(30c) Towards a Two-Level Superstructure Optimization Framework for Land Use Based on Food-Energy-Water Nexus

Authors: 
Xiao, X., Institute of Process Engineering, Chinese Academy of Sciences
Nie, Y., University of Chinese Academy of Sciences
Avraamidou, S., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Li, J., The University of Manchester
Pistikopoulos, S., Texas A&M University
Land use optimization is the result of competitions between different land types [1]. The main reasons of these competitions are the quantitative constraints of land scales and corresponding Food-Energy-Water (FEW) resources under multiple conflicting objectives [2, 3]. Despite FEW resources play critical important roles for sustaining and improving human life, increasing production demands and sustainable concerns intensify these competitions due to their limitation. How to coordinate these competitions is a complex task for reasonable land allocation based on limited FEW resources while promoting efficiencies and sustainability [4, 5]. A nexus thinking by considering the FEW flow and interactions in multi-objective land use systems has become apparently necessary for efficient use of FEW resources and trade-off decisions [6, 7]. However, challenges arise in representing quantitative FEW-Nexus that supports land-use decisions while encountering conflict objectives, limited data, and multiple scales [8, 9].

To address these challenges, we develop a multi-objective optimization framework by considering total profit, food production, resources use, and environmental penalty as multiple objectives for land use systems. The proposed framework suggests a two-level superstructure optimization method: (1) In the first level, models of all the production units are developed by data-driven modeling and global optimization methods based on limited realistic data. FEW flow among them are quantified and interlinked to construct interval models, which can be represented as interval superstructures. The small-scale MINLP problems can be solved efficiently due to the limited combinations of land units in the interval subsystems; (2) In the second level, multiple interval models with optimal land and FEW allocations are used to construct the extended systematic network and represented as a large-scale superstructure, which can be solved as a MILP problem. A series of FEW indices are provided for decision-makers to analysis nexus in the system, carry out quantitative assessment based on different objectives, and achieve trade-off solutions. The framework is illustrated by a case study on the crop-livestock system, which shows that it can provide multiple land allocation solutions and predicts corresponding yields for land units within the systems under flexible scales. Computational results from interval models indicate that we achieve optimal solutions in the interval subsystems, and valuable production models for yield prediction with high confidence. The performance of these models can be improved by increasing feedback data [10]. The systematic network provides an efficient method for extending the land allocation solutions to multiple land scales. For multiple objectives, the proposed FEW index can be applied to select strategies for optimal land allocation that maximizing food productivity and minimizing water and energy consumptions.

Reference

[1] Memmah, M. M., Lescourret, F., Yao, X., & Lavigne, C. (2015). Metaheuristics for agricultural land use optimization. A review. Agronomy for sustainable development, 35(3), 975-998.

[2] Bergstrom, J. C., Goetz, S. J., & Shortle, J. S. (2013). Land use problems and conflicts: Causes, consequences and solutions. Routledge.

[3] Keairns, D. L., Darton, R. C., & Irabien, A. (2016). The energy-water-food nexus. Annual review of chemical and biomolecular engineering, 7, 239-262.

[4] Miralles-Wilhelm, F. (2016). Development and application of integrative modeling tools in support of food-energy-water nexus planning—a research agenda. Journal of Environmental Studies and Sciences, 6(1), 3-10.

[5] Simpson, G., & Berchner, M. (2017). Water-energy nexus-Measuring integration: towards a water-energy-food nexus index. Water Wheel, 16(1), 22-23.

[6] Garcia, D. J., & You, F. (2016). The water-energy-food nexus and process systems engineering: a new focus. Computers & Chemical Engineering, 91, 49-67.

[7] Ringler, C., Bhaduri, A., & Lawford, R. (2013). The nexus across water, energy, land and food (WELF): potential for improved resource use efficiency?. Current Opinion in Environmental Sustainability, 5(6), 617-624.

[8] McCarl, B. A., Yang, Y., Schwabe, K., Engel, B. A., Mondal, A. H., Ringler, C., & Pistikopoulos, E. N. (2017). Model Use in WEF Nexus Analysis: a Review of Issues. Current Sustainable/Renewable Energy Reports, 4(3), 144-152.

[9] McCarl, B. A., Yang, Y., Srinivasan, R., Pistikopoulos, E. N., & Mohtar, R. H. (2017). Data for WEF Nexus Analysis: a Review of Issues. Current Sustainable/Renewable Energy Reports, 4(3), 137-143.

[10] Nie, Y., Avraamidou, S., Li, J., Xiao, X., & Pistikopoulos, E. N. (2018). Land use modeling and optimization based on food-energy-water nexus: a case study on crop-livestock systems. 13th International Symposium on Process Systems Engineering; Elsevier; Accepted.