Estimating energy consumption in cement mills is critical for the cement industry. Following data science practices and adopting machine learning technologies, SYMBIOLABS designed and implemented energy consumption prediction models for a cement mill of TITAN Inc. plant in Kamari Viotia. The models exploit historical milling process sensor measurements and operational data (e.g., raw material ratio, motor loads, output qualitative and quantitative measures, etc) and give prediction for energy consumption with accuracy better than almost one order of magnitude compared to models currently used by cement industry based on bibliographic methods.
SYMBIOLABS is carrying out the SymbioICT project, titled “Data analytics to support biomass and waste value chains in symbiotic networks”, funded by the state-aid Action “RESEARCH – CREATE – INNOVATE”, Operational Programme Competitiveness, Entrepreneurship and Innovation 2014-2020 (EPAnEK).
The project will support the exploitance of SYMBIOLAB’s R&D results to develop (a) data models for biomass and waste valorization that will link biomass/waste properties with valorization scenarios tailored to symbiotic networks, and (b) data analytics for biomass and waste valorization. The aim is to offer decision support solutions for biomass and waste valorization, and raw material search services.
SYMBIOLABS develops and offers to ommΑΙ visual analytics solutions for machine learning applications tailored to the fields of predictive maintenance and fault detection to optimize industrial production lines.
SYMBIOLABS develops and offers to DIADYMA / Waste Management of Western Macedonia S.A. digital services that support citizens of Western Macedonia to find the nearest recycling drop-off spots as well as special waste facilities nearest them.