• Energy Research

Abstract A parabolic trough is defined as a type of solar thermal collector that is straight in one dimension and curved as a parabola in the other two, lined with a polished metal mirror. Enhancing the thermal efficiency of this collectors is one of the major challenges of developing and growing of parabolic trough solar thermal power plants. Ferrofluids were proposed as a novel working fluid for industrial applications, due to their thermal performances. In this study, the convective heat transfer of Fe3O4-Therminol 66 ferrofluid under magnetic field (0–500 G) is evaluated using computational fluid dynamics. The ferrofluid with different volume fraction (1–4%) and the Therminol 66 (as the base fluid) are considered as the working fluids for a parabolic trough solar collector. Numerical analysis first validated using theoretical results, and then a detailed study is conducted in order to analyze the effect of the magnetic field on different parameters. The result demonstrated that using magnetic field can increase the local heat transfer coefficient of the collector tube, thermal efficiency as well as output temperature of the collector. In addition, increasing the volume fraction of nanoparticle in the base fluid and intensity of magnetic field increased the collector performance.

The LuxTurrim5G project is built on integrating different types of sensors and equipment that have been integrated into light poles in order to build new data-driven services. One additional service could be to harvest the waste heat produced in the electrical devices in the pole. In this research, we developed an intelligent model for heat transfer modeling of 5G Smart Poles. The input parameters used to construct the model are latitude of the station (deg), ambient temperature (°C), inside airflow (m3/min) and time (h). These input parameters are employed to predict heat flow (W) and maximum plate temperature (°C) inside the utility box. The results show that the ANFIS-PSO model provides an accurate prediction of R-value >0.95 for the test data, which is close to the maximum theoretically value of 1. The results showed that for the small amount of latitude, the maximum heat flow and temperature of the inside air is not detected at noon and the radiation heat flow to the vertical cylinder is maximized between sunrise and noon as well as between noon and sunset. The model also demonstrated that for the northern conditions, the temperature levels of heat generated over 30 °C are limited.

Abstract In natural gas measuring techniques, the natural gas mass flow rate is usually measured employing experimental facilities for density calculation, volume flow meters and other peripheral equipment such as temperature and pressure sensors. In order to simplify natural gas mass flow metering at natural gas pressure drop stations (CGS), this work presents an easy and precise method for measuring natural gas density. The method is based on a correlation that calculates the molecular weight and density of natural gas stream as a functional of the temperatures and pressures of natural gas before and after a throttle valve. These easily measurable properties at the natural gas pressure drop station are employed to calculate, firstly, the Joule Thomson (JT) coefficient and then density based on a developed correlation. For developing the corresponding correlation and also validating the results, a huge database including the practical data of 6 selected main natural gas fields of Iran is used. In fact, 50% of the available experimental data (three fields) have been used to develop the correlation and the remaining 50% (the other three fields) are used in accuracy assessment step. The results show that the error in calculating density doesn't exceed 1.2%, while for more than 70% of the states the error is even less than 0.6% and the average error is less than 0.4%. MAE (mean absolute error) and STD (standard deviation of absolute error) are also employed in order to validate the correlation results. The validation shows satisfactory calculation accuracy.

Abstract Assessment of solar potential over a location of interest is introduced as an important step for the successful planning of solar energy systems (photovoltaic or thermal). Due to the absence of meteorological stations and sophisticated solar sensors, solar data may be unavailable for every point of interest. Hence, empirical and intelligence methods are developed to estimate solar irradiance data. In this study, the idea of artificial intelligence methods is employed to predict the daily global solar radiation. The developed models are: group method of data handling (GMDH) type neural network, multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized with particle swarm optimization algorithm (ANFIS-PSO), ANFIS optimized with genetic algorithm (ANFIS-GA) and ANFIS optimized with ant colony (ANFIS-ACO). The data are collected from 12 stations in different climate zones of Iran. The input variables of the models are including month, day, average air temperature, maximum air temperature, minimum air temperature, air pressure, relative humidity, wind speed, top of atmosphere insolation, latitude and longitude. The results demonstrated that although the developed models can successfully predict the global horizontal irradiance, the GMDH model outperforms the other developed models. The values of root mean square error (RMSE), determination coefficient (R2) and mean square error (MSE) for the GMDH model were 0.2466 (kWh/m2/day), 0.9886 and 0.0608 (kWh/m2/day), respectively.

Abstract Geothermal energy is a renewable resource that is constantly available. The low geothermal well operating lifetime is the main challenge in using this type of renewable energy. This problem can be covered by the aid of solar system (hybrid system). For complicated renewable energy systems, finding the optimum design parameters and operating conditions require to develop experimental apparatus or sophisticated thermodynamic models. Hence, in this study, artificial intelligence (AI) approach is proposed for modeling the geothermal organic Rankin cycle (GORC) equipped with solar thermal unit. Indeed, the current study depicts how AI methods can meticulously simulate the operation of a complicated renewable energy system. The developed intelligent methods are adaptive neuro-fuzzy inference system (ANFIS) optimized with particle swarm optimization (PSO) algorithm (ANFIS-PSO) and multilayer perceptron (MLP) neural network optimized with PSO algorithm (MLP-PSO). The models are composed based on the main design parameters of the geothermal system that are solar radiation, well temperature, working fluid mass flow rate, turbine output pressure, surface area of the solar collector and preheater inlet pressure. The intelligent models use the mentioned input variables to predict the net power output, energy efficiency, exergy efficiency and levelized cost of energy (LCOE) of the GORC. Energy, exergy and economic analyses are carried out for the low global warming potential (GWP) refrigerants. It was found out that although the intelligent models can meticulously predict the targets, ANFIS-PSO performs better than MLP-PSO for modeling the GORC with solar system. Root mean square error of this model for prediction of power generation, energy efficiency, exergy efficiency and LCOE was 12.023 (kW), 3.587 × 10 - 4 , 3.278 × 10 - 4 and 1.332 × 10 - 4 , respectively.

Within the past decade, since impediments in nonrenewable fuel sources and the contamination they cause, utilizing green energies, such as those that are sun-oriented, in tandem with electric vehicles, is a developing slant. Coordinating electric vehicle (EV) charging stations with sun-powered boards (PV) reduces the burden of EV charging on the control framework. This paper presents a state-of-the-art literature review on remote control transmission frameworks for charging the batteries of electric vehicles utilizing sun-based boards as a source of power generation. The goal of this research is to advance knowledge in the wireless power transfer (WPT) framework and explore more about solar-powered electric vehicle charging stations. To do this, a variety of solar-powered electric vehicle charging station types are thoroughly studied. Following a study of many framework elements, the types of WPT components are explored in a different section. Within the wireless power transmission framework for solar-powered electric vehicle charging, compensators and various coil structures are also investigated, along with the advantages of each coil over the others. This study also discusses the use of artificial intelligence (AI) in WPT frameworks and highlights the important aspects of developing an AI model.

Geothermal energy has a great potential to be a competitor to other renewable resources, as it is abundant and plentiful, where one production well can be sufficient for a number of uses. However, geothermal energy systems are advancing to meet the growing demand for electrical power and other applications that utilize thermal energy as a source. In this work, a feasibility study was done in order to study the possibility of geothermal power plant implementation of 1 MW in Hasbaya, Lebanon. According to the available geothermal resource of 200°C at 3000 m depth, two geothermal plant types were pointed out, including single flash and double flash plants, showing that the double flash has a better performance in terms of actual plant efficiency of 8.16%. In addition, the condenser type affects the performance; thus, utilizing an air-cooled condenser will be suitable for application. In addition to the performance, the cost analysis shows that using air-cooled will reduce the capital cost and increase the net present value.