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Modeling Industrial Materials Flows to Reduce Energy Demand and Carbon Emissions

August 29, 2016

By Rebecca Hanes and Alberta Carpenter, NREL

Advanced modeling tools from the U.S. Department of Energy laboratory network support CEMAC's insights and analysis on the manufacturing of clean energy technologies. Among these, NREL's Materials Flow through Industry (MFI) supply chain modeling tool, developed with support from the Advanced Manufacturing Office, can identify and analyze opportunities to reduce the energy and carbon intensities from U.S. manufacturing.

The MFI tool tracks materials, resources, energy, water use, and greenhouse gas emissions throughout commodity supply chains, allowing users to determine the large-scale impacts and benefits of future manufacturing scenarios. Supply chain analyses also enable the identification of "hot spot" processes, which contribute a disproportionately large quantity to a supply chain's total energy consumption and emissions generation. Knowing where the hot spots occur in a particular supply chain—and which processes and materials are typically hot spots—helps prioritize research efforts towards prospective processes and materials that can offer significant benefits. The MFI tool is based on a database of 656 industrial commodities and 1,394 "recipes," or input-output models of material and fuel consumption by manufacturing processes. The tool combines the database with scenario parameters to derive commodity supply chain models under various manufacturing scenarios.

Red and green network diagram of the NREL's Materials Flow through Industry recipe database, where lines and dots resemble the relationship from one commodity to another.

Network diagram of the Materials Flow through Industry recipe databasee, where each node represents a commodity manufactured in the U.S. Green nodes have recipe information in the MFI database, and red nodes do not. A connection between nodes indicates that a commodity is used to produce another commodity.

Users can also define custom scenarios by changing technology mixes, implementing energy efficiency improvements, and implementing material substitutions. Technology mixes are the combination of processes, or recipes, from which each commodity is produced, and MFI has multiple recipe options for many commodities—including both currently implemented and next-generation technologies. The electricity grid mix can also be changed to any of the nineteen different grid mixes in the MFI database; alternatively, the user can define a custom grid mix. Efficiency potentials are used to model improvements in energy efficiency that results from process equipment upgrades, such as replacing an outdated water heater. These efficiency potentials can be implemented in one or several recipes, or throughout a supply chain to model a hypothetical best case efficiency scenario. Additionally, users have the option to leave all parameters at their baseline levels to model current U.S. industrial practice.

The flexible structure of the MFI tool allows for the easy addition of new recipes and a strong connection to the industry ecosystem. By default, no user-added data is visible to other users, although users can make their data publically available after NREL analysts review the data to ensure quality and functional correctness. By extending the database with custom recipes, users can model the supply chain of a specific manufacturing process and analyze the impacts of facility-level decisions—including the types of fuels used and equipment upgrades. Custom recipes also allow users to include the product use phase and end-of-life processing for a more complete, cradle-to-grave analysis.

Overall, the MFI tool provides results in five main categories:

  • Total energy consumption
  • Electricity consumption
  • Greenhouse gas emissions
  • Total material consumption
  • Water consumption.

Results are presented as supply chain totals that provide a high-level overview of the supply chain, in addition to subtotals for each step in the supply chain. Two additional sets of results that help identify hot spots show the materials that are consumed in the largest quantities throughout the supply chain, and the materials that account for the largest portion of the supply chain energy consumption.

As a demonstration of MFI functionality, NREL researchers analyzed 12 scenarios for the manufacture of cast aluminum, while changing the smelting technology (one of the most energy-intensive steps in the aluminum supply chain), the ratio of primary to secondary or recycled metal in the cast aluminum, and implementing energy efficiency improvements. Energy efficiency improvements were implemented at the process scale and at the supply chain scale, and the ratio of primary to secondary aluminum was changed incrementally from 48% primary and 52% secondary to 25% primary and 75% secondary. Two hypothetical "best case" scenarios combined next-generation technology with a supply chain increase in energy efficiency—and a significant increase in aluminum recycling. Seen below, the MFI results indicate that all alternative manufacturing scenarios offer reductions in fuel consumption and emissions over the baseline scenario.

Scatterplot graph showing supply chain emissions and fuel consumption for cast aluminum under several scenarios. Results show that alternative manufacturing scenarios offer fuel reduction.

Supply chain emissions and fuel consumption for cast aluminum under 12 manufacturing scenarios.

These results can be used in conjunction with information on the economics of implementation for each scenario to determine the best strategy to reduce fuel consumption and emissions in the aluminum industry. Advanced tools such as the MFI are what allow CEMAC to continually provide world class analysis and insight into the supply chains and manufacturing for clean energy technologies.