Neural network modelling of successive Ni2+-Zn2+-Cr3+ adsorption-desorption cycles in fixed bed column

• Grant number: 20/04216-1
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: June 01, 2020
• End date: May 31, 2022
• Field of knowledge: Engineering - Chemical Engineering - Industrial Operations and Equipment for Chemical Engineering
• Principal Investigator: Carlos Alexandre Moreira da Silva
• Grantee: Mateus de Paiva Moretto
• Host Institution: Instituto de Ciências Ambientais, Químicas e Farmacêuticas (ICAQF). Universidade Federal de São Paulo (UNIFESP). Campus Diadema. Diadema , SP, Brazil

Abstract

Several industries use heavy metals for their processes and there is a worldwide concern for the treatment of liquid effluents before discharge. One available solution is biosorption, which uses organic matter to adsorb metals and treat contaminated effluents. Besides, the use of adsorption-desorption cycles increases it potential due to the adsorbent reuse. The formulation of a kinetic model for this process can be complex and this is aggravated when dealing with residues with more than one substance. Artificial Neural Networks (ANNs) appear as an alternative to phenomenological modeling, since they are very adaptable and easy to generalize. In this project, we intent to obtain one ANN to describe the kinetic curves of seven successive adsorption-desorption cycles of Ni2+-Zn2+-Cr3+ mixture, in a fixed bed column. The Sargassum filipendula algae was used as adsorbent and the desorption occurred with eluent recirculation (H2SO4). All data are available in the literature. A systematic study will be performed, evaluating the number of intermediate layers and neurons, and different optimization methods, to determine the structure that effectively describes such curves. Also, we aim to contribute to the study of the treatment of effluents containing heavy metals at national and international ambit.