Responses of zinc recovery to temperature and mineral composition during sphalerite bioleaching process

Abstract

Temperature and energy resources (e.g., iron, sulfur and organic matter) usually undergo dynamic changes, and play important roles during industrial bioleaching process. Thus, it is essential to investigate their synergistic effects and the changes of their independent effects with simultaneous actions of multi-factors. In this study, we explored the synergistic effects of temperature and original mineral compositions (OMCs, energy resources) on the sphalerite bioleaching process. The microbial community structure was monitored by 16S rRNA gene sequencing technology and showed clear segregation along temperature gradients and Shannon diversity decreased at high temperature. On the contrary, the physicochemical parameters (pH and [Fe³⁺]) in the leachate were significantly affected by the OMCs. Interestingly, the influence of temperature on zinc recovery was greater at relatively simpler OMCs level, whereas the influence of OMCs was stronger at lower temperature. In addition, using [Fe³⁺], pH, relative abundances of dominant OTUs of microbial community and temperature as variable parameters, several models were constructed to predict zinc leaching efficiency, providing a possibility to predict the metal recovery efficiency under temperature change and variable energy resources.

Keywords: Microbial community; Models; Original mineral compositions; Temperature; Zinc leaching efficiency.

Fig. 1

Zinc leaching efficiency on day 30 under different temperature (A) and mineral composition (B)

Fig. 2

pH value and the concentration of ferric iron on day 30 under different temperature (a pH; b ferric iron) and different mineral composition (c pH; d ferric iron)

Fig. 3

Plot of physicochemical parameters of all samples under different temperature (a), mineral composition (b) and stages (c) using principal component analysis (PCA)

Fig. 4

Polts of microbial community composition at OTU level (a) and microbial community structure under different temperature (b), mineral composition (c) and stages (d) using detrended correspondence analysis (DCA)

Fig. 5

The direct and indirect effects of temperature and the original mineral composition on zinc leaching efficiency. The effects of temperature, mineral composition and physicochemical parameters (model 1)/microbial community structure (model 2)/microbial diversity (model 3) on zinc leaching efficiency, explored with Partial Least Squares Path Modeling (PLS-PM). Mineral composition and microbial community structure were represented by the top two axes (DCA1 and DCA2) of DCA. The whole physicochemical parameters was represent by the values of PC1 and PC2 (the top two axes) of PCA. The blue line represented the positive correlation, and the red line represented the negative correlation. The bold line showed the relation was significant (p < 0.05) or marginally significant (p < 0.1) and the regular line showed the relation was no significant. Models were assessed using goodness of fit (GoF) statistic. The GoFs for model 1–3 were 0.642, 0.599 and 0.642, respectively

Fig. 6

Models to predict zinc leaching efficiency using multiple linear regression analysis. The variable parameters were temperature, the concentration of ferric iron, pH value, OTU_1 (98% similarity to S. thermotolerans Kr1) or OTU_2 (98% similarity to A. caldus KU)

Fig. 7

Comparison of three models to predict zinc leaching efficiency. a MLR model; b nnet model; c neuralnet model. 50 random samples were used to construct the models, and then the remaining 30 samples were predicted (reshuffled 10 times)