The project at a glance Kanadevia Inova envisioned tackling one of the main challenges in Waste-to-Energy plants – bulky items that often disrupt the feeding process for combustion. Together with Zühlke, Kanadevia Inova developed an AI-based computer vision detection system to improve plant operation and maintenance. The jointly developed web application supports the plant operator in identifying bulky waste items, helping make better decisions and take eligible actions. Kanadevia Inova is a global Greentech company operating in Waste-to-Energy (WtE) and renewable gas sectors. Kanadevia Inova develops projects with its clients, drawing on its experience as a general engineering, procurement, and construction contractor, to deliver complex turnkey plants and system solutions for thermal and biological WtE recovery, gas upgrading, and power to gas plants. One of the main challenges in WtE plants are bulky waste items because they often lead to disruption in the feeding process for combustion. This can lead to severe consequences such as unplanned maintenance and downtime, fluctuation in steam condition, plant unavailability, and risk to staff safety. ' Zühlke's collaborative approach and cutting-edge solutions will enable us to tackle a critical challenge in bunker management, with the potential for smoother operations and reduced downtime. ' Miriam Rabacal Director Advanced Digitalisation at Kanadevia Inova With a multidisciplinary team, including data scientists, Kanadevia Inova developed a computer vision algorithm that helps plant operators to detect and handle bulky items, and improve plant operation and maintenance. Multidisciplinary know-how leads to a machine learning-based solution Zühlke joined the project after an initial proof of concept but before an existing minimum viable product (MVP) was created. A detection algorithm had to be developed to meet the required performance criteria and be fully deployable as a pilot product. Combining data science, data engineering, UI/UX, cloud platform technologies, software architecture and development, and Machine Learning Operations (MLOps) know-how, Zühlke supported the Kanadevia Inova team in developing a data processing pipeline and a computer vision detection algorithm for bulky waste items. ' With Zühlke's expertise in data science and MLOps, we were able to transform our concept into a reliable, operational AI system, enhancing bunker management safety and efficiency. ' Miriam Rabacal Director Advanced Digitalisation at Kanadevia Inova The Zühlke specialists developed a web application through which plant operators receive alarms (including key information) of detected bulky waste items from the algorithm and provide feedback. Zühlke also conceptualised and implemented an initial MLOps system with which the detection algorithm can be adapted and retrained for each plant individually and semi-automatically, accounting for the specific needs of each deployment site. This leads to significant time and cost savings for future implementation in other plants. Pilot demonstration on customer plants The image processing and bulky waste detection algorithm was deployed in two pilot WtE plants, one is operated by AVAG Umwelt AG in the city of Thun (CH) and the other is operated by Renergia Zentralschweiz in the city of Perlen (CH). The waste tipping bay gates are continuously monitored by a set of specific cameras and the recorded video stream is fed into the deployed detection pipeline. Newly arriving objects are analysed and potential bulky waste items are identified near real-time. Additionally, plant operators are notified about bulky waste items through the provided web application. That means that both pilot customers have the opportunity to test the live data throughout the pilot development process and challenge the result with the aimed value for the daily operation. ' Zühlke played an essential role in this development. Their comprehensive support from data processing to machine learning and MLOps brought our AI project to life. The initial detection performance goals were met and a better than-targeted failure rate was achieved. ' Miriam Rabacal Director Advanced Digitalisation at Kanadevia Inova By continuously developing the system together with its customers, Kanadevia Inova will be able to add value, including more reliable and safe processes, less downtime, and significant cost savings. Still confused about AI's potential? Let's talk! Our work Industrial Sector Brückner Maschinenbau leverages GenAI to optimise production efficiency Learn more Industrial Sector Sustainability vision and goals for innovative logistics Learn more Liebherr logo Industrial Sector Liebherr enhances operational efficiencies Learn more Go to case studies Deliver transformative impact with Zühlke. Speak to our team today. Get in touch
' Zühlke's collaborative approach and cutting-edge solutions will enable us to tackle a critical challenge in bunker management, with the potential for smoother operations and reduced downtime. ' Miriam Rabacal Director Advanced Digitalisation at Kanadevia Inova
' With Zühlke's expertise in data science and MLOps, we were able to transform our concept into a reliable, operational AI system, enhancing bunker management safety and efficiency. ' Miriam Rabacal Director Advanced Digitalisation at Kanadevia Inova
' Zühlke played an essential role in this development. Their comprehensive support from data processing to machine learning and MLOps brought our AI project to life. The initial detection performance goals were met and a better than-targeted failure rate was achieved. ' Miriam Rabacal Director Advanced Digitalisation at Kanadevia Inova