Optimization of hematite and quartz BIOFLOTATION by AN artificial neural network (ANN)

Merma, Antonio Gutiérrez; Olivera, Carlos Alberto Castañeda; Hacha, Ronald Rojas; Torem, Maurício Leonardo; Santos, Brunno F.

doi:10.1016/j.jmrt.2019.02.022

Cited by 27 publications

(15 citation statements)

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“…13 In order to shorten the whole procedure from isolation to identification of new bacterial strains and to uncover complex, nonlinear relationships in multivariate data, the prediction models obtained using artificial neural networks (ANNs) can be implemented. 14,15 To our knowledge, there have been no investigations based on the application of ANNs to the procedure of isolation and characterization of alkaliphilic microorganisms from soil. ANN models are recognized as a good modelling tool since they provide the empirical solution to the problems from a set of experimental data, and are capable of handling complex systems with nonlinearities and interactions between decision variables.…”

Section: Introductionmentioning

confidence: 99%

Temperature and pH growth profile prediction of newly isolated bacterial strains from alkaline soils

et al. 2019

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BACKGROUND: Soil microorganisms can form complex and varied communities which interact with each other in many different ways depending on environmental conditions. These microbial diversities are accompanied by different metabolic paths and adaptability reflected even in extreme environments. In recent decades, the biodiversity of microbes in extreme environments has been in scientific focus because such specifically adapted bacteria can improve bioremediation processes in industrial and agricultural applications. Instead of the time-consuming process of identification of new bacterial strains from habitats rich in microbiota, artificial neural networks have been proposed as a mapping model for resolving the problem of prediction of microbial behaviour. RESULTS:The occurrence and diversity of alkaliphilic sporogenic bacteria in alkaline soils were investigated. For this purpose, soil samples were collected from various locations: leached soil from the Danube river, cement factory wastewater accumulation, deposit of limestone near the Bešenovo lake and the Beli Majdan cave in the Fruška gora mountain. According to the obtained results, two empirical models were developed that gave a good fit to experimental data and were able to predict successfully the pH and temperature growth profiles of the natural isolates. The artificial neural network models showed a reasonably good predictive capability (overall R 2 for temperature growth profile was 0.727, while the overall R 2 for pH growth profile was 0.906).CONCLUSIONS: The developed mathematical models provided adequate precision for practical study in the microbiology laboratory and scale-up processes for a wide range of laboratory and industrial applications, where specifically adapted microbial communities are needed.

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Section: Introductionmentioning

confidence: 99%

Temperature and pH growth profile prediction of newly isolated bacterial strains from alkaline soils

et al. 2019

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“…A Figura 28 apresenta os espectros de absorbância do FTIR do biossurfactante. A banda de absorção 3346,34 cm -1 pode indicar a presença de grupos hidroxila e grupos amina (NH) (Merma et al, 2017;Hacha et al, 2018;Merma et al, 2019). A banda de absorção de 1629,52 cm -1 pode corresponder aos alcenos (C=C), grupos cetonas (Puelles, 2016).…”

Section: Espectroscopia No Infravermelho -Ftirunclassified

“…A banda de absorção de 1629,52 cm -1 pode corresponder aos alcenos (C=C), grupos cetonas (Puelles, 2016). As bandas de absorção entre 1500 cm -1 e 1300 cm -1 correspondem ao estiramento dos grupos CH 2 e CH 3 (Puelles, 2016;Merma et al, 2019). A banda de absorção 1040,57 cm -1 corresponde às vibrações do grupo funcional alcano (Puelles, 2016).…”

Section: Espectroscopia No Infravermelho -Ftirunclassified

“…As bandas de absorção características da hematita 465,99 cm -1 e 545,42 cm -1 que correspondem as vibrações de estiramento do grupo Fe-O são mantidas. Além disso, novas bandas de absorção são formadas como a banda de absorção 3426,29 cm -1 que pode indicar a presença de grupos hidroxila (Hacha et al, 2018;Merma et al, 2019). A banda de absorção 1084,56 cm -1 pode corresponder ao estiramento assimétrico dos grupos fosfato [PO 2 ] presentes nos fosfolipídios e nos núcleos (Puelles, 2016;Merma et al, 2017;Merma et al, 2019).…”

Section: Espectroscopia No Infravermelho -Ftirunclassified

“…Além disso, novas bandas de absorção são formadas como a banda de absorção 3426,29 cm -1 que pode indicar a presença de grupos hidroxila (Hacha et al, 2018;Merma et al, 2019). A banda de absorção 1084,56 cm -1 pode corresponder ao estiramento assimétrico dos grupos fosfato [PO 2 ] presentes nos fosfolipídios e nos núcleos (Puelles, 2016;Merma et al, 2017;Merma et al, 2019). A banda de absorção a 1632,92 cm -1 pode indicar a presença de grupos alcenos (C=C), grupo carbonila (C = O) e C=N (Puelles, 2016;Merma et al, 2019).…”

Section: Espectroscopia No Infravermelho -Ftirunclassified

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Contribution of bacterial cells as nucleation centers in microbiologically induced CaCO₃ precipitation—A mathematical modeling approach

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The microbiologically induced calcite precipitation (MICP) has been extensively studied for geotechnical engineering through simultaneous action of natural phenomena and engineering processes. The focus of bacterial contribution to the MICP has been directed to calcium carbonate productivity, while the additional bacterial role as a crystal nucleation center was not explained especially from a mathematical prediction modeling point of view. Therefore, this study provides explanations and a mathematical modeling approach of bacterial influence on the MICP induced by newly-isolated ureolytic Bacillus strains and Sporosarcina pasteurii DSM 33. Using the obtained results of low-cost, rapid, and simple assays, artificial neural network modeling was applied for cell surface predispositions, pH changes as well as calcium-involved function in biofilm formation during the MICP, for the first time. Based on the obtained contribution of the alkalophilic/alkaloresistant bacteria, calcite precipitation can be significantly directed by the presence, of ureolytic bacterial cells as nucleation centers during CaCO 3 precipitation as well as their morphology, surface characteristics, potential to form a biofilm, and/or generate pH changes.

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Optimization of hematite and quartz BIOFLOTATION by AN artificial neural network (ANN)

Cited by 27 publications

References 29 publications

Temperature and pH growth profile prediction of newly isolated bacterial strains from alkaline soils

Temperature and pH growth profile prediction of newly isolated bacterial strains from alkaline soils

Flotação De Hematita a Partir Do Rejeito De Minério De Ferro Com O Uso De Biossurfactante Extraído Da Bactéria Rhodoccocus Opacus

Contribution of bacterial cells as nucleation centers in microbiologically induced CaCO₃ precipitation—A mathematical modeling approach

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Optimization of hematite and quartz BIOFLOTATION by AN artificial neural network (ANN)

Cited by 27 publications

References 29 publications

Temperature and pH growth profile prediction of newly isolated bacterial strains from alkaline soils

Temperature and pH growth profile prediction of newly isolated bacterial strains from alkaline soils

Flotação De Hematita a Partir Do Rejeito De Minério De Ferro Com O Uso De Biossurfactante Extraído Da Bactéria Rhodoccocus Opacus

Contribution of bacterial cells as nucleation centers in microbiologically induced CaCO3 precipitation—A mathematical modeling approach

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Contribution of bacterial cells as nucleation centers in microbiologically induced CaCO₃ precipitation—A mathematical modeling approach