Dynamic Geospatial Modeling of the Building Stock To Project Urban Energy Demand.

In the United States, buildings account for more than 40% of total energy consumption and the evolution of the urban form will impact the effectiveness of strategies to reduce energy use and mitigate emissions. This paper presents a broadly applicable approach for modeling future commercial, residential, and industrial floorspace, thermal consumption (heating and cooling), and associated GHG emissions at the tax assessor land parcel level. The approach accounts for changing building standards and retrofitting, climate change, and trends in housing and industry. We demonstrate the automated workflow for California and project building stock, thermal energy consumption, and associated GHG emissions out to 2050. Our results suggest that if buildings in California have long lifespans, and minimal energy efficiency improvements compared to building codes reflective of 2008, then the state will face a 20% or higher increase in thermal energy consumption by 2050. Baseline annual GHG emissions associated with thermal energy consumption in the modeled building stock in 2016 is 34% below 1990 levels (110 Mt CO2eq/y). While the 2020 targets for the reduction of GHG emissions set by the California Senate Bill 350 have already been met, none of our scenarios achieve >80% reduction from 1990 levels by 2050, despite assuming an 86% reduction in electricity carbon intensity in our "Low Carbon" scenario. The results highlight the challenge California faces in meeting its new energy efficiency targets unless the State's building stock undergoes timely and strategic turnover, paired with deep retrofitting of existing buildings and natural gas equipment.

Machine learning for surrogate process models of bioproduction pathways.

Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.

Technoeconomic analysis for biofuels and bioproducts.

Technoeconomic analysis (TEA) is an approach for conducting process design and simulation, informed by empirical data, to estimate capital costs, operating costs, mass balances, and energy balances for a commercial scale biorefinery. TEA serves as a useful method to screen potential research priorities, identify cost bottlenecks at the earliest stages of research, and provide the mass and energy data needed to conduct life-cycle environmental assessments. Recent studies have produced new tools and methods to enable faster iteration on potential designs, more robust uncertainty analysis, and greater accessibility through the use of open-source platforms. There is also a trend toward more expansive system boundaries to incorporate the impact of policy incentives, use-phase performance differences, and potential impacts on global market supply.