Use of artificial intelligence for optimizing biosorption of textile wastewater using agricultural waste.

Most of the dyes are toxic and non-biodegradable in textile industry wastewaters. Therefore, removal of textile dye using agriculture waste becomes crucial for the environment. This can be accomplished by the biosorption process which is the passive uptake of pollutants by agricultural waste.

Response surface methodology and artificial neural network modelling for the performance evaluation of pilot-scale hybrid nanofiltration (NF) & reverse osmosis (RO) membrane system for the treatment of brackish ground water.

Artificial neural network (ANN) and response surface methodology (RSM) were employed to develop models for process optimisation of pilot scale nanofiltration (NF) and reverse osmosis (RO) membrane filtration system for the treatment of brackish groundwater. The process variables for this study were feed concentration, temperature, pH and pressure. The performance of NF/RO was assessed in terms of permeate flux, water recovery, salt rejection and specific energy consumption, which were considered as responses.