Rooftop photovoltaic system allocation to improve the distribution transformers life span using golden ratio optimization algorithm

Purpose This paper aims to evaluate the effect of optimal use of rooftop photovoltaic (PV) systems on improving the loss of life (LOL) of distribution transformers, reducing power losses as well as the unbalance rate of the 69-bus distribution network. Design/methodology/approach The problem is studied in three scenarios, considering different objective functions as multi-objective optimization in balanced and unbalanced operations. Meta-heuristic golden ratio optimization method (GROM) is used to determine the optimal size of the rooftop PV in the network. Findings The simulation results show that in all scenarios, the GROM by optimally installing the rooftop PV is significantly capable to reduce the transformer distribution loss of loss, unbalance rate and power loss as well as reduce the temperature of the oil and transformer winding. Also, the lowest %LOL, power loss and unbalance rate occurred in the second scenario for the balanced network and first scenario, respectively. In addition, the results showed that the unbalance of the network results in increased power losses and LOL of the distribution transformer. Originality/value The better capability of GROM is proved compared with the grey wolf optimization algorithm with better objective function and by achieving better values of LOL, unbalance rate and power loss. The results also showed that the %LOL, unbalance and power losses are weakened compared to without considering the PV cost but the achieved results are realistic and cost-effective.

Mulberry and Peppermint Leaves Mixing Ratio Optimization for Skin Beauty

Purpose: This study aims to determine the optimal mixing ratio of mulberry and peppermint leaves and evaluates their biological activities to identify whether the estimated ratio is suitable for use in inner beauty and cosmetic ingredients.Methods: Total polyphenol and flavonoid contents, 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical scavenging activities, and tyrosinase and elastase inhibition activities were measured to optimize the mixing ratio of mulberry and peppermint leaves.Results: The mixture of mulberry and peppermint leaves showed a total polyphenol content of up to 46.58 mg TAE/g, a total flavonoid content of up to 45.54 mg QE/g, and DPPH and ABTS radical scavenging activities of up to 74.18% and 40.60%, respectively. Tyrosinase and elastase inhibition activities were up to 67.46% and 35.01%, respectively. In the interest section, the maximum antioxidant and tyrosinase inhibitory activities were obtained at a mulberry:pepperint mixing ratio of 1.49:0.75 (g:g). In the experimental section, the maximum antioxidant and tyrosinase inhibitory activities were obtained at a mulberry:pepperint mixing ratio of 1.79:0.80 (g:g). Further, the maximum antioxidant and elastase inhibitory activities were obtained at a mulberry:pepperint mixing ratio of 1.11:0.75 (g:g).Conclusion: This study determined the superiority of the antioxidant activity, tyrosinase and elastase activity inhibition efficacies, and optimal mixing ratios of mulberry and peppermint leaves. Based on our findings, we believe that mulberry and peppermint leaves at an optimal mixing ratio will have considerable use as inner beauty and cosmetic ingredients.

A Sustainable Early Warning System Using Rolling Forecasts Based on ANN and Golden Ratio Optimization Methods to Accurately Predict Real-Time Water Levels and Flash Flood

Remote monitoring sensor systems play a significant role in the evaluation and minimization of natural disasters and risk. This article presents a sustainable and real-time early warning system of sensors employed in flash flood prediction by using a rolling forecast model based on Artificial Neural Network (ANN) and Golden Ratio Optimization (GROM) methods. This Early Flood Warning System (EFWS) aims to support decision makers by providing reliable and accurate information and warning about any possible flood events within an efficient lead-time to reduce any damages due to flash floods. In this work, to improve the performance of the EFWS, an ANN forecast model based on a new optimization method, GROM, is developed and compared to the traditional ANN model. Furthermore, due to the lack of literature regarding the optimal ANN structural model for forecasting the flash flood, this paper is one of the first extensive investigations into the impact of using different exogenous variables and parameters on the ANN structure. The effect of using a rolling forecast model compared to fixed model on the accuracy of the forecasts is investigated as well. The results indicate that the rolling ANN forecast model based on GROM successfully improved the model accuracy by 40% compared to the traditional ANN model and by 93.5% compared to the fixed forecast model.