Ash Depression in Fine Coal Flotation Using a Novel Polymer Aid

Abstract

The current study investigated the effects of novel hybrid polyacrylamide polymers as ash (slime) depressants in fine coal flotation to enhance combustible recovery and ash rejection. Coal samples at P 80 of approximately 45 um with ~25% ash content were floated in the presence of in-house synthesized hybrid aluminum hydroxide polyacrylamide polymers (Al(OH) 3 -PAM, or Al-PAM). All flotation experiments were carried out in a 5-L Denver flotation cell. Various influencing factors were examined to optimize the flotation process in the presence of the Al-PAM polymers, including the Al-PAM dosage, Al-PAM conditioning time, impeller rotation speed and pulp pH. Comparative and synergistic studies were also performed using organic polyacrylamide polymers (PAMs), commercial dispersants and Al-PAM/dispersant system. Results showed a significant improvement in both combustible recovery and ash rejection at an Al-PAM dosage of 0.25 mg/L. The maximum combustible recovery obtained, at natural pH, with Al-PAM and Al-PAM/dispersant system was determined to be 70% and 66% at ash content of 7.74% and 7.4%, respectively. Zeta potential values of both the raw coal and concentrate products showed a large shift toward more positive values (from −50 mV to −13 mV), indicating a significant decrease in ash-forming minerals (slimes) when Al-PAM polymers were applied.

Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model

“…As a final point, it is established that the hybrid model's prediction errors-quantified by the statistical parameters such as R 2 and RMSE-were dependably and considerably negligible compared to those conveyed in the literature that places emphases on prediction of outcomes of froth flotation processes. 2,[13][14][15][16][17][18][19]43,[51][52][53] It is recognized that RMSE and R 2 alone are not convincing proof that-compared to other ML models-the combination of RF and the firefly algorithm will reliably yield more accurate predictions (of flotation efficiency or other flotation outcomes). The comprehensive prediction ability of a given ML model is influenced by numerous aspects, and, as a result, it is challenging to assess (or rank) dissimilar, supervised ML models.…”

“…11 Ali et al used five different ML models: ANN, RF, adaptive neuro-fuzzy inference system (ANFIS), Mamdani fuzzy logic (MFL), and hybrid neural fuzzy inference system (HyFIS) to predict the froth ash content and the combustible recovery of fine high-ash coal. 13,14 Authors indicated that the performance difference among all models was marginal with the highest R 2 value of 0.92 obtained for MFL model. Mohanty developed an ANN-based model for controlling the froth layer depth in a laboratory scale coal flotation column by manipulating tailing flowrate.…”

“…Because of the highlighted limitations of more conventional modeling tools, as mentioned in the above paragraph, a focus has been placed on supervised and unsupervised utilizations of machine learning (ML) models for optimization and prediction of flotation processes. [8][9][10][11][12][13][14][15][16][17][18][19] ML models-if properly trained using high-quality datasets-have ample allure due to their ability to elucidate relationships from inherently non-linear relationships between inputs (ie, experimental process parameters) and outputs (eg, grade and concentration of metals) of the system, but are also able to perform predictions in previously untrained data-domains. If the prediction performance is excellent, a well-trained ML model can potentially be used to perform optimizations as well.…”

Optimization of flotation efficiency of phosphate minerals in mine tailings using polymeric depressants: Experiments and machine learning

“…A previous study reported the capability of this polymer to adsorb on ultrafine silica particles and promote their flocculation (Alagha et al, 2011). In addition, Hy-PAM was successfully applied in fine coal flotation to enhance the combustible recovery and reduce the ash contents (mainly fine clays and silicates) of coal concentrates ( Molatlhegi and Alagha, 2016). Preliminary investigations also indicated that this polymer had the potential to depress silicates in phosphate flotation (Alsafasfeh, 2020).…”

Flotation Behavior of Complex Sulfide Ores in the Presence of Biodegradable Polymeric Depressants

“…Depending on the integrated functional groups, polyacrylamides and their derivatives are being used as collectors, depressants, activators, or modifiers. Hybrid polyacrylamide polymer (Hy-PAM) was successfully applied in fine coal flotation to enhance the combustible recovery and reduce the ash contents of coal concentrates [2].…”