Sustainable manufacturing of platform chemicals from biomass

从可持续的原料中生产平台化学品是非常可取的。木质纤维素生物质(如木材和农业废弃物)是满足全球对平台化学品需求所需的一些碳和氢的潜在可再生来源，而不需要使用土地，否则土地将被用来种植粮食。这种生物质可以很容易地通过一种称为气化的过程转化为所谓的合成气，合成气中含有H2、CO的混合物，由于生物质的含氧量很高，还含有大量的二氧化碳。主要来自化石燃料的合成气已经在工业规模上转化为甲醇，并通过费托反应(FTR)转化为长链烃和含氧物。合成气中的任何二氧化碳都不会显著参与FTR，因此二氧化碳中的碳没有得到利用。此外，尽管FTR的产率很高，但选择性很差，生成的产品具有广泛的碳氢链长分布。该项目的总体目标是开发和优化可扩展的均相“受挫路易斯对”(FLP)催化剂和可持续溶剂的可扩展组合，选择性地将生物质衍生合成气(包括二氧化碳馏分)转化为平台化学品。将使用多个并行间歇反应器和一个流动反应器，结合在线和离线分析方法来生成催化剂性能数据；将使用动力学建模和/或人工智能等方法对数据进行分析，以阐明有关催化循环和非循环过程(如催化剂失活)的信息。这种数据驱动的方法可以用于优化催化剂结构，甚至通过自动化的LabVIEW界面实时优化流动中的反应条件。该项目最初的重点将是实现对乙烯的高选择性，乙烯很容易转化为各种产品，如聚合物。这种新颖的选择性催化剂应用于高价值平台化学品的定向合成时，将得到更好的利用，这些化学品随后用于制造长期使用的产品，这些产品可以隔离碳，而不是通常由FTR生产的低得多的运输燃料，这些燃料很快就会燃烧，向大气中释放更多二氧化碳，加剧全球变暖。

The manufacture of platform chemicals from sustainable feedstocks is highly desirable. Ligno-cellulosic biomass (e.g. wood and agricultural waste) is a potentially renewable source of some of the carbon and hydrogen required to meet the global demand for platform chemicals, without requiring the use of land, which would otherwise be used to grow food. This biomass can be readily converted by a process known as gasification to a so-called syngas, containing a mixture of H2, CO and, owing to the high oxygen content of biomass, a large amount of CO2. Synthesis gas, derived mainly from fossil fuels, is already converted on an industrial scale to methanol, and to long chain hydrocarbons and oxygenates by the Fischer-Tropsch reaction (FTR). Any CO2 in the syngas does not participate significantly in the FTR, thus the carbon in CO2 is not utilised. Furthermore, whilst the FTR has a good yield, the selectivity is poor, yielding a product with a wide distribution of hydrocarbon chain lengths.The overall goal of this project is to develop and optimise a scalable combination of homogeneous 'frustrated Lewis pair' (FLP) catalyst and sustainable solvent, to selectively convert biomass-derived syngas, including the CO2 fraction, into platform chemicals. Multiple parallel batch reactors and a flow reactor, coupled with on- and off-line analytical methods, will be used to generate catalyst performance data; this will be analysed using e.g. kinetic modelling and/or artificial intelligence to elucidate information on the catalytic cycle, and off-cycle processes such as catalyst deactivation. This data-driven approach may be used to optimise the catalyst structure, and even to optimise the reaction conditions in flow, in real time, through the automated Labview interface.The initial focus of the project will be on achieving high selectivity to ethene, which is readily converted into myriad products, such as polymers. Such novel, selective catalysts would be much better utilised when applied to the targeted synthesis of high value platform chemicals subsequently used to make long-lived products, which sequester carbon, rather than much lower value transport fuels typically produced by the FTR, which are quickly burned, releasing more CO2 into the atmosphere and contributing to global warming.