Laboratory for Intensification of Process Data Acquisition – DATAFAB

The design, evaluation and time required for the set up of chemical processes are very much linked to the number, diversity and reliability of the process data available at the time for taking decisions. The goal of the joint laboratory "LabCom" DATAFAB is to increase the competitiveness of the chemical industries and the related industries (pharmaceuticals, agrochemicals, fine chemicals, health care,...) by implementing technologies and methodologies for fast and reliable data acquisition.
DATAFAB will provide solutions to reach such a goal by transferring the know-how between the academic partner, the laboratory for catalytic process engineering (LGPC UMR5285 CNRS – CPE Lyon – Université Claude Bernard Lyon 1), and the industrial partner, PROCESSIUM, a small size enterprise based in Lyon and specialized in separation processes and thermodynamic.
The programme concerned by the3 years ANR contribution, encompasses 3 main phases: i) to set within a short time after the project start, traditional technologies for data acquisition, ii) to implement new intensified technologies demonstrated and available at the academic partner, and iii) at longer time, to discover innovative ground breaking technologies that are today at the proof-of-concept stage.
A further phase, starting at the end of year 3, will concern the capitalization of all the data collected within a knowledge data base for kinetics.
The addressed research area concerns chemical processes and more specifically, multiphase catalytic processes. Some of the key data required for designing assessing the profitability of a process are indeed process productivity and stability. Productivity, as well as selectivity, could be assessed through the knowledge of intrinsic kinetics, thus selectivity. Concerning process stability, one of the main recognize contributing factor is catalyst deactivation. Thus DATAFAB will design specific reactors and data mining tools to detect and quantify such deactivations, at very early stage of process design. Other mechanisms could lead to process instability such as solid formation and clogging which could results in shut-down of the process. Also, corrosion may lead to process shut-down, especially for high surface-to-volume equipment as encountered in intensified processes. A specific task will be devoted to collect case studies and data related to such process instability (catalyst deactivation, clogging, corrosion,...) to enrich the knowledge database.
DATAFAB will drive to innovations in the fields of laboratory reactors and equipment, which by itself is already a step forward to reach higher competitiveness. Furthermore, DATAFAB will target ground-breaking methodologies. Indeed, by coupling innovative reactor technologies with design-of-experiments methods, DATAFAB will built a new scientific paradigm named "learning-machines" which will result in 10x more time and effort saving through the optimal design of tests and experiments to provide more robust and more detailed data over larger ranges of operating conditions.