Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses

Physical and Chemical Properties of Biodiesel Obtained from Amazon Sailfin Catfish (Pterygoplichthys pardalis) Biomass Oil

Amazon sailfin catfish (Pterygoplichthys pardalis) is considered one of the greatest threats to the biodiversity of continental aquatic systems, causing serious economic and environmental problems in the regions. In this work, the production of biodiesel from Amazon sailfin catfish biomass oil is studied. The physical and chemical properties of biofuel produced were evaluated under the specifications of the European standard EN-14214 by using gas chromatography-mass spectrometry, infrared spectroscopy, and atomic absorption spectrometry analyses. The results show that the biodiesel complies with all the specifications of the standard, except the content of polyunsaturated methyl esters. The yields obtained from oil and biodiesel were 9.67 and 90.71% (m/m), respectively. The methyl ester concentrations study identified 17 components where 47.003% m/m corresponded to methyl esters with saturated chains, whereas 34.394% m/m was attributed to monosaturated methyl esters and the remaining (18.624% m/m) to polysaturated methyl esters. Finally, mineral analysis by atomic absorption showed the absence of heavy metals Cd, Ni, and Pb, as well as low concentrations of Ni, Fe, Cu, and Zn, demonstrating that the quality of the fuel is not compromised. The study indicates the feasibility of manufacturing biodiesel using Amazon sailfin catfish biomass oil as a low-cost raw material. It represents an environmental option to mitigate a global problem of atmospheric pollution, and at the same time, it shows a commercial alternative to reduce the ecological impact caused by this fish in the diverse ecosystems to which it has spread. In addition, the great adaptability of this fish provides the possibility of a profitable process to have very high rates of reproduction and growth, allowing the generation of large amounts of biomass for the production of biodiesel

Raman Spectroscopy and Chemometric Modeling to Predict Physical-Chemical Honey Properties from Campeche, Mexico

In this work, 10 chemometric models based on Raman spectroscopy were constructed to predict the physicochemical properties of honey produced in the state of Campeche, Mexico. The properties of honey studied were pH, moisture, total soluble solids (TSS), free acidity, lactonic acidity, total acidity, electrical conductivity, Redox potential, hydroxymethylfurfural (HMF), and ash content. These proprieties were obtained according to the methods described by the Association of Official Analytical Chemists, Codex Alimentarius, and the International Honey Commission. For the construction of the chemometric models, 189 honey samples were collected and analyzed in triplicate using Raman spectroscopy to generate the matrix data [X], which were correlated with each of the physicochemical properties [Y]. The predictive capacity of each model was determined by cross validation and external validation, using the statistical parameters: standard error of calibration (SEC), standard error of prediction (SEP), coefficient of determination of cross-validation (R2cal), coefficient of determination for external validation (R2val), and Student’s t-test. The statistical results indicated that the chemometric models satisfactorily predict the humidity, TSS, free acidity, lactonic acidity, total acidity, and Redox potential. However, the models for electric conductivity and pH presented an acceptable prediction capacity but not adequate to supply the conventional processes, while the models for predicting ash content and HMF were not satisfactory. The developed models represent a low-cost tool to analyze the quality of honey, and contribute significantly to increasing the honey distribution and subsequently the economy of the region

Analysis of Energy and Environmental Indicators for Sustainable Operation of Mexican Hotels in Tropical Climate Aided by Artificial Intelligence

This study assessed the energy-use index and carbon-footprint performance of nine medium-category Mexican hotels (two–four stars) located in tropical-climate regions. The consumption of electrical and thermal energies of each hotel was collected during audits. Based on this, various scenarios of the partial replacement of the most energy-consuming devices were evaluated and synthesized in an expert model based on artificial neural networks. The artificial-intelligence model was designed to simultaneously associate the energy-consumption indicators, environmental impact, and economic savings of hotels based on their category, location, room number, number of existing electrical or thermal devices, and their percentage of substitution with more energy-efficient technologies. The model was used to compare the various partial-technology-substitution alternatives in each hotel that could reduce energy consumption and CO2 emissions based on the current values reported by the energy-use and environmental-impact indicators. The results of the proposed approach showed that even without making total replacements of equipment such as interior and exterior lighting or air conditioners, it was possible to identify configurations that could reduce the hotels’ energy use per room-year by 9–12%. In the environmental case, using more efficient technologies could reduce environmental mitigation. The proposed methodology represents an attractive option to facilitate the analyses and the decision making of administrators according to the needs of the type of hotel to improve its performance, which also affects the reduction in operating costs