(204b) Techno-Economic Uncertainty Quantification of Algal-Derived Biocrude Via Hydrothermal Liquefaction Process | AIChE

(204b) Techno-Economic Uncertainty Quantification of Algal-Derived Biocrude Via Hydrothermal Liquefaction Process


Jones, S., Pacific Northwest National Laboratory
Zhu, Y., Pacific Northwest National Laboratory
Schmidt, A. J., Pacific Northwest National Laboratory
Billing, J. M., Pacific Northwest National Laboratory
Using microalgae for fuel production via hydrothermal liquefaction (HTL) has received increasing attention in recent years because of its high biocrude productivity and quality, cultivation flexibility, and potential for large scale operation. Most of the current knowledge on algae HTL is based on laboratory benchtop findings and/or simulation models. Additionally the conversion mechanism has not been well studied, while the simplified yield correlations or kinetics models were usually developed based on small batch equipment. The lack of commercial experience and knowledge gaps include, but not limited to, feedstock variability, reaction mechanism, overall reaction heat, optimal operating condition, reactor design, thermal-hydraulic properties of algae slurry, and nutrient recycling. These gaps can be addressed through techno-economic uncertainty quantification as a promising method to understand the process economics and potential risks. However, to the best of authors’ knowledge, vary limited stochastic simulations in the open literature considered both rigorous process model with parameter uncertainty and uncertainty regarding to economic assumptions, especially for algae HTL processes.

With the above motivations, techno-economic uncertainty quantification is conducted for algae HTL processes based on abundant operating data of PNNL’s continuous HTL reactor systems. Particularly, a “component additivity” model is developed to predict the yields and qualities of algae HTL process of a variety of algal biomass with model uncertainty quantified. Monte Carlo simulations are conducted using a rigorous process and economic model developed in Aspen Plus and Excel. Oracle Crystal Ball is used to generate semi-random Monte Carlo samples based on pre-determined probability distributions of model inputs, and perform statistical analysis of the results. Aspen Simulation Workbook and Excel VBA Macro are used to build dynamic links between those software. This presentation will focus on the framework of techno-economic uncertainty quantification, “component additivity” model of algae HTL reactor, plant-wide process and economic models, and the techno-economic uncertainties of algae HTL processes.