The most promising source of alumina in the 21st century is the coal fly ash (CFA) waste of coal-fired thermal plants. The methods of alumina extraction from CFA are often based on the pressure alkaline or acid leaching or preliminary roasting with different additives followed by water leaching. The efficiency of the alumina extraction from CFA under atmospheric pressure leaching is low due to the high content of acid-insoluble alumina phase mullite (3Al2O3·2SiO2). This research for the first time shows the possibility of mullite leaching under atmospheric pressure after preliminary desilication using high liquid to solid ratios (L:S ratio) and Na2O concentration. The analysis of the desilicated CFA (DCFA) chemical and phase composition before and after leaching has been carried out by inductively coupled plasma optical emission spectrometry (ICP-OES) and X-ray diffraction (XRD). The morphology and elemental composition of solid product particles has been carried out by scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX). An automated neural network and a shrinking core model (SCM) were used to evaluate experimental data. The Al extraction efficiency from DCFA has been more than 84% at T = 120 °C, leaching time 60 min, the L/S ratio > 20, and concentration of Na2O-400 g L-1. The kinetics analysis by SCM has shown that the surface chemical reaction controls the leaching process rate at T < 110 °C, and, at T > 110 °C after 15 min of leaching, the process is limited by diffusion through the product layer, which can be represented by titanium compounds. According to the SEM-EDX analysis of the solid residue, the magnetite spheres and mullite acicular particles were the main phases that remained after NaOH leaching. The spheric agglomerates of mullite particles with non-porous surface have also been found.
Keywords: alkaline; coal fly ash; kinetics; leaching; machine learning; mullite; neural network; shrinking core model.