Analysis and Modelling of an Industrial Pressure Filtration using Process Data * *The authors would like to acknowledge the Technical University of Denmark (DTU) and BIOPRO2. The project received financial support from Innovation Fund Denmark through the BIOPRO2 strategic research center (Grant number 4105-00020B)
| Autor: | Jens Abildskov, Paloma de Gracia Andrade Santacoloma, Jakob Kjøbsted Huusom, Franz D. Bähner |
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| Rok vydání: | 2017 |
| Předmět: |
Work (thermodynamics)
Steady state Operations research Process (engineering) business.industry Flow (psychology) 02 engineering and technology 021001 nanoscience & nanotechnology law.invention Filtration theory 020401 chemical engineering Control and Systems Engineering law Viscosity (programming) Political science Production (economics) 0204 chemical engineering 0210 nano-technology Process engineering business Filtration |
| Zdroj: | IFAC-PapersOnLine. 50:12137-12142 |
| ISSN: | 2405-8963 |
| Popis: | In order to understand a series of pressure leaf filters located in the downstream line of a bio-based production site, historical process data have been analysed. In general, changing raw materials induce variability into the pressure profiles and thereby cycle durations of the manually reinitialised dead-end filtrations. The absence of a true steady state results in uncertainty about the optimal way of running the filters, and staff members alter the operational specifications frequently. It appears that, in some cases, this propagates disturbances rather than ameliorate them. Statistical analyses are carried out to illustrate the current situation and especially allow quantifying the extent of the uncertainties. Furthermore, significant correlations between process variables are revealed and economically motivated operational objectives are identified. Secondly, working towards on-line predictions of filtration performance, a model is presented. It is based on classical filtration theory and requires only commonly available measurements (pressure, flow, viscosity). The generated predictions are found to be acceptable for many cycles, but in some cases fail due to non-modelled effects, motivating further work. |
| Databáze: | OpenAIRE |
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