Developing a novel tool to optimize life cycle greenhouse gas emissions related to renewable diesel and lubricant production

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The proposed project aims to develop a novel tool to optimize life cycle greenhouse gas (GHG) emissions across a new process that generates bio-based lubricants and diesel products from biomass. The technology is the property of the industrial partner, Altranex Corporation. The proposed tool will allow Altranex to a) determine GHG emissions across the putative process, b) benchmark these emissions against conventional lubricant and diesel products, and c) dynamically adjust process elements to identify minimal GHG emissions across the process. With respect to the last point, the tool will essentially link process elements, including the choice of feedstock (virgin feedstocks versus waste oils) and the reuse of waste heat and synthesis gas produced during the process, so that changes at one point in the process will affect upstream and downstream stages. The optimization will be automated in order to test a wide range of configurations and conditions. A spreadsheet-based life cycle assessment (LCA) model will be established in accordance with ISO guidelines, and the optimization tool will be developed in Visual Basic. The results of this project will provide Altranex with a novel methodology for undertaking dynamic optimization of LCA results and help them to identify the best configuration for their process to facilitate future scale up and commercialization. Although the current focus is on GHG emission, the methodology could also be used in other midpoints and endpoints in future. The PI (Dr. Warren Mabee) and the supporting partner (Altranex Corporation) will cooperatively share the process data and the LCA model/optimization tool to accomplish the six-month research project. The novelty of the project is rooted in the combination of optimization tools with LCA and siting the prospective plant and availability of feedstocks in real-world geographies. The proposed project offers high-quality training for one PDF in multiple aspects related to LCA modeling and biotechnology, specifically related to application and commercialization in the clean tech industry.