## 1 Introduction  
Bieleaching is a way of metal extraction via biogenically produced metabolites and thus considered as a green alternative to chemical leaching processes [1]. The application of microbial processes to extract metals from nearly insoluble ones is commonly referred to as bieleaching, and understanding of this bacterial leaching has been rapidly established over the past decades. The main metals extracted include copper, cobalt, nickel, zinc, and uranium [2]. Although bieleaching is an eco-friendly technology for the recovery of useful metals, it is difficult to predict due to various factors affecting the trend [3]. According to our previous studies, the tendency of bieleaching varied greatly depending on many conditions such as the origin of microorganisms, the composition of minerals, and the temperature.  
Artificial neural network (ANN) is a data-driven self-adaptive technique, which does not require an understanding of the complex nature of the underlying processes [4]. The ANN architecture is composed of input layers, hidden layers, and output layers along with neurons (perceptrons) to model nonlinear complex systems. The neurons are non-linearly connected to the neurons at the next layer via transfer functions, weights, and biases [5]. The ANN model produces the required response via modification of weights and biases of neurons in the network [6]. In ANN model development, the training process is initiated by generating output values from input values through internal calculations. Based on the differences between calculated output values and observed (target) output values, backpropagation is performed to reduce these differences by adjusting the weights and biases.  
This training process is called feedforward backpropagation and is the most widely used method in ANN architecture [7]. This nature of ANN gives efficient results in the prediction of its output with phenomena for which it is difficult to understand the interrelationship between inputs and outputs. Therefore, ANN can be used to predict the bioleaching when it is difficult to clarify the effects of various factors.  
The prediction of metal bioleaching using ANN has been reported by several researchers. [PERSON] [8] used neural network procedures to predict the metal (Cu, Zn, and Cd) solubilization percentages in municipal sludge treated with a continuous process using _Thiobacillus ferrooxidans_. [PERSON] [9] optimized the amount of iron removal by bioleaching of kaolin sample with high iron impurities with _Aspergillus niger_. [PERSON] [10] proposed the application of ANN to predict the effect of operating parameters on the dissolution of Cu, Mo, and Re from molybden concentrates via mesoacidophilic bioleaching. The initial pH, solid concentration, inoculation percent, and time (days) were used as input operating parameters for prediction. [PERSON] [11] predicted Mo bioleaching using ANN from experimental data of spent catalyst using _Escherichia coli_ and predicted bioleaching using ANN.  
The aim of this study was to predict the leachate's dynamic characteristics from bioleaching in batch conditions using ANN modeling. Bioleaching data used in this study were collected from our previous eight studies about bioleaching [12; 13; 14; 15; 16; 17; 18; 19]. Most of the results of the eight studies were used for ANN learning and optimization. We also evaluated the predictability of the optimized ANN model by adapting the selected one result for each output variable.