SCHEME is a private lab providing R&D and expertise in modelling and environment. Our area of expertise are: - Predictive modelling of water quality/quantity at the catchment scale (hydraulics / hydrology / agronomy) and testing of change scenarios: we help stakeholders to design detailed action plans regarding water quality / quantities issues. - Direct simulation of fluid mechanics and reactive transport in porous media: we help industrials to design and optimise new engineering processes such as waste management through composting and methane production - Data mining and machine learning: we speed up model design by high level data analysis, and we deliver operational models directly to clients, based on their data and adapted to their needs, in a very short cycle. Some References: Evaluation of the different action plans for the green algae bloom reduction on Britanny coasts: Test of change scenarios over 14 river catchments using the spatialised agro-hydrological model TNT2 over a 25 year projection. (client: French government) Design of an operational Nitrogen / Phosphorous simplified modelling tool. Application over all new Zealand, non-instrumented river catchments. (client: the National institute of Water and Atmospheric research (NIWA), New Zealand) Design of an industrial waste composting model on OpenFOAM platform. (Client: SUEZ Environnement) Multi-spectral signature analysis and back reconstruction using machine learning for post-process quality control. (Client: COFELY / SUEZ environnement)
SCHEME is a private lab providing R&D and expertise in modelling and environment. Our area of expertise are: - Predictive modelling of water quality/quantity at the catchment scale (hydraulics / hydrology / agronomy) and testing of change scenarios: we help stakeholders to design detailed action plans regarding water quality / quantities issues. - Direct simulation of fluid mechanics and reactive transport in porous media: we help industrials to design and optimise new engineering processes such as waste management through composting and methane production - Data mining and machine learning: we speed up model design by high level data analysis, and we deliver operational models directly to clients, based on their data and adapted to their needs, in a very short cycle. Some References: Evaluation of the different action plans for the green algae bloom reduction on Britanny coasts: Test of change scenarios over 14 river catchments using the spatialised agro-hydrological model TNT2 over a 25 year projection. (client: French government) Design of an operational Nitrogen / Phosphorous simplified modelling tool. Application over all new Zealand, non-instrumented river catchments. (client: the National institute of Water and Atmospheric research (NIWA), New Zealand) Design of an industrial waste composting model on OpenFOAM platform. (Client: SUEZ Environnement) Multi-spectral signature analysis and back reconstruction using machine learning for post-process quality control. (Client: COFELY / SUEZ environnement)
Company Information
- Industry
- Renewables & Environment
- Company Type
- Privately Held
- Founded
- unknown
- Employee Range
- Myself Only
- Revenue Range
- Not available
Location
- Address
- LE RHEU RENNES
- City
- Region
- Postal Code
- 35000
- Country
- France