The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.
The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To obtain a better accuracy of prediction data, search optimization algorithms (SOA) such as genetic algorithms (GA) and the particle swarm optimization algorithm (PSO) were used to optimize the prediction accuracy by searching the model parameters. This article presents a review of different hybrid SVM models with SOA applied to obtain the best parameters to reduce the prediction error of solar radiation using meteorological variables. Research articles from the last 5 years on solar radiation prediction using SVM models and hybrid SMV optimized models with SOA were studied. The results show that SVM with GA presents a better performance than the classical SVM models using the Radial basis kernel function for prediction parameters.
Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.
In an organic Rankine cycle (ORC), the study of the cycle efficiencies and the turbine is essential to know the performance in the generation of electrical energy. The proper selection of a working fluid is relevant, because it must be environmentally friendly and compatible with the ORC plant. This article presents an experimental study for the analysis of the cycle and thermal efficiencies on a 1-kW ORC system and the isentropic efficiency of its scroll expander. The test was performed on a 1-kW ORC with scroll expander system with R245fa as the working fluid. Furtheremore, it was considered a working temperature below 100 • C, which is used in waste heat recovery systems, to determine the performance of the ORC. The enthalpy is estimated with the Coolprop software. For estimating the cycle and thermal efficiency, the net power and the thermal power, which are supplied to evaporate the working fluid, were considered. The isentropic efficiency of the expander was calculated by the scroll mechanical work and the hydraulic work at the scroll expander. The results show that the mean isentropic efficiency of the fluid in the prototype test for ORC in the generation of 1000 W was about 60%, a promising value for the generation of electrical energy using the residual heat from industry.
Humanity has developed recycling activities over time due to their benefits, the shortage of raw materials, or the footprint with regard to the environment. The absence of a recycling culture in Mexico has not allowed its development and growth despite the benefits. In 2012, Mexico only recycled less than 10% of urban solid waste. Most recycling activities are focused on plastic, paper, and cardboard products due to their prices in local markets. This article presents a semi-automated prototype focused on recycling glass bottles using the thermal shock phenomenon. It aims to develop a sustainable glass recycling culture by creating a new branch for the integral glass recycling process and a proposal base on Integrated Sustainable Waste Management (ISWM) and the Quintuple Helix Model. It helps to reduce waste and resource recovery from recycling and upcycling glass bottles. The products obtained from upcycling fulfill new uses and acquire new value, while glass leftovers continue the integral recycling process for glass. Additionally, this paper demonstrates the relation between the ISWM and the Quintuple Helix Model and the opportunity to implement the twelfth Sustainable Development Goal (SDG).
In Mexico and many parts of the world, land cargo transport units (UTTC) operate at high speeds, causing accidents, increased fuel costs, and high levels of polluting emissions in the atmosphere. The speed in road driving, by the carriers, has been a factor little studied; however, it causes serious damage. This problem is reflected in accidents, road damage, low efficiency in the life of the engine and tires, low fuel efficiency, and high polluting emissions, among others. The official Mexican standard NOM-012-SCT-2-2017 on the weight and maximum dimensions with which motor transport vehicles can circulate, which travel through the general communication routes of the federal jurisdiction, establishes the speed limit at the one to be driven by an operator. Because of the new reality, the uses and customs of truck operators have been affected, mainly in their operating expenses. In this work, a mathematical model is presented with which the optimum driving speed of a UTTC is obtained. The speed is obtained employing the equality between the forces required to move the motor unit and the force that the tractor has available. The required forces considered are the force on the slope, the aerodynamic force, and the friction force, and the force available was considered the engine torque. This mathematical method was tested in seven routes in Mexico, obtaining significant savings of fuel above 10%. However, the best performance route possesses 65% flat terrain and 35% hillocks without mountainous terrain, regular type of highway, and a load of 20,000 kg, where the savings increase up to 16.44%.
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