Recovery of iron from copper slag using jatropha oil as a green reductant: Investigation of pyrolysis analysis, reaction kinetic and artificial neural network model prediction

Abstract

Jatropha oil (JO) is a non-edible vegetable oil and a potential renewable energy source. In this paper, JO was proposed as a reductant to recover iron from copper slag (CS), and the effects of JO dosage, reduction temperature and reduction time on the recovery of iron by deep reduction of CS were investigated. Based on Coats-Redfern method, a thermogravimetric analyzer was used to explore the reactivity, mass loss behavior, and kinetic mechanism of CS reduction by JO, and to compare the reduction characteristics of anthracite and JO char when used as a reductant. Additionally, artificial neural network (ANN) modeling was used to predict the effects of reductant type, temperature, and heating rate on mass loss during CS reduction. The results showed that the optimum condition for CS reduction when JO was used as the reductant was 3 wt% JO at 1250 °C for 45 min, which resulted in 84.97 % Fe recovery and 94.58 % Fe grade in CS; the reduction mechanism of CS followed a one-dimensional diffusion model (D1); and ANN5 (15*1) was the optimum ANN model to predict the reduction of CS. This research introduces innovative strategies for the environmentally friendly and efficient recovery of iron from iron-containing furnace slag.