Estimating Soil Carbon Sequestration of Jatropha for Sustainable Aviation Fuel Pathway

作者： Zhang   Zongwei     Li   Junqi     Wang   Zihan     Liu   Haonan     Wei   Keheng   来源： Water, Air, & Soil Pollution 年份： 2023 文献类型 ： 期刊 关键词： emission   Oil   Aviation   use   sequestration   carbon   Fuel   Sustainable   Land   Jatropha  

描述： two methods for producing Jatropha oil–based aviation kerosene (pathway 1: oil residue used for

Estimating Soil Carbon Sequestration of Jatropha for Sustainable Aviation Fuel Pathway

作者： Zhang   Zongwei     Li   Junqi     Wang   Zihan     Liu   Haonan     Wei   Keheng   来源： Water, Air, & Soil Pollution 年份： 2023 文献类型 ： 期刊 关键词： emission   Oil   Aviation   use   sequestration   carbon   Fuel   Sustainable   Land   Jatropha  

描述： two methods for producing Jatropha oil–based aviation kerosene (pathway 1: oil residue used for

Numerical Modeling of Chemical Kinetics, Spray Dynamics, and Turbulent Combustion towards Sustainable Aviation

作者： Arvid   Åkerblom     Martin   Passad     Alessandro   Ercole     Niklas   Zettervall     Elna   J.   K.   Nilsson     Christer   Fureby   来源： Aerospace 年份： 2023 文献类型 ： 期刊 关键词： spray   Aviation   turbulence   simulations   numerical   combustion   chemical   engines   Fuel   jet   LES   Sustainable   kinetics   supersonic  

描述： jet fuels, Jet A and JP-5, and two alternative jet fuels, C1 and C5, are targeted. The laminar

Life‐cycle analysis of sustainable aviation fuel production through catalytic hydrothermolysis

作者： Peter   Hua   Chen     Uisung   Lee     Xinyu   Liu     Hao   Cai     Michael   Wang   来源： Biofuels, Bioproducts and Biorefining 年份： 2023 文献类型 ： 期刊 关键词： grease   assessment   Aviation   biofuel   Fuel   SAF   catalytic   Sustainable   brown   cycle   life   hydrothermolysis  

描述： Catalytic hydrothermolysis (CH) is a sustainable aviation fuel (SAF) pathway that has been recently approved for use in aircraft fuel production. In alignment with broader sustainable aviation goals, SAF production through CH requires a quantitative assessment of carbon intensity (CI) impacts. In this study, a current‐day life‐cycle analysis (LCA) was performed on SAF produced via CH to determine the CI. Various oily feedstocks were considered, including vegetable oils (soybean, carinata, camelina and canola) and low‐burden oils and greases (corn oil, yellow grease and brown grease). Life‐cycle inventory data were collected on all processes within the CH LCA boundary: feedstock cultivation and/or collection, preprocessing, hydrothermal cleanup and CH, biocrude refining, fuel transportation and end use through combustion. Baseline results show that the CH‐produced SAF can be generated with CI reductions ranging from 48 to 82% compared with conventional jet fuel. Modest improvements to CI can be achieved through incremental changes to the brown grease CH process, such as relaxing the dewatering specification and implementing renewable natural gas and electricity, which could decrease the CI from 22.9 to 7.9 g CO₂e/MJ. Total CH fuel production potential was also assessed on the basis of current or near‐future feedstock availability and CI. The total biofuel production potential of CH (SAF and renewable fuel co‐products) in the US sums to approximately 3487 million gallons per year, with 97% of these volumes having a CI below 50% of that for petroleum jet fuel. The study shows that from an LCA perspective, CH offers a viable SAF pathway that is comparable with existing SAF pathways like hydroprocessed esters and fatty acids.

Numerical Modeling of Chemical Kinetics, Spray Dynamics, and Turbulent Combustion towards Sustainable Aviation

作者： Arvid   Åkerblom     Martin   Passad     Alessandro   Ercole     Niklas   Zettervall     Elna   J.   K.   Nilsson     Christer   Fureby   来源： Aerospace 年份： 2023 文献类型 ： 期刊 关键词： spray   Aviation   turbulence   simulations   numerical   combustion   chemical   engines   Fuel   jet   LES   Sustainable   kinetics   supersonic  

描述： jet fuels, Jet A and JP-5, and two alternative jet fuels, C1 and C5, are targeted. The laminar

Life‐cycle analysis of sustainable aviation fuel production through catalytic hydrothermolysis

作者： Peter   Hua   Chen     Uisung   Lee     Xinyu   Liu     Hao   Cai     Michael   Wang   来源： Biofuels, Bioproducts and Biorefining 年份： 2023 文献类型 ： 期刊 关键词： grease   assessment   Aviation   biofuel   Fuel   SAF   catalytic   Sustainable   brown   cycle   life   hydrothermolysis  

描述： Catalytic hydrothermolysis (CH) is a sustainable aviation fuel (SAF) pathway that has been recently approved for use in aircraft fuel production. In alignment with broader sustainable aviation goals, SAF production through CH requires a quantitative assessment of carbon intensity (CI) impacts. In this study, a current‐day life‐cycle analysis (LCA) was performed on SAF produced via CH to determine the CI. Various oily feedstocks were considered, including vegetable oils (soybean, carinata, camelina and canola) and low‐burden oils and greases (corn oil, yellow grease and brown grease). Life‐cycle inventory data were collected on all processes within the CH LCA boundary: feedstock cultivation and/or collection, preprocessing, hydrothermal cleanup and CH, biocrude refining, fuel transportation and end use through combustion. Baseline results show that the CH‐produced SAF can be generated with CI reductions ranging from 48 to 82% compared with conventional jet fuel. Modest improvements to CI can be achieved through incremental changes to the brown grease CH process, such as relaxing the dewatering specification and implementing renewable natural gas and electricity, which could decrease the CI from 22.9 to 7.9 g CO₂e/MJ. Total CH fuel production potential was also assessed on the basis of current or near‐future feedstock availability and CI. The total biofuel production potential of CH (SAF and renewable fuel co‐products) in the US sums to approximately 3487 million gallons per year, with 97% of these volumes having a CI below 50% of that for petroleum jet fuel. The study shows that from an LCA perspective, CH offers a viable SAF pathway that is comparable with existing SAF pathways like hydroprocessed esters and fatty acids.

Crew recovery optimization with deep learning and column generation for sustainable airline operation management

作者： Ahmet   Herekoğlu     Özgür   Kabak   来源： Annals of Operations Research 年份： 2023 文献类型 ： 期刊 关键词： Artificial   generation   Crew   disruptions   Airline   learning   recovery   Management   Machine   intelligence   problem   optimization   Column   Sustainability   AutoML   Sustainable   Business  

描述： In today’s competitive marketplace, businesses face the ongoing challenge of meeting evolving customer demands while maintaining sustainable practices. For airlines, sustainability is a critical consideration that involves optimizing resource usage. This study addresses the crew recovery problem, an essential aspect of building sustainable business models for airlines. The primary objective is to minimize costs associated with crew disruptions while considering constraints, including flight time limitations. Recovery strategies, realized through actions known as recovery actions, play a pivotal role in addressing crew disruptions. Leveraging historical data, learning-based approaches have the potential to enhance algorithms for large-scale optimization problems. They provide insights that may be overlooked through traditional methods, improving the success of the recovery process. This study presents a column generation-based solution approach for the crew recovery problem, utilizing a customized deep learning model to provide recovery actions as inputs. The methodology is applied to a major European airline company. The results indicate that the model, supported by deep learning outputs, outperforms traditional methods in terms of solution quality and efficiency.

Crew recovery optimization with deep learning and column generation for sustainable airline operation management

作者： Ahmet   Herekoğlu     Özgür   Kabak   来源： Annals of Operations Research 年份： 2023 文献类型 ： 期刊 关键词： Artificial   generation   Crew   disruptions   Airline   learning   recovery   Management   Machine   intelligence   problem   optimization   Column   Sustainability   AutoML   Sustainable   Business  

描述： In today’s competitive marketplace, businesses face the ongoing challenge of meeting evolving customer demands while maintaining sustainable practices. For airlines, sustainability is a critical consideration that involves optimizing resource usage. This study addresses the crew recovery problem, an essential aspect of building sustainable business models for airlines. The primary objective is to minimize costs associated with crew disruptions while considering constraints, including flight time limitations. Recovery strategies, realized through actions known as recovery actions, play a pivotal role in addressing crew disruptions. Leveraging historical data, learning-based approaches have the potential to enhance algorithms for large-scale optimization problems. They provide insights that may be overlooked through traditional methods, improving the success of the recovery process. This study presents a column generation-based solution approach for the crew recovery problem, utilizing a customized deep learning model to provide recovery actions as inputs. The methodology is applied to a major European airline company. The results indicate that the model, supported by deep learning outputs, outperforms traditional methods in terms of solution quality and efficiency.