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The growing population and increasing urbanization have led to a surge in domestic wastewater generation, posing significant challenges for effective and sustainable treatment. The present study demonstrates a novel and sustainable approach for the onsite treatment of domestic wastewater using an integrated settler-based biofilm reactor (ISBR) with efficient biogas generation. The ISBR provides an optimized environment for the growth of biofilm, facilitating the removal of organic pollutants and pathogens. Moreover, the ISBR enables the recovery of a valuable resource in the form of biogas, thus enhancing the overall utility of the treatment process. The performance of the ISBR was comprehensively evaluated at laboratory scale through treating the actual domestic wastewater generated from the hostel of Manipal University Jaipur. The ISBR system was operated under an ambient environment at a hydraulic retention time (HRT) of 24 h. The results demonstrated remarkable efficiency in terms of chemical oxygen demand (COD), total suspended solids (TSS), and coliforms removal, with average removal efficiency being more than 90%. According to the COD mass balance analysis, 48.2% of the influent COD was recovered as bioenergy. The chromatogram revealed a high percentage of methane gas in the collected biogas sample. The field emission scanning electron microscope (FESEM) analysis of the accumulated sludge in the ISBR system depicted the morphology of methanogenic bacteria. Both the experimental and theoretical results confirmed the feasibility and sustainability of the ISBR system at the onsite level.

Abstract This article introduces an effective stochastic operation framework for optimal energy management of the shipboard power systems including large, nonlinear and dynamic loads. The proposed framework divides the ship power system into several agents, which coordinate with each other based on their demands/supplies until. The alternating direction method of multipliers (ADMM) is deployed as the multi-agent framework to solve the reformulated distributed energy management problem in the ship. Two types of turbo-generators are considered in the proposed system model, including single-shaft and twin-shaft models, to increase the part-load efficiency in certain times when facing variable speed operation. The proposed distributed framework is equipped with a recursive mechanism, which helps the ship system for running optimal load scheduling when facing insufficient power generation. In order to model the uncertainty effects associated with the forecast error in the interval-ahead load demand, a stochastic framework based on unscented transform is devised which can work in the nonlinear and correlated environments of shipboard power systems. Due to the nonlinear cost function in each agent, a powerful optimization algorithm based on modified θ-firefly algorithm (Mθ-FOA) is proposed. This is a phasor algorithm, which helps for escaping from premature convergence and getting trapped in local optima. The appropriate performance of the proposed stochastic model is examined on the real dataset of a ship power system. The simulation results show the high robustness, guarantied consensus, economic operation and feasible solution when power generation shortage based on load shedding in the system.

The synthesis of biodiesel from Jatropha curcas by transesterification is kinetically controlled. It depends on the molar ratio, reaction time, and temperature, as well as the catalyst nature and quantity. The aim of this study was to explore the transesterification of low-cost, inedible J. curcas seed oil utilizing both homogenous (potassium hydroxide; KOH) and heterogenous (calcium oxide; CaO) catalysis. In this effort, two steps were used. First, free fatty acids in J. curcas oil were reduced from 12.4 to less than 1 wt.% with sulfuric acid-catalyzed pretreatment. Transesterification subsequently converted the oil to biodiesel. The yield of fatty acid methyl esters was optimized by varying the reaction time, catalyst load, and methanol-to-oil molar ratio. A maximum yield of 96% was obtained from CaO nanoparticles at a reaction time of 5.5 h with 4 wt.% of the catalyst and an 18:1 methanol-to-oil molar ratio. The optimum conditions for KOH were a molar ratio of methanol to oil of 9:1, 5 wt.% of the catalyst, and a reaction time of 3.5 h, and this returned a yield of 92%. The fuel properties of the optimized biodiesel were within the limits specified in ASTM D6751, the American biodiesel standard. In addition, the 5% blends in petroleum diesel were within the ranges prescribed in ASTM D975, the American diesel fuel standard.

AbstractPower companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest,K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection.

Abstract Recently, biodiesel has been gaining market share against fossil-origin diesel due to its ecological benefits and because it can be directly substituted for traditional diesel oils. However, the high cost of the raw materials required to produce biodiesel make it more expensive than fossil diesel. Therefore, low-priced raw materials, such as waste cooking oil and animal fats, are of interest because they can be used to drive down the cost of biodiesel. We have produced biodiesel from camel fat using a transesterification reaction with methanol in the presence of NaOH. The experimental variables investigated in this study were the temperature (30–75 °C), reaction time (20–160 min), catalyst concentration (0.25–1.5%), and methanol/fat molar ratio (4:1–9:1). A maximum biodiesel yield of 98.6% was obtained. The fuel properties of biodiesel, such as iodine value, saponification value, density, kinematic viscosity, cetane number, flash point, sulfur content, carbon residue, water and sediment, high heating value, refractive index, cloud point, pour point, and distillation characteristics, were measured. The properties were compared with EN 14214 and ASTM 6751 biodiesel standards, and an acceptable level of agreement was obtained.

Solar photovoltaic (PV) panel parameter estimation is vital to manage solar-based microgrid operations, for which several techniques have been developed. Solar cell modeling using metaheuristic algorithms is found to be one of the accurate techniques. However, it requires experimental datasets, which may not be available for most of the industrial modules. Therefore, this study proposed a new model to estimate the solar parameters for two types of PV panels using manufacturer datasheets only. In addition, two optimization techniques called particle swarm optimization (PSO) and genetic algorithm (GA) were also investigated for solving this problem. The predicted results showed that GA is more accurate than PSO, but PSO is faster. The new model was tested under different solar radiation conditions and found to be accurate under all conditions, with an error which varied between 7.6212 × 10−4 under standard testing conditions and 0.0032 at 200 W/m2 solar radiation. Further comparison of the proposed method with other methods in the literature showed its capability to compete with other models despite not using experimental datasets. The study is of significance for the sustainable energy management of newly established commercial PV micro grids.

Accurate prediction of future electrical power demands greatly facilitates the task of power generation reliably and economically. In this paper, the new soft computing technologies, namely neuro-fuzzy techniques are adopted to improve precision of medium to long-term load forecasting. Performance of the proposed methodology is verified using simulations of some data pertaining to the Riyadh power system. This approach is compared with time series and neural networks design methods. It is demonstrated that the proposed methodology surpasses other methods by producing very accurate peak load forecasts. Keywords: Fuzzy logic, Neural networks, Neuro-fuzzy, Load forecasting