Modeling of copper removal from aqueous solutions by using carbon-based adsorbents derived from hazelnut and walnut shells by artificial neural network

Abstract

The aim of this study is to model the removal of copper from an aqueous solution using Artificial Neural Network (ANN). Two different carbon-based materials (biochar) obtained by pyrolysis of hazelnut and walnut shells were used as adsorbents in a batch adsorption system. The surface area of biochars with a porous structure obtained from hazelnut and walnut shells were 124.347 m2/g and 256.931 m2/g, respectively. In adsorption experiments, initial copper concentration, adsorbent amount, temperature, pH, contact time and mixing speed were the adsorption parameters considered for the system and defined as inputs in the modeling studies. Experimental results for removing copper ions were found by changing pH 2.5-5, initial heavy metal concentration 15-45 ppm, adsorbent amount 1-3 g/L, mixing speed 200-600 rpm and temperature between 25-45oC. The % copper removal, which is tried to be maximized during the modeling phase, was selected as the model estimation parameter and defined as output to the system. ANN training was done with the Levenberg–Marquardt (LM) feed-forward algorithm and the data was categorised into 50% training, 25% validation and 25% testing. The maximum epoch value was determined as 8 iterations. Correlation coefficient (R2) values of the system were determined as 99% for education, validation and testing for the two different carbon-based adsorbents.