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In this article, the optimization of the specific urease activity (SUA) and the calcium carbonate (CaCO3) using microbially induced calcite precipitation (MICP) was compared to optimization using three algorithms based on machine learning: random forest regressor, artificial neural networks (ANNs), and multivariate linear regression. This study applied the techniques in two existing response surface method (RSM) experiments involving MICP technique. Random forest-based models and artificial neural network-based models were submitted through the optimization of hyperparameters via cross-validation technique and grid search, to select the best-optimized model. For this study, the random forest-based algorithm is aimed at having the best performance of 0.9381 and 0.9463 in comparison to the original r2 of 0.9021 and 0.8530, respectively. This study is aimed at exploring the capability of using machine learning-based models in small datasets for the purpose of optimization of experimental variables in MICP technique and the meaningfulness of the models by their specificities in the small experimental datasets applied to experimental designs. This study is aimed at exploring the capability of using machine learning-based models in small datasets for experimental variable optimization in MICP technique. The use of these techniques can create prerogatives to scale and mitigate costs in future experiments associated to the field.
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Redes Neurais de Computação , Algoritmo Florestas Aleatórias , Algoritmos , Aprendizado de Máquina , Carbonato de CálcioRESUMO
Significant amounts of mining tailings are generated and disposed of every year in dams, leading to potentially serious environmental and safety problems. To identify alternatives for the disposal of these wastes, research works involving their potential application as precursors in the development of alkaline-activated materials have been published in recent years. In this context, the objective of this paper is to present an overview of the main contributions already made on the subject, identified through a bibliometric review and content analysis in the Scopus and Web of Science databases. There was an exponential growth of interest in the subject in the period 2019-2021, when more than 50% of the papers were published. The most used tailings and sub-areas of research were also identified.
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Álcalis , Bibliometria , Bases de Dados FactuaisRESUMO
Knowledge of the partition mechanisms in the agrochemical environment is fundamental for understanding their behavior within an ecosystem and mitigating possible adverse effects of these products. In this review, the objective was to present the main transport mechanisms, physical-chemical properties, and atmospheric monitoring methodologies of the most diverse types of agrochemicals used in agriculture that can reach the atmosphere and affect different compartments. It has been verified that volatilization is one of more considerable significance of the various forms of transport since a significant part of the applied pesticides can volatilize in a few days. As for monitoring these compounds in the atmosphere, both passive and active sampling have their advantages and disadvantages. Passive samplers allow sampling in large quantities and at remote locations, in addition to making continuous measurements, while active samplers have the advantage of being able to detect low concentrations and continuously. Since a significant portion of the applied pesticides is directed to the atmosphere, monitoring makes it possible to understand some properties of the pesticides present in the air. This monitoring can be done from different existing methodologies based on adopted criteria and existing technical standards. Graphical representation of mobility and environmental monitoring of atmospheric pollutants from pesticides.
RESUMO
Exposure of geomaterials to acidic leachates may compromise their structure and functionality due to changes in physicochemical, mineralogical, and hydraulic behavior. The literature identifies the need to evaluate changes in a pure state and in conditions of extreme acidity. This study aimed to evaluate changes in the chemical, mineralogical, and morphological properties of Osorio fine uniform sand (OFS), basalt residual soil (BRS), kaolin (KAO), and bentonite (BEN) exposed to sulfuric acid in concentrations of 0.00 mol/L (distilled water), 0.01 mol/L, and 1.00 mol/L. The tested samples were characterized using X-ray fluorescence spectrometry, X-ray diffraction, thermogravimetry, differential scanning calorimetry, and scanning electron microscopy. The acid attack on geomaterials by contact with the solution 1.00 mol/L has resulted in the solubilization of some constituent minerals, as well as the formation of sulfate minerals, changes in the water dehydration peak in the pores, and mass loss. The morphology of the sand and bentonite particles did not change with exposure to sulfuric acid. The acidic attack resulted in changes in the morphology of the particles for BRS and KAO. The results of this study are important for determining operational parameters of waste containment systems and contaminated areas, as well as for applying geomaterials as founding materials.