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

Golmohammadi, Hassan; Rashidi, Alimorad; Safdari, Seyed Jaber

doi:10.2298/ciceq120403066g

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…A quantitative structure-property relationship study based on partial least squares method and the results revealed that the developed model shows high reliability and good predictivity for ferric iron prediction in the bioleaching system. 24 Pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution was predicted efficiently with the use of ANNs. Here, the inputs of the network were ozone concentration, Fe 3+ concentration, solid-liquid ratio, aeration (O 2 ), leaching duration, and temperature whereas the percentage of iron extracted from the gold concentrate is the output of the network.…”

Section: Abstract Electronic Waste Printed Circuit Boards Bimentioning

confidence: 99%

Neural network prediction of bioleaching of metals from waste computer printed circuit boards using Levenberg‐Marquardt algorithm

Annamalai

Gurumurthy

2020

Computational Intelligence

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The applicability of artificial neural network (ANN) to predict the bioleaching of metals using from computer printed circuit boards (CPCB) and the influence of process parameters were studied. The influence of process parameters initial pH (1.6-2.4), pulp density (2%-13%), and the initial volume of Inoculum (5%-25%) were investigated on the rate of bioleaching of metals from CPCB. Network inputs were fed as initial pH, pulp density, and inoculum volume and with the extraction of Cu, Ag, and Au as output. The ANN was developed using the Levenberg-Marquardt algorithm and trained for modeling and prediction. The most fitting architectures for Cu, Ag, and Au were [

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Section: Abstract Electronic Waste Printed Circuit Boards Bimentioning

confidence: 99%

Neural network prediction of bioleaching of metals from waste computer printed circuit boards using Levenberg‐Marquardt algorithm

Annamalai

Gurumurthy

2020

Computational Intelligence

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“…Utilizing backpropagation ANN, prediction models were constructed and applied in sintering pot experiments to optimize humidity and fuel ratio, ultimately improving sintering drum strength. Golmohammadi et al [ 29 ] developed a Quantitative Structure–Property Relationship (QSPR) using Partial Least Squares (PLS) and ANN to predict the precipitation of trivalent iron during bioleaching. The neural network model exhibited reliable and accurate predictive capabilities during the bioleaching process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Biomass–Humic Acid Limonite Pellets Using Artificial Neural Network Model

et al. 2023

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Due to the detrimental impact of steel industry emissions on the environment, countries worldwide prioritize green development. Replacing sintered iron ore with pellets holds promise for emission reduction and environmental protection. As high-grade iron ore resources decline, research on limonite pellet technology becomes crucial. However, pellets undergo rigorous mechanical actions during production and use. This study prepared a series of limonite pellet samples with varying ratios and measured their compressive strength. The influence of humic acid on the compressive strength of green and indurated pellets was explored. The results indicate that humic acid enhances the strength of green pellets but reduces that of indurated limonite pellets, which exhibit lower compressive strength compared to bentonite-based pellets. Furthermore, artificial neural networks (ANN) predicted the compressive strength of humic acid and bentonite-based pellets, establishing the relationship between input variables (binder content, pellet diameter, and weight) and output response (compressive strength). Integrating pellet technology and machine learning drives limonite pellet advancement, contributing to emission reduction and environmental preservation.

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“…Recently, quantitative structure-property relationship (QSPR) method was used in many fields to extract and link chemicals properties to their molecular structures [22][23][24][25][26][27][28][29][30]. In QSPR modelling different computational techniques have been used, such as multiple linear regression (OLS or MLR), PLS, ANN, SVR and ANFIS [31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

Laidi

Abdallah

Si‐Moussa

et al. 2021

CI&CEQ

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As quantitative structure-property relationship (QSPR) technique provides a suitable tool to predict the critical micelle concentration (CMC) of Gemini surfactants from their structure descriptors. In this study, a comparative work was conducted to model the CMC property of 211 diverse Gemini surfactants based on their structural characteristics using linear and non-linear quantitative structure-property relationship models. Least squares model (OLS) and partial least squares (PLS) against k-nearest neighbours regression model (KNN), artificial neural network (ANN) and support vector regression (SVR) have been developed to model the CMC. Molecular descriptors were calculated and screened to remove unsuitable descriptors and improve the learning. Results indicate that the improved performance of support vector regression when the hyper-parameters are optimized using Dragonfly algorithm (SVR-DA) was highly capable of predicting the pCMC (-logCMC) values with an average absolute relative deviation (AARD) of 0.666 and coefficient of determination (R?) of 0.9971 for the global dataset.

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

Cited by 8 publications

References 27 publications

Neural network prediction of bioleaching of metals from waste computer printed circuit boards using Levenberg‐Marquardt algorithm

Neural network prediction of bioleaching of metals from waste computer printed circuit boards using Levenberg‐Marquardt algorithm

Prediction of Compressive Strength of Biomass–Humic Acid Limonite Pellets Using Artificial Neural Network Model

CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

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