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<p>Hydrogen is universally recognized as a highly viable and sustainable energy carrier. However, transporting hydrogen to customers is a significant challenge. A cost-effective method for transporting hydrogen on a larger scale involves blending it with natural gas (NG). Nevertheless, there is uncertainty regarding the maximum quantity of hydrogen that can be transported by blending it with natural gas (NG) while adhering to practical constraints. Owing to the limitations of the maximum operating pressure and velocity constraints, each natural gas pipeline network (NGPN) has a different proportion of hydrogen that can be blended. In this scenario, the paper presents a novel computational optimization model to enhance the operational plan of the hydrogen blend natural gas pipeline network (HB-NGPN). The model’s primary objective is to manage hydrogen blending percentages in the natural gas pipeline network (NGPN). The model comprehensively articulates the fundamental principles governing the hydraulics of gas pipelines and compressor stations, facilitating the simulation of the HB-NGPN. An NGPN, initially intended to transport natural gas (NG), was used to implement the developed model. An evolutionary algorithm, ‘improved ant colony optimization (IACO),’ optimizes the HB-NGPN. The operational variables were investigated and compared with previously published work to determine how varying the quantities of hydrogen affect them. Our results show an increase in permissible hydrogen content (10.40%) compared to previously published work utilizing the generalized reduced gradient (GRG) technique (6.65%) and the ant colony optimization (ACO) technique (9.85%). We examined this issue using a bi-, tri-, and tetra-objective scale. The novelty of this paper lies in its review of HB-NGPN on tri-objective and tetra-objective scales, which few researchers have previously focused on. The study is expected to function as a decision-support tool for repurposing NGPN by including hydrogen injection.</p> Graphical Abstract <p></p>

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Optimized computational framework for hydrogen blending in natural gas pipeline networks: analyzing hydrogen injection algorithms and operational parameters

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • Devanshi Srivastava,
  • Adarsh Kumar Arya,
  • Murali Pujari,
  • Bhalchandra Shingan

摘要

Hydrogen is universally recognized as a highly viable and sustainable energy carrier. However, transporting hydrogen to customers is a significant challenge. A cost-effective method for transporting hydrogen on a larger scale involves blending it with natural gas (NG). Nevertheless, there is uncertainty regarding the maximum quantity of hydrogen that can be transported by blending it with natural gas (NG) while adhering to practical constraints. Owing to the limitations of the maximum operating pressure and velocity constraints, each natural gas pipeline network (NGPN) has a different proportion of hydrogen that can be blended. In this scenario, the paper presents a novel computational optimization model to enhance the operational plan of the hydrogen blend natural gas pipeline network (HB-NGPN). The model’s primary objective is to manage hydrogen blending percentages in the natural gas pipeline network (NGPN). The model comprehensively articulates the fundamental principles governing the hydraulics of gas pipelines and compressor stations, facilitating the simulation of the HB-NGPN. An NGPN, initially intended to transport natural gas (NG), was used to implement the developed model. An evolutionary algorithm, ‘improved ant colony optimization (IACO),’ optimizes the HB-NGPN. The operational variables were investigated and compared with previously published work to determine how varying the quantities of hydrogen affect them. Our results show an increase in permissible hydrogen content (10.40%) compared to previously published work utilizing the generalized reduced gradient (GRG) technique (6.65%) and the ant colony optimization (ACO) technique (9.85%). We examined this issue using a bi-, tri-, and tetra-objective scale. The novelty of this paper lies in its review of HB-NGPN on tri-objective and tetra-objective scales, which few researchers have previously focused on. The study is expected to function as a decision-support tool for repurposing NGPN by including hydrogen injection.

Graphical Abstract