<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-22T07:57:45Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/64DDD07D-9A54-41EA-9BF9-3D5022751DA0" ns1:id="64DDD07D-9A54-41EA-9BF9-3D5022751DA0"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/ABECC644-5071-424E-8AED-9D3D53869410" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/709C8D66-D346-4AFE-B8DA-5612802676D7" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/709C8D66-D346-4AFE-B8DA-5612802676D7" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2023-09-29T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/6221C302-16EB-4756-B56F-7D5B754D2B0B" ns1:rel="FUND" ns1:start="2022-09-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10031051</ns2:identifier></ns2:identifiers><ns2:title>AI Process Mapper for the Waste &amp;amp; Recycling Industry</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>SortFlow Limited is a London-based cleantech company which has developed, a platform to design, model and virtually test waste recycling processes.

In this project, we will develop a new software module which takes data collected from optical sorting equipment and Artificial Intelligence (AI) vision systems in waste sorting and recycling plants, and integrates it into an AI powered live process map. The AI Process Mapper will provide to plant operators real time actionable insight into process performance, causes for underperformance and corrective action recommendations.

As part of the project, we will process the data collected from pilot plants through machine learning models using 'supervised learning' to identify patterns and connections between the output qualities measured and sorting equipment infeed data in the sorting lines to understand the reasons behind performance variations and identify opportunities to improve, optimise processes and make performance predictions.

The solution will optimise plant recycling processes and provide better recovery and purity of recyclable waste materials, which will in turn help to reduce the amount of recyclables being landfilled or incinerated (and the associated CO2 emissions) and increase revenue for waste and recycling facilities.</ns2:abstractText></ns2:project>