Deep learning-based intelligent management for sewage treatment plants

Wan, Keyi; Du, Boxin; Wang, Jianhui; Guo, Zhiwei; Dong, Feng; Gao, Xu; Shen, Yu; Yu, Keping

doi:10.1007/s11771-022-5036-3

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…e particle filter algorithm is a typical nonlinear tracking filtering method, which is an optimal regression Bayesian filtering algorithm based on Monte Carlo simulation. A state estimate can be computed [8]. Compared with other filtering algorithms, such as the extended Kalman filter (EKF) algorithm and the trajectory-free Kalman filter algorithm, the particle filter method is not limited by linearization error or Gaussian noise assumption and is suitable for any state or model in any environment.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Gray Prediction Model Based on Fuzzy Theory

2022

International Transactions on Electrical Energy Systems

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In order to improve the forecasting effect of the gray prediction model, this paper combines the fuzzy theory to construct the gray prediction model and explores its forecasting accuracy. Moreover, this paper uses the entropy weight method to obtain the objective weight to correct the subjective weight, which makes the weight calculation more reasonable. In view of the uncertainty of the control signal of the research object, this paper introduces the gray system theory to conduct cluster analysis on the fire control computer and mainly introduces the general whitening weight function. Furthermore, this paper adopts the center point mixed with a triangular whitening weight function to carry out gray clustering according to the difficulty of defining the gray class boundary and gives the solution steps to obtain the intelligent gray prediction model. Finally, this paper verifies that the intelligent gray prediction model based on fuzzy theory has a good effect through experiments, which can effectively improve the prediction effect of the intelligent prediction model.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Gray Prediction Model Based on Fuzzy Theory

2022

International Transactions on Electrical Energy Systems

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“…The proposed model has higher prediction accuracy with faster convergence. Wan et al [20] proposed to combine the grey correlation algorithm (GRA) and the GRU network into a prediction model to predict wastewater operations, and the GRA-GRU model predicted influent wastewater conditions with better accuracy than GRU, LSTM, CNN, and MLR models. Li et al [21] and Liang et al [22] proposed a combined CNN-LSTM deep learning approach based on flow prediction that helps to estimate water availability and flood warnings for watershed management, while in the presence of complex circumstances, the model's forecast accuracy will decline.…”

Section: Related Workmentioning

confidence: 99%

FDA-SCN Network Based Soft Sensor for Wastewater Treatment Process

Du,

Yao

2023

Pol. J. Environ. Stud.

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The primary conflict between the social economy's rapid development and industrial technology's prompt advancement is the scarcity of water resources, the pollution of the water environment and the damage to water ecology make this contradiction more prominent, therefore, in addition to ensuring ecological security, encouraging the resource usage of wastewater can also help to resolve the imbalance between the supply and demand of water resources. In addition, the supervision and implementation of increasingly strict ecological and environmental management standards and policies make it difficult for the effluent quality of many wastewater treatment plants to meet the discharge standards, of which the total nitrogen (TN) in

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“…In recent years, many researchers have applied the ant colony algorithm to task allocation, but the ant colony algorithm is not able to be searched quickly in the initial search due to insufficient accumulation of pheromone information in the initial stage, so that the pheromone cannot be searched locally in the initial stage [5]. The adaptive artificial firefly algorithm is able to quickly find the optimal value in a local range and has a fast convergence rate [6][7][8]. Therefore, the integration of the adaptive artificial firefly algorithm with the ant colony algorithm can make up for the disadvantage of the slow initial solution speed of the ant colony algorithm and find the global optimal solution faster, providing new ideas and methods for solving the task allocation problem [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Engineering archive management model based on big data analysis and deep learning model

Du¹,

Liu²,

Xu³

et al. 2023

Applied Mathematics and Nonlinear Sciences

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In the background of the era of big data, the information management system of engineering archives has become more comprehensive and perfect because of the application of information technology. The application of deep learning model makes the management of engineering archives more systematic, scientific and standardized, which greatly improves the quality and efficiency of engineering archives management. The progress of society and the development of the times have put forward higher requirements for digital storage technology. This paper combines the characteristics of the new technological era, analyses the characteristics of traditional information management in the context of data processing, artificial intelligence, deep learning and other data, proposes a method for developing and managing web archives based on Bootstrapping technology, introduces an information meta-evaluation mechanism to improve the quality of mining, and uses a long and short-term memory model to extract multi-type fine-grained archival information elements in the corpus. Finally, the Alex network was established to manage the archives in a categorised manner. The experimental results show that the query results of the proposed method for the target archives are 100% accurate, and the query time for individual archives is basically within 5s, which has good management effect.

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Deep learning-based intelligent management for sewage treatment plants

Cited by 8 publications

References 36 publications

An Intelligent Gray Prediction Model Based on Fuzzy Theory

An Intelligent Gray Prediction Model Based on Fuzzy Theory

FDA-SCN Network Based Soft Sensor for Wastewater Treatment Process

Engineering archive management model based on big data analysis and deep learning model

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