Data-driven performance analyses of wastewater treatment plants: A review

Newhart, Kathryn B.; Holloway, Ryan W.; Hering, Amanda S.; Cath, Tzahi Y.

doi:10.1016/j.watres.2019.03.030

Cited by 289 publications

(133 citation statements)

References 88 publications

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“…Campbell, Wang, Liu, and Daigger (2019) used laboratory completely mixed reactors to demonstrate the benefits of operation at high sludge ages (40 days) which minimized filamentous organisms and their associated high viscosity and low oxygen transfer, while maximizing settleability and oxygen transfer efficiency. Newhart, Holloway, Hering, and Cath (2019) discussed developments in assessing treatment effectiveness and provided recommendations for improvements.…”

Section: Control and Automationmentioning

confidence: 99%

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

Sweeney

Kabouris

2020

Water Environment Research

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Section: Control and Automationmentioning

confidence: 99%

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

Sweeney

Kabouris

2020

Water Environment Research

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“…It has been shown that these flexible machine learning soft-sensors may outperform the traditional linear regression models. Aside from theory-based science approaches (i.e., applying mechanistic models or empirical knowledge), nowadays the modeling methodologies using theory-free data-driven soft-sensors are gaining more attention [19].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Soft Sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study

et al. 2021

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Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With the increase of installed WWTPs worldwide, the modeling and forecast of their energy consumption have become a critical factor in WWTP design to meet environmental and economic requirements. The accurate and swift energy consumption forecasting soft-sensors are not only supportive to the daily electric and financial budgeting by WWTP practitioners on the micro-scale, but also beneficial to local municipal operation and fundamental to regional environmental impact estimation on the macro-scale. Energy consumption in WWTPs is influenced by different biological and environmental factors, making it complicated and challenging to build soft-sensors. This paper intends to provide short-term forecasting of WWTP energy consumption based on data-driven soft sensors using traditional time-series and deep learning methods. Ten data-driven soft sensors, including the ordinary least square, exponential smoothing state space, local regression, auto-regressive integrated moving average (ARIMA), structural time series model, Bayesian structural time series, non-linear auto-regressive, long short-term memory with and without updates, and gated recurrent units have been investigated and compared for WWTP energy consumption forecasting. Energy consumption time-series data from a membrane bioreactor-based WWTP in the middle east is used to evaluate the performances of the proposed soft-sensors. Results showed that ARIMA achieved slightly improved performances, among others. The employment of adaptive deep learning-based soft sensors is expected to enhance the capabilities of the deep models to quickly and accurately follow the trend of future data.

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“…To alleviate this limitation, non-parametric models, such as machine learning, have been introduced. One of the most popular softsensors categories is artificial neural network (ANN) [16].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

et al. 2020

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The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs, and endorse optimization of overall performances. Deep learning technologies as proven data-driven soft-sensors should be developed for WWTP applications to tackle the process of non-linearity and the dynamic nature of environmental data. This study adopts deep learning-based models as softsensors to forecast WWTP key features, such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption. We constructed six deep learning models derived from long short-term memory (LSTM) and gated recurrent unit (GRU), namely traditional LSTM and GRU, the exponentially smoothed LSTM, and the adaptive version of LSTM and smoothed LSTM. The employment of a smoothed LSTM technique is expected to reduce the outlier effect and to improve forecasting accuracy. Meanwhile, the usage of adaptive deep models will enhance the capabilities of the LSTM to quickly and accurately follow the trend of future data. We compared the performance of these models with Bi-directional LSTM (BiLSTM) and the seasonal decomposition using local regression. The historical records from a coastal municipal WWTP in Saudi Arabia are used to verify the investigated models' effectiveness. The proposed models provide promising forecasting results but require no assumptions on the data distributions. In terms of efficiency, GRU based models converge faster than LSTM based models. In terms of accuracy, the LSTM soft-sensor shows overall the optimal result for all key features followed by the exponentially-smoothed GRU and LSTM. By contrast, the adaptive models achieved the lowest forecasting performance compared to the other models. These findings will benefit practitioners to achieve data-driven WWTP management.

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Data-driven performance analyses of wastewater treatment plants: A review

Cited by 289 publications

References 88 publications

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

A Data-Driven Soft Sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study

Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

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