Application of artificial neural networks and response surface methodology for dye removal by a novel biosorbent

Toxic dyes found in industrial effluent must be treated before being disposed of due to their harm-ful impact on human health and aquatic life. Thus, Musa acuminata (banana leaves) was employed in the role of a biosorbent in this work to get rid of methylene blue derived from a synthetic solu-tion. The effects of five process parameters such as temperature, pH, biosorbent dosage, initial methylene blue concentration, using a central composite design, the percentage of dye clearance was investigated (CCD). The response was modelled using a quadratic model based on the CCD. The analysis of variance revealed the most influential element on experimental design response. Temperature of 44.3 degrees C, pH of 7.1, biosorbent dose of 0.3 g, starting methylene blue concentra-tion of 48.4 mg/L, and 84.26% dye removal were the best conditions for M. acuminata (banana leave powder). At these ideal conditions, the experimental percentage of biosorption was 76.93. The surface area of 40 m2/g was provided by M. acuminata and the pore volume of M. acuminata was observed as 0.265 cm3/g and the scanning electron microscopy images are also showed before and after biosorption. The link between the estimated results of the developed artificial neural networks (ANN) model and the experimental results defined the success of ANN modeling. As a result, the study's experimental results were found to be quite close to the model's predicted outcomes.