From Mass Balance models to Digital Twin technology: How SortFlow is looking to put new life into MRF data

From Mass Balance models to Digital Twin technology: How SortFlow is looking to put new life into MRF data

From Mass Balance models to Digital Twin technology: How SortFlow is looking to put new life into MRF data

Material Recovery Facilities (MRFs) provide the critical in-between stage of sorting and segregating recyclable materials. MRFs face mounting pressure to meet the increasing demand for sustainability, but their performance is not easily monitored and the material they process vary in composition – sometimes significantly – impacting the whole process. As a result, it is currently difficult for MRFs to work to their full potential, at least not constantly. To add to the complexity, degradations in performance are not always perceivable, and are sometimes identified days and even weeks after the problem starts occurring.

At SortFlow, we started our journey by developing a software application, SortFlow Process, to build mass balance models for sorting processes. The mass balance method is applied in our industry for the performance modelling of these sorting processes. These models provide great insight into the process expected performance and additional calculations can be derived from these such as commercial performance and financial modelling. If such a tool provides huge benefits when it comes to modelling new and existing facilities, mass balance models are only representative of the average plant performance and are not designed to identify any issues occurring live nor do optimisation work on plant settings.

This is what inspired us to develop the first digital twin application for MRFs and recycling facilities, SortFlow AI Mapper. It enables monitoring in real time the performance of a sorting process using live data. One of the key strengths of SortFlow AI Mapper is that it collects data from multiple sources and brands of equipment to create a digital representation of the sorting process – which can be used to make data-driven decisions to optimise the sorting process.

As MRFs become more advanced, operators must keep track of all the equipment and technologies involved in the sorting process. AI Mapper makes this task more manageable by providing intuitive and user-friendly dashboards and reports. The system highlights any critical issues or deviations, enabling operators to address them proactively. The benefits of SortFlow AI Mapper extend beyond optimising sorting processes and improving efficiency. The tool aims to help MRF operators meet regulatory compliance, reduce residue disposal and cut costs.

With the first phase of development completed and pilot tests still ongoing, this is just the beginning of the journey for our digital twin application. As for all our applications, we plan to continuously release new features and functionalities, so stay tuned over the coming months as we will be sharing more information and releasing exciting product updates.

With SortFlow AI Mapper, we are launching a pioneering Digital Twin application for the waste and recycling industry. By creating a digital representation of the facility, collecting data from multiple sources, and providing real-time analytics and simulations, SortFlow helps operators optimise their operations, reduce waste disposal and meet regulatory compliance.

Luc Mallinger

CEO & Founder of SortFlow Limited