• Energy Research

In this paper, an artificial neural network inverse (ANNi) model is applied to optimize the thermal performance (η) of parabolic trough concentrators. A feedforward neural network architecture is trained using an experimental database from parabolic trough concentrators operations. Rim angle (φr), inlet (Tin) and outlet (Tout) fluid temperatures, ambient temperature (Ta), water flow (Fw), direct solar radiation (Gb), and the wind velocity (Vw) were used as main input variables within the neural network model to estimate the thermal performance with a correlation coefficient of R2 = 0.9996 between experimental and simulated values. The sensitivity analysis is carried out to verify the effect of all input variables. The optimal operation conditions of parabolic trough concentrators are established using artificial neural network inverse modeling (ANNi) to achieve optimal operation conditions of parabolic trough concentrators. The results indicated that ANNi is a feasible tool for Parabolic Trough Concentrators optimization.

Solar and geothermal energies are considered cleaner and more useful energy sources that can be used to avoid the negative environmental impacts caused by burning fossil fuels. Several works have reported air-conditioning systems that use solar energy coupled to geothermal renewable energy as a thermal source. In this study, an Absorption Air-Conditioning System (AACS) used sodium hydroxide-water (NaOH-H2O) instead of lithium bromide-water to reduce the cost. Low enthalpy geothermal heat was derived from two shallow wells, 50 and 55 m deep. These wells are of interest due to the thermal recovery (temperature vs. time) of 56.2 °C that was possible at the maximum depth, which can be used for the first stage of the process. These wells were coupled with solar energy as a geothermal energy application for direct uses such as air-conditioning systems. We studied the performance of an absorption cooling system operating with a NaOH-H2O mixture and using a parabolic trough plant coupled with a low enthalpy geothermal heat system as a hybrid heat source, as an alternative process that can help reduce operating costs and carbon dioxide emissions. The numerical heat transfer results showed the maximum convective heat transfer coefficient, as function of fluid velocity, and maximum temperature for a depth higher than 40 m. The results showed that the highest temperatures occur at low fluid velocities of less than or equal to 5.0 m/s. Under these conditions, reaching temperatures between 51.0 and 56.2 °C in the well was possible, which is required of the geothermal energy for the solar energy process. A water stream was used as the working fluid in the parabolic trough collector field. During the evaluation stage, the average experimental storage tank temperature achieved by the parabolic trough plant was 93.8 °C on October 23 and 92.9 °C on October 25, 2017. The numerical simulation used to evaluate the performance of the absorption cycle used a generator temperature of 90 °C, a condenser and absorber temperature at 35 °C, and an evaporator temperature of 10 °C. The Coefficient of Performance was calculated as 0.71 under design conditions.