1. Introduction
Cyanidation has remained a dominant technology in the gold industry for more than a century because of its simplicity and economy. However, there are some disadvantages, such as its high toxicity and slow kinetics. In some cases, gold occurs as submicroscopic particles in carbonaceous minerals; sulfides; and minerals containing arsenic, copper, and manganese, which can cause a high cyanide consumption and low gold recovery using the cyanide leaching method [
1]. Therefore, it is necessary to research non-cyanide leaching reagents for gold for the depletion of ores which are open to cyanidation and to improve the environmental requirements. Proposed alternatives include thiourea (TU), thiosulfate, thiocyanate, bromine, and iodine [
2,
3,
4]. Of the non-cyanide reagents, TU and thiosulfate have gained significant research interest over the last few decades [
5,
6,
7]. Thiosulfate is considered a promising alternative, but it has not been applied widely due to complex chemical reactions and passivation and recovery problems [
8]. The TU leaching of gold features a low environmental impact, good selectivity, and fast kinetics of leaching, and has been used in commercial production [
9,
10].
The initial gold leaching rates at high concentrations of TU are comparable to those of cyanide leaching [
11]. However, TU is unstable and easily converts to other unwanted products (via thermal degradation, side-reactions, etc.), which causes a high TU consumption and the passivation of the gold surface, thus reducing the dissolution rate of gold [
8]. In general, the leaching time (Lt), initial pH, temperature (T), TU dosage (Td), and stirring speed (Ss), etc., are the important parameters when optimization studies for the TU gold leaching process are considered [
12]. TU is a kind of water-soluble organic compound which is relatively stable in acidic solution and rapidly decomposed in alkaline solution. Therefore, the TU leaching of gold is generally carried out in the pH range of 1–2 [
13]. However, the TU consumption increases as the pH decreases [
14]. Birich et al. [
3] found that the leaching temperature has a significant effect on the mean dissolution rate of gold in TU solutions, and a trial at 45 °C was up to two times faster than a 25 °C trial. Nevertheless, when the temperature exceeds 45 °C the TU solution evaporates, resulting in a drop in pH and thus slowing down the leaching kinetics. Previous research has revealed that the gold extraction increased with the increase in the TU concentration due to the production of formamidine disulfide in the medium. Olyaei et al. [
12] found that as the ferric sulfate/TU molar ratio increases, the gold recovery tends to increase and reaches a highest value at the ratio of 1/1. Additionally, the dissolution rate did not increase significantly after 5 h of leaching. Tanriverdi et al. [
14] discovered that the maximum gold extraction was found at the stirring speed of 480 rpm, with 14.09 g/t Au in the ore and a 20% solid/water ratio.
In industrial processes, the goal of controlling experimental conditions is to gain the optimum performance; especially, the goal is the optimal selection of the parameters from the economic and environmental points of view. However, the simultaneous evaluation of gold recovery and TU consumption is complicated due to the numerous interactions between the operational conditions and process performance. Moreover, quantitative data on TU leaching systems are limited. It therefore seems of interest to study the matching relationship between various parameters and gold recovery and TU consumption.
A gold recovery and TU consumption evaluation simultaneously in the leaching process by appropriate modeling can optimize experimental conditions and minimize the experiment cost. Recently, artificial neural network (ANN) models have been widely used to capture and interpret relationships where the relations between the input and output parameters are complex and nonlinear [
15]. Each network consists of three parts: the first part constitutes the input layer, where the data are inputted into the network; the second part consists of the hidden layers where these collective inputs are processed; the last one is the output layer, where the target prediction values of the network model are outputted in the current state [
16]. An ANN can be considered as a black box consisting of a complex set of formulas, and the outputs of the network can be related to the actual data used as inputs and the expected outputs in samples to reflect the complex mapping of multi-samples [
17]. However, the mathematical properties and distribution laws of some data are not obvious, and even ANN cannot reflect the internal complex mapping relationship well. Grey relational analysis (GRA) is an important multivariate analysis in the field of grey system theory, which aims to find the optimal system parameters of a grey system and make the dynamic behavior of the system most suitable for problem analysis [
18]. One of the major advantages of GRA is that a small amount of data is needed to describe the system behavior, and the relational degree can be obtained to reflect the correlation between the comparison sequence and the reference sequence. With the excellent performance of GRA in analyzing the incomplete information system, GRA is particularly suitable for the analysis of processes, such as the process of the TU leaching of gold, which have limited data, complexities, uncertainties, and no prototype.
In general, the pretreatment of the refractory gold concentrate is required to improve the gold extraction. The main pretreatment technologies include roasting [
19], chemical oxidation [
20], pressure oxidation [
21], and biological oxidation [
22], and bio-oxidation technology has been proved to be cleaner than conventional methods [
23]. At present, the commonly used leaching bacteria mainly include
Acidithiobacillus ferrooxidans (
A. ferrooxidans),
Leptospirillum ferrooxidans,
Acidithiobacillus thiooxidans, ect. [
24]. In this work, the
A. ferrooxidans was used to oxidize the refractory gold ore, and then the effects of various operational parameters such as the leaching time, initial pH, temperature, TU dosage, stirring speed, and ferric iron concentration on the gold recovery and TU consumption in the leaching process were investigated. Then, the GRA method was adopted to explore the relationship between the six influencing variables and the gold recovery and TU consumption. Finally, an ANN model was established to predict the effects of the influencing variables on the gold recovery and TU consumption. These methods can provide effective guidance for the control of experimental conditions in the TU gold leaching process.
4. Conclusions
In this paper, the optimum conditions of effective parameters for the TU leaching of gold were determined. According to the results, 77.6% of gold was extracted at a leaching time of 4.0 h, with an initial pH of 1.5, temperature of 35 °C, TU concentration of 6.0 g/L, stirring speed of 400 r/min, and ferric iron concentration of 9.33 g/L using 8.1 kg/t of TU. In addition, an ANN model was established to simultaneously predict the gold recovery and TU consumption in the process of leaching gold with TU. Prior to establishing the ANN model, the GRA was performed to investigate the relational degree between the selected parameters and the gold recovery and TU consumption, which is critical for the TU leaching process prediction. The main conclusions were summarized as follows:
- (1)
The relational degree of all the influencing parameters collected from the TU leaching process related to the gold recovery and TU consumption was analyzed in GRA. The results showed that the temperature, leaching time and amount of agents have a significant influence on the gold recovery and TU consumption, but the initial pH and stirring speed had a little influence.
- (2)
The prediction results obtained by the ANN model were quite satisfactory. It achieved quite a high correlation coefficient (R > 0.99) for the training, testing, and validation stages. The performance evaluation showed that the MAE was 1.9226 and the RMSE was 2.0785 for gold recovery, and the MAE was 0.2809 and the RMSE was 0.3352 for TU consumption. In addition, the prediction accuracy reached over 94% for gold recovery and 95% for TU consumption. These results showed that the predicted values were in excellent agreement with the experimental values, and the validity of the model was verified.
In summary, the GRA and the ANN model can reflect the practice efficiently and also provide effective suggestions for controlling the optimum parameters in the leaching process. Furthermore, more operating conditions and experimental data need to be considered in future work in order to train the neural network model with a better performance. Besides, the technical, economic, and environmental benefits should be considered together when establishing the ANN model.