Data-driven system efficiency prediction and production parameter optimization for PW-LHM

Tan, Chaodong; Deng, Hanwen; Feng, Ziming; Li, Bing; Zhen-hua, Peng; Feng, Gang

doi:10.1016/j.petrol.2021.109810

Cited by 8 publications

(1 citation statement)

References 10 publications

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“…We used the PCC method to eliminate irrelevant and highly correlated feature values of PAEs and retain the main features for model construction [73] to reduce the feature value dimension while avoiding the problems of overfitting and low training efficiency [74]. The PCC is defined as the quotient of covariance and standard deviation between two variables [75] and can be calculated as follows:…”

Section: Dimensionality Reduction Of the Eigenvalues Of Paes Using Pe...mentioning

confidence: 99%

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis

Li,

Yang,

et al. 2023

IJMS

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Phthalate esters (PAEs) are widely exposed in the environment as plasticizers in plastics, and they have been found to cause significant environmental and health hazards, especially in terms of endocrine disruption in humans. In order to investigate the processes underlying the endocrine disruption effects of PAEs, three machine learning techniques were used in this study to build an adverse outcome pathway (AOP) for those effects on people. According to the results of the three machine learning techniques, the random forest and XGBoost models performed well in terms of prediction. Subsequently, sensitivity analysis was conducted to identify the initial events, key events, and key features influencing the endocrine disruption effects of PAEs on humans. Key features, such as Mol.Wt, Q+, QH+, ELUMO, minHCsats, MEDC-33, and EG, were found to be closely related to the molecular structure. Therefore, a 3D-QSAR model for PAEs was constructed, and, based on the three-dimensional potential energy surface information, it was discovered that the hydrophobic, steric, and electrostatic fields of PAEs significantly influence their endocrine disruption effects on humans. Lastly, an analysis of the contributions of amino acid residues and binding energy (BE) was performed, identifying and confirming that hydrogen bonding, hydrophobic interactions, and van der Waals forces are important factors affecting the AOP of PAEs’ molecular endocrine disruption effects. This study defined and constructed a comprehensive AOP for the endocrine disruption effects of PAEs on humans and developed a method based on theoretical simulation to characterize the AOP, providing theoretical guidance for studying the mechanisms of toxicity caused by other pollutants.

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Section: Dimensionality Reduction Of the Eigenvalues Of Paes Using Pe...mentioning

confidence: 99%

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis

Li,

Yang,

et al. 2023

IJMS

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A novel hybrid efficiency prediction model for pumping well system based on MDS–SSA–GNN

Ma,

Dong

2024

Energy Science & Engineering

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The prediction of the efficiency of oil well pumping systems plays an important role in optimizing the energy efficiency parameters of these systems. Currently, the prediction of oil well pumping system efficiency relies primarily on mechanistic models, but these models are often overly complex in predicting efficiency. Some researchers have attempted to use deep learning to predict system efficiency, but due to insufficient consideration of influencing factors and the causal relationships between these factors and system efficiency, they often include irrelevant variables as influencing factors, leading to less accurate prediction models. In this paper, a hybrid model (MDS–SSA–GNN) is proposed for the prediction of pumping well system efficiency. The model consists of six parts: Pearson's product moment correlation coefficient (PPMCC), multidimensional scaling (MDS) transform, maximum–minimum normalization, sparrow optimization algorithm (SSA), graph neural network (GNN), and maximum–minimum inverse normalization. First, the size of the correlation coefficient between each influencing factor and the system efficiency is quantitatively calculated by using PPMCC. Second, the main influencing factors are downscaled by using MDS and normalized based on the principle of maximum–minimum normalization. Third, the GNN algorithm is used for the prediction of the pumping unit system efficiency, and the SSA algorithm is used for the optimization of the initial values of the network parameters. Finally, the prediction results are obtained by the maximum–minimum antinormalization. To validate the model's accuracy, this study randomly selected 100 actual oil wells for comparative analysis and analyzed the impact of structural parameters of the hybrid algorithm on the prediction accuracy of system efficiency. The analysis results demonstrate that the proposed model can effectively predict system efficiency and has a certain role in improving the accuracy of oil well pumping system efficiency predictions.

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Synthesis of environmentally friendly neonicotinoid insecticide with proper functional properties by theoretical methodologies

Zhao,

Yang,

Chen

et al. 2023

Ecotoxicology and Environmental Safety

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Data-driven system efficiency prediction and production parameter optimization for PW-LHM

Cited by 8 publications

References 10 publications

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis

A novel hybrid efficiency prediction model for pumping well system based on MDS–SSA–GNN

Synthesis of environmentally friendly neonicotinoid insecticide with proper functional properties by theoretical methodologies

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