INTELLIGENT SOFTWARE ANALYZER DESIGN FOR PARAMETERS EVALUATION OF TERNARY HEAVY METAL IONS REMOVAL BY IMMOBILIZED FUNGAL BIOMASS

In this paper the fuzzy logic method improved by adaptive learning of a fuzzy inference system, based on anfis, was used to demonstrate a software analyzer design for parameters evaluation of ternary heavy metal ions removal. The studied process was conducted to investigate metal binding ability of the novel hybrid hydrogel, obtained by entrapping Penicillium cyclopium biomass into chemically cross-linked poly (vinyl alcohol) (PVA) network toward Cu2+, Co2+ and Fe3+ from ternary aqueous solution. The performance of the biosorbent was evaluated by determining the values of heavy metal uptake and heavy metal removal efficiency in the ternary metal mixture. The innovative immobilization technology developed provides an attractive strategy for the developing high-affinity biosorption system for the treatment of wastewater containing heavy metals in low concentration. The obtained results of both - the studied process and software analyzer design and implementation are illustrated and discussed.