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.

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.