ServicenavigationHauptnavigationTrailKarteikarten


Research unit
EU RFP
Project number
99.0438-2
Project title
Knowledge-driven batch production

Texts for this project

 GermanFrenchItalianEnglish
Key words
-
-
-
Anzeigen
Alternative project number
-
-
-
Anzeigen
Research programs
-
-
-
Anzeigen
Short description
-
-
-
Anzeigen
Partners and International Organizations
-
-
-
Anzeigen
Abstract
-
-
-
Anzeigen
References in databases
-
-
-
Anzeigen

Inserted texts


CategoryText
Key words
(English)
Batch production; batch process control; batch optimization;
Education; Training; Scientific Research; Social Aspects
Alternative project number
(English)
EU project number: HPRN-2000-00039
Research programs
(English)
EU-programme: 5. Frame Research Programme - 4.1.1 Research training networks
Short description
(English)
See abstract
Partners and International Organizations
(English)
Coordinator: Technical University of Denmark; Lyngby (DK)
Abstract
(English)
Two key areas of concern in the chemical and biochemical processing industries are how to achieve greater efficiency at lower cost with existing plants, and how to maximize the benefits of modern multi-purpose agile manufacturing technology. As many companies are now looking to manufacture a wide variety of products, often in small batches, there is a requirement for generic types of models and control and optimization tools that can encompass a range of products and recipes. The Research Training Network 'BatchPro' is a major contributor to the modeling, monitoring, control and optimization of batch production systems.

More specifically, the ability to use knowledge from different sources to drive batch processes is of crucial importance across many sectors. In order to satisfy the identified needs, advanced measurement techniques and leading edge multivariate data processing and optimizing process control procedures are required. It is essential, therefore, that process engineers, chemists, mathematicians, statisticians and control engineers collaborate to develop the appropriate tools through multi-disciplinary research.

The knowledge-driven approach consists of three research themes:
· Modeling through knowledge integration,
· Multivariate measurements, performance monitoring and state estimation,
. Nonlinear control and dynamic optimization.
References in databases
(English)
Swiss Database: Euro-DB of the
State Secretariat for Education and Research
Hallwylstrasse 4
CH-3003 Berne, Switzerland
Tel. +41 31 322 74 82
Swiss Project-Number: 99.0438-2