A Hinkley Center supported research project
Evaluating and Optimizing the Value of Anaerobic Digestion of Food Waste Using Sensitivity Analysis and Machine Learning
About • Team • Technical Advisory Group & Quarterly Reports
About the Project
This project aims to advance the state-of-the-art of anaerobic digestion (AD) of food waste by evaluating the variability and uncertainty of AD performance as a function of food waste composition and AD operating parameters, and to develop a machine-learning based predictive tool that can estimate AD performance and corresponding economic and environmental impact metrics.
Food waste (FW) is one of the largest fractions of wet-organic waste. It’s estimated that one-third of all food produced for human consumption is wasted globally (FAO, 2011). In the U.S., 35 million of tons of FW is landfilled representing a significant resource and economic loss (EPA,2020). Anaerobic digestion (AD) is a biological conversion process that can be used as an alternative to landfilling to reduce negative environmental impacts and support valuable resource recovery from FW (Cruz et al, 2012; Choi et al., 2022). Two limitations to the adoption of AD technology include economic viability (Cruz et al, 2022), and performance stability due to compositional variability of the FW. For low volumes of waste, the capital cost associated with AD and biogas upgrading can outweigh the potential value of the biogas. However, AD has been gaining increasing attention due to recent policy and economic incentives aimed at reducing greenhouse gas emissions by either diverting FW from landfills or incentivizing renewable natural gas (RNG) production. In addition, the inherent compositional variability of FW (e.g., carbohydrate, protein, lipid content) can have a significant effect on AD performance and stability. Therefore, by developing tools that allow us to better understand and predict the performance of AD of FW we can more easily address potential process stability challenges and improve its economic viability.
Results from this project will be shared with the project’s Technical Advisory Group, which includes city, county, and industry stakeholders, to aid in development of future AD projects and to support sustainable FW management.
About • Team • Technical Advisory Group & Quarterly Reports
