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Inman, Daniel J

Publications and source records attributed to Inman, Daniel J.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Data-Driven Modeling of Spatially Explicit Air Pollutant Inventories for Agricultural and Forestry Feedstock Production

As biomass feedstock production expands, air pollutants generated from feedstock supply chain activities may impact air quality and human health. The National Renewable Energy Laboratory's Feedstock Production Emissions to Air Model (FPEAM) generates county-scale inventories of direct air pollutant emissions produced by feedstock supply chain activities. FPEAM can also be used to generate local, temporally resolved inventories for input into air quality models. This presentation provides an overview of FPEAM functionality and discusses several recent use cases prior to FPEAM's scheduled public release in September 2019. FPEAM is a modular, data-driven model. Each module contains calculations for an activity type, except for the feedstock transportation and on-farm equipment operation modules which connect to the U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES) and Nonroad models, respectively. The data-driven aspect of FPEAM means that it is the input data provided for a scenario rather than the model itself that determines the pollutants and supply chain activities included in the scenario scope. Modules within FPEAM can also be turned on or off manually to adjust the scenario scope. Default input data packaged with the FPEAM code base can be used to calculate inventories of volatile organic compounds, particulate matter, ammonia, nitrogen oxides, sulfur oxides, and carbon monoxide generated by agricultural and forestry equipment operation, application of nitrogenous fertilizers, herbicide and insecticide, and feedstock transportation. Custom, user-developed data can be used in place of the default data to analyze scenarios with additional or alternate feedstocks, pollutants, and supply chain activities.

09 BIOMASS FUELS↗
DOE OSTI2019

Chapter 1: Insights From Over 10 Years of Cellulosic Biofuel Modeling

TBDWe present insights gained from over 10 years of system dynamic modeling of the cellulose to biofuel industry in the United States. We use a publicly-available Biomass Scenario Model to explore the impact of logistics system, economies of scale, and shared industrial learning on the developing cellulose-to-biofuels industry in the United States. One theme from this study as well as from the work performed over the last decade is the importance of the movement of the system toward maturation, both in terms of the supply system and the conversion processes. Mature processes imply lower investment risk, better yields, and better process economics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Spatially Explicit Modeling of Criteria Air Pollutants from Agricultural and Forestry Feedstock Production

The Feedstock Production Emissions to Air Model (FPEAM) is a spatially explicit supply chain model that quantifies criteria air pollutants and precursors generated by biomass production, harvest and transportation to biorefineries. FPEAM was most recently used for the 2016 Billion Ton Study (BTS) published by the U.S. Department of Energy Bioenergy Technologies Office. This presentation describes several use cases for the model, and provides an overview of recent restructuring that increases FPEAM's usability and functionality. FPEAM currently estimates emissions of volatile organic compounds, particulate matter, nitrogen oxides, sulfur oxides, and carbon monoxide from on-farm equipment operation, on-farm fugitive dust, on-farm chemical (fertilizer, herbicide and insecticide) application, and biomass transportation. These calculations are implemented within FPEAM as modules, which independently perform calculations for each emissions category. This structure allows users to include or exclude emissions categories according to the goal and scope of their analysis. Each module's input data defines the activities taking place with information including fuel combusted, chemicals applied, and equipment used. Users can replace or augment the default input data to quantify additional pollutant types, emissions categories, farming practices and biomass types. Emissions from biomass transportation and on-farm equipment operation are by default calculated by the U.S. Environmental Protection Agency's Motor Vehicle Emission Simulator (MOVES) and NONROAD model, respectively. These models - which are computationally intensive but provide detailed results - can optionally be replaced with user-provided emissions factors for fuel combustion and vehicle operation. This approach reduces FPEAM's runtime and allows users to run sensitivity analyses around biomass production and transportation scenarios. Other enhancements currently under development are the use of farm-to-biorefinery route information to increase the spatial resolution of transportation emissions, and a module to connect FPEAM and the Intervention Model for Air Pollution (InMAP), which will enable the quantification of human health impacts.

09 BIOMASS FUELS↗
DOE OSTI2018

Assessing Potential Air Pollutant Emissions from Agricultural Feedstock Production using MOVES

Biomass feedstock production is expected to grow as demand for biofuels and bioenergy increases. The change in air pollutant emissions that may result from large-scale biomass supply has implications for local air quality and human health. We developed spatially explicit emissions inventories for corn grain and six cellulosic feedstocks through the extension of the National Renewable Energy Laboratory's Feedstock Production Emissions to Air Model (FPEAM). These inventories include emissions of seven pollutants (nitrogen oxides, ammonia, volatile organic compounds, particulate matter, sulfur oxides, and carbon monoxide) generated from biomass establishment, maintenance, harvest, transportation, and biofuel preprocessing activities. By integrating the EPA's MOtor Vehicle Emissions Simulator (MOVES) into FPEAM, we created a scalable framework to execute county-level runs of the MOVES-Onroad model for representative counties (i.e., those counties with the largest amount of cellulosic feedstock production in each state) on a national scale. We used these results to estimate emissions from the on-road transportation of biomass and combined them with county-level runs of the MOVES-Nonroad model to estimate emissions from agricultural equipment. We also incorporated documented emission factors to estimate emissions from chemical application and the operation of drying equipment for feedstock processing, and used methods developed by the EPA and the California Air Resources Board to estimate fugitive dust emissions. The model developed here could be applied to custom equipment budgets and is extensible to accommodate additional feedstocks and pollutants. Future work will also extend this model to analyze spatial boundaries beyond the county-scale (e.g., regional or sub-county levels).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model

Variance-based sensitivity methods can provide insights into large computational models. We present a novel application of sensitivity analysis to the Biomass Scenario Model (BSM) a large and complex system dynamics model of the developing biofuels industry in the United States. We apply a two-stage sensitivity approach consisting of an initial sensitivity screening, followed by a variance decomposition approach. Identifying key system levers and quantifying their strength is not straightforward in complex system dynamics models that have numerous feedbacks and nonlinear results. Variance-based sensitivity analysis (VBSA) offers a systematic, global approach to assessing system dynamics models because it addresses nonlinear responses and interactive effects. Especially when a large model's size makes manual exploration of the input space difficult and time-consuming, the approach can help to provide a comprehensive understanding of interactions that drive model behaviors.

97 MATHEMATICS AND COMPUTING↗