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

Abstract

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.

Probabilistic Intraday Wastewater Treatment Plant Inflow Forecast Utilizing Rain Forecast Data and Sewer Network Sensor Data

“…Langeveld et al (2017) developed an empirical model for inflow quality prediction, modeling different water quality processes for individual inflow dynamics regimes. F. Li and Vanrolleghem (2022) utilized a multi-objective genetic algorithm to train an LSTM network with respect to influent average behavior and variability. In the reviewed literature, various data-driven methods are explored to forecast the WTP inflow rates and pollution levels across different forecasting horizons, employing a diverse array of input variables.…”