Experimental analysis, modelling, and optimisation of alkaline leaching in coal fly ash treatment

Abstract

The present study introduces a novel integration of Gaussian Process Regression (GPR) modelling and Particle Swarm Optimisation (PSO) to improve the efficiency of alkaline leaching of coal fly ash (CFA). The selected operating variables for the alkali leaching process include temperature, leaching time, concentration of the alkalis (NaOH and KOH), and the liquid-to-solid ratio. A GPR model was employed for data fitting of the leaching process, yielding high predictive accuracy with R2 values of 0.9978 for SiO<inf>2</inf> dissolution, 0.9742 for Al<inf>2</inf>O<inf>3</inf> dissolution, and 0.9945 for Al/Si ratio in the NaOH-treated CFA process. In the KOH-treated CFA process, the GPR model achieved R2 values of 0.9645 for SiO<inf>2</inf> dissolution, 0.9873 for Al<inf>2</inf>O<inf>3</inf> dissolution, and 0.9960 for Al/Si ratio. Under optimised conditions, both NaOH- and KOH-treated leaching processes demonstrated an effective desilication of CFA, with NaOH showing higher silica dissolution and KOH yielding greater alumina recovery. The resulting Al/Si ratios further confirmed the efficiency of treatment, with the higher ratio in the NaOH process reflecting more effective silica removal. These findings demonstrate the efficacy of using PSO in conjunction with GPR models to optimise leaching processes, offering a significant advancement in the efficient processing of CFA through precise control of operational parameters. © 2025 Canadian Institute of Mining, Metallurgy and Petroleum.