(383y) A Multiscale Modeling and Optimization Framework for the Techno-Economic and Impact Assessment of Novel Technology Pathways

Policy intervention has the potential to accelerate the adoption of low-cost and sustainable technology pathways. Measures include tax credits and incentives, and targeted investment. Moreover, novel technology pathways can also take advantage of untapped resources (e.g. exhaust, agricultural waste) or improve efficiency at scale, which provides benefits such as energy security and economic growth. Nevertheless, emerging technologies need to be bench-marked against extant technologies with higher technology readiness, and thus a more certain cost and efficiency profile. To this end, we present a framework to evaluate and compare multiple technologies under uncertainty in multiple aspects, such as technology cost, efficiency, resource price, market demand, etc.

Our framework is demonstrated over one promising techno-economic avenue, i.e. CO₂ valorization, where carbon dioxide from flue gases is captured and converted into value-added products such as biofuels, bioplastics, chemicals, and fertilizers. These products can substitute fossil-based alternatives, supporting decarbonization and advancing circular economy goals. Despite their potential, CO₂ valorization technologies face significant challenges - particularly the transition from lab to commercial scale. Sensitivity and uncertainty analysis allows decision-makers to hedge against the risk posed by market fluctuations and the uncertainty in scale-up. Essentially, allowing an informed and uncertainty-aware bench-marking of novel technology pathways against extant systems.

The proposed integrated multi-scale framework spans numerous temporal and spatial dimensions. At the process scale, high-fidelity models are developed to support optimization, control, and model-based design of experiments. At the plant and supply chain level, techno-economic analysis (TEA) and life cycle assessment (LCA) are used, in tandem, to evaluate commercial as well as environmental performance. As a case study, the framework is demonstrated on a novel algae-based CCUS system, where a fast-growing, resilient microalgae strain converts CO₂ captured from a traditional natural gas power plant into value-added products such as bio-fuels, lipids, proteins, and biofertilizers. The analysis identifies key cost drivers and trade-offs, providing actionable insights for researchers, policymakers, and potential investors.

Research Interests -

1) Machine Learning / Deep Learning

2) Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA)

3) First Principles based Modeling, Optimization and Control