3.2. Analysis of Variance
Table 6 shows the results obtained for the experimental treatments of the statistical design; replicates are indicated as “2”. The average recovery is indicated in the last column.
The analysis of variance is detailed in
Table 7. It is evident that all factors and their interactions significantly influence the coal flotation process as indicated by
p-values below 0.05. pH was the only factor that exhibited a
p-value greater than 0.05 (i.e., 0.147).
The F-statistic value is interpreted inversely to the p-value; thus, a higher value means a greater effect of the factor on the response variable. Accordingly, it is observed that the interaction between calcium and sulfate ions exerts a substantial influence (F = 438.3) on the response variable.
To complement the analysis of variance, individual and interaction effect graphs were constructed. Individual effects denote the differences between groups solely attributable to a particular factor (e.g., pH), while interaction effects consider multiple factors influencing the response variable.
The individual effects of pH and calcium and sulfate ion concentrations are presented in
Figure 2. Regarding pH, as the level increased from −1 to 1 (i.e., from 7 to 9), coal recovery decreased by 2%, from 77 to 75%. This variation can be attributed to the influence of the pH on the hydrophobicity of coal particles by altering surface charge and their interaction with floatation reagents, primarily the collector. However, pH is not deemed a significant factor according to its
p-value of 0.147.
When calcium and sulfate ion concentration was increased the coal recovery also increased. Evaluating calcium concentration effects at levels −1 and 1 (i.e., 400 and 800 mg/L) resulted in recoveries of 73% and 79%, respectively. Meanwhile, when sulfate concentration effects were varied, it resulted in recoveries of 71% and 82% at levels of −1 and 1 (960 and 1920 mg/L), respectively.
Apparently, the results contradict the findings reported in the literature, as Wan et al. [
30] suggest that a substantial presence of calcium ions in process water may lead to a low flotation coal index. However, it is imperative to note that Wan et al. solely examined the individual effect of calcium ions. Therefore, with sulfate ions present, they may act as activators, promoting collector adsorption on the coal surface [
31]. Consequently, sulfate ions enhance particle hydrophobicity, contributing to a higher recovery.
Figure 3 shows the interaction matrix among the factors pH, calcium and sulfate at the designated levels of interest. In
Figure 3A, the interaction between calcium and sulfate factors can be observed. When assessing the −1 level of calcium and sulfate (i.e., 400 and 960 mg/L, respectively), a coal recovery of approximately 60% was achieved. However, maintaining this calcium level and increasing sulfate concentration to level 1 (1920 mg/L) significantly enhances recovery, reaching up to 90%. This effect can be attributed to an augmentation in the surface charge of coal particles due to the adsorption of calcium species. This facilitates the subsequent adsorption of SO
42- ions (reducing the surface charge), resulting in increased collector adsorption.
Figure 3 shows the interaction between calcium concentration and pH at the two designated levels. Recovery is higher when employing level 1 of both factors (i.e., 800 mg/L of Ca
2+ and pH 9) and decreases when employing pH level −1 (i.e., 7). This behavior mirrors the one observed between sulfate and pH interactions (see
Figure 3D). This behavior may be associated with an increased adsorption of CaOH
+ on the coal surface, as this species exhibits higher activity at this pH, whereas at pH 7, the predominant species will be Ca
2+ [
30].
According to Raichur et al. [
32], pH and zeta potential are closely related. These authors reported that sub-bituminous coal at neutral pH (i.e., 7) exhibits a surface charge ranging from −10 to −30 mV, while at alkaline pH (i.e., 9), the potential varies between −35 and −45 mV. This decrease is attributed to the adsorption of OH
– ions on the particle surface.
Figure 3C,E,F correspond to the reverse interaction of
Figure 3A,B,D, without affecting the response variable.
Figure 4 presents a Pareto diagram of standardized effects in which the studied factors are illustrated in order of importance. The diagram shows a reference line at 2.31 to indicate which factors are statistically significant. The longest bar represents the factor with the greatest influence on the process, which corresponds to the calcium × sulfate interaction. As can be seen, pH, as an individual factor, is the last bar, and it is below the Pareto line, indicating that it does not have statistical significance on its own within the range that was studied.
The simultaneous effect of Ca2+, SO42− and alkaline pH in the flotation process enables carbon yields between 37.5% and 90.3%. To maximize coal recovery, it is important to control the process conditions such as calcium, sulfate and pH, to improve the efficiency of the flotation process. The use of excessive resources, such as flotation reagents, can result in an increase in operational cost and the formation of undesirable species which can negatively impact the coal recovery. Statistical analysis and graphical representations of the effect of factors also contribute to the state of the art and allow for a better understanding of the interfacial chemistry of coal in the flotation media.
Equation (1) presents the regression equation, which describes the behavior of the variable of interest as a function of the individual factors and their interaction.
In Equation (1), the coded values −1 and +1 represent the lower and upper levels of each factor, as indicated in
Table 3 of the experimental design. The variable ‘a’ corresponds to pH levels in the range of [−1, 0), while ‘
A’ is used for the range of [0, +1]. For intermediate pH values, such as 0.5, interpolated or extrapolated values could be applied within these ranges. This approach is similarly applied to other factors, such as calcium ions (
b and
B) and sulfate ions (
c and
C). Therefore, this equation provides an effective means to estimate coal recovery within the studied factor range.
3.3. Relationship Between Coal Recovery and Ash Content
Figure 5 shows the wt.-% of floated material and its ash content as a function of calcium and sulfate concentration. The results at pH 7 are depicted in
Figure 5A. The highest recovery, approximately 88.6%, was obtained when utilizing 400 mg/L of Ca
2+ and 1920 mg/L of SO
42−, yielding an ash content of 12.1%. Conversely, maintaining the SO
42− concentration while increasing the Ca
2+ concentration (i.e., 800 mg/L) adversely affected recovery, resulting in 59.9% with a slightly elevated ash content of 12.6%. The results at pH 9 are depicted in
Figure 5B. Analogous to the previous scenario, the highest recovery was achieved with a Ca
2+/SO
42− ratio of 400/1920 mg/L, attaining recoveries of 90.4% and an associated ash content of 13%. Conversely, the lowest recovery, 37.6%, was observed at the lowest concentrations of Ca
2+ and SO
42− (i.e., 400/960 mg/L). Additionally, under these conditions, the highest ash content was observed, reaching 20.9%. For the remaining tests, the ash content remained below 13%.
The correlation between recovery and ash content fluctuated based on coal characteristics and process conditions, as constituents of ash (i.e., inorganic compounds) tend to float alongside coal, thereby diminishing the quality of the final product.
Figure 6 presents the X-ray diffraction of the ash of a coal concentrate obtained under the conditions of test 6, described in
Table 6, namely pH 9, 400 mg/L of Ca
2+ and 1920 mg/L of SO
42−. The diffraction pattern reveals constituents such as anhydrite (CaSO
4), quarts (SiO
2) and hematite (Fe
2O
3). It is important to determine the ash composition in order to evaluate its suitability for diverse industrial applications, environmental impact and implications for public and occupational health.