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Comparison ofphenomenologicalmembrane bioreactoractivated sludgebiological modelswith alternativeversions based ontime seriesinput-outputapproaches Parneet Paul2011 год

Comparison of phenomenological membrane bioreactor activated sludge biological models with alternative versions based on time series input-output approaches
статья из журнала

Страница публикацииПубликация в OpenAlex
Аннотация: Data was taken from a full scale sidestream membrane bioreactor (MBR) plant that has been treating industrial wash water from a salad processing factory based in Worcestershire. Various online and offline measurements were taken during an intensive sampling period for the complete flow train. Offline tests included measuring extra-cellular polymeric substance (EPS) and soluble microbial product (SMP) levels in the sludge water which are the main foulants on the associated membrane [1]. Two modified phenomenological Activated Sludge models based upon the ASM1 and ASM3 that included EPS/SMP concentrations were calibrated and validated using these offline and online measurements [2–6]. In order to see whether a simpler model structure could be formulated for advanced control purposes that was based wholly upon measured historical data sets, further conceptual models were developed based on system identification procedures and input-output times series analysis methods [7]. These model forms utilised autoregressive, state-space and subspace formulations, and were calibrated and validated in Matlab© using the collated plant data. A poor model fit was shown for the modified Oliveira and Lu Activated Sludge models, although their parameters were changed from the original models' values to reflect an MBR system [5,6]. In comparison the subspace and ARX formulated biological models were reasonably accurate when compared to the Activated Sludge version, although a much longer historical data set is needed to confirm these initial findings.
Год издания: 2011
Авторы: Parneet Paul
Издательство: Taylor & Francis
Источник: Desalination and Water Treatment
Ключевые слова: Fault Detection and Control Systems, Advanced Control Systems Optimization, Control Systems and Identification