M.A. Escalante Soberanis

Efficiency of a parabolic trough collector as a water heater system in Yucatán, Mexico

The performance of a parabolic trough collector (PTC) manufactured in Merida, Yucatan, was evaluated under the ANSI/ASHRAE 93-1986 standard. The water heating system for testing with a constant flow limited to a maximum temperature of 55 °C was built; thus the tests were at low temperatures. Using water as working fluid, it was found that the maximum efficiency of the collector was 5.43%, with a flow rate of 0.022 kg/s at a direct solar irradiance with incidence angle 0°. The evaluation methodology and design of the system for testing the collector is reported in this paper.

Modeling and optimization of a solar parabolic trough concentrator system using inverse artificial neural network

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 Concentra...

Photovoltaic Module Temperature Estimation: A Comparison Between Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems Models

The main objective of this paper is to present a comparison between two models for estimation of a photovoltaic system’s module temperature (T\(_{mod}\)) using Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems. Both estimations use measurements of common operation variables: current, voltage and duty cycle (d) from a power converter of the photovoltaic system as input variables and T\(_{mod}\) as a desired output. The models used the same database for the training process, different training strategies were evaluated with the objective to find which model has the best estimation with respect to the T\(_{mod}\). Subsequently, the output results from these architectures are validated via the Root Mean Squared Error, Mean Absolute Percentage Error and correlation coefficient. Results show that the Artificial Neural Network model in comparison with Adaptive Neuro Fuzzy Inference System model provides a better estimation of T\(_{mod}\) with \(R = 0.8167\). Developed models may have an application with smart sensors on cooling systems for photovoltaic modules with the objective of improving their operation efficiency.

Efficiency curves analysis of a parabolic trough solar collector in the Yucatan Peninsula

The application of parabolic trough collector (PTC) technology is a clean and new way to produce industrial process heat. The experimental evaluation of the efficiency of first law in a PTC as the solar angle of incidence changes around the noon is presented in this paper. A brief introduction on the PTC operating conditions is included. The efficiency was obtained under the standards of the ASHRAE norms, by means of the thermal analysis depending on the fluid inlet and outlet temperature. Finally, the characteristic curves of the collector were established, as well as its removal factor. To fulfill the previous results, the following measurements were carried out: the inlet and outlet temperatures of the working fluid in the receptor of the PTC, and the solar angle of incidence referred to the azimuthal angle, with the axis located during the noon. These data were analyzed and the angle correction factor was calculated, as well as the instantaneous optical efficiency of the PTC.

An Approach to Codification Power on the Behavior of Genetic Algorithms

Genetics Algorithms (GAs) are based on the principles of Darwins evolution which are applied to the minimization complex function successfully. Codification is a very important issue when GAs are designed to dealing with a combinatorial problem. An effective crossed binary method is developed. The GAs have the advantages of no special demand for initial values of decision variables, lower computer storage, and less CPU time for computation. Better results are obtained in comparison the results of traditional Genetic Algorithms. The effectiveness of GAs with crossed binary coding in minimizing the complex function is demonstrated.

Optimization operation of a parabolic trough collector using artificial neural network

The present work describes the thermal efficiency optimization of parabolic trough collectors by combining a model of artificial neural network and computational optimization techniques. A feedforward neural network architecture is trained using experimental database from parabolic trough collector operations. Rim angle, inlet and outlet fluid temperatures, ambient temperature, water flow, direct solar radiation, and wind velocity were used as main input variables within the neural network model to estimate the thermal performance. The optimal operation conditions of parabolic trough collectors are established using the hybridization of optimization technique with neural network model to achieve optimal operation conditions of parabolic trough collector. The result indicated that methodology implemented is a feasible tool for parabolic trough collectors optimization.