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Digitale Medien

A Modeling and Optimization Tool for the Expandable Polystyrene Batch Process (2008)

Heikkinen, Mikko ; Nurminen, Ville ; Hiltunen, Teri ; [weitere]

Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)

Chemical product and process modeling 3 (2008), S. 3

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ISSN: 1934-2659

Quelle: Berkeley Electronic Press Academic Journals

Thema: Chemie und Pharmazie , Werkstoffwissenschaften, Fertigungsverfahren, Fertigung

Notizen: Expandable Polystyrene (EPS), a material of the insulation productions and packages, is commonly produced in a batch process. The control of the batch process is based on predefined process recipes and process parameters such as mixing properties. The EPS production has to be able to satisfy the aims and quality requirements of the markets, which causes additional demands on the process control. In this paper we demonstrate the optimization and modeling application for the EPS-batch process. The application consists of a production optimization tool and a simulation tool for the process parameters based on the Multi-Layer Perceptron network (MLP) with retrain properties. The software can be used at the operational level as well as at the process management level. The features are programmed into standalone software built up in the Matlab environment and it is tailored for the needs of the EPS production company StyroChem Ltd. The results show that the application can offer an efficient tool to economize production and storage costs.

Materialart: Digitale Medien

URL: http://www.bepress.com/cppm/vol3/iss1/3

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Digitale Medien

Forecasting Air Quality Parameters Using Hybrid Neural Network Modelling (2000)

Kolehmainen, Mikko ; Martikainen, Hannu ; Hiltunen, Teri ; [weitere]

Springer

Environmental monitoring and assessment 65 (2000), S. 277-286

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ISSN: 1573-2959

Schlagwort(e): air quality ; forecasting ; neural networks ; Self-Organizing Map ; Sammon's mapping

Quelle: Springer Online Journal Archives 1860-2000

Thema: Energietechnik

Notizen: Abstract Urban air pollution has emerged as an acute problem in recent years because of its detrimental effects on health and living conditions. The research presented here aims at attaining a better understanding of phenomena associated with atmospheric pollution, and in particular with aerosol particles. The specific goal was to develop a form of air quality modelling which can forecast urban air quality for the next day using airborne pollutant, meteorological and timing variables. Hourly airborne pollutant and meteorological averages collected during the years 1995–1997 were analysed in order to identify air quality episodes having typical and the most probable combinations of air pollutant and meteorological variables. This modelling was done using the Self-Organising Map (SOM) algorithm, Sammon's mapping and fuzzy distance metrics. The clusters of data that were found were characterised by statistics. Several overlapping Multi-Layer Perceptron (MLP) models were then applied to the clustered data, each of which represented one pollution episode. The actual levels for individual pollutants could then be calculated using a combination of the MLP models which were appropriate in that situation. The analysis phase of the modelling gave clear and intuitive results regarding air quality in the area where the data had been collected. The resulting forecast showed that the modelling of gaseous pollutants is more reliable than that of the particles.