Technoeconomic analysis of fermentative-catalytic biorefineries: model improvement and rules of thumb
The NSF Engineering Research Center for Biorenewable Chemicals (CBiRC) has as its primary mission the transformation of the US chemical industry from one that relies primarily on fossil carbon (i.e., petroleum derivatives) to one that uses biorenewable carbon (i.e., photosynthetically derived carbon – typically sugar) as a primary feedstock. The work reported in this thesis, at its core, aimed to provide CBiRC with better tools to predict the costs and lifecycle impacts of proposed pathways to new biorenewable chemicals. The CBiRC LCA team has developed and/or used multiple tools to assess the economic viability – or more specifically, the estimated minimum selling price (e.g., cost of production) – for several biorenewable chemicals. The CBiRC LCA team has developed and/or used multiple tools to assess the economic viability – or more specifically, the estimated minimum selling price (e.g., cost of production) – for several biorenewable chemicals. Specifically, they have developed proof-of-concept technoeconomic analyses (TEAs) based on approximately six key parameter estimates. They have also conducted detailed TEAs on later-stage processes using commercially available software such as Aspen™ and SuperPro®. The Raman group also developed a model that is more complex than the proof-of-concept TEA, but simpler than SuperPro-based models, initially referred to as BioPET (Biorenewables Process Evaluation Tool).
This thesis begins in Chapter 2 with a reorganizing and expansion of BioPET. The improved and expanded version was given the new moniker of ESTEA (Early Stage Technoeconomic Analysis). Specific improvements made in the transition of BioPET to ESTEA included the following: (1) clarifying data flow and overall spreadsheet organization, (2) revising labor costs (3) accounting for solvent costs (4) accounting for consumption of organic material (5) adding additional hydrolysis unit operations (6) adding a greenhouse gas emission estimation block and (7) providing new macro-based graphs and analyses. The latter part of chapter 2 describes validation activities related to ESTEA. ESTEA was run with process parameters appropriate to the production of Ethanol, Succinic Acid and Adipic Acid. The resulting MESP values were compared with estimates from more detailed process models in SuperPro (Intelligen Inc., Scotch Plains, NJ), BioPET using results from a previous Masters student in the Raman group, Joshua T. Claypool (Claypool and Raman, 2013), and literature values.
In Chapter 3, we use ESTEA as a framework for examining the impact of key process parameters on estimated unit cost of product. Specifically, computer code was written to allow exploration of a large parameter-cost-space, and the results were analyzed to develop simple generalizations, or “rules-of-thumb” regarding the relationship between key process parameters and MESP. Computer code (written in Microsoft VBA within Excel) enabled the systematic manipulation of key inputs while recording the impact on the minimum estimate selling process (MESP) predicted by ESTEA. This provided insight into the influence of process parameters on overall cost. Finally, Chapter 4 summarizes the key findings from this work.