Biologically Fe2+ oxidizing fluidized bed reactor performance and controlling of Fe3+ recycle during heap bioleaching: an artificial neural network-based model

Abstract

The performance of a biological Fe(2+) oxidizing fluidized bed reactor (FBR) was modeled by a popular neural network-back-propagation algorithm over a period of 220 days at 37 degrees C under different operational conditions. A method is proposed for modeling Fe(3+) production in FBR and thereby managing the regeneration of Fe(3+) for heap leaching application, based on an artificial neural network-back-propagation algorithm. Depending on output value, relevant control strategies and actions are activated, and Fe(3+) production in FBR was considered as a critical output parameter. The modeling of effluent Fe(3+) concentration was very successful, and an excellent match was obtained between the measured and the predicted concentrations.

Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: Performance and artificial neural network (ANN) modelling studies

“…In the ANN modelling of CSTR, the procedure given by Ozkaya et al [13] was followed. A neural network is defined as a system of simple processing elements, called neurons, which are connected to a network by a set of weights (Fig.…”

Modeling effluent heavy metal concentrations in a bioleaching process using an artificial neural network technique

“…The prediction models such as ANNs demonstrate stronger and more realistic behavior in predicting the complex non-linear and ever changing conditions of biological processes. ANN applications were previously used in some studies to predict the leaching of metals for bioleaching processes with similar or different microorganism groups and sludges from different industries and applications (Du et al, 1994;Acharya et al, 2006;Ozkaya et al, 2008;Liu et al, 2008;Jorjani et al, 2007;Nurmi et al, 2010;Laberge et al, 2000). In this study, a three layer feed forward MLP neural network model was developed to predict the effluent heavy metal concentrations of bioleaching technique involving A. ferrooxidans in dewatered metal plating sludge containing no sulfide or sulfate compounds.…”

“…The application of ANN to issues related to wastewater treatment and water resources conservation is rapidly gaining popularity due to their immense power and potential in the mapping of nonlinear system data. In the context of hydrological forecasting, recent studies have reported that ANN technique may offer a promising alternative for bioleaching (Acharya et al, 2006;Ozkaya et al, 2008;Liu et al, 2008;Jorjani et al, 2007;Nurmi et al, 2010;Laberge et al, 2000), rainfall-runoff modeling (Lin and Chen, 2004), stream-flow prediction (Raman and Sunilkumar, 1995;Kisi, 2004a), suspension of sediments (Kisi, 2004b), water resources (Cobaner et al, 2008), reservoir inflow forecasting (Coulibaly et al, 2005) and treatment of wastewater (Elmolla et al, 2010;Pai et al, 2009;Chen and Lo, 2010). The variation in the characteristics of a bioleaching system may be non-linear and multivariate, and the variables involved may have complex inter-relationships.…”

“…The authors have demonstrated that a neural network with input variables of type of sludge, initial metal concentrations and pH could satisfactorily predict heavy metal solubilization. Since the 1990s, the studies on biological systems and bioleaching has shown that ANN-based models demonstrated better prediction performance for complex biological systems with numerous non-linearly correlated parameters as compared to conventional mathematical and statistical models (Acharya et al, 2006;Ozkaya et al, 2008;Liu et al, 2008;Jorjani et al, 2007;Nurmi et al, 2010;Laberge et al, 2000). In this study, a new ANN model was proposed for the estimation of heavy metal concentrations in a completely mixed batch reactor as an alternative to the conventional methods.…”

Prediction of ferric iron precipitation in bioleaching process using partial least squares and artificial neural network

“…They also modeled the performance of a biological Fe 2+ oxidizing fluidized bed reactor (FBR) by a popular neural network-back-propagation algorithm under different operational conditions [16]. Yetilmezsoy and Demirel used a three-layer artificial neural network (ANN) model to predict the efficiency of Pb(II) ions removal from aqueous solution by Antep pistachio (Pistacia vera L.) shells based on 66 experimental sets obtained in a laboratory batch study [17].…”