Adsorption behaviors of shale oil in kerogen slit by molecular simulation

Yang, Yongfei; Liu, Jie; Ye, Jun; Kou, Jianlong; Li, Zheng; Wu, Ta‐You; Zhang, Kai; Zhang, Lei; Sun, Hai

doi:10.1016/j.cej.2020.124054

Cited by 181 publications

(116 citation statements)

References 39 publications

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“…19,20 Computation simulation, especially atomistic and molecular modeling, enables visualization of the displacement process and can provide the atomistic details of fluid flow in confined channel. [21][22][23][24][25][26] Wu et.al 27 investigated the flow of water confined in nanopores with different wettability and dimensions by molecular dynamic (MD) simulations. An accurate model was proposed to calculate fluid flux and compared the results with theoretical analysis.…”

Section: Introductionmentioning

confidence: 99%

Insight into the pressure-induced displacement mechanism for selecting efficient nanofluids in various capillaries

Wang

Zhang

2020

Environ. Sci.: Nano

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Section: Introductionmentioning

confidence: 99%

Insight into the pressure-induced displacement mechanism for selecting efficient nanofluids in various capillaries

Wang

Zhang

2020

Environ. Sci.: Nano

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“…As discussed by many scholars, intelligence techniques have a high capability to undertake non-linear and complicated calculations [7][8][9][10][11][12][13][14]. A large number artificial intelligence-based practices are studied, for example, in the subjects of environmental concerns [15][16][17][18][19][20][21], sustainability [22], quantifying climatic contributions [23], pan evaporation and soil precipitation prediction [22,24,25], air quality [26], optimizing energy systems [27][28][29][30][31][32][33][34], water and groundwater supply chains [17,[35][36][37][38][39][40][41][42][43], natural gas consumption [44], face or particular pattern recognition [23,[45][46][47][48][49], image classification and processing …”

Section: Introductionmentioning

confidence: 99%

Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen

Moayedi¹,

Mosavi²

2021

Preprint

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The great importance of estimating dissolved oxygen (DO) dictates utilizing proper evaluative models. In this work, a multi-layer perceptron (MLP) network is trained by three capable metaheuristic algorithms, namely multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE) for predicting the DO using the data of the Klamath River Station, Oregon, US. The records (DO, water temperature, pH, and specific conductance) belonging to the water years 2015 - 2018 (USGS) are used for pattern analysis. The results of this process showed that all three hybrid models could properly infer the DO behavior. However, the BHA and SCE accomplished this task by simpler configurations. Next, the generalization ability of the developed patterns is tested using the data of the 2019 water year. Referring to the calculated mean absolute errors of 1.0161, 1.1997, and 1.0122, as well as Pearson correlation coefficients of 0.8741, 0.8453, and 0.8775, the MLPs trained by the MVO and SCE perform better than the BHA. Therefore, these two hybrids (i.e., the MLP-MVO and MLP-SCE) can be satisfactorily used for future applications.

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“…With recent advances in computational intelligence, many scholars have replaced traditional methods with economical and accurate machine learning , deep learning [1][2][3][4][5][6][7], decision making [8; 9], and artificial intelligence-based tools [10][11][12][13][14]. These novel approximation techniques are well employed in various engineering field such as in evaluating the environmental concerns [15][16][17][18][19][20][21][22][23][24][25], implications for natural environmental [26][27][28][29][30][31][32][33][34], water resources management [35][36][37][38][39][40][41], energy efficiency [42][43][44][45][46][47][48]…”

Section: Introductionmentioning

confidence: 99%

A Water Cycle-based Error Minimization Technique in Predicting Bearing Capacity of Foundation

Moayedi

Mosavi

2021

Preprint

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Selecting the appropriate training technique is a significant step in utilizing intelligent approaches. It becomes even more important when it comes to critical problems like analyzing the bearing capacity of foundations. This study investigates the feasibility of a capable metaheuristic algorithm, called water cycle algorithm (WCA), for training a multi-layer perceptron (MLP). The WCA-MLP is applied to a large finite element dataset to predict the settlement. The results of this model are compared with electromagnetic field optimization (EFO) and shuffled complex evolution (SCE) benchmarks. With reference to the obtained Pearson correlation factors (larger than 0.88 in all stages), all employed models are suitable for the mentioned objective. Moreover, it was observed that the training error of the WCA was 5.84 and 3.89% smaller than the EFO and SCE, respectively. Likewise, the accuracy of the WCA-MLP was 1.85 and 2.04% larger in the testing phase. Also, a predictive equation is finally elicited for practical applications in compatible circumstances.

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Adsorption behaviors of shale oil in kerogen slit by molecular simulation

Cited by 181 publications

References 39 publications

Insight into the pressure-induced displacement mechanism for selecting efficient nanofluids in various capillaries

Insight into the pressure-induced displacement mechanism for selecting efficient nanofluids in various capillaries

Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen

A Water Cycle-based Error Minimization Technique in Predicting Bearing Capacity of Foundation

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