Sewage sludge utilization with optimized process control and emission reduction through intelligent early agglomeration detection
The Klär-vision project investigates the influence of oxyfuel combustion and further develops and integrates an agglomeration monitoring system using AI models to enable early detection and control of agglomeration while supporting more stable and efficient sewage sludge utilization.
Funding code: 03EI5496C
Project duration: 01.01.2026 – 31.12.2028
Eigenbetrieb Stadtentwässerung Stuttgart (SES)
Thermische Verwertung Mainz GmbH (TVM)
Gemeinschaftsklärwerk Bitterfeld-Wolfen GmbH (GKW)
Kläranlage Haßfurt, Stadt Haßfurt
Entsorgung + Recycling Zürich (ERZ), Stadt Zürich
Zweckverband Wasserversorgung und Abwasserbehandlung Rügen (ZWAR)
RWE Power – Klärschlamm-Monoverbrennungsanlage am Standort Knapsacker Hügel
Badische Anilin- & Soda-Fabrik (BASF SE)
Enercity Klärschlammverbrennungsanlage Hannover-Lahe
The aim of the Klär-vision project is to make the thermal treatment of sewage sludge in stationary fluidised bed combustion systems more efficient and sustainable. Central to the project is a further-developed, online-capable agglomeration monitor, enhanced by AI-based prediction models that enable emerging agglomeration processes to be detected early and countermeasures to be initiated in good time. This is intended to prevent process instabilities and achieve targeted reductions in emissions of both nitrous oxide (N₂O) and nitrogen oxides (NOx). In addition, the project investigates how oxyfuel combustion — the use of pure oxygen rather than air as the oxidant — affects agglomeration behaviour and emission profiles, as this approach is expected to deliver significant gains in combustion efficiency.
Schematic illustration of the Klär-vision project
A key component of the project is experimental work aimed at optimising operating strategies for a more resource-efficient combustion of sewage sludge. At the same time, Klär-vision pursues consistent digitalisation: real-time sensor data from laboratory and industrial plants are collected centrally, analysed using big-data methods, and the resulting insights fed directly back into control algorithms. In this way, downtime can be reduced, greenhouse gas emissions lowered, and the safe operating limits of existing plants gradually extended.
The project thus makes a concrete contribution to Germany’s energy transition and the achievement of national climate targets. The close involvement of more than a dozen industrial and infrastructure partners — including both plant manufacturers and operators of fluidised bed combustion facilities — not only provides a solid big-data foundation, but also ensures that the solutions developed are fit for practical application and ready to find their way to market.
Big-data basis of the Klär-vision project: Geographic distribution of the locations of the project partners (dark blue), associated partners (red, plant operators, or orange, plant designers B&E), and subcontractor KMT (light blue).
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