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Neural network for modeling the capture of lead and cadmium ions from wastewater using date palm stones

Faisal, AAH, Nassir, ZS, Rashid, HM, Al-Hashimi, OA, Shubbar, AAF and Saleh, B (2022) Neural network for modeling the capture of lead and cadmium ions from wastewater using date palm stones. International Journal of Environmental Science and Technology. ISSN 1735-1472

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The current theoretical and experimental study was to thoroughly examine the capability of date stones for scavenging cadmium and lead ions from simulated wastewater. Three layers-artificial neural network (ANN) with 115 batch tests proved that the best conditions achieved the highest sorption efficiency (>63% for Cd(II) and > 91% for Pb(II)) where time 1 h, pH 5–6, dosage 5 g/100 mL, speed 100 rpm and temperature 25 °C. A satisfactory matching between the measurements and the ANN outputs was recognized with coefficient of determination greater than 99%. The ANN has also revealed throughout the sensitivity analysis that the initial pH and contact time with importance of 25 and 39% for cadmium and lead ions respectively were considered to be the most influential parameters in the removal process. Among Langmuir, Freundlich, and ANN models, the latter one was well fitted the sorption data. This model was substituted in solute transport equation to describe the spatial and temporal distribution of metal ions through the packed column. From the breakthrough curves, the well agreement between the theoretical and measurements (Willmott’s index almost greater less than 0.97), the date stones sorbent have had greater tendency to sorb lead ions than that of cadmium ones.

Item Type: Article
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TD Environmental technology. Sanitary engineering
T Technology > TP Chemical technology
Divisions: Civil Engineering & Built Environment
Publisher: Springer Verlag
Date Deposited: 09 Feb 2022 12:19
Last Modified: 09 Feb 2022 12:30
DOI or ID number: 10.1007/s13762-021-03883-1
URI: https://researchonline.ljmu.ac.uk/id/eprint/16270
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