Application of Artificial Neural Network and Particle Swarm Optimization for modelling and optimization of biosorption of Lead(II) and Nickel(II) from wastewater using dead cyanobacterial biomass

Removal of heavy metals through biosorption using biomass offers several advantages over other conventional techniques such as low cost, high efficiency, environmentally friendly, etc. In the present article, biosorption of Nickel(II) and Lead(II)was investigated using dried biomass of cyanobacterial consortium. OFAT (one-factor-at-atime) analysis was used to assess the effect of input parameters on the removal of potentially toxic elements by varying initial metal ion concentration (2-10 mgL(-1)), adsorbent dose (0.1-1.0 gL(-1)), pH (for Pb(II): 2-6, for Ni(II): 2-8) and temperature (25 degrees C-45 degrees C) individually, at constant shaking speed of 150 rpm. Results showed that removal using biomass attained highest values in as short time as 15 min. The investigations also showed the removal is highly effective at lower initial concentrations of heavy metals. Maximum removal of Lead(II) (87.27 +/- 1.75%) and Nickel(II) (92.57 +/- 0.77%) was obtained at pH 6 and 45 degrees C and at pH 7 and 25 degrees C, respectively, within 15 min with 0.1 gL(-1) biomass. Both the Langmuir model and Freundlich model were seen to fit the equilibrium data. Further, Artificial Neural Network was used to model the biosorption process. Subsequently, Particle Swarm Optimization was applied to optimize the operating conditions for the removal of both the metals.

Automated summary

The removal of heavy metals through biosorption using biomass is effective, especially at low initial concentrations, achieving highest removal values in a short period of time. Both Langmuir and Freundlich models fit the equilibrium data well, with Artificial Neural Network and Particle Swarm Optimization used for modeling and optimization.