Probabilistic design of wastewater treatment plants

Talebizadeh, Mansour; Belia, Evangelina; Vanrolleghem, Peter

doi:10.2175/193864715819541017

Cited by 1 publication

(1 citation statement)

References 85 publications

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“…However, because of a lack of adequate input datasets, their full potential remains underexploited. For example, for WRRF design, engineers usually make initial sizing by using design guidelines based on average loads and safety factors (Talebizadeh et al 2015). Also, controller performance can benefit from influent forecasting, e.g.…”

Section: Introductionmentioning

confidence: 99%

An influent generator for WRRF design and operation based on a recurrent neural network with multi-objective optimization using a genetic algorithm

Vanrolleghem

2024

Modelling for Water Resource Recovery

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Nowadays, modelling, automation and control are widely used for Water Resource Recovery Facilities (WRRF) upgrading and optimization. Influent generator (IG) models are used to provide relevant input time series for dynamic WRRF simulations used in these applications. Current IG models found in literature are calibrated on the basis of a single performance criterion, such as the mean percentage error or the root mean square error. This results in the IG being adequate on average but with a lack of representativeness of, for instance, the observed temporal variability of the dataset. However, adequately capturing influent variability may be important for certain types of WRRF optimization, e.g., reaction to peak loads, control system performance evaluation, etc. Therefore, in this study, a data-driven IG model is developed based on the long short-term memory (LSTM) recurrent neural network and is optimized by a multi-objective genetic algorithm for both mean percentage error and variability. Hence, the influent generator model is able to generate a time series with a probability distribution that better represents reality, thus giving a better influent description for WRRF design and operation. To further increase the variability of the generated time series and in this way approximate the true variability better, the model is extended with a random walk process.

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Section: Introductionmentioning

confidence: 99%