Reducing Pollution From Agriculture and Mining
Agriculture and mining are two important industries in Minnesota, but they are also associated with significant environmental concerns due to the carbon-intensive production of nitrogen fertilizers and the high energy intensity of mining operations. To decarbonize these two industries, it may be possible leverage the versatility of green ammonia that is produced from renewable resources and can serve as both a sustainable fertilizer and a carbon-free fuel. Moreover, green ammonia allows further decarbonization of related manufacturing processes such as steelmaking. However, in Minnesota, solar and wind availability, farming activities, and mining operations are spatially and temporally distributed, such that their integration becomes a complex systems-engineering problem.
As a PhD student and UMII MnDRIVE PhD Graduate Assistant, Hanchu Wang (Chemical Engineering) worked on a project called “Decarbonizing the Agricultural and Mining Industries in Minnesota via Green Ammonia and Synergistic Supply Chain Optimization.” This project developed a state-wide computational network model that captures the major operations and interactions, including wind-based electricity generation, green ammonia production, transportation, utilization as fertilizer and energy carrier, as well as relevant mining and manufacturing operations. The model allows determination of the optimal locations of wind farms, ammonia plants, distribution centers, and transportation links.
To reduce computational complexity, geographical discretization was employed, dividing Minnesota into regions based on 5-degree intervals of latitude and longitude. A wind potential profile is provided for each of these regions. Agricultural fertilizer demand is determined at the county level, and six significant iron ore mines are incorporated to account for mining energy requirements. The objective is to minimize overall costs while ensuring that all energy and fertilizer demands are met with renewable resources.
The mathematical models were implemented in AIMMS and solved using CPLEX. The model was solved to its optimality, and the results are illustrated in the figure below. The heat map provides insights into wind potential, while the dashed lines indicate ammonia transportation. The yellow solid lines represent electricity transmission, and the factory icons denote the green ammonia production plants, with node size reflecting their capacity. The lighting icons represent wind farms, and the diamond icons represent iron ore sites. In the optimal network design, one observes two significant wind farms, one located near mining sites and another in proximity to agricultural areas. Regarding the trade-off between electricity generation and transmission, wind farms are not located in areas with the highest wind potential. Multiple green ammonia production facilities are recommended in southern Minnesota and northeast Minnesota to fulfill local fertilizer and fuel requirements, respectively.
The project also includes a user interface. This interface empowers users with more precise datasets to optimize network designs and potentially assists policymakers in the decarbonization efforts of both the agricultural and mining sectors in Minnesota.
Dr. Wang was awarded her PhD in 2023. She did this research under the supervision of MSI PIs Qi Zhang (assistant professor, Chemical Engineering and Materials Science) and Prodromos Daoutidis (professor, Chemical Engineering and Materials Science) and used MSI resources for the project.
The UMII MnDRIVE Graduate Assistantship program supported U of M PhD candidates pursuing research at the intersection of informatics and any of the five MnDRIVE areas:
- Robotics
- Global Food
- Environment
- Brain Conditions
- Cancer Clinical Trials
This project was part of the Environment MnDRIVE area.
The Graduate Assistantship program has been converted to the Data Science Initiative-MnDRIVE Graduate Assistantship program. Research supported by the program is at the intersection of data science and the five MnDRIVE areas. Proposals must align with one of three data science tracks: Foundational Data Science; Digital Health and Personalized Health Care Delivery; and Agriculture and the Environment. The deadline for the program beginning in the Spring Term 2025 is 5 pm CDT, October 4, 2024.
Image description: Optimal design and supply network based on 2020 data.
