Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

Abstract

Biomass provides a renewable pathway to support current and future energy needs for liquid transportation fuels, and is also being investigated as a low net carbon feedstock for electricity generation. To leverage this renewable source of energy requires the development and utilization of biomass resources beyond the current production levels. One source of renewable biomass energy feedstock is agricultural residues. However, a recent study[1] identified six factors that limit sustainable agricultural residue removal, and it stated that a comprehensive assessment of sustainable residue removal limits must consider each of the six factors. These factors are: (1) soil organic carbon, (2) wind and water erosion, (3) plant nutrient balances, (4) soil water and temperature dynamics, (5) soil compaction, and (6) off-site environmental impacts. Each of these factors is described by a set of disparate and heterogeneous models that are not currently integrated together. In addition each of the models has been validated, developed, and is currently maintained by a subject area expert separate from the other models. Recoding the complete set of models into a single monolithic software structure is impractical due to the time needed to develop and validate the completed set of models. Instead an extensible software framework is needed that can integrate the model set together enabling analysis and optimization of agricultural residue harvest for energy usage. This paper presents an integrated modeling strategy that incorporates these model sets together with the needed GIS information within a single integrated computational engineering framework. This integrated computational engineering framework has been implemented to facilitate high fidelity spatial assessments of biomass resource management. A case study demonstrating initial implementations of the resulting interactive analysis and optimization framework is presented. The case study demonstrates how multiple constraints can be simultaneously considered as a part of assessing sustainable agricultural residue removal potential.

Simulated impact of the renewable fuels standard on US Conservation Reserve Program enrollment and conversion

“…For residue harvesting, we assume residues must be retained at regionally specific levels estimated by Muth et al . () to assure that wind and rain erosion are kept at tolerable levels and soil carbon is maintained. But, if the required residues were to change, it could change the harvestable residue quantities, land‐use changes, and prices to meet RFS2.…”

“…The amount of crop residue produced depends on the crop yield and the harvest index or ratio of residue to grain (1 to 1 for corn) (United States Department of Energy, 2011). The amount that can be sustainably removed is governed by the retention coefficients, which are estimated from application of the revised Universal Soil Loss Equation 2 (RUSLE2) and the Wind Erosion Prediction System (WEPS) models incorporating the soil conditioning index and tillage (Muth et al, 2011). The retention coefficients for corn range from 0.13 to 11.67 Mg ha À1 with an average of 3.86 Mg ha À1 .…”

U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry

“…The amount that can be sustainably removed is governed by the retention coefficients, which are estimated from application of RUSLE2 and WEPS models incorporating the soil conditioning index and tillage (Muth et al, 2011). The amount that can be physically removed depends on the combined efficiency of the collection equipment (e.g., shredders, rakes, and balers).…”

Dynamic pricing model for cruising taxicab based on system dynamics

“…The cruising taxi pricing system is a multi-variable, multi-stakeholder (taxi drivers, operators, passengers), multi-feedback complex system. 14,15 There are many uncertain factors in the process of its development and evolution. Meanwhile, the various factors interact with each other and each variable is constantly evolving and changing.…”