In this study, diesel fuel was enriched with hydrogen and boron, which are used as environmentally friendly energy sources in recent years. A special fuel additive (octamix) was obtained by mixing ethanol, ammonia boron and trioctyl borate and the effects of octamix-diesel fuel blends on diesel engine responses were analyzed at different engine loads (from 500 W to 3000 W) and a constant engine speed (3000 rpm). Test fuels were prepared by adding 0.5% (0.5 OCT-D), 1% (1 OCT-D), 2% (2 OCT-D) and 3% (3 OCT-D) octamix into the diesel (100D) by volume. Brake thermal efficiency (BTE) increased while brake specific fuel consumption (BSFC) decreased with increasing octamix up to 1% percentage. With 1 OCT-D fuel, the maximum BTE was achieved as 26.2% at 3000 W load, while the minimum BSFC was 180 g/kWh at 2500 W load. An increase of 3.19% and a decrease of 18.18% were achieved, respectively, compared to the 100D. On the other hand, nitrogen oxide (NO_x), carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbon (HC), and smoke emissions have been reduced by up to 1% octamix percentage. Compared to the 100D, a decrease of 15.66%, 46.67%, 23.63%, 24.24%, and 10.71% was achieved, respectively. Graphical Abstract

With the increasing integration of wind power systems into conventional power systems, an accurate wind speed (WS) forecasting technique is essential for the reliable and stable operation of the power grid. The availability of wind energy increases with the correct WS estimation technique. Linear statistical methods such as the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model have been popular in recent times for short-term and very short-term forecasting of WS. However, studies are continuing the effect of forecast errors resulting from different disaggregated time series on the resulting WS forecast error. In this paper, we present a state-of-the-art machine learning model, the Prophet model, which has not been used in short-term wind forecasting before in the literature. The performance reliability and accuracy of the developed Prophet model’s results were compared to those of the SARIMA model. The success of forecasting time-series data was evaluated using Mean Absolute Error (MAE) and Mean Square Error (RMSE) results. The models’ reliability, accuracy, fit, and performance were evaluated using standardized residuals, autocorrelation function (ACF), and partial autocorrelation (PACF). The 7-day performance metrics of the Prophet model are 1.9, 2.5, 4.1, 3.8, 4.0, 8.4, and 2.1 for MAE, and 2.3, 2.9, 4.7, 5.0, 4.4, 8.6, and 2.5 for RMSE, respectively. The performance measures of the SARIMA model are 3.3, 4.1, 5.1, 4.8, 5.1, 1.8, and 5.4 for the MAE, and 4.2, 5.1, 6.2, 6.2, 5.7, 2.6, and 6.2 for the RMSE, respectively. The Prophet model gave higher accuracy than SARIMA for all other days except 1 day (06.01.2018). As a result, the Prophet model outperformed the SARIMA model in predicting WS measurements 24 h ahead using quarterly training data.

At least 16 bulk polymorphs of linked icosahedrons exist in boron that are not found in other materials, due to the low covalent radius and sp2 hybridization capacity of boron atoms. One of these is borophene, an exciting new nanomaterial with a wide range of possible energy uses. The existence of borophene, a two-dimensional (2D) material, has been proven by both theoretical and experimental studies. Borophene's high theoretical specific capacities, magnetic conductivity, and ion transport properties make it a promising candidate in energy applications. In this study, firstly, the structure, chemical, and physical properties of borophene were mentioned. Then, in terms of synthesis approaches, both top-down and bottom-up techniques (such as atomic layer deposition (ALD), chemical vapor deposition (CVD), exfoliation by sonochemistry, molecular beam epitaxy (MBE), and multi-step thermal decomposition (MTD) for ultrahigh-vacuum borophene deposition technologies) were discussed. Finally, its use as a catalyst in high-metal-ion batteries, hydrogen storage (HS), Nanoelectronics applications hydrogen evolution reaction (HER) was mentioned.

The need for energy in the world is increasing day by day and various energy production methods are used to meet this need. Production of hydrogen from biomass is one of these methods. Hydrogen production from biomass is a promising process to produce hydrogen and energy which has advantages such as the ability to use sustainable energy sources like biomass and solid waste, being carbon neutral, and increasing energy independence thanks to the variation of resources and the availability of local resources. The catalysts used in this process which can be conducted in three separate ways, affect hydrogen and energy production positively or negatively. One of the most important steps in effectively acquiring the ideal amount of product is predicting the outcomes of this procedure. This article compares a support vector regression (SVR) and random forest (RF) model to predict how various inputs used to produce hydrogen from biomass will affect hydrogen output. Additionally, the effect of catalyst addition on hydrogen yield in biomass processes was examined. In this context, 57 experimental studies from the literature were selected as a data set. From this data, 90% was selected for training and 10% for testing. The outputs were evaluated according to parameters such as R2, RMSE and MSE. The results show that RF and SVR models can significantly predict catalyst activity and hydrogen production.