Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.     

Synthesis and characterization of new hybrid inorganic–organic polymer nanocomposite as efficient catalyst for oxidative desulfurization of real fuel

Sulfur-containing compounds are responsible for much air pollution, and therefore eliminating these compounds is of importance. Herein, a hybrid organic–inorganic recyclable nanocatalyst (TBA-PW₁₁Ni@PANI) was synthesized successfully to investigate its effects on the catalytic oxidative desulfurization (CODS) process of real gasoline/model fuel. To this end, the Keggin-based mono-lacunary polyoxometalate [PW₁₁NiO₃₉] was prepared and modified with quaternary cation tetrabutylammonium (TBA). Then, this was further immobilized on polyaniline (PANI) via the sol–gel method. The synthesized nanocomposite was characterized using various techniques. The high dispersion of polyoxometalate on PANI was confirmed. Also, it was found that the crystalline structure remained unaltered after immobilization. In addition, the effects of various parameters such as dosage and temperature on the CODS of model fuel in the presence of H₂O₂–acetic acid (1:2 v/v) were studied in detail. Moreover, the kinetics of the CODS process was also studied and a mechanism proposed. According to the results, TBA-PW₁₁Ni@PANI showed an efficiency of up to 97% with 0.1 g at 35°C (optimum values) which implies its good catalytic functionality in the CODS process. Finally, the TBA-PW₁₁Ni@PANI catalyst displayed long-term stability and good reusability after five runs.     

Synthesis of new dihybrid nanofluid of TiO2/MWCNT in water–ethylene glycol to improve mixture thermal performance: preparation,characterization, and a novel correlation via ANN based on orthogonal distance regression algorithm

Journal of Thermal Analysis and Calorimetry - Nanofluid refers to the mixture of fluid and solid nanoparticles. If this mixture contains more than one NP or fluid, it is called “hybrid...