This paper explores the application of Building Information Modeling(BIM)technology in pipeline collision detection and optimization for a station and operation section project of Line 2 in a specific city.By leveragi...This paper explores the application of Building Information Modeling(BIM)technology in pipeline collision detection and optimization for a station and operation section project of Line 2 in a specific city.By leveraging BIM for 3D modeling,the study facilitates the identification of pipeline conflicts,enabling comprehensive optimization of the pipeline layout.Navisworks software is used for visualizing the model,providing an intuitive platform for detecting clashes and refining the pipeline design.The proposed BIM-based approach not only enhances construction efficiency by reducing rework and conflicts but also improves project quality through more accurate,coordinated designs.While the focus is on the construction industry,the methods discussed are applicable to various fields,offering broader potential for improving integration and efficiency in other types of construction projects.展开更多
Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-p...Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-pressure flows.Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells,such as the precise planning of airflow paths,pressure distribution,and compression.This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions.The strategy aims to improve production stability and optimize system efficiency.The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization,such as reservoir depletion,wellbore conditions,and equipment performance.Additionally,the model predicts the timing and impact of liquid loading,which can significantly affect production.The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions,while the strategy involves adjusting compressor settings,optimizing flow rates,and planning pressure distribution across the network to maximize productivity while maintaining system stability.By implementing these strategies,this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system.The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields,ensuring more robust and sustainable pipeline operations.展开更多
In order to reduce the carbon emissions of natural gas pipelines,based on the background of different energy structures,this paper proposes a general low carbon and low consumption operation model of natural gas pipel...In order to reduce the carbon emissions of natural gas pipelines,based on the background of different energy structures,this paper proposes a general low carbon and low consumption operation model of natural gas pipelines,which is used to fine calculate the carbon emissions and energy consumption of natural gas pipeline.In this paper,an improved particle swarm optimization(NHPSO-JTVAC)algorithm is used to solve the model and the optimal scheduling scheme is given.Taking a parallel pipeline located in western China as an example,the case is analyzed.The results show that after optimization,under the existing energy types,the pipeline system can reduce 31.14%of carbon emissions,and after introducing part of new energy,the pipeline system can reduce 34.02%of carbon emissions,but the energy consumption has increased.展开更多
As the proportion of natural gas consumption in the energy market gradually increases,optimizing the design of gas storage surface system(GSSS)has become a current research focus.Existing studies on the two independen...As the proportion of natural gas consumption in the energy market gradually increases,optimizing the design of gas storage surface system(GSSS)has become a current research focus.Existing studies on the two independent injection pipeline network(InNET)and production pipeline network(ProNET)for underground natural gas storage(UNGS)are scarce,and no optimization methods have been proposed yet.Therefore,this paper focuses on the flow and pressure boundary characteristics of the GSSS.It constructs systematic models,including the injection multi-condition coupled model(INM model),production multi-condition coupled model(PRM model),injection single condition model(INS model)and production single condition model(PRS model)to optimize the design parameters.Additionally,this paper proposes a hybrid genetic algorithm based on generalized reduced gradient(HGA-GRG)for solving the models.The models and algorithm are applied to a case study with the objective of minimizing the cost of the pipeline network.For the GSSS,nine different condition scenarios are considered,and iterative process analysis and sensitivity analysis of these scenarios are conducted.Moreover,simulation scenarios are set up to verify the applicability of different scenarios to the boundaries.The research results show that the cost of the InNET considering the coupled pressure boundary is 64.4890×10^(4) CNY,and the cost of the ProNET considering coupled flow and pressure boundaries is 87.7655×10^(4) CNY,demonstrating greater applicability and economy than those considering only one or two types of conditions.The algorithms and models proposed in this paper provide an effective means for the design of parameters for GSSS.展开更多
Solution styrene-butadiene rubber(SSBR)finds wide applications in high performance tire design and various other fields.This study aims to create a quantitative structure–property relationship(QSPR)model linking SSBR...Solution styrene-butadiene rubber(SSBR)finds wide applications in high performance tire design and various other fields.This study aims to create a quantitative structure–property relationship(QSPR)model linking SSBR's glass transition temperature(Tg)to its structural properties.A dataset of 68 sets of data from published literature was compiled to develop a predictive machine learning model for SSBR's structural design and synthesis using small sample sizes.To tackle small sample sizes,a framework combining generative adversarial networks(GAN)and the Tree-based Pipeline Optimization Tool(TPOT)is proposed.GAN is first used to generate additional samples that mirror the original dataset's distribution,expanding the dataset.The TPOT is then applied to automatically find the best model and parameter combinations,creating an optimal predictive model for the mixed dataset.Experimental results show that using GAN to enlarge the dataset and TPOT regression models significantly enhances model performance,increasing the R2 value from 0.745 to 0.985 and decreasing the RMSE from 7.676 to 1.569.The proposed GAN–TPOT framework demonstrates the potential of combining generative models with automated machine learning to improve materials science research.This combination accelerates research and development processes,enhances prediction and design accuracy,and introduces new perspectives and possibilities for the field.展开更多
文摘This paper explores the application of Building Information Modeling(BIM)technology in pipeline collision detection and optimization for a station and operation section project of Line 2 in a specific city.By leveraging BIM for 3D modeling,the study facilitates the identification of pipeline conflicts,enabling comprehensive optimization of the pipeline layout.Navisworks software is used for visualizing the model,providing an intuitive platform for detecting clashes and refining the pipeline design.The proposed BIM-based approach not only enhances construction efficiency by reducing rework and conflicts but also improves project quality through more accurate,coordinated designs.While the focus is on the construction industry,the methods discussed are applicable to various fields,offering broader potential for improving integration and efficiency in other types of construction projects.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057 and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028.
文摘Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-pressure flows.Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells,such as the precise planning of airflow paths,pressure distribution,and compression.This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions.The strategy aims to improve production stability and optimize system efficiency.The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization,such as reservoir depletion,wellbore conditions,and equipment performance.Additionally,the model predicts the timing and impact of liquid loading,which can significantly affect production.The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions,while the strategy involves adjusting compressor settings,optimizing flow rates,and planning pressure distribution across the network to maximize productivity while maintaining system stability.By implementing these strategies,this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system.The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields,ensuring more robust and sustainable pipeline operations.
基金supported by the CNPC Science and Technology Major Project(2013B-3410)the Graduate Research And Innovation Fund project of Southwest Petroleum University in 2021(2021CXYB51)
文摘In order to reduce the carbon emissions of natural gas pipelines,based on the background of different energy structures,this paper proposes a general low carbon and low consumption operation model of natural gas pipelines,which is used to fine calculate the carbon emissions and energy consumption of natural gas pipeline.In this paper,an improved particle swarm optimization(NHPSO-JTVAC)algorithm is used to solve the model and the optimal scheduling scheme is given.Taking a parallel pipeline located in western China as an example,the case is analyzed.The results show that after optimization,under the existing energy types,the pipeline system can reduce 31.14%of carbon emissions,and after introducing part of new energy,the pipeline system can reduce 34.02%of carbon emissions,but the energy consumption has increased.
基金funded by the National Natural Science Foun-dation of China,grant number 51704253 and 52474084。
文摘As the proportion of natural gas consumption in the energy market gradually increases,optimizing the design of gas storage surface system(GSSS)has become a current research focus.Existing studies on the two independent injection pipeline network(InNET)and production pipeline network(ProNET)for underground natural gas storage(UNGS)are scarce,and no optimization methods have been proposed yet.Therefore,this paper focuses on the flow and pressure boundary characteristics of the GSSS.It constructs systematic models,including the injection multi-condition coupled model(INM model),production multi-condition coupled model(PRM model),injection single condition model(INS model)and production single condition model(PRS model)to optimize the design parameters.Additionally,this paper proposes a hybrid genetic algorithm based on generalized reduced gradient(HGA-GRG)for solving the models.The models and algorithm are applied to a case study with the objective of minimizing the cost of the pipeline network.For the GSSS,nine different condition scenarios are considered,and iterative process analysis and sensitivity analysis of these scenarios are conducted.Moreover,simulation scenarios are set up to verify the applicability of different scenarios to the boundaries.The research results show that the cost of the InNET considering the coupled pressure boundary is 64.4890×10^(4) CNY,and the cost of the ProNET considering coupled flow and pressure boundaries is 87.7655×10^(4) CNY,demonstrating greater applicability and economy than those considering only one or two types of conditions.The algorithms and models proposed in this paper provide an effective means for the design of parameters for GSSS.
基金supported by the National Science Fund for Excellent Young Scholars(52122311)the National Natural Science Foundation of China(52373222,92367111).
文摘Solution styrene-butadiene rubber(SSBR)finds wide applications in high performance tire design and various other fields.This study aims to create a quantitative structure–property relationship(QSPR)model linking SSBR's glass transition temperature(Tg)to its structural properties.A dataset of 68 sets of data from published literature was compiled to develop a predictive machine learning model for SSBR's structural design and synthesis using small sample sizes.To tackle small sample sizes,a framework combining generative adversarial networks(GAN)and the Tree-based Pipeline Optimization Tool(TPOT)is proposed.GAN is first used to generate additional samples that mirror the original dataset's distribution,expanding the dataset.The TPOT is then applied to automatically find the best model and parameter combinations,creating an optimal predictive model for the mixed dataset.Experimental results show that using GAN to enlarge the dataset and TPOT regression models significantly enhances model performance,increasing the R2 value from 0.745 to 0.985 and decreasing the RMSE from 7.676 to 1.569.The proposed GAN–TPOT framework demonstrates the potential of combining generative models with automated machine learning to improve materials science research.This combination accelerates research and development processes,enhances prediction and design accuracy,and introduces new perspectives and possibilities for the field.
