(361f) A Computational Platform for Assessing Organic Waste Management Strategies in the Food-Energy-Water Nexus

The areas of food, energy, and water are crucial sectors in human life, and they are posing significant challenges in economic, environmental, and social areas as the population keeps growing. One of the main challenges is to properly manage the organic waste generated from those sectors to support the well-functioning of the whole society [1]. However, the nature of interactions and interdependencies makes the food, energy and water (FEW) sector a very complex nexus system, and this nature is inherited to the organic waste management problem in the FEW nexus. For example, the dairy industry in the State of Wisconsin contains 1,280,000 dairy cows which can produce dairy food products, but also 6,400,000 kilogallons of manure annually [2]. The cow manure, in turn, can be used in anaerobic digestion to produce biogas and energy. In addition, the organic waste generated in the nexus, such as animal manure and food waste, are highly distributed and usually needs collection and central processing [3]. Considering the complexity of the nexus and the nature of the organic waste, supply chain management (and optimization) has been proven as an efficient method for analyzing such type of holistic, integrated, and multi-media problem and estimating its overall economic and environmental impacts [4].

In the field of process systems engineering, many works have studied the design and optimization of supply chains of organic waste management. A generalized modeling network has been proposed to capture the fundamental elements in the supply chain [5]. In addition, there are numerous case studies which focus on a specific spatial area and contain different types of biomass as basic feedstock, such as cow manure, wastewater sludge, and crop residues [6,7]. These case studies demonstrate how appropriate waste processing can achieve monetary savings and environmental improvement for communities, and how this information is valuable for different stakeholders and policy-makers. However, such types of work highly rely on dependable data inputs, especially in product conversion factors and product conversion costs. And for some cutting-edge technologies (e.g. struvite recovery, co-digestion of different feedstocks), it is difficult to get trustable data.

To provide best practices for integrating computational and experimental work in organic waste and nutrient management science, and to enhance stakeholder engagement, we have developed an easy-to-use computational platform. The platform has two modes: a demo mode and an expert mode. In the demo mode, we have included results and conclusions from several pre-solved cases to allow stakeholders to do parametric studies and observe how individual-level changes (e.g., investment on certain technologies) can impact the whole supply chain system. And in the expert mode, the stakeholders are able to define the scope of their own organic waste management problem using a graphic interface and upload their own data (e.g., product conversion factors for certain technologies). Some basic databases containing data from published literature, the technical support for modeling and solving the supply chain problem will be provided. The results visualization is also automated using a back-end GIS tool. The capability of this platform will be discussed, and a simple tutorial will be given.

References:

[1] Finley J W, Seiber J N. The nexus of food, energy, and water[J]. Journal of agricultural and food chemistry, 2014, 62(27): 6255-6262

[2] USDA National Agricultural Statistic Service: Milk Production and Mild Cows. https://www.nass.usda.gov/Charts_and_Maps/Milk_Production_and_Milk_Cows/

[3] Angelidaki I, Ellegaard L. Codigestion of manure and organic wastes in centralized biogas plants[J]. Applied biochemistry and biotechnology, 2003, 109(1-3): 95-105

[4] Pan S Y, Du M A, Huang I T, et al. Strategies on implementation of waste-to-energy (WTE) supply chain for circular economy system: a review[J]. Journal of Cleaner Production, 2015, 108: 409-421.

[5] Sampat A M, Martin E, Martin M, et al. Optimization formulations for multi-product supply chain networks[J]. Computers & Chemical Engineering, 2017, 104: 296-310.

[6] Hu Y, Scarborough M, Aguirre-Villegas H, et al. A Supply Chain Framework for the Analysis of the Recovery of Biogas and Fatty Acids from Organic Waste[J]. ACS Sustainable Chemistry & Engineering, 2018, 6(5): 6211-6222.

[7] You F, Wang B. Life cycle optimization of biomass-to-liquid supply chains with distributed–centralized processing networks[J]. Industrial & Engineering Chemistry Research, 2011, 50(17): 10102-10127.