between ITENAS Bandung and MATE Godollo will be elaborated, including the output and the outcome of the activities, and plan activities involving other university partners, Slovak University of Agriculture in Nitra – Slovakia and Czech University of Life Science Prague, Czech Republic.

In the course of drying process one of the main problems is to exactly determine the moisture content in the material bed. The measurement is an option, but its accuracy is not satisfactory. Another way to determine the moisture distribution is to use physically based or blackbox models. Physically based models give a moderately good result, but it normally takes a great effort to identify their parameters, and to solve the model itself. Derivation of the classical black-box models seems to be an uncomplicated approach. However, the application of such models is mainly limited mainly for process control. Therefore, this paper overviews the application opportunities of the use of artificial intelligence methods. Among those methods (neural network, Fuzzy modelling, genetic algorithm, etc) the main emphasize is given to the use of artificial neural network (ANN) modelling especially for grain drying as a widely applied dehydration technology. A methodology is given to the selection aspects of neural network structure and specifically to the main influencing model parameters as sampling time, randomised training, different training algorithms, number of hidden neurones, number of linked data series and type of validation data. Based on the recent study, it is concluded that the ANN can be used during effectively in case of post-harvest processes especially for estimation the temperature and moisture content distribution.

An attempt is established to investigate the heat transfer from a heated chimney of an indirect solar dryer and predicted the chimney outlet temperature using energy balance equation. Tests were carried out with no-load condition under natural convection mode. Data of air velocity, temperatures, ambient relative humidity and solar radiation of the drying process are presented in order to solve the heat transfer coefficients. The result shows that temperature difference between mean air inside a chimney and ambient was 23.8 °C at maximum solar radiation of 812 W/m², where maximum airflow is observed. The predicted chimney outlet temperature was found in good agreement with that of the experimental value. Temperature variation, correlation coefficient, and average absolute error are less than 10%, 0.977, and 5%, respectively. Replacing the normal chimney by heated chimney can significantly reduce the heat loss and increase the chimney efficiency.

In this work, the examination of an experimental air-cooled photovoltaic-thermal (PV/T) module is introduced. Two different shapes of fin fixed to the copper absorber plate are examined for the classical unit of an air-cooled PV/T collector. Vertical and louver-shaped fins (with the same surface area) are investigated and thermally evaluated in this study. The experiments aimed to increase the temperature difference between inlet and outlet air to reach high thermal performance. The enhancement result showed that the thermal efficiency of the louver fin unit increased by 48% and 54% compared to the vertical fin and unfinned units, respectively. Thus, louver fins are better than vertical fins with the same surface area [ABSTRACT FROM AUTHOR]

This paper gives an overview of the status of implementation of photovoltaic (PV) systems among 34 member countries involved in the COST Action PEARL PV in the year 2018. Besides this, influencing factors that have an impact on the implementation of PV systems in each country have been assessed. The implementation of PV systems varies in European countries due to differences in solar irradiation and weather conditions, electricity consumption, national economic situation, and governmental approaches to promote PV. For this paper, data sets covering, among others, numbers installed PV systems, and the variables mentioned above, were collected, processed, and synchronized. Subsequently, relations between data were analyzed by geographic mapping and mutual correlations. The 34 countries evaluated produced together 120 TWh of PV electricity in 2018. It was notable that Germany produced the highest amount (45 TWh) and has the highest share (8.4%) of PV in its electricity generation. Weak correlations were found between the different variables and the gross domestic product, annual global irradiation, number of subsidy schemes, cumulative installed capacity, annual electricity generated by PV, the share of RE, and share of PV. Logically, as expected, strong correlations (R2 = 0.9) were found between the number of inhabitants and the annual electricity production and consumption. From our analysis, we conclude that four different groups of countries can be identified to categorize the usability of PV. One type covers countries with a significant unused PV potential while local–global irradiation is redundantly available, such as Turkey, Spain, Cyprus, Portugal, and Israel. [ABSTRACT FROM AUTHOR]