A novel long short-term memory artificial neural network (LSTM)-based soft-sensor to monitor and forecast wastewater treatment performance

Xu, Boyan; Pooi, Ching Kwek; Tan, Kar Ming; Huang, Shujuan; Shi, Xueqing; Ng, How Yong

doi:10.1016/j.jwpe.2023.104041

Cited by 30 publications

(4 citation statements)

References 24 publications

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“…Xu et al [21] developed an LSTM network-based soft sensor to monitor and forecast specific performance parameters, such as chemical oxygen demand (COD), ammonium cation, and total nitrogen in a two-staged anoxic-oxic process for wastewater treatment. In that work, the proposed soft sensor received data from lower-cost sensors.…”

Section: Related Workmentioning

confidence: 99%

A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

Alencar,

Fernandes,

Moura Duarte

et al. 2024

Automation

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The fourth industrial revolution has transformed the industry, with information technology playing a crucial role in this shift. The increasing digitization of industrial systems demands efficient sensing and control methods, giving rise to soft sensors that have the potential to replace traditional physical sensors in order to reduce costs and enhance efficiency. This study explores the implementation of an artificial neural network (ANN) based soft sensor model in a water supply system to predict flow rates within the system. The soft sensor is centered on a Long Short-Term Memory (LSTM) artificial neural network model using Monte Carlo dropout to reduce uncertainty and improve estimation performance. Based on the results of this work, it is concluded that the proposed soft sensor (with Monte Carlo dropout) can predict flow rates more precisely, contributing to the reduction in water losses, as well as cost savings. This approach offers a valuable solution for minimizing water losses and ensuring the efficient use of this vital resource. Regarding the use of soft sensors based on LSTM neural networks with a careful choice of Monte Carlo dropout parameters, when compared to the multilayer perceptron model, the LSTM model with Monte Carlo dropout showed better mean absolute error, root mean square error, and coefficient of determination: 0.2450, 0.3121, and 0.996437 versus 0.2556, 0.3522, and 0.9954. Furthermore, this choice of Monte Carlo dropout parameters allowed us to achieve an LSTM network model capable of reducing uncertainty to 1.8290, keeping the error metrics also at low levels.

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Section: Related Workmentioning

confidence: 99%

A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

Alencar,

Fernandes,

Moura Duarte

et al. 2024

Automation

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“…5 Due to these complex characteristics, many previous studies have predicted and analyzed the different components of wastewater by using soft measurement techniques and mathematical models for advanced prediction and treatment. 6–8…”

Section: Introductionmentioning

confidence: 99%

Optimized deep learning models for effluent prediction in wastewater treatment processes

Yang,

Guo,

Geng

et al. 2024

Environ. Sci.: Water Res. Technol.

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“…Furthermore, such models are presented for the penicillin fermentation process [22], the industrial polyethylene process [23], the grinding-classification process [24], the polypropylene process [25], the roasting process of oxidizing zinc sulfide concentrates [26], plasma enhanced chemical vapor deposition (PECVD)-the most important process in the manufacturing of solar panels [27], and the industrial hydrocracking process [28]. There are several recent works related to emissions [29][30][31]. There are several recent works on the application of LSTM-based models related to the steel industry [32], the paper industry [33], and the latest industrial processes related to the production of green hydrogen and decarbonization [34].…”

Section: Introductionmentioning

confidence: 99%

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Herceg,

Ujević Andrijić,

Rimac

et al. 2023

Mathematics

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Dynamic neural networks (DNNs) are a type of artificial neural network (ANN) designed to work with sequential data where context in time is important. Unlike traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if the dynamic of a certain process is emphasized. They are commonly used in natural language processing, speech recognition, and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial isomerization process, it is crucial to measure the quality attributes that affect the octane number of gasoline. Process analyzers commonly used for this purpose are expensive and subject to failure. Therefore, to achieve continuous production in the event of a malfunction, mathematical models for estimating product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNNs), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM), and dynamic polynomial models. The obtained results are satisfactory, suggesting a good possibility of application.

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A novel long short-term memory artificial neural network (LSTM)-based soft-sensor to monitor and forecast wastewater treatment performance

Cited by 30 publications

References 24 publications

A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

A Soft Sensor for Flow Estimation and Uncertainty Analysis Based on Artificial Intelligence: A Case Study of Water Supply Systems

Optimized deep learning models for effluent prediction in wastewater treatment processes

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

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