Towards good modelling practice for parallel hybrid models for wastewater treatment processes.
Water Sci Technol
; 89(11): 2971-2990, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38877625
ABSTRACT
This study explores various approaches to formulating a parallel hybrid model (HM) for Water and Resource Recovery Facilities (WRRFs) merging a mechanistic and a data-driven model. In the study, the HM is constructed by training a neural network (NN) on the residual of the mechanistic model for effluent nitrate. In an initial experiment using the Benchmark Simulation Model no. 1, a parallel HM effectively addressed limitations in the mechanistic model's representation of autotrophic bacteria growth and the data-driven model's incapability to extrapolate. Next, different versions of a parallel HM of a large pilot-scale WRRF are constructed, using different calibration/training datasets and different versions of the mechanistic model to investigate the balance between the calibration effort for the mechanistic model and the compensation by the NN component. The HM can improve predictions compared to the mechanistic model. Training the NN on an independent validation dataset produced better results than on the calibration dataset. Interestingly, the best performance is achieved for the HM based on a mechanistic model using default (uncalibrated) parameters. Both long short-term memory (LSTM) and convolutional neural network (CNN) are tested as data-driven components, with a CNN HM (root-mean-squared error (RMSE) = 1.58 mg NO3-N/L) outperforming an LSTM HM (RMSE = 4.17 mg NO3-N/L).
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Eliminación de Residuos Líquidos
/
Modelos Teóricos
Idioma:
En
Revista:
Water Sci Technol
/
Water science and technology
Asunto de la revista:
SAUDE AMBIENTAL
/
TOXICOLOGIA
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido