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Bioethanol is a source of energy for the future when petroleum is going to finish. The aim of this study is to assess bioethanol production from watermelon waste using Saccharomyces cerevisiae (S. cerevisiae) yeast and fermentation method. The test was replicated three times in 35 hr at three different fermenter agitator speeds and three levels of yeast. The results showed that about 35.5 g of bioethanol was obtained from each kilogram of watermelon. Investigating different variables shows that the fermenter agitator speed of 120 rpm and the yeast amount of 5 g could lead to the best yield in the process of fermenting watermelon waste for the purpose of producing bioethanol. The results of the evaluating the artificial neural network (ANN) model and adaptive neuro‐fuzzy inference system (ANFIS) in predicting bioethanol production from watermelon waste with the highest coefficient of determination (R2) were 0.9895 and 0.9993, respectively. These results indicate that ANNs and ANFIS are effective in predicting bioethanol production from watermelon waste. Practical Applications Advanced, green, clean, and sustainable processing technologies to reduce watermelon waste. Fermentation of agricultural waste in the shortest time (30 hr). Investigating important factors in waste fermentation process. The performance of ANN and ANFIS network in predicting bioethanol production.

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Besides economical issues, reliability and environmental emissions have become major concerns whenever an energy system is designed. The objective of this study is to design both an autonomous and non-autonomous hybrid green power system (HGPS) to supply a specific load demand considering economics, reliability indices, and environmental emissions. The HGPS includes wind turbine (WT), photovoltaic (PV) array, fuel cell, and the actual data used for simulation which are annual solar irradiation and wind speed for the state of Illinois. For reliability analysis, it is assumed that only WT, PV, DC/AC convertor, and electrical network might have failure in supplying power. To evaluate the actual reliability level of the HGPS equipments, all possible conditions should be considered and for each one of them, the reliability indices are estimated by determining the quantity and probability of load demands not supplied. However, in this study, an approximate model is utilized to examine the reliability indices. In the approximate model, the average power generation of WT units and PV array is utilized (instead of considering the outage of each WT unit and PV array separately) and then the mathematical expectation of reliability indices is calculated. The results show that the reliability indices are activated when there is no backup (electrical network) for the HGPS. The utilization of HGPS in parallel with electrical network, when cost is given the highest importance, leads to the purchase of all power from the electrical network. However, when emission is considered as the second objective function in the fuzzy multi-objective problem; the degree of importance given to each function (total cost and emission) plays a major role in the optimal design of HGPS.

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