Dynamic Response Evaluation for Single-Hole Bench Carbon Dioxide Blasting Based on the Novel SSA–VMD–PCC Method

Zhou, Shengtao; Yao, Yingkang; Luo, Xuedong; Jiang, Nan; Niu, Shuaishuai

doi:10.1061/(asce)gm.1943-5622.0002589

Cited by 20 publications

(8 citation statements)

References 37 publications

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“…The use of IVMD, optimized using the SSA, for the decomposition of PV power data marks a significant advancement in the preprocessing stage of forecasting [53][54][55]. This methodological choice allows for a refined extraction of the intrinsic modes within the power generation data, facilitating a more detailed and accurate analysis of the power output fluctuations.…”

Section: Discussionmentioning

confidence: 99%

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Fu,

Zhang,

Xie

2024

Electronics

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Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.

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Section: Discussionmentioning

confidence: 99%

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Fu,

Zhang,

Xie

2024

Electronics

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“…Considering the above shortcomings, new fracturing methods such as pulse fracturing, circulating thermochemical fluid fracturing, and CO 2 fracturing have been proposed, and they have achieved good effects in recent years. Among them, CO 2 fracturing is a much better and more effective way to enhance the permeability of coal seams, as there have been various CO 2 fracturing applications in low-permeability coal seams. − Research on the fracturing and propagation mechanism of coal during CO 2 fracturing plays a vital role in improving the recovery of coalbed methane and the CO 2 injection rate. This technique can also reduce gas disasters by degassing …”

Section: Introductionmentioning

confidence: 99%

Strain Field Characteristics of Coal-like Material under CO₂ Fracturing

Wang,

Pan,

Fujii

2023

ACS Omega

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Considering the problems related to hydrofracturing, CO2 fracturing is a more effective way to enhance the permeability of coal seams. Conventional research has focused on the dynamic behavior of coal and rock under CO2 fracturing. There is a lack of quantitative descriptions of the fracturing stages. Therefore, CO2 fracturing experiments were carried out with coal-like material at several pressures. The maximum principal strain field was obtained by analyzing the surface images of the specimens. Nine characteristic parameters of typical grayscale and texture images were calculated from the strain field. The correlation between the maximum principal strain and the characteristic parameters was examined using gray relational analysis, and the effects of pressure on those parameters were investigated. We obtained some interesting results, for example, the standard deviation increased from 50 to 100, and kurtosis decreased from −1.0 to −1.5 with the fracturing pressure. The thresholds of the characteristic function F at different fracturing stages were determined, too. This research provides a basis for designing the drilling hole location and determining the millisecond delay of multihole fracturing, as well as for automatically dividing the fracturing stages with artificial intelligence based on the maximum principal strain field.

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“…Their tensile strength is approximately 3-4 times higher than that of ordinary rebar, and their density is approximately 1/3-1/4 of that of prestressed steel bars. They can not only resist static loads, but also resist dynamic loads including fluctuating pressure (Zheng et al, 2022), explosion (Feng et al, 2017), and blast vibration (Zhou et al, 2023). Moreover, since they are resistant to acid and alkali corrosion (Inman et al, 2017;Xu et al, 2019;Li et al, 2021;Mohamed et al, 2021), BFRP bars could provide a reliable way to solve the corrosion of steel prestressed anchor cables.…”

Section: Introductionmentioning

confidence: 99%

Experimental study on the mechanical properties and failure modes of BFRP bar anchor systems under static tension loading

et al. 2023

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Basalt fiber-reinforced polymer bars are lightweight composite materials with high strength, low density, and excellent corrosion resistance. The anchor system made from basalt fiber-reinforced polymer bars is worthy of being developed and expected to be used in rock anchoring projects. In this work, four different basalt fiber-reinforced polymer anchor systems were designed, the influences of different design parameters on the ultimate bearing capacity of the anchor system were investigated through tension tests, and the failure modes of different anchor systems were elucidated. The test results indicated that failure modes, such as the transverse fracture of these bars and debonding of the bonding medium, were widely present in the wedge-modified anchor system and the steel-pipe-protected anchor system. These two anchor systems performed poorly with the wedge anchorage, whereas the basalt fiber-reinforced polymer bars protected by seamless steel pipes burst under the tension imposed by a universal testing machine. The threaded steel-pipe-bonded anchor system and the steel strand–basalt fiber-reinforced polymer bar composite anchor system had maximum anchorage efficiency coefficients of 97.7% and 98.5%, respectively. The bars in the corresponding test groups all exhibited burst failure, indicating that these two anchoring structures achieved effective anchorage of the basalt fiber-reinforced polymer bars.

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Dynamic Response Evaluation for Single-Hole Bench Carbon Dioxide Blasting Based on the Novel SSA–VMD–PCC Method

Cited by 20 publications

References 37 publications

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Strain Field Characteristics of Coal-like Material under CO₂ Fracturing

Experimental study on the mechanical properties and failure modes of BFRP bar anchor systems under static tension loading

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Dynamic Response Evaluation for Single-Hole Bench Carbon Dioxide Blasting Based on the Novel SSA–VMD–PCC Method

Cited by 20 publications

References 37 publications

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Strain Field Characteristics of Coal-like Material under CO2 Fracturing

Experimental study on the mechanical properties and failure modes of BFRP bar anchor systems under static tension loading

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Strain Field Characteristics of Coal-like Material under CO₂ Fracturing