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Self learning model for intelligent predictive control system for crystallization processes

Objectif

Due to unpredictable crystallisation mechanisms, industrial crystallisation (up to 70% of all chemicals) is seldom operated under automatic control of product size distribution. This frequently results in non-reproducibility, unacceptable product qualities and excess energy consumption. Objective of SINC-PRO is to increase efficiency (reproducibility) and effectiveness (-20% energy, -5% cost, reduced time-to-market) by developing advanced techniques for on-line measurement and control. Results: flexible process modelling tool, observer/feedback system integrated with on-line measurement techniques, toolbox comprising of both Model Predictive Control and self-learning neural network type control. Exploitation: modelling and control tool box (two software developers), integrated modelling and control system with new measurement techniques (5 end-user industries), improved insight in crystallisation mechanisms and control (2 RTD institutes).

Appel à propositions

Data not available

Régime de financement

CSC - Cost-sharing contracts

Coordinateur

DSM RESEARCH BV
Contribution de l’UE
Aucune donnée
Adresse
Koestraat 3
6160 MD GELEEN
Pays-Bas

Voir sur la carte

Coût total
Aucune donnée

Participants (11)