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Island-type petrochemical parks have gradually become the 'trend' in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was "safer". The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks. [ABSTRACT FROM AUTHOR]

Efficient operation of the gas field supply chain is an important guarantee for oil and gas energy security, and it needs to dynamically adapt to upstream and market fluctuations. This paper proposes an innovative design and operation optimization mixed integer nonlinear programming (MILP) method for distributed supply chain based on skid equipment. Unlike the traditional methods, the proposed MILP method can simultaneously obtain upstream production planning, midstream modular equipment and processing capacity allocation, and downstream transportation allocation schemes for various natural gas products such as liquefied natural gas (LNG), compressed natural gas (CNG) and pipeline natural gas (PNG) of each time periods by making the maximum net present value (NPV) of the full development cycle as the target. In order to prove the superiority and usability of the proposed method, four operation scheduling modes are compared through two comprehensive case analysis. Additionally, the effects of gas well productivity, market demand and product price are investigated through sensitivity analysis. The results show that comparing with the traditional method, the proposed method can effectively improve the loading rate of the processing equipment, increase the overall revenue of gas field development, integrate the operation in the upstream, midstream and downstream of the supply chain, dynamically adapt to the gas production and marketing fluctuations. This study provides a creative way to obtain better profit, reduce energy utilization and promote cleaner production for sustainable supplies management in gas industry. • A sustainable supply chain of distributed multi-product gas field based on skid equipment. • Production planning of upstream gas wells and allocation of downstream multiple products are considered. • Multi-product, modular facilities and multiple transportation mode are integrated. • The practicality of the optimization method is testified by two case studies. • Sensitivity analysis is employed to investigate the impact of uncertainty parameters. [ABSTRACT FROM AUTHOR]

• An integrated MILP method for gathering pipeline networks is proposed. • Hydraulic characteristics and actual field technical constraints are considered. • A piecewise linearization method is applied to this model. • Three case studies are adopted to confirm the method's feasibility. High investment highlights the importance of optimizing gathering pipeline networks. In view of the problem of ignoring hydraulic characteristics in previous studies, an integrated mixed-integer linear programming (MILP) model for gathering pipeline networks is developed in this study. Taking the minimum total construction cost as the objective function, the proposed model considers the economic and technical constraints such as obstacles, three-dimensional terrain, pipeline topological structures, pipe diameters, wellhead back pressure, and pressure equipment. The ant colony optimization algorithm is used for route optimization to provide parameters for the proposed model. Moreover, a piecewise method is employed to linearize the nonlinear hydraulic equations. Then, the model is solved by the CPLEX solver to obtain the optimal solution integrally, including the optimal connection topology, the location of the central processing facility, the position of pressure equipment, the diameter and detailed route of each pipeline, the pipeline flowrate and the node pressure. Finally, two real-world gas fields and a virtual oil field are taken as examples to demonstrate the feasibility and practicality of the model. The optimal results illustrate that this model can be implemented as a decision-support tool to optimize gathering pipeline networks in the actual design process. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the natural gas trading market, enhancing the flexibility of multi-product natural gas production has practical significance for improving the whole life cycle benefits of gas fields. At present, the collaborative relationship between multi-product process optimization and modular equipment has not been studied enough; there is still a lack of integrated model coupling process optimization, modular equipment design, and scheduling optimization. Therefore, this paper utilizes the convenient scheduling technique across multiple blocks and establishes a distributed multi-product natural gas processing model based on life cycle thinking, facilitating the enduring, dynamic configuration and upkeep administration of modular equipment. The established optimization model is formulated to maximize the net present value of profits, allowing for the selection of optimal production strategy for various natural gas products, such as LNG (Liquefied Natural Gas), CNG (Compressed Natural Gas), NG (Natural Gas), mainly including volume distribution, capacity of gas, and installation, utilization, inter-block scheduling, and recovery of modular equipment, etc. Modular equipment within multiple blocks can be dynamically adjusted based on the production status of the gas field and the market demand for multi-product natural gas. By researching actual production cases, the effectiveness and reliability of this approach have been verified. The results indicate that the net present value of the equipment cost is 89 % of the traditional equipment, which significantly improves production system flexibility and economic benefits of the gas field. This study proposes new operational and management ideas for future distributed multi-product production models in gas fields by considering the efficiency of natural gas resource development and utilization, as well as the risk resistance of the multi-product natural gas industry chain. • An optimization framework for the life cycle of multi-product natural gas production planning. • The framework considers process selection and life cycle management of modular equipment. • The framework takes into consideration both the production capacity forecast and market demand. • Distributed and centralized processing patterns are compared. • A case study shows the superiority of the modular equipment scheduling model. [ABSTRACT FROM AUTHOR]

Third-party damage has become a significant factor leading to pipeline failures and leaks, resulting in severe ecological damage and human casualties. Traditional safety risk analysis methods in the past have focused on severing the causal chain of accidents, while neglecting the unique characteristics of different pipeline segments and environmental factors on their ability to bearing disasters. This paper presents a framework for evaluating the disaster-bearing capacity of natural gas pipeline third-party damage based on probabilistic neural networks. A comprehensive disaster-bearing capacity index system for evaluating third-party damage to natural gas pipelines has been constructed by analyzing the causal factors and potential consequences of third-party damage risk incidents. Next, the cluster analysis and indicator weighting information is used for achieving the comprehensive disaster-bearing capacity assessment. Finally, the data samples are constructed with the index levels and comprehensive disaster-bearing capacity levels of each evaluation unit. An optimization algorithm, Particle Swarm Optimization (PSO), is utilized to improve the parameter selection process of the Probabilistic Neural Network (PNN) model, in order to evaluate the disaster-bearing level of each unit. A typical long-distance natural gas pipeline in Zhejiang Province was selected as the case study, and a total of 200 research units were obtained through pipeline analysis. Furthermore, 14 indicator data were obtained by leveraging multiple sources of data surrounding the pipeline. With the utilization of cluster analysis, it was determined that Gaussian mixed model clustering method ultimately identified a total of 5 different levels of disaster-bearing capacity. The optimal smoothing factor can be accurate from 0.5 to 0.588 by using the PSO-PNN model, and the final accuracy rate is improved from 88% to 98%, which is obviously the introduction of PSO optimization algorithm in the PNN model can improve the accuracy of the model. This represents a significant improvement in accuracy when compared to traditional PNN models. The proposed evaluation method can provide scientific basis for the prevention and control of third-party damage in oil and gas pipelines and pipeline planning. • A third-party damage comprehensive disaster bearing capacity framework for evaluating natural gas pipeline. • Using clustering analysis to eliminate the influence of subjective evaluation. • Evaluation of disaster bearing capacity based on probabilistic neural network. • PSO-PNN optimization model effectively improves the accuracy of evaluation. [ABSTRACT FROM AUTHOR]

Under the background of "One national network", the balance between the natural gas supply side and the demand side is prominent. This paper proposes a supply-demand balance method of natural gas pipeline network coupled with the hydraulic and thermal characteristics, and decides the transportation scheme through flow rate allocation. This paper designs a two-stage relaxation optimization algorithm, coupled the thermal process into the hydraulic calculation to improve the hydraulic calculation accuracy, making the natural gas transportation scheme more applicable to engineering. The result shows the feasibility of transportation decision-making and the accuracy of hydro thermal calculation of the proposed model through a real case. The relative error of thermal calculation and TGNET is below 2%, and the hydraulic calculation error of coupled thermal can also be within 10%. After optimization, the carbon emissions decrease from 418,100 tons to 260,000 tons, reducing by about 37.81%. This study also analyzes the impact of hydraulic calculation on carbon emissions calculation, and further illustrates the necessity of coupled thermal characteristics in transportation scheme. This study can provide decision support for the transportation operation and help the natural gas pipeline network to meet the dual carbon requirements and achieve sustainable development. • A flow rate allocation method combining operation process is proposed. • A convex relaxation optimization algorithm is designed for energy consumption calculation. • Two stage relaxation iteration algorithm is designed to couple thermal and hydraulic characteristics. • Carbon emission objective is added to the optimization model for environmental benefit. • An actual case is used to prove the practicability of the method. [ABSTRACT FROM AUTHOR]

In the context of achieving carbon neutrality, reducing CO 2 emissions has become an essential task for many countries and poses challenges for various industries. With global trade, the shipping industry is overgrowing, bringing economic benefits but also significant CO 2 emissions. To achieve the goal of carbon neutrality, the fuel mix of ships needs to be planned appropriately. As an extension of the base method of Carbon Emission Pinch Analysis, this study develops a new Carbon Pinch Point analysis method for shipping fuel planning. Optimising the energy mix of ships in different period is used to reduce CO 2 emissions from shipping fuels and achieve the set emission reduction targets. Taking the Zhoushan sea area in China as an example, emission reduction targets are set in three period with a five-year period from the national policy level. The first period (2020–2025) achieves a 14.5% reduction relative to 2020; the second period (2025–2030) achieves a 32.2% reduction relative to 2020; and the third period (2030–2035) achieves a 57.2% reduction relative to 2020. Each period is divided into Scenario 1 (without heat loss), and Scenario 2 (with heat loss), and two options are developed for each scenario by constraining different low-carbon fuels. The study shows that it is more economical to use LNG as an alternative fuel in the first period, requiring an additional 457.74 kt of LNG in Scenario 1 and 225.26 kt of LNG in Scenario 2. Using biodiesel as an alternative fuel in the second period is more economical, requiring an additional 343.55 kt of biodiesel in Scenario 1313.04 kt of biodiesel in Scenario 2. The use of biodiesel as an alternative fuel in the third period is more economical, requiring an additional 371.73 kt of biodiesel in Scenario 1 and 466.23 kt of biodiesel in Scenario 2. • A multi-phase energy plan approach to shipping fuel planning. • Expanding the application and development of carbon emissions pinch point analysis. • A case study based on one region of China illustrates this approach. • Determining fuel options for 2025–2035. [ABSTRACT FROM AUTHOR]

• A methodology is proposed for dynamic probability assessment of natural gas pipeline for third-party damage. • Failure factors are identified into two aspects of intentional factors and unintentional factors. • The probabilities of third-party damage failure causations are estimated by Fuzzy set theory and Dempster-Shafer evidence theory. • Fault tree analysis and event sequence diagram are coupled and transformed into dynamic Bayesian network model to eliminate uncertainty. • A real-field case study is used to prove the practicality and advantages of the proposed method. Failure and leakage of natural gas pipelines can lead to serious ecological losses and casualties. Third-party damage has become an important cause of pipeline failure and leakage, which urgently needs an accurate risk assessment method to assess the risk. Conventional qualitative risk analysis methods can only point out the critical events of failure accidents but fails to predict the failure probability. This paper proposes a dynamic risk probability analysis method based on Dynamic Bayesian network (DBN), which is validated by a third-party damage case under uncertainty. First, human factors are taken as the main analysis object in the risk analysis, by which two subcategories of intentional and unintentional factors are classified. A complete risk factor analysis is performed by combining expert recommendations with the fault tree analysis method and developing a coupled model with the event sequence diagram. Second, in order to deal with the uncertainty of risk factors, the coupled model is mapped to a DBN model. The prior probabilities of the input DBN model are obtained by database, fuzzy set theory, and Dempster-Shafer evidence theory. Weibull distribution is applied to construct the probability transfer process between time segments, which better fits the characteristics of third-party disruptive factors in onshore pipelines. Finally, the practicality and advantages of the proposed method are demonstrated by a real case study, which identifies 6 critical events and predicts the probabilistic information in different time slices. Furthermore, the method predicts the probability of failure events and potential consequences by processing the time series information, and it is found that the probability of structural damage and explosion is higher than other consequences. In this way, some risk management countermeasures are proposed in a targeted manner. The results show that compared with the conventional BN model which only performs probabilistic inference once, the DBN model can perform temporal dynamic inference to achieve the prediction of failure probability, and it can effectively achieve the numerical prediction of risk failure probability. [ABSTRACT FROM AUTHOR]

Pressure-exchange ejector is a novel equipment to overcome the pressure mismatch in multi-pressure gathering pipeline network. Hydraulic simulation plays an important role in the safety monitoring and operation optimization of gathering pipeline network. However, there is currently no hydraulic simulation model of the gathering pipeline network with the pressure-exchange ejector. This paper proposes a steady-state hydraulic simulation model, which consists of pipeline network structure sub-model, pipeline flow sub-model, and pressure-exchange ejector sub-model. Pipe flow, mass balance and detailed characteristics of pressure-exchange ejector are taken into consideration in the model to determine the unknown pressure and flow parameters. The proposed model is solved by the Newton-Raphson iterative method and validated by experimental and field data. The installation schemes of pressure-ejector in a real-world gas field are determined by scenario analysis to improve gas field production. This study provides an analytical tool for multi-pressure gathering pipeline network and guidance for gas field production. • A novel hydraulic model of gathering pipeline network with pressure-exchange ejector. • A hydraulic model of gathering pipeline network with pressure-exchange ejectors is proposed. • A solution based on Newton-Raphson iterative is presented. • Experimental and field data are used to validate the proposed model. • The production of a real-world gas field is improved by scenario comparison. [ABSTRACT FROM AUTHOR]

Modular equipment is becoming popular in unconventional gas field development due to cost savings and flexibility in capacity adjustment by dynamic allocation. It is necessary to make long-term investment and maintenance planning to give full play to the advantages of modular equipment. This paper fills the gap of considering individual scheduling and the impact of utilization time on the value of the equipment in the unconventional gas field development by presenting a comprehensive method that considers the various costs of modular equipment and can facilitate tracking the usage of the equipment. A mixed integer linear programming model is developed to get the solution for the modular equipment, including the processing capacity selection, the installation and use plans, mobile scheduling, and depreciation sale plan, to enhance gas field production flexibility and achieve the best net present value. The changes of production and market demand, and the impact of utilization time on equipment depreciation are taken into account comprehensively. A real case study is used to prove the practicality and advantages of the proposed model. The results show the equipment utilization rate has been increased from 60% to 75%, achieving 10% higher economic benefits when using modular equipment compared with the traditional method in gas field production. In addition, the sensitivity analysis is implemented to investigate the influence of uncertain parameters such as output and market demand, equipment service life, and type of equipment on the equipment dynamic allocation and the economic performance. This study provides a powerful decision-making tool for gas field development to reduce costs, increase efficiency and promote clean practical production. • An equipment allocation model with long-term investment and maintenance planning is developed. • The impact of service life on modular equipment performance and value is considered. • Results yield 10% higher net present value compared to traditional method. • Sensitivity analysis is employed to investigate the impact of uncertainty parameters. [ABSTRACT FROM AUTHOR]

Erosion caused by solid particles in a pipeline is one of the main problems endangering the safety production of the oil and gas industry, which may lead the equipment to malfunction or even fail. However, most of the previous studies focused on the standard elbow, and the erosion law of right-angle elbow and blind tee is rarely reported in the literature. This work aims to investigate the erosion law of different pipeline structures including 90° elbow, right-angle pipe, and tee pipe based on the production characteristics and engineering parameters of the gas field. An integrated CFD-DPM method is established including a realizable k-ε turbulence model, discrete phase model, and erosion rate prediction model. The accuracy of the model is evaluated by a series of experimental data of flow conditions of our previous work. Further, the erosion rate, pressure distributions, and particle trajectories in 90° elbow, right-angle pipe, and tee pipe under different flow velocities, particle mass flow rate, pipe diameter are investigated by applying the presented model. The results show that the blind tee has the most obvious growth rate, and the most serious erosion is located in the blind end of the pipe wall. The maximum erosion rate of the 1.5D is greater than that of the 3D elbow as a whole, and the 1.5D elbow is more concentrated in the serious erosion area. Furthermore, the erosion rate of the bend weld is much greater than that of the straight pipe weld. This study can provide a basis for the selection of different structural pipe fittings, thereby reducing the pipeline erosion rate and improving the integrity of the management of gas pipelines. [ABSTRACT FROM AUTHOR]

Erosion is one of the most common forms of material failure and equipment damage in gas transmission pipelines. Shale gas fields use hydraulic fracturing whereby solid particles are often carried in the gas flow, and the pipeline is in a high-pressure state, which is more likely to cause erosion. The prediction of particle erosion regulation in gas-solid two-phase flow is an effective means to ensure the safe operation of shale gas fields. In this paper, an integrated CFD-DPM model is established to investigate the erosion of 90° elbow in a shale gas field under gas-solid two-phase flow, employing the realizable k-ε turbulence model, discrete phase model, and erosion rate prediction model. The reliability of the proposed numerical models is verified by comparing the predicted data with the experimental data. Moreover, the effects of six important factors on maximum erosion rate are analyzed, including gas velocity, mass flow rate of sand particles, particle diameter, shape coefficient of sand particles, pipeline diameter, elbow radius of curvature. Specifically, the results indicate that the gas velocity, mass flow rate and shape coefficient of sand particles are positively correlated with the maximum erosion rate, while the pipe diameter and the elbow radius of curvature are negatively correlated with the maximum erosion rate. A new correlation was developed, which included four dimensionless groups, namely Reynolds number, diameter ratio, density ratio and particle number. The correlation can be used to predict maximum corrosion rate of elbows. This work can provide data reference and theoretical basis for mitigating the erosion rate of pipelines and managing the integrity of gas pipelines. [ABSTRACT FROM AUTHOR]

Distributed fiber optic sensing (DFS) systems are an effective method for long-distance pipeline safety inspections. Highly accurate vibration signal identification is crucial to DFS. In this paper, we propose an end-to-end high-accuracy fiber optic vibration signal detection and identification algorithm by extracting features from the time domain and frequency domain by a one-dimensional convolutional neural network and two-dimensional convolutional neural network, respectively, and introducing a self-attentive mechanism to fuse the features of multiple modes. First, the raw signal is segmented and normalized according to the statistical characteristics of the vibration signal combined with the distribution of noise. Then, the one-dimensional sequence of vibration signal and its two-dimensional image generated by short-time Fourier transform are input to the one-dimensional convolutional neural network and two-dimensional neural network, respectively, for automatic feature extraction, and the features are combined by long and short-time memory. Finally, the multimodal features generated from the time and frequency domains are fused by a multilayer TransformerEncoder structure with a multiheaded self-attentive mechanism and fed into a multilayer perceptron for classification. Experiments were conducted on an urban field database with complex noise and achieved 98.54% accuracy, which demonstrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• A novel optimization framework for shale gas production planning is proposed. • The framework considers the dynamic allocation of modular infrastructure. • Production decline curve of shale gas wells is considered rigorously. • A case study shows the benefit of scheduling modular infrastructure. • A load factor index is proposed to quantitatively compute the device utilization. Compared with the conventional method utilizing permanent processing facilities, modular infrastructure has shown greater potential to hedge against the uncertainty due to the rapidly declining characteristic of shale gas production. However, there is still a lack of a comprehensive approach that integrates the production planning and the dynamic allocation of the modular infrastructure based on the production curve of shale gas wells. Therefore, a systematic optimization framework is developed to simultaneously optimize the production planning and modular infrastructure allocation over a given time horizon, maximizing the net present value (NPV) of the system, considering the production curve and gas well status, the processing capacity as well as the scheduling of modular infrastructure. The proposed mixed-integer linear programming model combines the decisions relevant to shale gas production, transportation, and processing together with the decisions regarding modular infrastructure, including allocation, capacity selection, installment planning, moving scheduling, and salvage operation. A case study deciding a development strategy for 24 multi-well pads over a period of 15 years is implemented to illustrate the applicability of the proposed model. The results show that the dynamic allocation of the modular infrastructure can adapt to productivity fluctuations. The use of the modular approach increases the NPV by 9.12% and has a high utilization efficiency of processing devices compared to the conventional method. This work reveals that the synergistic interaction of the production planning and modular infrastructure dynamic allocation can increase efficiencies in the uses of energy, resources, and human capital to promote cleaner production practices. [ABSTRACT FROM AUTHOR]

High summer temperatures pose numerous challenges to the oil and gas recovery process in oil depots, including reduced adsorption tank recovery rates and decreased absorption tower desorption efficiency. This paper introduces a coupling design approach that integrates chemical process design with computational fluid dynamics simulation. The proposed approach is then utilized to investigate the optimal design and performance of the heat exchanger within the oil depot's oil and gas recovery system. First, according to the given process design parameters, the heat exchanger is preliminary designed to determine the required heat exchange area and heat load. Based on the preliminary design results, a detailed design is carried out, resulting in the following calculations: the hot fluid has inlet and outlet temperatures of 40 °C and 29.52 °C, respectively, with an outlet flow velocity of 9.89 m/s. The cold fluid exhibits inlet and outlet temperatures of 25 °C and 26.98 °C, respectively, with an outlet flow velocity of 0.06 m/s. The specific structure and dimensions of the heat exchanger are determined, including the shell type, pipe specifications, and pipe length. Finally, CFD numerical simulation is utilized to analyze the flow field, velocity field, and pressure field within the designed heat exchanger. The calculations reveal the following findings: the hot fluid exhibited inlet and outlet temperatures of 40 °C and 29.54 °C, respectively, along with an outlet flow velocity of 9.94 m/s. On the other hand, the cold fluid shows inlet and outlet temperatures of 25 °C and 26.39 °C, respectively, with an outlet flow velocity of 0.061 m/s. The results show that the chemical process design and CFD numerical simulation results are consistent and can be mutually verified. The designed heat exchanger can efficiently cool oil and gas from 40 °C to 30 °C, and the oil and gas processing capacity can reach 870 m3/h, which is conducive to realizing the goals of energy saving, environmental protection, and safety. [ABSTRACT FROM AUTHOR]

Hydrogen energy, characterized by its high calorific value and sustainability, represents a secondary clean energy source. It is predominantly stored, transported, and utilized through pipeline networks. However, a comprehensive optimization approach for the design for the ground pipeline network of hydrogen storage is currently lacking. This paper embarks on a systematic exploration of the layout, station type and parameter design for the ground pipeline network of hydrogen storage. Addressing various application perspectives, the study constructs two distinct pipeline network structures: the Star-Star Global Optimization Model (SSGO-Model) and the Star-Tree Global Optimization Model (STGO-Model). Three cases are discussed under six different constraint scenarios. These scenarios focus on layout optimization, variable capacity layout optimization, and the overall optimization of layout and parameters. An overall optimization strategy is proposed to solve the models. A detailed analysis of the iteration process, layout schemes, station and pipeline constraints, investment, and flow parameters of the pipeline network in the optimization results is presented. Through systematic analysis, the correctness and effectiveness of the model are verified, demonstrating the variability of optimization results under different topological structures and constraint conditions. Moreover, when conducting integrated optimization, the total investment is minimized, proving its superiority. The findings demonstrate the applicability of these models in guiding the construction for the ground pipeline network of hydrogen storage, thereby fostering the advancement of hydrogen storage technology. Concurrently, this research contributes significantly to the development of hydrogen energy, offering strategic insights and practical solutions for its efficient deployment. • Integrated optimization model for hydrogen storage ground pipeline network. • The model considers layout, station type and parameter design. • Overall optimization strategy is proposed. • Three cases verify optimization model. [ABSTRACT FROM AUTHOR]

The accuracy of pipeline temperature monitoring using the Brillouin Optical Time Domain Analysis system depends on the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. The Non-Local Means noise reduction algorithm, due to its ability to use the data patterns available within the two-dimensional measurement data space, has been used to improve the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. This paper studies a new Non-Local Means algorithm optimized through the Black Widow Optimization Algorithm, in view of the unreasonable selection of smoothing parameters in other Non-Local Means algorithms. The field test demonstrates that, the new algorithm, when compared to other Non-Local Means methods, excels in preserving the detailed information within the Brillouin Gain Spectrum. It successfully restores the fundamental shape and essential characteristics of the Brillouin Gain Spectrum. Notably, at the 25 km fiber end, it achieves a 3 dB higher Signal-to-Noise Ratio compared to other Non-Local Means noise reduction algorithms. Furthermore, the Brillouin Gain Spectrum values exhibit increases of 9.4% in Root Mean Square Error, 12.5% in Sum of Squares Error, and 10% in Full Width at Half Maximum. The improved method has a better denoising effect and broad application prospects in pipeline safety. [ABSTRACT FROM AUTHOR]

China aims to reduce carbon dioxide emissions and achieve peak carbon and carbon neutrality goals. Natural gas, as a high-quality fossil fuel energy, is an important transition resource for China in the process of carbon reduction, so it is necessary to predict China's natural gas demand. In this paper, a novel natural gas demand combination forecasting model is constructed to accurately predict the future natural gas demand. The Lasso model and the polynomial model are used to build a combinatorial model, which overcomes the shortcomings of traditional models, which have low data dimensions and poor prediction abilities. In the modeling process, the cross-validation method is used to adjust the modeling parameters. By comparing the performance of the combinatorial forecasting model, the single forecasting model and other commonly used forecasting models, the results show that the error (2.99%) of the combinatorial forecasting model is the smallest, which verifies the high accuracy and good stability advantages of the combinatorial forecasting model. Finally, the paper analyzes the relevant data from 1999 to 2022 and predicts China's natural gas demand in the next 10 years. The results show that the annual growth rate of China's natural gas demand in the next 10 years will reach 13.33%, at 8.3 × 1011 m3 in 2033, which proves that China urgently needs to rapidly develop the gas supply capacity of gas supply enterprises. This study integrates the impact of multiple factors on the natural gas demand, predicts China's natural gas demand from 2023 to 2033, and provides decision-making support for China's energy structure adjustment and natural gas import trade. [ABSTRACT FROM AUTHOR]

The rapid development of natural gas pipelines has highlighted the need to utilize SCADA (supervisory control and data acquisition) system data. In this paper, a heat transfer model of a natural gas pipeline based on data feature extraction and first principle models, which makes full use of the measured temperatures at each end of the pipeline, is proposed. Three methods, the NARX neural network (nonlinear autoregressive neural network with exogenous inputs), time series decomposition, and system identification, were used to model the changes of gas temperatures of the pipeline. The NARX neural network method uses a cyclic neural network to directly model the relationship of temperature between the start and the end of the pipeline. The measured temperature series at the pipeline inlet and outlet were decomposed into trend items, fluctuation items, and noise items based on the time series decomposition method. Then the three items were fitted separately and combined to form a new temperature prediction series. The system identification method constructed the first-order and second-order transfer function to model the temperature. The simulation of the three data-driven models was compared with those of the physics-based simulation models. The results showed that the data-driven model has great advantages over the physics-based simulation models in both accuracy and efficiency. The proposed models are more suitable for applications such as online simulation and state observation of long-distance natural gas pipelines. [ABSTRACT FROM AUTHOR]

• The effect of blending pattern on hydrogen concentration distribution is studied. • Hydrogen is injected from the bottom at an angle of 120° to facilitate full mixing. • The hydrogen proportion is not directly related to the local enrichment. • Increasing the kinetic energy ratio to 1.2 can improve the mixing uniformity. After hydrogen injected into a natural gas pipeline, the inner flow field of the pipeline will change greatly and hydrogen may be locally enriched. This is an important reason for the hydrogen-induced cracking of the pipeline. Therefore, there is an urgent need to investigate the effect of blending patterns on hydrogen concentration distribution in a methane-hydrogen pipeline. At this work, a multi-angle hydrogen injection model that is different from the traditional T-junction is established to understand the factor of hydrogen blending ratio and geometry (diameter ratio, hydrogen injection angle) on the blending effects. Firstly, the model is validated with experimental data under the corresponding conditions and then pure hydrogen injected into the natural gas pipeline under different injection patterns is analyzed computationally. The result shows that hydrogen injected from the bottom with angle α = 120 ° is helpful to form a low hydrogen concentration zone at the bottom of the pipeline and has the best blending effect compared to other angles. Based on the 120 ° model, the blending effect can be further improved by increasing the kinetic energy ratio to 1.2. This study can provide some references for improving the blending uniformity of hydrogen-blended natural gas pipelines, enhancing the safety of blending operation and preventing hydrogen embrittlement. [ABSTRACT FROM AUTHOR]

• A methodology to determine the target gas supply reliability is proposed. • An optimization model based on cost-benefit analysis is developed. • The unit's reliability considering the improvement measures impact is assessed. • The impact of units' operational age on target gas supply reliability is analyzed. In this study, a methodology to determine the target gas supply reliability of the natural gas pipeline system based on cost-benefit analysis is proposed. In the methodology, an optimization model is developed firstly, and the objective function is constructed to maximize the difference between the benefits and investment when improving system reliability. Secondly, the investment and the benefits of reduced unreliability cost due to the implementation of the reliability improvement measures are considered in the optimization model. Thirdly, the virtual age model is employed to quantify the impact of reliability improvement measures on the unit's reliability, and the gas supply reliability evaluation method of the natural gas pipeline system is developed. Finally, the optimization model used to determine the target gas supply reliability is solved by a heuristic genetic algorithm. Furthermore, a real natural gas pipeline system located in China is applied to confirm the feasibility of the methodology, and the target gas supply reliability is determined, with a value of 0.998608. At last, the effect of system operational age on the target gas supply reliability is investigated through sensitivity analysis. The results show that the target gas supply reliability decreases with the increase of the operational age, which is consistent with the engineering practice. [ABSTRACT FROM AUTHOR]

In recent years, the scale of gas transmission networks has been continuously expanding, and the operational conditions are becoming increasingly complex. This poses higher requirements for the centralized control of the pipeline network operation. Relying solely on the experience of scheduling personnel may not comprehensively address the operational issues within the network. There is an urgent need for efficient automatic response methods (ARM) to assist in formulating operation schemes and ensuring the safe and stable operation of the pipeline network. Therefore, this paper proposes an automatic response framework of large complex natural gas pipeline operation optimization based on data-mechanism coupling to provide operation schemes that ensure safety, stability, and economic benefits. First, two ARM is proposed, namely the Data-Based ARM using operation scheme database, and the Opti-Model ARM based on optimization modeling. Subsequently, the rapid response features of Data-Based ARM and the optimal response characteristics of Opti-Model ARM are combined to establish the Integrated ARM. Finally, these three ARMs are compared and analyzed through a regional natural gas pipeline network in China. The result indicates that the Data-Based ARM can quickly produce a variety of matched solutions but cannot ensure economic optimality. Response solutions obtained through Opti-Model ARM reduce the compressor energy costs by 4.6–10.8 % compared to on-site operational schemes, but they take longer in response time. In contrast, the economic attributes of solutions derived from the Integrated ARM are on par with Opti-Model ARM, but with a 58.7 % improvement in response speed. The proposed Integrated ARM can swiftly and accurately offer economically viable operation schemes tailored to the varying needs of pipeline operators, which can help to address the energy demand challenges of the future, fostering sustainable development. • An automatic response framework based on hybrid-driven is proposed. • Data-Based method quickly responds to a set of matching feasible scheme. • Opti-Model method responds to economically optimal scheme. • A real case is performed to verify the effectiveness of the framework. [ABSTRACT FROM AUTHOR]

Due to high investment costs and technological uncertainties, the large-scale deployment of carbon capture, utilization and storage (CCUS) technologies faces significant challenges. Consequently, effective policy incentives are essential for catalyzing its growth. As a critical incentivization policy within the dual-carbon strategic objectives, carbon emissions trading can provide crucial support for the investment and operation of CCUS projects. This study aims to identify optimal flows of CO 2 and allocation of carbon allowance, integrating carbon tax and carbon emissions trading policies, while minimizing the regional emission reduction costs. Coal-fired power plants are taken as carbon capture objects, enhanced coal bed methane reservoirs, enhanced oil recovery formations, deep saline aquifers and depleted gas fields are taken as storage objects, and pipelines are taken as transportation mode, respectively. A mixed-integer linear programming model allowing for inter-source carbon allowance trading is developed to minimize the total annualized investment and operational costs of CCUS during the project's operational phase. The proposed model is applied to a specific case study and solved using GAMS solver. The results indicate that, under a framework that permitted carbon allowance trading among emission sources, the case achieves geological storage and utilization of 25.72 million tons of CO 2 annually, with a marginal CO 2 abatement cost of 139 CNY/t. This validates the reliability of the proposed CCUS model under the established carbon emissions trading policy and underscores the effectiveness of permitting inter-source carbon allowance trading measures in reducing the overall costs of carbon abatement projects. • A CCUS model is eatablished with carbon tax and trading policies. • The optimal scheme is determined based on source-sink data. • The model decides the economically optimal deployment of CCUS chain. • A real case is performed to verify the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Mixed obstacles have a great influence on the deflagration process of liquefied petroleum gas (LPG)-air premixed combustible gas with concentration gradient. The arrangement of mixed obstacles may further stimulate overpressure and flame propagation. In this work, based on experimental and numerical simulations, this paper analyzes the flame and overpressure, and mainly studies the coupling relationship among the explosion overpressure characteristics, the structure of flame and the speed of flame propagation. The result shows that when the rectangular obstacle is 100 mm away from the ignition source, not only the speed of flame is the fastest, but also the time required to reach the maximum over-pressure is the shortest. In this configuration, an elongated flame is formed between a rectangular obstacle and a flat obstacle, and an obvious backflow structure appears. In addition, the average growth rate of overpressure has a minimum value, reaching at −35 MPa/s. The existence of rectangular obstacles further stimulates the overpressure. When the rectangular obstacle is 400 mm away from the ignition source, the maximum overpressure value is the highest among the four configurations. Besides, the time when the maximum area of flame appears in the simulation is almost the same as the time when the maximum overpressure is obtained. In addition, the average growth rate of overpressure increases significantly after touching the rectangular obstacle, which coincides with the mutation time of the front tip of the flame, overpressure and area of flame after the flame encounters the rectangular obstacle. This research has an important theoretical guiding significance for preventing LPG leakage and explosion accidents in a long and narrow space. [ABSTRACT FROM AUTHOR]

The arrangement of obstacles can significantly impact the deflagration behavior of combustible gases. In the actual pipeline accident site, liquefied petroleum gas (LPG) and other gases often show non-uniform distribution after leakage owing to diffusion and gravity, and the deflagration mechanism is also more complex. In this paper, based on the non-uniform distribution of combustible gases, the flame behavior and overpressure characteristics of LPG–air combustible gas deflagration are carried out by a combination of experiments and numerical simulations with obstacles arranged in increasing and decreasing blockage height. The results show that in the increasing blockage height arrangement, the flame forms a "straw hat" cavity, finally forming an elliptical region. In the decreasing blockage height arrangement, the flame appears as a "ribbon-shaped" narrow, blank area, which gradually becomes longer with time. By observing the overpressure and the structure of flame propagation in the coupled state, it is found that the explosion overpressure is maximum when the height of the obstacle is consistent, and the moment of the maximum area of flame appears slightly earlier than the appearance of the maximum overpressure peak. At the same time, without considering the change in height of the obstacle, the three arrangements all have an accelerating effect on the flame of deflagration. And the decreasing blockage height arrangement condition has the most obvious effect on the flame acceleration, which makes the peak of area of flame and the overpressure peak appear at first, and finally leads to the formation of a positive feedback mechanism among the speed of flame propagation, the area of flame and overpressure. In addition, in the case of the non-uniform distribution of combustible gases, the acceleration obtained by the flame at the initial stage is very important for the overall acceleration of the flame. The results of this paper can provide a reference for the placement of equipment and facilities in long and narrow spaces such as various pipe galleries, and to make predictions about the impact of the shape of some objects on the explosion and provide a theoretical basis for the prevention and management of gas explosions. [ABSTRACT FROM AUTHOR]

Natural gas plays an important role in the transition from fossil fuels to new energy sources. With the expansion of pipeline networks, there are also problems with the safety of pipeline network operations in the process of transportation. Among them, third-party damage is a key factor affecting the safety of pipelines. In this paper, the risk factors of third-party damage are analyzed, and an evaluation model of natural gas pipeline damage is established using the GeNIe Modeler. Through Bayesian network reverse reasoning and a maximum cause chain analysis from the four aspects of personnel, environment, management, and equipment, it was found that the top five factors that have significant influence on third-party damage, are safety investment, the completeness of equipment, safety inspection frequency, the management of residents along the pipeline, and safety performance, with the posteriori probability in the model of 97.3%, 95.4%, 95.2%, 95.1%, 95.1%, respectively. Consequently, it is necessary for pipeline operation companies to secure investment on safety, to make sure that the safety equipment (system) works and is in a good condition, to maintain the safety inspection frequency in an organization, to build a management system for residents along the pipeline, and to conduct routine safety performance assessments accordingly. [ABSTRACT FROM AUTHOR]

Given the spatial structures and functional requirements, there are a number of different types of obstacles in long and narrow confined spaces that will cause a premixed gas explosion to produce greater overpressure and influence the flame behavior for different obstacles. Because the volume fraction of unburned gas changes with the changing height of the U-type obstacles, we can further study the influence on the volume fraction of the unburned premixed gas for the characteristics of the overpressure and the flame behaviors in the closed tube with the obstacles. The results show that after the premixed gas is successfully ignited in the pipe, the overpressure in the pipe greatly increases as the unburned premixed gas burns between the adjacent plates. Moreover, the increase of the overpressure in the closed duct becomes faster when the decrease of unburned gas becomes faster. The high-pressure areas between the plates move inversely compared with the direction of flame propagation when the height of the U-type increases, whereas the high pressure in the front of the flame moves further when the flame propagation passes all obstacles. In addition, the reversed flow structure of the flame is a coupling result for the overpressure caused by the flame propagation and the vortex between the plates. From the perspective of production safety, this study is a significant basic subject about the characteristics of overpressure and flame behaviors in a closed tube with obstacles. [ABSTRACT FROM AUTHOR]

Underground Gas Storage (UGS) is an important facility for natural gas peak adjustment and ensuring the balance between energy supply and demand. The daily gas injection and production capacity of large UGS can reach several hundred million or even billions of cubic meters, and the energy consumed by the injection and production equipment is not to be underestimated, which also contains a large amount of pressure energy that can be utilized for power generation. In this context, the optimization of low-carbon operation of UGS is of great practical significance in engineering. In this study, an innovative UGS system operation optimization method is proposed to integrate a natural gas differential pressure generator set in the UGS system. Considering the complex interconnections among multiple storage areas, multiple wells and wellsites, and the hydro-thermodynamic constraints during the operation of injection and production pipeline network, and with the objective of minimizing the carbon emission, the UGS integrated with Differential Pressure Power Generation System (UGSIDPPGS) is established. In order to solve this complex model, this paper designs an optimization solution framework based on variational assignment, which effectively avoids the problem of falling into inferior solutions during the solution process. The optimization model is applied to the case study of a large-scale depleted gas reservoir in China, and the optimal operation schemes under three scenarios are successfully obtained. The results show that the UGSIDPPGS optimization model, not only effectively utilizes the pressure energy, generates up to 56.908 × 106 kW h per month, and reduces the CO 2 emission by 56,738 tons, but also achieves the low-carbon and economic operation of the UGS. In conclusion, the operation strategy proposed in this study is of great value for optimizing the operation of UGS, and provides practical guidance and suggestions for the low carbon and environmental protection of UGS and the utilization of new energy sources. • A UGS operation model integrating differential pressure generation is proposed. • An optimization model of UGS integrated with differential pressure power generation is established. • A framework for high solution efficiency is proposed. • Validation of the model in a real case. [ABSTRACT FROM AUTHOR]

The accurate simulation of upward multiphase flow of hydrate slurry in the annulus is one of the key scientific unsolved issues in natural gas hydrate solid fluidization exploitation. In this work, the upward multiphase flow of hydrate slurry in a vertical concentric annulus is simulated. The hydrate slurry hydrodynamic models suitable for pseudo-single-phase flow, bubbly flow, slug flow, and annular flow are proposed, respectively. Finally, the hydrate decomposition kinetic model is combined with the established annulus hydrate slurry multiphase flow model to simulate the multiphase flow of hydrate slurry in the annulus. The factors affecting flow behaviors are analyzed. During the upward flow in the annulus, the hydrate slurry temperature first decreases and then increases. As the inlet temperature increases, the fluid temperature, hydrate decomposition rate, and gas superficial velocity increase. During the upward flow in the annulus, hydrate may be formed again, which indicates that the error may be magnified due to ignoring hydrate formation. The larger the flow rate, the smaller the length of the slug flow. The larger the hydrate volume fraction, the higher the starting point of hydrate decomposition. These findings are of practical value to give a further understanding of hydrate slurry multiphase flow, which can promote further engineering application of natural gas hydrate solid fluidization exploitation. [ABSTRACT FROM AUTHOR]

China has formed a national natural gas pipeline network, and reasonable and accurate pipeline capacity allocation scheme has important significance for improving the utilization rate of pipeline network, improving the income of pipeline network operators and ensuring the gas consumption of downstream users, which poses greater challenges to the formulation of pipeline capacity allocation rules and mechanisms. This paper takes the natural gas pipeline network system as the research object, and studies the rules and mechanisms of pipeline capacity allocation. A capacity allocation model of natural gas pipeline network is proposed with the net present value of pipeline network operator's income as the goal, considering the gas consumption characteristics of downstream users, the dynamic change of gas source node capacity, pipeline hydraulic characteristics, pipeline flow direction, compressor and other constraints. Based on the simulation of the actual pipeline network by the proposed model, seven allocation rules and one mechanism are proposed. Specifically, the user priority of non-interruptible type, the pipeline path adjustable type and the gas source node adjustable type is higher than the corresponding comparison type. In the pipeline capacity allocation mechanism, the amount compensation mechanism for pipeline capacity transactions is as follows: When the pipeline capacity transaction occurs between users, the transaction information is submitted to the pipeline network operator. The pipeline network operator proposes the corresponding amount compensation according to the transaction information, and the transaction parties negotiate the proportion of the amount compensation and submit it to the pipeline network operator. In addition, it is recommended that pipeline network operators vigorously develop LNG receiving stations and downstream interruptible and compressible users in the future. • A capacity allocation model for complex pipeline network is constructed. • The model considers users type, user allocation weight and user purchase plan. • The allocation scheme considers process constraints to ensure technical feasibility. • The rules of pipeline capacity allocation are proposed by analyzing different allocation scenarios. [ABSTRACT FROM AUTHOR]

Under the guidance of the "Dual Carbon" goal, integrated energy supply stations have gradually become an essential facility for the energy transition. Promoting user repurchase has become a vital marketing strategy for integrated energy supply station enterprises. This paper proposes a prediction method based on the user profiling method to predict user repurchase behavior accurately. First, using an improved RFM model and the K-means algorithm, this paper constructs user profiles by dividing 10,000 users into three clusters: general-value developmental users, high-value new users, and low-value loyal users. Next, this paper uses the random forest, light gradient boosting machine, and extreme gradient boosting to predict the repurchase behavior of non-clustered users and the three clusters and compares their prediction performance. In addition, this paper adopts the stacking method for model fusion to improve the prediction performance further. The results show that the accuracies of the best prediction models for the three clusters are 93.28 %, 93.68 %, and 92.84 %, respectively. Finally, this paper provides each cluster with the corresponding prediction model of user repurchase behavior and marketing strategy. For the application scenario of integrated energy supply stations, this study accurately predicts the repurchase behavior of each cluster with unique consumption characteristics. It helps to provide personalized services for new energy vehicle consumers, optimize their consumption experience, and facilitate sustainable consumption. • A method for predicting user repurchase behavior for integrated energy supply stations based on the user profiling method. • This paper clusters users based on the unique consumption characteristics and constructs user profiles for each cluster and individual. • Three mainstream ensemble learning algorithms to predict the repurchase behavior of each cluster. • Stacking algorithm for model fusion to improve the prediction performance further. [ABSTRACT FROM AUTHOR]

Large sections of gas accumulation easily form in continuously undulating liquid pipelines to impede the commissioning process affecting the safe operation of the pipeline. To solve the gas resistance and overpressure during the commissioning of continuously undulating pipeline in complex terrain, a whole process commissioning simulation model is proposed to quantitatively calculate the location and volume of gas accumulation and to monitor the pressure drop across the line in the real time. Based on a single V-shaped pipe compression process model, the proposed model considers the influence of back pressure, gas-liquid migration and integrates different process such as high point exhausting, pigging process and pump-pipe coordination. Multiple operating conditions of two real pipelines are simulated, and the results show that the proposed model has an accuracy of within ±15% for predicting the pressure along the pipeline and tracking the pigging device, indicating that the gas transport model of the continuous undulating pipeline production process has acceptable accuracy. In addition, the applicability of the proposed model under different operating condition is also verified. Specifically, the gas accumulation ratio and safety pressure of the entire pipeline are calculated by considering multiple sets of different exhaust Schemes for different flow rates, high points, and the use of pigging devices on two different real pipelines, demonstrating that the model has good generality and feasibility. Secondly, the model can calculate the pressure drop at different positions along the entire pipeline, monitor the position of the pigging device in real time, and simulate comprehensive production exhaust Schemes, demonstrating that the model has comprehensive functionality and some generality. In summary, the proposed model can provide validation and guidance for the subsequent production of related continuous undulating liquid pipelines. • A whole commissioning process simulation model is proposed. • The key process of high point exhausting, pigging and pump-pipe coordination are considered. • The location and volume of gas accumulation and the pipeline pressure drop can be obtained. • Real pipelines are used to prove the practicability of the method. [ABSTRACT FROM AUTHOR]

With the expansion of the scale of the petrochemical industry, the occurrence of safety production accidents has seriously affected people's livelihood and property. Many countries have developed emergency response systems for preventing and responding to kinds of accidents. In this paper, an emergency response framework whose mainly includes the configuration and management of emergency supplies is proposed by a multi-objective optimization method, considering pre-disaster and post-disaster stages. In the pre-accident stage, the configuration plan of emergency materials is obtained by solving the mathematical model according to the actual production of the enterprise. In the post-accident phase, based on the number of materials stored in the previous stage, third-party emergency forces are considered to optimize the delivery of materials by a multi-objective model. Finally, a petrochemical enterprise in Zhoushan, Zhejiang is taken as an example to illustrate the validity and practicability of the proposed framework. The framework can complete rescue work under the requirements of enterprise budget and rescue time, so as to reduce the safety and environmental impact caused by accidents, such as air pollution caused by fire accidents, marine pollution caused by leakage accidents, etc. In the analysis, all scheme in model can complete more than 80% of the commodity scheduling within half an hour, which is better than the actual situation of the enterprise. This study provides the way to reduce material utilization and promote cleaner production for sustainable supplies management in petrochemical enterprise. • A multi-objective method for emergency response of in petrochemical enterprise is proposed. • The multi-objective framework including pre-disaster and post-disaster stages integrates costs and rescue time. • Scheduling in the post-disaster stage considers the role of the third-party emergency force in the petrochemical. • The model gives a variety of schemes that can be chosen, which are beneficial to economical and rescue time. • A real-field case study is used to prove the practicality and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

• An online simulation framework is proposed for natural gas pipeline network. • Kriging-GA method is used to enhance the efficiency of parameter identification. • NARX model is adopted for error compensation caused by physics-based model. • Two cases are performed to verify the effectiveness of the framework. The natural gas pipeline network is an important part of the integrated energy system. The optimal control of the integrated energy system requires accurate transient online simulation of the natural gas pipeline network. However, the existing relevant research does not make enough use of the measured data, resulting in low simulation accuracy. Therefore, a high-accuracy online transient simulation framework is proposed from four perspectives: transient simulation model selection, model parameters settings, measured data processing, and model error compensation. In this framework, measured data is fully used to improve simulation accuracy by integrating multiple physics-based and data-driven methods. First, the simulation model based on the finite volume method is selected as the basic physics-based model. Then measured data are processed based on the 3 times standard deviation method and moving average filtering to drive the simulation model. To set suitable model parameters, a data-driven method combining with the Kriging model and genetic algorithm is proposed to simultaneously identify the two key parameters of roughness and heat transfer coefficient. In addition, the final simulation errors of the physics-based model are compensated by combining the data-driven model in three modes, namely supplement, embedment, and integration. Two cases of an actual single pipeline and a complex pipeline network illustrate the applicability of the proposed framework and verify the advantage of the integrated data-driven and physics-based methods. Compared with measured data, the final simulation errors of mass flow and temperature are 0.3279% and 0.1036%, respectively. Under the trend of delicacy and intelligent operation of natural gas pipeline network, the proposed framework provides a theoretical basis for the online simulation application. [ABSTRACT FROM AUTHOR]

The "dual carbon goal" and "one country network" indicate the greater challenge of balancing supply and demand in natural gas pipeline networks. This paper proposes an optimization method for supply and demand balance of large and complex natural gas pipeline network to respond the demand changes of downstream users by making the peak shaving and flow rate allocation scheme, considering carbon emission targets to improve economic and environmental benefits. This method considers the pipeline network elements including bidirectional pipeline, multiple pipeline structure, compressor station and gas storage so on, and couples the hydraulic characteristics to accurately describe the hydraulic situation of the pipeline under each allocation scheme. Case 1 shows that the economic efficiency is optimized by 15.58% and compensates for the overgrowth demand generated through the initial stage. Compared with TGNET, the average relative error of hydraulic was only 2.89%. Case 2 shows that the optimal flow rate allocation scheme can be further improved in the case of multiple pipelines. Case 3 for a large-scale pipeline network reduces 58% annual carbon emissions by adjusting the flow rate allocation scheme. This study can provide decision support for the allocation and operation of natural gas pipeline network. • A method is proposed to describe the complex natural gas pipeline network system. • A multi-period optimization model of supply and demand balance is proposed. • Economic and environmental benefits are taking as objective function. • The peak shaving and the hydraulic characteristics are considered. [ABSTRACT FROM AUTHOR]

The natural gas pipeline networks play a vital role in Integrated Energy System (IES). Simultaneously, with the increase in the number and types of users, the operational model needs to be transformed from plan-oriented to demand-oriented. Hence, higher requirements are put forward for the flexibility and rapidity of the control methods. In this paper, a novel rapid operation control method for natural gas pipeline networks is proposed based on multi-user demand prediction and pipeline flow state inversion. First, the Nonlinear autoregressive (NAR) neural network is used to analyze the historical consumption of various users and predict each user's demand in the short term. Second, the rapid transient inversion method is derived from the partial differential control equation to realize the inverse of the pipeline flow state. Third, combined with the equipment model, the control method is formed to realize rapid control of the pipeline networks. The control method is applied to classical triangular networks and existing pipeline networks to demonstrate accuracy and effectiveness. The time-consuming ratio of the commercial software and proposed model for the triangular pipeline networks is 1:0.22. For the existing pipeline networks, six users' average demand prediction errors are 5.58%, and the control errors of pressure at three markets are 1.00%, 1.22%, and 0.81%. The results show that the proposed method can respond to users' demands rapidly and provide decision support for the safe and efficient operation of the natural gas pipeline networks. [Display omitted] • A rapid control method is proposed for the natural gas pipeline networks. • Multi-user demand prediction provides advanced guidance for network operation. • An Out-to-In model is derived to inverse the transient flow state of the network. • Two cases are performed to verify the accuracy and effectiveness of the method. [ABSTRACT FROM AUTHOR]

The purpose of flow allocation is to give full play to the transmission capacity of the pipeline network system and complete the transportation task with less operation cost. When the flow allocation cannot balance the changes of upstream gas source and downstream demand, the pipeline network system needs to be planned and constructed. Therefore, this paper aims to propose an optimization method allowing the simultaneous optimization of infrastructure planning and the flowrate allocation in each decide cycle by explicit consideration of the corresponding operational constraints. The constraints include the limit of gas supply and user demand, the transmission capacity of pipeline network, the balance of node flowrate, and the capacity of gas storage. The injection, storage and production capacity of gas storage is considered. A case study including multi scene settings shows the feasibility of the proposed method. The results show that compared with the annual scale, the half-year scale can better consider the peak regulation effect of gas storage, and the utilization rate of gas storage is improved by 12.88%. Multiple options that allow for both expansion and new pipelines are more economical than a single option. Specifically, the total cost under multi-option planning is 8.24% lower than only expansion is allowed, and 8.32% lower than only new construction is allowed. In addition, the sensitivity analysis is carried out to study the impact of the unit pipeline transmission cost, unit construction cost and lead time of pipeline on the pipeline construction scheme. The improvement of gas storage utilization rate, gas storage operation and pipeline network transmission capacity verify that the proposed planning model has the ability to improve the system operation efficiency and reduce the total cost. • A multi-period planning model integrating flowrate allocation is proposed. • The injection, storage and production capacity of gas storage is considered. • A case study for multi-scene settings shows the feasibility of the proposed method. • Sensitivity analysis shows the impact of various factors on the optimal results. [ABSTRACT FROM AUTHOR]

• A methodology is developed to assess gas supply reliability of a pipeline system. • Three aspects of uncertainty and system's hydraulic characteristic are considered. • The gas supply strategy of the natural gas pipeline system is proposed. • The uncertainty in the gas injection/production capacity is considered. Underground gas storage plays a significant role in ensuring gas supply reliability of natural gas pipeline systems. However, the specific hydraulic characteristics of the underground gas storage and the uncertainties in its gas injection/production capacity are generally overlooked when evaluating the gas supply reliability. Therefore, an integrated methodology to assess gas supply reliability of the natural gas pipeline system is developed in this study, and three aspects of uncertainty and hydraulic characteristic of the natural gas pipeline system are both considered. Based on system's gas supply strategy, the amounts of gas supplied by the transmission pipeline system and required by the consumers are calculated firstly. The underground gas storage is then employed to regulate the supply-demand imbalance between the transmission pipeline system and market demand by performing its gas injection/production function. Moreover, the operational reliability of the underground gas storage is evaluated to determine whether it is able to complete the specified gas injection/production task. Then, the total daily amount of gas supplied to the consumers, is obtained, and two indicators proposed to quantify the gas supply reliability are then calculated. Finally, the expected gas supply reliability is assessed based on a large number of Monte Carlo trials. Moreover, the methodology is applied to a simplified gas pipeline system to confirm its feasibility, and system's ability to satisfy the consumers demand is evaluated, and the weakest consumer node is identified. Furthermore, the gas supply reliability is overestimated without considering the uncertainties in the underground gas storage's gas injection/production capacity. [ABSTRACT FROM AUTHOR]