MINLP model and algorithm for superstructure optimization of algae processing network

Abstract

Abstract This work sheds light on the economic viability of algal biorefinery for biological carbon sequestration and hydrocarbon biofuel production. We develop a new superstructure of the algal biorefinery and manage to incorporate a plethora of state-of- the-art technology pathways into seven stages of the process, which are cultivation, harvesting, primary dewatering, secondary dewatering, cell disruption, lipid extraction and upgrading. We include four types of bioreactors for algae cultivation: open pond, flat plate photobioreactor, bubble column photobioreactor, tubular photobioreactor; six types of flocculants for harvesting: polyelectrolyte, sodium hydroxide, polyaluminium chloride, aluminium sulfate, chitosan and poly γ-glutamic acid; two primary dewatering techniques: centrifugation and pressure filtration; two advanced dewatering methods: freeze-drying and thermal drying; five cell disruption methods: grinding, high pressure homogenization, bead beating, microwave and sonication; organic solvent extraction with different types of extractants and supercritical carbon dioxide extraction; several transesterification pathways to produce biodiesel and glycerol as well as two hydroprocessing pathways ending up with renewable diesel and propane in the final upgrading stage. We propose a mixed-integer nonlinear programming model to minimize the unit carbon sequestration and utilization cost. In order to enhance the computation efficiency, we apply a tailored branch-and-refine algorithm on the basis of successive piecewise linear approximation to solve the problem to global optimality. If the biorefinery selects open pond, polyelectrolyte, pressure filtration, butanol extraction and sodium-methoxide-catalysed transesterification under the current market conditions, we are able to sequester the CO2 at an economically competitive cost of $0.644/t CO2.