Study of Heavy Metal ion adsorption from Synthetic Wastewater with the help of biomass and analysis of Mathematical Model

Abstract

The biofilm matrix is a mixture of secreted polymers, metabolites and nutrients ingested, lysis solution products, and even particle debris. This matrix, which is polyanionic by nature, is essential for the activated carbon of metal cations. In this study, the heavy metal adsorption mechanism in biofilms is modelled in one spatial dimension. The numerical method is a free-boundary value issue for nonlinear parabolic and quadratic partial differential equations. Parabolic equations control the evolution of the substrate, while hyperbolic equations control the growth of cellulose and exogenous polymeric substances (EPS). Every equation is related to every other equation. The model is allencompassing and may be used to a wide range of microbe populations, EPS, and substrates. In numerical analysis, the spatial rivalry between heterotrophic and autotrophic organisms using
oxygen as a common substrate is considered. The model can replicate the transport and adsorption of heavy metals into biofilms as well as the distribution pattern of microbial species and substrate concentrations. It can also represent the dynamics of biofilm development. Using the approach of
characteristics, numerical simulations are created for typical cases. Results show that the model can capture the key aspects of the heavy metal adsorption system on EPS. The biosorption procedure considers several process variables, including concentration, contact duration, ionic strength, energy, pores, pore volume, available sites, velocity, and factors related to activity, diffusion, and dispersion. In this review article, we outline the main physical and chemical mechanisms in the adsorbents of heavy metals on numerous types of widely used biosorbents. The most popular dynamic and steady  state mathematical models for bioremediation in group and resolved columns are compiled here. Coupled nonlinear partial differential equations are produced because of the mathematical modelling of dynamic process models. It is recommended to use approximate approaches to research the
sensitive analysis of key parameters