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Self learning model for intelligent predictive control system for crystallization processes

Objective

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).

Call for proposal

Data not available

Coordinator

DSM RESEARCH BV
Address
Koestraat 3
6160 MD Geleen
Netherlands

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Participants (11)

DANISCO SUGAR OY
Finland
Address
Sokeritethaantie 20
02460 Kantvik

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DELFT UNIVERSITY OF TECHNOLOGY
Netherlands
Address
Leeghwaterstraat 44
2628 CA Delft

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INNOVA SPA
Italy
Address
Via Della Scrofa 117
00186 Roma

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INTELLIGENT SYSTEM MODELING AND CONTROL N.V.
Belgium
Address
Technologielaan 11 / 0101
3001 Leuven

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IPCOS TECHNOLOGY BV
Netherlands
Address
Bosscheweg 145 A
5282 WV Boxtel

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KEMIRA OYJ
Finland
Address
Porkkalankatu 3
00101 Helsinki

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LABOR S.R.L.
Italy
Address
Via Della Scrofa 117
Roma

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PROCESS SYSTEMS ENTERPRISE LTD.
United Kingdom
Address
Blackfriars Lane 20
EC4V 6HD London

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PURAC BIOCHEM BV
Netherlands
Address
Arkelsedijk 46
4200 AA Gorinchem

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ROQUETTE ITALIA SPA
Italy
Address
Via Serravalle 26
15063 Cassano Spinola

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UNIVERSITY OF ROME "LA SAPIENZA"
Italy
Address
Via Eudossiana 8
00184 Roma

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