Design of Predictive Models of Products from Thermochemical Conversion Processes – COMPPIT

The LabCom COMPPIT proposed between IMT Atlantique, a leading engineering school with international recognition, and S3d Ingénierie, a leading company on the subjects related to biomass and waste recovery, aims to design a decision-making tool on the choice of the best thermochemical conversion process depending on the nature of the input processed and the recovery of the envisaged products.

Currently, developers projects of biomass/waste recovery using thermochemical methods are faced with a lack of data on the quality of the products (e.g. composition, physicochemical characteristics, presence of pollutants, etc.) and on material/energy balances, particularly for new feedstock. However, this information is essential for conducting feasibility studies. The challenge is to reduce the risk on new investments by providing informed advice to project leaders.
It will therefore involve implementing predictive models of gaseous, liquid and solid products from different biomasses and fatal waste. These models, based on machine learning techniques, will be fed from experimental results obtained on existing or to-be-built equipment.

Various scientific and technological obstacles must be overcome since access to data from continuous pilots, which are more representative of industrial production, is limited (data mainly available for batch systems). These include the non-linearity of process parameters and operating conditions as well as the availability of robust, large-quantity, high-quality data sets. In addition, missing data must be collected from test campaigns to be carried out. However, collecting data for model training is costly and time-consuming because the data must be pre-processed to eliminate noise and redundancies.

The LabCom will mobilize scientific, technical, and engineering skills to meet these challenges. Thus, the pooling of research and expertise resources between the two partners, IMT Atlantique and S3D Ingénierie, will make it possible, through the ambitions expressed in the roadmap, to carry out effective research and innovation actions in the service of the economic sector.