Soft-sensing with qualitative trend analysis for wastewater treatment plant control

Thürlimann, Christian M.; Dürrenmatt, David J.; Villez, Kris

doi:10.1016/j.conengprac.2017.09.015

Cited by 43 publications

(10 citation statements)

References 28 publications

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“…Wastewater treatment is one key technology to potentially provide additional water supplies, and it is very important for the functioning of the economy and society. Wastewater treatment has been attracting a lot of attention, since it can not only remove organic wastes to reduce the environmental burden, but also offer the advantage of producing a renewable source of water [5,6]. Wastewater treatment is a very complex process with a variety of physical and biochemical reactions since it presents nonlinear dynamic behavior, time delay and uncertainty [7].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

Zhou

Zhang

Jing

et al. 2022

Sensors

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This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.

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Section: Introductionmentioning

confidence: 99%

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

Zhou

Zhang

Jing

et al. 2022

Sensors

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“…Since real-time reporting of wastewater treatment characteristic parameters are difficult but wanted, soft-sensors are introduced to estimate process variables that are not directly accessible, to avoid long analyzing duration and expensive investment cost [13]. Applications in WWTPs showed short response time, compatibility with conventional hard sensor networks, and good transferability [14]. Additionally, soft-sensors may be challenged in practice due to the lack of transparency and the calibration or fine-tuning requirements.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we would build deep learning forecasting models as soft-sensors for wastewater treatment key features, including influent flow, influent temperature, influent biochemical oxygen demand (BOD) concentration, effluent chloride concentration, effluent BOD concentration, and power consumption during the treatment. Inflow shows the overall treatment workload, together with aeration demands and costs [14]. Temperature as a hydrological variable is a key factor in biological reactions with a close connection to the further prediction of microbial water quality [30], [31], and is also an important input for potential heat recovery models [32].…”

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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“…But, due to the complicated physical backgrounds and harsh conditions of industrial plants, when using first-principle approaches, it is difficult to model the entire process when that phase is considered. Nevertheless, data-driven (blackbox models) are built on historical data that can be obtained from industrial processes and built without any operational experience or prior knowledge, making it an acceptable choice for soft sensor modeling of complex processes [5]. For the development of data-driven soft sensors, an abundance of multivariate statistical methods and machine learning methods such as Partial Least Squares (PLS), Principal Component Analysis (PCA), Fuzzy Logic, Support Vector Regression (SVR), and Artificial Neural Network (ANN) have been used [6].…”

Section: Introductionmentioning

confidence: 99%

Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN

Osman

2020

Journal of Control Science and Engineering

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Real-time measurements of key effluent parameters play a highly crucial role in wastewater treatment. In this research work, we propose a soft sensor model based on deep learning which combines stacked autoencoders with neural network (SAE-NN). Firstly, based on experimental data, the secondary variables (easy-to-measure) which have a strong correlation with the biochemical oxygen demand (BOD5) are chosen as model inputs. Moreover, stochastic gradient descent (SGD) is used to train each layer of SAE to optimize weight parameters, while a strategy of genetic algorithms to identify the number of neurons in each hidden layer is developed. A soft sensor model is studied to predict the BOD5 in a wastewater treatment plant to evaluate the proposed approach. Interestingly, the experimental results show that the proposed SAE-NN-based soft sensor has a better performance in prediction than the current common methods.

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Soft-sensing with qualitative trend analysis for wastewater treatment plant control

Cited by 43 publications

References 28 publications

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

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

Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN

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