New deterministic tools to systematically investigate fouling occurrence in membrane bioreactors

[Display omitted] •Various connectionist tools are developed to predict the membrane fouling resistance in MBRs.•The LSSVM outperforms other models in estimating the fouling resistance.•GEP technique leads to development of a mathematical expression.•This research investigation helps to make better...

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Veröffentlicht in:Chemical engineering research & design 2019-04, Vol.144, p.334-353
Hauptverfasser: Hamedi, Hamideh, Ehteshami, Majid, Mirbagheri, Seyed Ahmad, Zendehboudi, Sohrab
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Sprache:eng
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Zusammenfassung:[Display omitted] •Various connectionist tools are developed to predict the membrane fouling resistance in MBRs.•The LSSVM outperforms other models in estimating the fouling resistance.•GEP technique leads to development of a mathematical expression.•This research investigation helps to make better operating and policy decisions while employing MBRs.•The importance of the input parameters is ranked as Flux > TMP > Temperature > MLSS. Membrane fouling as a major concern in development and optimization of membrane bioreactor (MBR) technologies has been the focus of numerous engineering and research investigations. Considering the complexity of membrane fouling occurrence, mathematical modelling techniques have been progressively proposed to forecast this phenomenon for optimizing MBR performance. A majority of the models are not reliable and accurate enough in terms of theoretical and practical prospects. In this research work, smart methods including artificial neural network (ANN), gene expression programming (GEP), and least square support vector machine (LSSVM) are suggested to avoid utilization of complex modelling methodologies and costly and time-consuming measurements. The developed models relate fouling resistance to key parameters such as permeate flux, temperature, and transmembrane pressure. To enhance the performance of conventional connectionist tools, particle swarm optimization (PSO) algorithm with global optima is utilized. This study aims to simulate the MBR efficiency by calculating membrane fouling resistance. The performance of the smart models is evaluated based on the mean squared error (MSE), maximum absolute percentage error (MAAPE), minimum absolute percentage error (MIAPE), and coefficient of determination (R2). The results reveal that the developed LSSVM tool has the lowest MSE (0.0002), MAAPE (3.18), and MIAPE (0.01), and the highest R2 (0.99) in the testing phase. The transmembrane pressure and permeate flux are the most important parameters affecting the membrane fouling resistance. This study can help to obtain a better understanding of membrane fouling process to achieve optimal conditions for MBR systems in terms of design, operation, and optimization prospects.
ISSN:0263-8762
1744-3563
DOI:10.1016/j.cherd.2019.02.003