Decarbonizing the Gas-to-Liquid (GTL) Process Using an Advanced Reforming of Methane Process

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2023-12-11 DOI:10.1021/acsengineeringau.3c00025
Zeinab Ataya, Mohamed Challiwala, Gasim Ibrahim, Hanif A. Choudhury, Mahmoud M. El-Halwagi and Nimir O. Elbashir*, 
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Abstract

The gas-to-liquid (GTL) process is a promising technology for converting natural gas into synthetic fuels and chemicals. However, its high carbon dioxide (CO2) emissions present significant challenges. Methane reforming contributes up to 60% of GTL’s CO2 emissions, necessitating decarbonization. Dry reforming of methane (DRM) shows potential for CO2 conversion. Still, it faces challenges such as high energy requirements, catalyst deactivation, and an incompatible hydrogen-to-carbon monoxide (H2/CO) ratio for GTL processing, requiring extensive research. A previous study proposed a two-reactor system known as CARGEN that co-produces solid carbon (in the form of multiwalled carbon nanotubes [MWCNTs]) and syngas, reducing CO2 emissions by 40% compared to the benchmark autothermal reforming (ATR) process through life cycle assessment (LCA) studies. This paper presents a comprehensive simulation of the advanced DRM process used to retrofit an existing ATR-based GTL plant─initially, a 50,000 bbl./day ATR-based GTL plant is simulated. The advanced reformer process replaces ATR through retrofitting. Comparative analysis shows a remarkable 73% reduction in net CO2 emissions and the potential coproduction of 243 kg of MWCNTs per barrel of syncrude, equivalent to 12,150 tons/day of MWCNTs. However, the advanced reformer-based GTL plant requires 61% more natural gas feedstock while utilizing 79% less oxygen than the ATR-based plant. Furthermore, a separate techno-economic analysis examines the advanced reformer-based GTL plant based on a calculation for 13,100 tons/day of CO2 feedstock to co-produce 3,277 tons/day of MWCNTs and 50,000 barrels/day of syncrude. This analysis, considering a 25% tax rate, 25-year plant life, and zero salvage value, demonstrates an attractive 10-year payback period at selling prices of 80 USD/bbl. for syncrude and 10 USD/kg for MWCNTs. These results provide a process system-level perspective, showcasing the advanced reformer-based GTL plant (CARGEN Process) as an effective solution for low-carbon GTL production.

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利用先进的甲烷重整工艺实现气液化 (GTL) 过程的去碳化
气变液(GTL)工艺是将天然气转化为合成燃料和化学品的一项前景广阔的技术。然而,其较高的二氧化碳(CO2)排放量带来了巨大挑战。甲烷重整产生的二氧化碳排放量占 GTL 排放量的 60%,因此必须实现脱碳。甲烷干重整(DRM)显示了二氧化碳转化的潜力。但它仍然面临着高能量要求、催化剂失活、氢气与一氧化碳(H2/CO)的比例与 GTL 处理不相容等挑战,需要进行广泛的研究。之前的一项研究提出了一种名为 CARGEN 的双反应器系统,该系统可同时生产固体碳(以多壁碳纳米管 [MWCNTs] 的形式)和合成气,通过生命周期评估 (LCA) 研究,与基准自热转化 (ATR) 工艺相比,可减少 40% 的二氧化碳排放量。本文全面模拟了用于改造现有 ATR 型 GTL 工厂的先进 DRM 工艺--首先模拟了一个日产 50,000 桶 ATR 型 GTL 工厂。先进的重整工艺通过改造取代了 ATR。对比分析表明,二氧化碳净排放量显著减少了 73%,每桶合成原油可生产 243 千克的微小纤维无机氮化合物,相当于每天生产 12,150 吨微小纤维无机氮化合物。然而,基于先进重整装置的 GTL 工厂所需的天然气原料比基于 ATR 的工厂多 61%,而使用的氧气却比后者少 79%。此外,一项单独的技术经济分析还对基于先进重整装置的 GTL 工厂进行了研究,该工厂以 13100 吨/天的二氧化碳原料为计算基础,联合生产 3277 吨/天的 MWCNTs 和 50000 桶/天的 syncrude。考虑到 25% 的税率、25 年的工厂寿命和零残值,该分析表明,在同步原油 80 美元/桶和 MWCNTs 10 美元/千克的销售价格下,10 年的投资回收期极具吸引力。这些结果提供了一个工艺系统级的视角,展示了基于先进重整装置的 GTL 工厂(CARGEN 工艺)是低碳 GTL 生产的有效解决方案。
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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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