The mining industry of the last few decades recognizes that it is more profitable to simulate model using historical data and available mining process knowledge rather than draw conclusions regarding future mine exploitation based on certain conditions. The variability of the composition of copper leach piles makes it unlikely to obtain high precision simulations using traditional statistical methods; however the same data collection favors the use of softcomputing techniques to enhance the accuracy of copper recovery via leaching by way of prediction models. In this paper, a predictive modeling contrasting is made; a linear model, a quadratic model, a cubic model, and a model based on the use of an artificial neural network (ANN) are presented. The model entries were obtained from operation data and data of piloting in columns. The ANN was constructed with 9 input variables, 6 hidden layers, and a neuron in the output layer corresponding to copper leaching prediction. The validation of the models was performed with real information and these results were used by a mining company in northern Chile to improve copper mining processes.
Due to the complexity of extraction mining worldwide, computer models are becoming an essential tool for reducing production costs [
Copper mining in Chile is the most profitable industry and contributes to approximately 12% of world copper production [
Softcomputing is the branch of Artificial Intelligence that groups paradigms and techniques working with incomplete and imprecise information in critical processes, in order for the company to obtain useful solutions for tasks such as prediction or discovery of information or knowledge [
This paper describes the work done with ANN to generate behavior predictions of the pile in the copper leaching domain. The use of ANNs has been used in combination with other techniques commonly used as a predictor in the mining industry, such as linear regression, with the aim of contrasting and enhancing the quality of results. An ANN can be defined as a set of computational units (neurons) that are highly interconnected. Each neuron is also called node which represents the biological neuron, and the connection among them represents the biological neuronal network [
This paper describes the copper recovery prediction process for an extraction mine in northern Chile, using both a statistical model (methods traditionally used in the copper leaching industry) and a neural model known as multilayer perceptron with backpropagation [
The process of applying different techniques to adjust the weights related to each of the input variables of neurons in an ANN is known as learning [
Currently, the applications that include ANN are in all areas of science and engineering, that is, energy production estimation systems [
In [
The SCM Franke Company uses three industrial processes widely known in the copper industry to produce metallic copper via hydrometallurgy: leaching in dynamic cells, solvent extraction, and electrowinning. The goal in these processes is to achieve the highest copper production by saving resources and having the lowest possible environmental impact. In this sense, the company considered carrying out simulations to predict the copper production through leaching processes. The objective was to produce an efficient model (with an accuracy greater than 95%) for the estimation of copper leaching recovery based on historical data.
A previous analysis by the SCM Franke Company conducted to determine which of these processes is the least controlled (number of influencing factors and homogeneity of irrigation) found it to be the leaching process in dynamic stacks. Based on this previous analysis, the parameters to be used in the prediction models and the desired quality level (95%) were identified and employed as the desired adjustment value in the simulation to test the results of the models.
The study summarized the prediction of copper extraction in dynamic stacks, applying mathematical models (statistical model) that require complete information to give precise results and an ANN, which is considered to have advantages over the treatment of incomplete information in terms of generating predictions in industrial production.
Pile leaching copper is a percolation process that operates above ground. The procedure is illustrated in Figure
Process flow diagram of the pile leaching process.
A sprinkler system is installed on top of each heap allowing diluted sulfuric acid to be fed uniformly over the ore. While the solution percolates through the pile, copper is leached out of the ore. The pregnant leaching solution is collected by a drainage system below the pile and is led through a collection ditch into a pond.
The research consists of two stages, the first called “copper recovery modeling” in which models are generated with each of the aforementioned methods and the second stage called “Evaluation” where the results are compared and the quantity and quality of information are obtained.
Both stages are beneficial for the SCM Frank Company in order to understand the leaching process with the characteristics of the ore and to be able to predict the recovery of copper in dynamic stacks, focused on achieving a less than 5% margin of error in estimation. These two stages are possible due to the previous analysis and selection of variables and parameters representing the inputs to the prediction methods; Figure
Work scheme for copper recovery prediction in dynamic stacks.
In order to perform the modeling, the most recent literature in leaching process was considered, taking into account the variables that affect the recovery of copper. In addition, the historical operational results data and process-related pilot testing were taken into account, which we call pilot data. Pilot tests consist of testing columns with strict control measures regarding irrigation rates, acid concentrations in irrigation solutions, and operating cycles, conditions that vary depending on the test to be performed. A case study was generated with a database of approximately 30,000 pieces of data.
For the purpose of this study, the historical database corresponding with industrial and piloting performance from SCM Franke Company was used. The data stored in the database correspond to both plant data operation (weighted values and accumulated daily) and pilot data.
The operating plant data were obtained at a frequency of 4 hours for 1 year. For some periods, the irrigation was stopped on some cells or modules in service; due to these periods, inconsistent results were disregarded and corresponding data was not considered for the data collection process; some “noise” in the system and useless information were disregarded as well. Regarding the pilot data, the method of information collection was the same used for the data of operation.
The next step was to identify the parameters that affect the copper recovery and to form a robust database with this information for use in the preparation and evaluation of process models using different techniques in order to determine one that meets the plant requirements (adjustment greater than 95%) with respect to plant and pilot operating data. The parameters considered in the statistical model are detailed in Table
Entry parameters to the statistical model.
Notation | Name and description | Optimum values |
---|---|---|
|
Monoclass granulometry (refers to the mineral) | Between 11,5 mm and 15 mm |
|
Irrigation rates | Between 14 ( |
|
Total acid added | Between 0,5 g/l and 100 g/l |
|
Pile high | Between 1 m and 5 m |
|
Total copper grade | Between 0,5% and 2% |
|
CO3 grade | Between 0,5% and 10% |
|
Leaching ratio | Less than 15 m3/TMS in pilot and Less than 8 m3/TMS in plant |
|
Operation days | Between 90 and 120 days |
|
Soluble copper grade stacked | 70% of the total copper grade |
A search was performed to find the combination of suitable variables to produce the most successful model for the desired response. The system response was defined as the percentage of copper extraction. To validate the model, the response variable was monitored. The model was monitored in order to obtain the best fit for the operating conditions and variations. When model misalignment was below acceptable (much lower than the desired 95%), the model was readjusted and remade considering the operating ranges that were initially not considered; parameters that were initially not considered were now carried out.
To select the subset of variables that ensure a practical model, coefficients were generated using the Minitab software. In detail, the calculated coefficients were as follows: Vars,
Originating from the preliminary model and utilizing the Minitab tools, three statistical models were generated: linear adjustment model, quadratic adjustment model, and cubic adjustment model. Table
Comparative of
Model |
|
---|---|
Lineal model | 69,8% |
Quadratic model (standard deviation = 9,4) | 89,9% |
Cubic model (standard deviation = 8,3) | 92,3% |
ANN model | 97,9% |
For the lineal model (
To perform the RNA modeling, the MATLAB program was applied. For the network programming, the following parameters were used: 9 income variables (see Table
In the variables selection the following was considered: the experience accumulated by operation experts of the SCM Franke Company, the results of the piloting, bibliography, and the results observed with the statistical models described above. In the ANN configuration, nine input variables were used (see Table
The output layer was formed by a corresponding neuron to the prediction of copper recovery in dynamic piles, equally focusing on achieving a less than 5% error in the prediction estimation. The mathematical expression used in each neuron to obtain the output value is indicated in (
Table
The best value corresponding to the ANN model during the learning phase and validation of the ANN, a 97,9% of adjustment, was obtained. Figure
ANN validation model.
The result obtained through linear modeling was far from the required (89.9% v/s 95%), which indicates that the capacity to make the needed adjustment far exceeds what can be delivered by this type of modeling. The adjustment results of the quadratic model were approximately 22% better than those obtained previously and the
The use of ANN for system modeling led to an improvement from 92.3% to 97.9%, compared to the cubic model. It was observed that the use of ANN achieved the objective of adjusting to fit, which was due to the complex network that this process uses to adjust the system response to the existing parameters.
To achieve these results through the use of ANN, the use of a program such as MATLAB or another advanced calculation program is required. Due to the high complexity of such a modeling technique, it requires a database with an abundance of information so as to give the program the information necessary to evaluate as many cases as possible. On the other hand, ANN has the disadvantage of not being easily detected compared to statistical models, so this could present a problem for new users.
SCM Franke has carried out simulations on other copper production processes and knows from experience that the prediction is much cheaper than the experimental work, but, in this case, our study describes a prediction model that represents the behavior of the leaching plant, considering the variables initially defined with adjustment results higher than 95% obtained.
Accordingly, it was determined that ANN was the best model for SCM Franke mining leach plant, due to the high variability of the existing plant results and tests. The adjustment obtained was 97.9%, which is higher than the adjustment of 95% initially requested.
This study has served to obtain a comparison between prediction models and it can be intuited by the precision of the adjustments that the neural model has potential for use in future copper production prediction process. Furthermore, experience was gained in defining the model of an ANN, which can be used in future process simulations related to the improvement of copper attainment via softcomputing techniques in the SCM Franke Company or in similar companies.
The authors declare that they have no conflicts of interest.
Thanks are due to SCM Franke for supporting this project, specifically for the collaboration on data provision and storing and for the experience made available to the authors to select the parameters and criteria used for modeling.