Superstructure optimization of a microalgal biorefinery design with life cycle assessment-based and economic objectives

Abstract

We present a multi-objective optimization framework for the design of an algal biorefinery with multiple target products. Four environmental endpoint indicators and the economic performance are used as objective functions following the life cycle assessment (LCA) methodology. The process alternatives are modeled as a superstructure covering a total of 720 feasible routes. The proposed method optimizes not only the superstructure route but simultaeously discrete and continuous parameters within the process units. A multi-objective genetic algorithm (MOGA) is used to solve this nonlinear mixed-integer optimization problem to design the extraction procedures for all macromolecular fractions. For the extractions, liquid–liquid equilibria (LLE) are predicted with quantum chemical calculations. A microalgae biorefinery for the marine diatom Phaeodactylum tricornutum is considered as case study, including the cultivation and extraction-supported fractionation of the wet algal biomass to harvest the target products eicosapentaenoic acid (EPA), laminarin and fucoxanthin. 2-Butanol proved to be the preferred solvent for the initial extraction step of wet biomass. Nutrients and solvent production cause most of the environmental impact of the overall process. A Python package for integrating LCA with multi-objective superstructure optimization is provided as open source software. It is applicable to any process system design task involving environmental objectives.