Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar.

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. [Display omitted] • Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. • RODL-HMSEP technique aims for predicting the sorption performance of HMs of various biochar features. • The RODL-HMSEP model uses density-based clustering (DBSCAN) technique for simulating the features of metal adsorption data. • Deep belief network (DBN) model performs the next phase of data clustering. • RODL-HMSEP technique ensured promising performance on the prediction of sorption efficiency onto biochar over other methods. [ABSTRACT FROM AUTHOR]