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Trash is cash – Turning unmerchantable crop and forest residues into high-value

Canadian PI: Dr. Anthony Lau                   

Canadian Institution: The University of British Columbia

Indian PI: Dr. Sonal Thengane

Indian Institution: Indian Institute of Technology (IIT) Roorkee

Project Summary:

Most of the world’s fertilizers are produced in large-scale, capital-intensive, centralized facilities, and then shipped to rural areas (FAO, 2016). Because of the mark-up from long-distance logistics, farmers in remote communities often pay 2-5 times the world price for fertilizers. During COVID and the Russian-Ukrainian war, the interrupted global supply chain has caused fertilizer shortages and price spikes, leading to severe food insecurity. A representative farmer, Mrs. Wacecilia, told us: “I’m afraid that we’ll not have any food from this land for our children.” 

Using a new patent-pending chemical process (oxygen-lean torrefaction), we are developing a new generation of small-scale, portable, low-cost bioconversion systems that can support a network of decentralized, self-sufficient, and carbon-negative bioeconomies in rural, underserved communities, helping them recover from the pandemic-related supply chain disruptions in a carbon-negative way. Using locally available crop/forest residues that would otherwise be burned in open-air as the input feedstock, our process can engineer the residues into higher-value, carbon-negative bioproducts such as fertilizer blends that improve moisture/nutrient retention in the soil, leading to reduced need for synthetic chemical fertilizers. Furthermore, through an internet-of-things (IoT)-based real-time control architecture, we can customize the reaction conditions in response to specific soil/crop types, thereby optimizing the fertilizer blend at the single-farm specificity. Finally, by verifying/aggregating the carbon credits on their behalf, our IoT architecture allows rural, decentralized communities to access carbon credits for the first time, thereby achieving climate justice. 

In this project, researhcers will focus on a new machine learning approach to optimize, control, and coordinate smart, real-time fleets of these reactors, and test the hybrid hardware-software approach with commercialization partners both in India and Canada. In addition to implementation, commercialization, and scale-up by our industrial partners, the work output will be open-sourced codes/datasets that can apply to other industrial processes to build self-sufficient rural communities in light of supply chain crises caused by COVID and the Russian-Ukrainian war.