Application of Artificial Neural Network and Shrinking Core Model for Copper (Ii) and Lead (Ii) Leaching from Contaminated Soil Using Ethylenediaminetetraacetic Acid
Musamba Banza, Hilary Rutto, Tumisang Seodigeng
Abstract
This work examined the kinetics of Copper (II) and Lead (II) leaching in EDTA acid. The impacts of various factors such as pH, acid concentration, soil-to-solution ratio, and stirring speed were investigated to improve the leaching conditions and assess the leaching kinetics. According to the findings, copper (II) and lead (II) leaching was enhanced by an increase in leaching temperature, stirring speed and acid concentration, as well as a drop in solid-liquid ratio and pH. The results indicated that after 120 minutes of leaching in EDTA acid with a concentration of 1.5 M and a solid/liquid ratio of 5 g/200 mL, pH of 3, and stirring speed of 375 rpm, 90.98% of copper (II) and 95.43% of lead (II) were removed from the sample. Diffusion across the product layer was the rate-controlling phase during the leaching; according to the experimental results, a diffusion-controlled model best matched. The activation energy was determined to be 15.58 kJ/mol and 15.70 kJ/mol for copper (II) and lead (II), indicating the diffusion-controlled process. The ANN Multi-layer, Feed-forward, and Back-Propagation Learning algorithms are trained to optimize the leaching process parameters. The ANN algorithm was developed with two neurons as output layers corresponding to copper (II) and lead (II) leaching recovery, 15 hidden layers, and 5 input variables describing the leaching parameters. The optimal trained neural network illustrates the validation, testing, and training steps with R2 values of 0.992, 0.994 and 0.999, respectively. The leaching mechanism was validated by (XRF) X-Ray Fluorescence, (FTIR) infrared spectroscopy and (SEM) scanning electron microscope analysis of the original sample.