Objective The CLEAN project developed a system for automatic on-line process optimisation in pulp and paper plants. Optimisation is here defined as producing pulp or paper to a given specification consisting of permitted ranges for several measurable quality and environmental parameters, while minimising the cost per tonne of output subject to these constraints. This was achieved by implementing an adaptive controller using 'Case-Based Learning' techniques, originally developed under research into artificial intelligence, supported by neural nets modelling some of the parameters. Quantified results are presented for one pulp mill and one paper mill. The technique is applicable to other industries where the variable quality of the raw materials causes control problems.The goal of clean is to create a system for automatic on-line high-level parameter setting in pulp and paper plants.Automated systems used at present perform the classic automation functions such as measuring, open-loop and closed-loop control. They also perform some of the online functions which optimise the individual plant operations. However, the high-level operations of optimising the set-points and control parameters and checking the model and process constants, assumptions and optimisation criteria are only performed infrequently, offline, manually, and generally by a highly qualified technolgist.The present project aims at developing an automated online approach based on recent advances in case-based learning methods. The system will make the best use of existing literature and historical data, of applicable analytical approaches, of heuristic rules and deep AI, in assessing the overall performance of the pland and in resetting its high-level parameters. At the same time the system will not be static and circumscribed by the rules and values input: it will have in itself the capability to learn from experience and improve its policies.It is expected that the utilization of the system will lead to cuts in energy, pollution and the use of auxiliary chemicals, a rise in production yield and turnover, and an increase in profit margins Fields of science social sciencessociologyindustrial relationsautomationnatural sciencesearth and related environmental sciencesenvironmental sciencespollutionnatural sciencescomputer and information sciencesartificial intelligenceheuristic programming Programme(s) FP3-BRITE/EURAM 2 - Specific programme (EEC) of research and technological development in the field of industrial and materials technologies, 1990-1994 Topic(s) 2.2.1 - Tools, techniques and systems for high quality manufacturing Call for proposal Data not available Funding Scheme CSC - Cost-sharing contracts Coordinator BRITISH MARITIME TECHNOLOGY LTD Address Waldegrave road, 1 orlando house TW11 8LZ Teddington United Kingdom See on map EU contribution € 0,00 Participants (4) Sort alphabetically Sort by EU Contribution Expand all Collapse all COMPANHIA DE CELULOSE DO CAIMA SA Portugal EU contribution € 0,00 Address Rua joaquim antonio aguira41.3 1000 Lisboa See on map KUL Belgium EU contribution € 0,00 Address De croylaan 46 3001 Heverlee See on map ROERMOND PAPIER BV Netherlands EU contribution € 0,00 Address Po box 1225 mijnheerkensweg 18 6040 KE Roermond See on map Siemens AG Germany EU contribution € 0,00 Address Werner-von-siemens-straße 50 91052 Erlangen See on map
