Value chain optimization for the industrial deployment of novel photochemical synthetic pathways
Value chain optimization for the industrial deployment of novel photochemical synthetic pathways
批准号:
2896293
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
Photochemistry is capable of disruptively changing value and supply chains by considering alternative, preferentially renewable and green feedstock. Taking these feedstocks rather than oil and natural gas will change the chemical industry as we know it and as it has developed since the onset of industrialization: Commodity chemicals are produced on large scale in multiple steps often starting from steam cracking. In many cases supply chains are integrated on a single site ("Verbund"-site) for improved heat integration logistics, and safety in pipes. Therefore, changing the initial feedstock from natural gas to bio-based methanol for example will have major implications on the supply and value chains. While this change is widely accepted on our way to carbon neutrality, the technical and economical impact on the chemical industry needs to be considered to maintain commercial viability and competitiveness. Photochemistry is a prime example of technology that has the potential to utilize biogenic feedstock for chemical conversions. In this project we plan to pave the way toward implementing photochemistry in the large scale chemical industry by investigating the technoeconomical aspects and the changes of value and supply chains. We will consider changing boundary conditions, including the shift to renewable feedstock and CO2-taxation. We will pick up examples investigated within BASF's photochemistry cluster at Imperial, including the fabrication of di-acids with CO2 as a reagent to be used in the polyester and polyamide fabrication and the conversion of methanol to mono-ethyleneglycol (MEG). The project will be holistic along the R&D workflow and along the value and supply chains and aim at benchmarking and optimizing future processing options. This project will require an interest in holistic challenges of chemical engineering. It will consider machine learning algorithms and theoretical assessments rather than experimental work.
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