Using BP Neural Networks to Prioritize Risk Management Approaches for China’s Unconventional Shale Gas Industry

Dong, Cong; Dong, Xiucheng; Gehman, Joel; Lefsrud, Lianne

doi:10.3390/su9060979

Cited by 14 publications

(3 citation statements)

References 48 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input layer of each neural network is the process parameter corresponding to the type of CNC instruction. The containing layer is determined by empirical formula (12) [38].…”

Section: Energy Consumption Prediction Model Of Cnc Machine Tools Bas...mentioning

confidence: 99%

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

et al. 2021

View full text Add to dashboard Cite

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

show abstract

“…The input layer of each neural network is the process parameter corresponding to the type of CNC instruction. The containing layer is determined by empirical formula (12) [38].…”

Section: Energy Consumption Prediction Model Of Cnc Machine Tools Bas...mentioning

confidence: 99%

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Fossil fuels (especially coal and oil) account for 65% of carbon emissions [1], and China is a country with a fossil-based energy system ( Figure A1), which has committed to peak its carbon emissions by 2030 at the Paris climate change agreement [2]. Fuel switching is a pivotal and vital way to mitigate climate change and environmental pollution [3,4]. Shale gas, a clean-burning and efficient energy resource, has become a hot topic, particularly in regard to the advantages of reducing a country's overdependence on high-pollution energy resources [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

Water Availability Assessment of Shale Gas Production in the Weiyuan Play, China

Xia

Guan

et al. 2019

Sustainability

View full text Add to dashboard Cite

Innovations and improvements in hydraulic fracturing and horizontal well technologies have contributed to the success of the shale gas industry; however, the industry is also challenged by freshwater use and environmental health issues, and this makes precise quantification of water consumption important. The objective of this study was to better understand water sustainability and availability of the projected shale gas from 2018 to 2030 in the Weiyuan play, China. The water footprint framework was used to quantify the potential water use and environmental impacts on different time scales. The results showed that the water use per well ranged from 11,300 to 60,660 m3, with a median of 36,014 m3, totaling ~ 3.44 Mm3 for 97 wells. Yearly evaluation results showed that the gray water footprint was the main contributor and accounted for 83.82% to 96.76%, which was dependent on the different treatment percentage scenario. The monthly environmental impact results indicated that the annual streamflow statistics were more likely to prevent water withdrawal. Water quality issues may be alleviated through recycling and retreatment measures that improve current waste water management strategies. Resource regulators should manage their water resources by matching water demand to water availability or replenishment.

show abstract

“…Fossil fuels (especially coal and oil) account for 65% of carbon emissions [1], and China is a country with a fossil-based energy system (Fig. S1), which has committed to peak its carbon emissions by 2030 at the Paris climate change agreement [2].Fuel switching is a pivotal and vital way to mitigate climate change and environmental pollution [3,4].Shale gas, a clean-burning and efficient energy resource, has become a hot topic, particularly in regard to the advantages of reducing a country's overdependence on high-pollution energy resources [3,5,6]. China's shale gas reserves are 31.57 trillion m 3 , ranking first in the world's shale gas reserves, and are the largest global reserves of technically recoverable shale gas [7,8].…”

Section: Introductionmentioning

confidence: 99%

Water Availability Assessment of Shale Gas Production in the Weiyuan Play, China

Wu¹,

Xia²,

Baoshan³

et al. 2019

Preprint

View full text Add to dashboard Cite

Innovations and improvements in hydraulic fracturing and horizontal well technologies have contributed to the success of the shale gas industry; however, the industry is also challenged by freshwater use and environmental health issues. Increasing water impact makes precise quantification of water consumption important. The objective in this study was to better understand water sustainability and availability of the projected shale gas from 2018 to 2030 in the Weiyuan play, China. The water footprint framework was used to quantify the potential water use and environmental impacts on different time scales. The results showed that the water use per well ranged from 11351.3 to 60664.73 m3, with a median of 36013.94 m3, totaling ~3.44 Mm3 for 97 wells. Yearly evaluation results showed that the gray water footprint was the main contributor and accounted for 83.82% to 96.76%, which was dependent on different scenarios of treatment percentages. The monthly environmental impact results indicated that the annual streamflow statistics were more likely to prevent water withdrawal. Water quality issues may be alleviated through recycling and retreatment measures that improve current waste water management strategies. Resource regulators should manage their water resources by matching water demand to water availability or replenishment.

show abstract

Using BP Neural Networks to Prioritize Risk Management Approaches for China’s Unconventional Shale Gas Industry

Cited by 14 publications

References 48 publications

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Water Availability Assessment of Shale Gas Production in the Weiyuan Play, China

Water Availability Assessment of Shale Gas Production in the Weiyuan Play, China

Contact Info

Product

Resources

About