Computational modelling of geochemical speciation of the trace metals in the wastewater treatment process optimization

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Abstract

The speciation of trace metals in the wastewater treatment plants determines its ultimate fate in natural surface waters due to biological and chemical processes. The quantification of the trace metals speciation studies was undertaken in the WWTP and was of special concern due to their persistence and recalcitrance in the biosphere. The metals of interest included: Al, Co, Cr, Cd, Fe, Cu, Ni, Mn, Mo, Zn, Pb and Ti. Trace metals accumulation was determined using geochemical modelling-mass balance. The mass balance model had a numerical impact on cost optimization procedure that uses steady state with a set of pre-defined constraints to evaluate operation points, controller parameters and plant dimensions. The mass balance model allowed detection of inconsistencies within the trace metals datasets and assisted in identifying the systematic errors in the metal reduction. It quantified the overall removal and fate of trace metals in biological treatment plants. Mass balances comprising seasonal programmable sampling showed a significant reduction in the number of trace metals. Removal of metals from biological treatment processes was mainly by complexation of the metals with microorganisms, precipitation and adsorption. The comparison of the measured data indicated an increasing trend of high concentration in the sludge (biomass) that could be of danger to human health and environment. Geochemical modelling and computation of the speciation of the trace metals offer a powerful tool for the process design, troubleshooting and optimization representing a multi-variable system that cannot be effectively handled without appropriate computer-cased technique and modelling.

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Matheri, A. N., Mohamed, B., Ntuli, F., Ngila, J. C., & Zvinowanda, C. (2022). Computational modelling of geochemical speciation of the trace metals in the wastewater treatment process optimization. Applied Water Science, 12(12). https://doi.org/10.1007/s13201-022-01805-1

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