Cu/ZnO/ZrO2-SAPO-34串联体系用于CO2和H2合成二甲醚:催化剂优化、技术经济和碳足迹分析

IF 4.3 Q2 ENGINEERING, CHEMICAL
Jasan Robey Mangalindan, Fatima Mahnaz, Jenna Vito, Navaporn Suphavilai and Manish Shetty*, 
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引用次数: 0

摘要

为了减轻与人为排放相关的有害影响,使用CO2和H2作为原料,通过串联催化剂将其转化为二甲醚(DME)是一种有吸引力的可持续途径。首先,我们研究了Cu- zno - zro2 (CZZ)和硅铝磷酸SAPO-34双功能外加剂对CO2加氢制二甲醚的催化活性,并优化了它们的反应活性,重点确定了CZZ的最佳合成条件,包括Cu:Zn:Zr的摩尔比和老化和煅烧温度。CZZ-611在40℃时效和500℃煅烧条件下甲醇(MeOH)产率最高,为10.8 mol kgcat-1 h-1。当与SAPO-34偶联时,CZZ/SAPO-34在优化条件下(260°C, 500 psig, 2000 mL gczz1 h - 1)达到20%的CO2转化率和56%的DME选择性,并且稳定50 h,活性略有降低。接下来,我们进行了动力学建模,将实验室规模的发现转化为工业填充床反应器,然后进行了技术经济分析(TEA),并进行了从摇篮到闸门的环境足迹评估,以评估其工业适用性。一个20000吨/年二甲醚工厂的TEA显示,原材料成本是主要的运营成本驱动因素(H2成本占总成本的47%)。考虑到绿色H2(4美元/公斤H2)和捕获的CO2作为饲料,二甲醚的最低销售价格(MDSP)为3.21美元/公斤,比市场价格(1.2美元/公斤)高出2.7倍。使用灰色H2时,MDSP降至1.99美元/公斤(1美元/公斤H2),随着资本支出(±30%)和其他经济因素的变化,MDSP波动为±0.14美元。植物碳足迹主要受H2源的影响。绿色和灰色H2的排放量分别为0.21和4.4 kg CO2当量/kg二甲醚。重要的是,通过使用直接从空气中捕获的绿色H2和CO2,可以实现负碳足迹。总的来说,我们的工作表明,串联催化是一种很有前途的可持续二甲醚生产方法,并确定了使其与化石燃料相比具有成本竞争力的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tandem Cu/ZnO/ZrO2-SAPO-34 System for Dimethyl Ether Synthesis from CO2 and H2: Catalyst Optimization, Techno-Economic, and Carbon-Footprint Analyses

To alleviate detrimental effects associated with anthropogenic emissions, the use of CO2 and H2 as feedstocks for their conversion to dimethyl ether (DME) with tandem catalysts is an attractive and sustainable route. First, we investigated the catalytic activity of bifunctional admixtures of Cu-ZnO-ZrO2 (CZZ) and a silicoaluminophosphate, SAPO-34, for CO2 hydrogenation to DME and optimized their reactivity with an emphasis on identifying optimum synthesis conditions for CZZ including Cu:Zn:Zr molar ratio and aging and calcination temperatures. The highest methanol (MeOH) productivity (10.8 mol kgcat–1 h–1) was observed for CZZ-611 aged at 40 °C and calcined at 500 °C. When coupled with SAPO-34, CZZ/SAPO-34 reached 20% CO2 conversion and 56% DME selectivity at optimized conditions (260 °C, 500 psig, and 2000 mL gCZZ–1 h–1) and was stable for 50 h time-on-stream, with a slight reduction in activity. Next, we performed kinetic modeling to translate lab-scale findings to industrial packed-bed reactors followed by a techno-economic analysis (TEA) with cradle-to-gate environmental footprint evaluation to evaluate its industrial applicability. A TEA of a 20,000 tpy DME plant revealed raw material costs as the main operating cost drivers (H2 cost comprises 47% of total cost). Considering green H2 ($4/kg H2) and captured CO2 as feed, the minimum DME selling price (MDSP) was $3.21/kg, ∼2.7× higher than the market price ($1.2/kg). MDSP drops to $1.99/kg with gray H2 ($1/kg H2) and fluctuates ±$0.14 with changes in CAPEX (±30%) and other economic factors. The plant’s carbon footprint was mainly affected by the H2 source. Green and gray H2 resulted in emissions of 0.21 and 4.4 kg CO2 eq/kg DME, respectively. Importantly, a negative carbon footprint can be achieved by using green H2 and CO2 captured directly from air. Overall, our work shows tandem catalysis as a promising approach toward sustainable DME production and identifies the pathway toward making it cost-competitive with fossil fuels.

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来源期刊
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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