(486a) Responsible Innovation in Chemical Process Design: Stakeholder-Driven Multi-Objective Optimization of a Modular Food Waste Valorization Process | AIChE

(486a) Responsible Innovation in Chemical Process Design: Stakeholder-Driven Multi-Objective Optimization of a Modular Food Waste Valorization Process


Abramovitch, M. - Presenter, University of California, Santa Barbara
Ierapetritou, M., University of Delaware
Food waste is produced at staggering rates (1.3 billion tons/year),[1] consumes vast amounts of energy (26 exajoules/year) and is a significant source of land, water, and air pollution.[2,3] Food waste is also a major component of the organic matter that flows into landfills, and it imposes substantial costs—through collection and disposal—on individuals, businesses, and local and state governments.[4] In addition to poor economics and environmental footprint, the odors, increased traffic, and public health impacts related to landfills are shown to disproportionately burden low-income and minority communities.[5] It is therefore imperative to not only develop technologies that divert food waste from landfills, but to do so through a systems approach that simultaneously considers economic, environmental, and societal objectives.

Since food waste is a geographically distributed feedstock, modular processing at the sources (e.g., restaurants, cafeterias, grocery stores, etc.) can minimize overall costs and environmental impact by eliminating the need to transport raw materials to a centralized processing facility. When designing distributed chemical or biological processes for implementation in such public areas, it is critical to work closely with all stakeholders involved; these include business owners and employees, municipal and state policymakers, and members of the general public. Each of these groups has their own set of priorities and constraints which must be met to ensure the process is practical, supported by policy, and safe to implement.

To address these challenges, we develop a stakeholder-driven multi-objective optimization framework for the design of modular processes that upgrade food waste to valuable chemical products. Our approach combines SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis with a fuzzy analytic hierarchy process (FAHP) [6] to, for the first time, allow public and private-sector stakeholders to both propose and weight the quantitative metrics used as objectives in process optimization. For example, business owners will naturally want to maximize profitability metrics such as net present value, while policymakers may prioritize emissions reduction. By having stakeholders generate objectives via SWOT analysis and then systematically quantifying the priority of each objective via FAHP, we minimize the non-technological barriers to adoption of the feasible process alternatives resulting from the optimization. This novel approach to “responsible innovation” [7] in chemical process design increases the likelihood that the proposed technologies have beneficial real-world impact.

As a proof-of-concept, we apply this method to the optimization of a simplified model for a modular food waste valorization process involving the following basic steps: (1) fungal aerobic digestion of mixed food waste to produce liquid “digestate,” (2) de novo synthesis of aromatics from digestate using engineered microbes, (3) separation (e.g., adsorption) of products from the reactor effluent for subsequent purification. Process optimization is carried out using standard routines, and techno-economic analysis and life-cycle assessment are implemented to evaluate economic and environmental objectives, respectively. We demonstrate the effect of different stakeholder priorities on the optimal process design and the resulting economic and sustainability metrics. Additionally, scenario analysis is conducted to assess the sensitivity of the optimal design to uncertain economic and physical parameters and other assumptions. Future work will include the development of more detailed empirical and semi-empirical models of each process unit, allowing the proposed multi-objective optimization framework to be used for process synthesis.

  1. (2019) Key facts on food loss and waste you should know! http://www.fao.org/save-food/resources/keyfindings/en/
  2. DSM Environmental Services (2017) Analysis of Organic Diversion Alternatives.
  3. United States Environmental Protection Agency (2010) Greenhouse Gas Reporting Program (GHGRP). https://www.epa.gov/ghgreporting/ghg-reporting-program-data-sets
  4. (2016) A Roadmap to Reduce U.S. Food Waste by 20 Percent. https://www.refed.com/downloads/ReFED_Report_2016.pdf
  5. Mohai P, Saha R (2015) Which came first, people or pollution? Assessing the disparate siting and post-siting demographic change hypotheses of environmental injustice. Environmental Research Letters, 10:1–17. https://doi.org/10.1088/1748-9326/10/11/115008
  6. Darshini D, Dwivedi P, Glenk K (2013) Capturing stakeholders’ views on oil palm-based biofuel and biomass utilisation in Malaysia. Energy Policy, 62:1128–1137. https://doi.org/10.1016/j.enpol.2013.07.017
  7. Stilgoe J, Owen R, Macnaghten P (2013) Developing a framework for responsible innovation. Research Policy, 42(9):1568–1580. https://doi.org/10.1016/j.respol.2013.05.008


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