Investigation of potato peel-based bio-sorbent efficiency in reactive dye removal: Artificial neural network modeling and genetic algorithms optimization

Over the last few years, a number of investigations have been conducted to explore the low cost sorbents for the decontamination of toxic materials. Undoubtedly, agricultural waste mass is presently one of the most challenging topics, which has been gaining attention during the past several decades. Wastes are very cheap and easily available material in production of sorbent. Therefore, the Reactive Red 198 (RR198) removal efficiency from aqueous solutions by potato peel powder based sorbent (PP) was examined in this study. The Taguchi method was used in combination with full factorial methods to design the experiments. Based on the design of experiment outputs, 18 experimental sets were designed and the experiments were done in accordance with the experimental design. The sorption handmade batch reactor consists of a 200 ml beaker, 100 RPM magnetic stirrer, and a sampling port. Then, the experimental data were collected under desired conditions. In each sample sorbent was separated using a centrifuge (3000 rpm and 5 minutes). Then, dye concentrations were determined based on Beer’s law and calibration plots using a UV-visible spectrophotometer. The wavelength resolution and the bandwidth were, respectively, 1 and 0.4 nm. The length of the optical path in glass cell was 1 cm. The maximum absorption wavelength was determined in each run to compensate the matrix effects. The results revealed that PP is effective for the sorption of RR198 from aqueous solutions. The maximum sorption of PP from RR198 solution was determined as 93 mg/g. Artificial neural network (ANN) model of dye removal efficiency (DR%) was developed based on the experimental data sets. The ANN model was strongly validated using statistical tests. The R2 and RMSE of the test set were 0.98 and 4.3, respectively. The results demonstrate that PP can be successfully used as sorbent for RR198 removal from aqueous solutions. The results revealed that experimental parameters strongly influence the DR% and different experimental conditions cause different DR% (from 0 to 93).

Keywords:

Sorption , Potato Waste Powder , Design of Experiment , Artificial Neural Network , Genetic Algorithm , Dye Removal