Solar fuel production via supercritical water gasification of algae biomass
Creators: name: Rahbari, Alireza
Publisher: name: The Australian National University
Year: 2020

description: Greenhouse gas (GHG) emissions from liquid fuel consumption account for nearly one third of the global anthropogenic emissions. Sustainable carbon-neutral fuel production is imperative to meet the global GHG emissions reduction targets set by the Paris Agreement. Thermochemical conversion of biomass can provide carbon-neutral high calorific-value fuels for subsequent conversion to liquid fuels. Supercritical water gasification (SCWG) is such a process, and converts wet biomass and carbonaceous waste. Compared to conventional gasification, SCWG offers more flexibility in terms of feedstock, lower char/tar formation, higher yield, and reduced feedstock-drying costs. It can be argued that algae are the ideal feedstocks for the SCWG process. Algae, as a renewable biomass source, is not a seasonal crop, has a high growth rate, can be cultivated even in brackish water, and contains high fixed carbon. Integrating the SCWG process with a concentrated solar thermal (CST) heat source offers a renewable and carbon-free replacement to traditional routes, with higher energy efficiency and syngas yield. Production of syngas via SCWG of algae typically results in methane which requires a reforming step before the syngas is suitable for downstream Fischer-Tropsch (FT) or methanol synthesis (MS) process. There is a lack of analysis of the system-level challenges and economic feasibility of these concepts in the literature. Motivated by this research gap, this thesis investigates the techno-economic performance of the proposed solar fuel plant. Steady-state physical models of the upstream solar-SCWG-reforming plant at a fixed CST input of 50 MWth are developed in Aspen Plus software. The methane reforming techniques considered are steam methane reforming, autothermal reforming and partial oxidation/dry reforming. In order to obtain a compatible composition of syngas for a downstream application, two scenarios are evaluated here: (i) discarding carbon in the form of carbon dioxide from the SCWG reactor, and (ii) supplying renewable hydrogen from PV-electrolysis into the algae-derived syngas. Optimal process parameters are determined through an exergy-based optimisation of the integrated plant. The subsequent conversions of solar-syngas to synthetic gasoline/diesel and to methanol are modelled in Aspen Plus software. In order to mitigate the influence of solar fluctuations, there is an on-site syngas storage acting as a buffer between the upstream gasification and downstream FT/MS units. To explore the dynamic behaviour of the plant under the variable solar resource throughout the year, a system-level energy model is developed using polynomial performance curves of the gasification/FT/MS units along with control logic for cut-off points, syngas storage dynamics, and predictive dispatch. The cost analysis of the captured scenarios includes the system capital, operating and maintenance costs, from which the levelised cost of fuel (LCOF) is calculated. Single- and multi-objective genetic algorithms have been formulated to investigate the economic and overall system performance objectives individually and collectively, and to speculate how each impacts the optimal design of the considered configurations. There is a detailed uncertainty analysis with respect to key economic parameters and ramping times of SCWG-reforming and FT/MS reactors at the optimal design of the plant from the economic and system performance perspectives. A range of optimal configurations are found, which vary greatly based on the available cost of renewable hydrogen for both gasoline/diesel and methanol processes. Although the LCOF of the proposed solar fuel plant is relatively high compared with conventional petroleum-based fuels, further opportunities to lower the LCOF are foreseen through cost savings in algae farming and hydrogen production, upscaling the solar field, and moving the plant to an area of higher DNI.