Multi-period optimization of hydrogen supply chain utilizing natural gas pipelines and byproduct hydrogen

Yoon, Ha-Jun; Seo, Sang-Beom; Lee, Chul‐Jin

doi:10.1016/j.rser.2022.112083

Cited by 37 publications

(12 citation statements)

References 44 publications

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“…Comparing with other functions. To compare the proposed model's performance, the function type is replaced with the sine type function for constraint (6). Changing the constraint is still the model becomes Linear Programming (LP).…”

Section: Mathematical Model Consequently It Is Necessary To Make the ...mentioning

confidence: 99%

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A robust and resilience machine learning for forecasting agri-food production

Lotfi

Gholamrezaei

Kadłubek

et al. 2022

Sci Rep

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This research proposes a new framework for agri-food capacity production by considering resiliency and robustness and paying attention to disruption and risk for the first time. It is applied robust stochastic optimization by adding robustness to the constraint's objective function and resiliency situation. This research minimizes the mean absolute deviation and coefficient of standard deviation errors by linear function in the agri-food capacity production. This study suggests agri-food managers and decision-makers use this mathematical method to forecast and improve production management. The results of this research lead to better decision-making and are compared with other sine functions. The main model's Robust and Resiliency Mean Absolute Deviation (RRMAD) value is 1.28% lower than other sine-type functions. The conservativity coefficient, confidence level, weight factor, resiliency coefficient, and probability of the scenario vary. The main model's RRMAD value is 1.28% lower than other sine-type functions. Growing the weight factor will result in an increase in RRMAD and a smooth decline in R-squared. Additionally, as the resilience coefficient rises, the RRMAD function increases while the R-squared declines. By altering the probability of the scenario, the RRMAD function drops, and the R-squared goes up.

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Section: Mathematical Model Consequently It Is Necessary To Make the ...mentioning

confidence: 99%

“…Constraint (5) shows the amount of forecasted production in year i under scenario s. Constraint(6) presents linear regression function that must be fitted. Therefore, polynomial regression is suggested.…”

mentioning

confidence: 99%

A robust and resilience machine learning for forecasting agri-food production

Lotfi

Gholamrezaei

Kadłubek

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…[22] for the first time assessed the economic advantages, the techno-economic feasibility and the central role of reusing by-product hydrogen in the early phase of hydrogen infrastructure in the northern Spain region. A multi-period programming was designed in [23] to make use of existing infrastructure for by-product hydrogen and natural gas (NG) pipelines, which demonstrated the economic benefits of byproduct hydrogen. Even though, the potential of by-product hydrogen remains to be discovered.…”

Section: B Literature Reviewmentioning

confidence: 99%

The Design of By-product Hydrogen Supply Chain Considering Large-scale Storage and Chemical Plants: A Game Theory Perspective

Cao¹,

Li²,

Jia³

et al. 2022

Preprint

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“…Viewed from a broad perspective that covers the whole world, the biggest challenges in hydrogen development are the unavailability of adequate infrastructure, the level of risk of using hydrogen, lack of government support, integration of systems such as codes, regulations and code standards, and requires large costs [34]. In South Korea itself, the toughest challenge in hydrogen development is that the cost of raw materials and construction of infrastructure is still quite expensive [35]. In China, precisely in Guangdong province, a wind power plant has been built to be used as a supplier of electrical energy in the electrolysis of water into hydrogen, but the challenge in its construction is the high construction cost and China has only invested around CNY 6.17 million in infrastructure development [36].…”

Section: Challenges Of Hydrogen Development In the World And In Indon...mentioning

confidence: 99%

Potential and Challenges of Hydrogen Development as New Renewable Energy in Indonesia

Manullang¹,

Sinaga²

2022

r.e.m

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The use fossil energy consumption in Indonesia is increasing along with population growth and industrial development. Fossil energy oil and coal can produce greenhouse gas emissions and environment pollution. The largest producer of CO2 emissions comes from exhaust gases in the production process and motor vehicles. Indonesia's energy mix target in 2025 is around 23% from New and Renewable Energy (NRE). Given the importance of using NRE, Indonesia is starting to look for alternative energy that is environmentally and sustainable, like hydrogen energy. Green hydrogen technology has the potential to be developed in Indonesia. Hydrogen production processes commonly carried out are through the electrolysis of water, methanol, and biomass. The use of hydrogen can be applied to motor vehicles and power plants. Hydrogen can reduce greenhouse gas emissions. However, Indonesia has problems in developing green hydrogen technology, one of which is the high production cost, so it requires other parties to develop it.

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Multi-period optimization of hydrogen supply chain utilizing natural gas pipelines and byproduct hydrogen

Cited by 37 publications

References 44 publications

A robust and resilience machine learning for forecasting agri-food production

A robust and resilience machine learning for forecasting agri-food production

The Design of By-product Hydrogen Supply Chain Considering Large-scale Storage and Chemical Plants: A Game Theory Perspective

Potential and Challenges of Hydrogen Development as New Renewable Energy in Indonesia

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