The characterization of a specific fuel has always been an important point for developing and designing new components or systems with the maximum efficient possible. Studying the laminar burning velocity can lay a necessary prerequisite for the accurate poststudy of the turbulent range and to understand how the combustion process takes place. The study of the combustion products from a specific reaction is a requisite for any system in order to understand the elements that are taken in the process and if it is possible to improve it. In this study, a new open code methodology was developed for the determination of combustion products, flame temperature and laminar burning velocity using numerical methods (Newton–Raphson, Taylor series and Gaussian elimination) in an application codified in MATLAB. The MATLAB application was applied for the study of Saudi LPG setting parameters such as initial temperature, pressure and equivalence ratio that are meaningful because they have a great effect on the results. In addition, simulation in Ansys Chemkin using San Diego and RedSD mechanisms was carried out. The results from the MATLAB application were compared with other experimental research and Ansys Chemkin simulation. These are presented in different plots and it is shown that: (1) For the laminar burning velocity results, the numerical method agrees with the experimental results for ratios (0.6–1.2) by other authors and the simulation in Ansys Chemkin. (2) For the highest studied equivalence ratios (1.3–1.7) the laminar burning velocity results between all the resources have more difference. (3) The combustion products calculated by the MATLAB application agree with those simulated in Ansys Chemkin except N and NO. (4) The MATLAB application gives a maximum value of 40.35 cm/s, that is greater than 35 ± 0.91, the one determined by Bader A. Alfarraj. (5) The flame temperature calculated by the MATLAB application overestimates that simulated in Ansys Chemkin but has the same behavior for all the calculated ratios. (6) The MATLAB application has also been developed for the study and analysis of other fuels.

Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin”. For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation–extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.

There is great interest in the development of a simple system that could identify adulteration or counterfeiting of Peruvian Pisco (a grape-based alcoholic drink). In this study, sensors based on SnO₂-TiO₂ and SnO₂-MoO₃ composites with different weight composition ratios were synthesized and characterized. These sensors were tested with aqueous solutions of EtOH/MeOH and Pisco samples of Italia and Quebranta varieties in order to explore their capacity to identify variations in these beverages. The response profile of the most sensitive sensors showed an enhanced response to alcoholic samples with greater content of ethanol up to a concentration of 45%v/v, while the increased content of methanol in the range of 0.1 to 0.3% v/v diminished the intensity of the sensor response. Differences in the composition of methanol and ethanol in the Pisco varieties studied (Italia and Quebranta) were correlated to the capacity of the composite-based sensors to differentiate them with greater performance. Sensors based on SnO₂-TiO₂-1/2 composites showed greater reproducibility in their response profile over time in comparison to SnO₂-TiO₂-1/1 and SnO₂-MoO₃ composites. The PCA showed that composite sensors were able to differentiate Pisco samples according to the varieties, Italia and Quebranta.

El objetivo del trabajo que se presenta es mejorar el rendimiento térmico de cocinas industriales que utilizan gas licuado de petróleo (GLP) a fin de promover el buen uso del recurso en la ciudad de Lima. Los experimentos fueron realizados en un banco de ensayos, donde se registraron variables físicas que utiliza el modelo del prototipo. Luego se determinó la potencia y rendimiento de varias cocinas industriales del mercado peruano para comparar con el diseño de la cocina prototipo considerando dos normas internacionales. El quemador dio un amplio rango de potencia según la presión de trabajo utilizado y un alto rendimiento de 59% comparado con las cocinas que se comercializan a nivel nacional que en promedio están en 45%. La ventaja de esta cocina prototipo es que usa presiones de operación entre 28 a 60 mbar comparada con las cocinas del mercado peruano que usan presiones entre 3 a 6 bar, disminuyendo de esta manera los riesgos asociados a incendio y explosión.