Search

Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The recent increase in photovoltaic (PV) power generation and its extensive use worldwide has led to the development of complex distributed generation systems, which has caused an increase in PV faults. These defects lead to considerable power losses, significantly impacting the reliability and performance of the PV system. Several approaches have been implemented, but an accurate solution has not been found. Therefore, an optimal Deep Belief with Buffalo Optimization (DB-BO) algorithm is applied in the grid-connected system for detecting faults and regulating its classes. Moreover, principal component analysis is used to analyze the power loss issues, and linear discriminant analysis is utilized to mitigate the voltage deviation issues. MATLAB or Simulink is used as the implementation process, and simulation outcomes are compared with recent conventional models. It has been revealed that the developed DB-BO algorithm has reduced the power loss to 3.4 mW. Also, total harmonic distortion (THD) is improved compared to the existing security methods. Thus, the efficiency of the model that was built has been proven by getting the best results in accuracy, total harmonic distortion (THD), and power loss. The computation time of the proposed model (0.238 s) is compared with metaheuristic algorithms such as CSE (0.315629 s) and GWA (3.636 s). [ABSTRACT FROM AUTHOR]

The increasing global need for freshwater has led to the widespread implementation of photovoltaic (PV) assisted desalination facilities as a viable and environmentally friendly remedy. The necessity for these plants resides in executing an effective power management strategy to provide dependable and economically feasible water generation. This paper utilizes a mixed architecture consisting of a convolutional neural network (CNN) and a deep Q‐learning Network (DQN) to implement a hybrid deep learning‐based power management strategy. The presented approach is modeled and executed in matrix laboratory (MATLAB) language, and the experimental findings validated that this algorithm achieved a computational time of 0.2 s and an energy loss of 0.01 megawatts, which is lower than the conventional models. Furthermore, the proposed strategy achieved a remarkable fit with an accuracy rate of 0.99, demonstrating its effectiveness in handling diverse load power and solar profiles in power management. Implementing this hybrid approach holds promise for a substantial reduction in operational expenditures and the advancement of sustainability in freshwater generation, making a valuable contribution toward a more environmentally friendly and optimized future for the desalination sector. [ABSTRACT FROM AUTHOR]