Artificial Neural Networks in Modeling of Dewaterability of Sewage Sludge
Mechanical dewatering is a key process in the management of sewage sludge. However, the drainage efficiency depends on a number of factors, from the type and dose of the conditioning agent to the parameters of the drainage device. The selection of appropriate methods and parameters of conditioning a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/6/1552 |
| _version_ | 1797541734013468672 |
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| author | Mariusz Kowalczyk Tomasz Kamizela |
| author_facet | Mariusz Kowalczyk Tomasz Kamizela |
| author_sort | Mariusz Kowalczyk |
| collection | DOAJ |
| description | Mechanical dewatering is a key process in the management of sewage sludge. However, the drainage efficiency depends on a number of factors, from the type and dose of the conditioning agent to the parameters of the drainage device. The selection of appropriate methods and parameters of conditioning and dewatering of sewage sludge is the task of laboratory work. This work can be accelerated through the use of artificial neural network (ANNs). The paper discusses the possibilities of using ANNs in predicting the dewatering efficiency of physically conditioned sludge. The input variables were only four parameters characterizing the conditioning methods and the dewatering method by centrifugation. These were the dose of the sludge skeleton builders (cement, gypsum, fly ash, and liquid glass), sonication parameters (sonication amplitude and time), and relative centrifugal force. Dewatering efficiency parameters such as sludge hydration and separation factor were output variables. Due to the nature of the research problem, two nonlinear networks were selected: a multilayer perceptron and a radial neural network. Based on the results of the prediction of artificial neural networks, it was found that these networks can be used to forecast the effectiveness of municipal sludge dewatering. The prediction error did not exceed 1.0% of the real value. ANN can therefore be useful in optimizing the dewatering process. In the case of the conducted research, it was the optimization of the sludge dewatering efficiency as a function of the type and parameters of conditioning factors. Therefore, it is possible to predict the dewatering efficiency of sludge that has not been tested in the laboratory, for example, with the use of other doses of physical conditioner. However, the condition for correct prediction and optimization was the use of a large dataset in the neural network training process. |
| first_indexed | 2024-03-10T13:19:57Z |
| format | Article |
| id | doaj.art-09b8907a194347179906ed4b922dce67 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:19:57Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-09b8907a194347179906ed4b922dce672023-11-21T10:04:45ZengMDPI AGEnergies1996-10732021-03-01146155210.3390/en14061552Artificial Neural Networks in Modeling of Dewaterability of Sewage SludgeMariusz Kowalczyk0Tomasz Kamizela1Faculty of Infrastructure and Environment, Częstochowa University of Technology, Dabrowskiego 69, 42-201 Czestochowa, PolandFaculty of Infrastructure and Environment, Częstochowa University of Technology, Dabrowskiego 69, 42-201 Czestochowa, PolandMechanical dewatering is a key process in the management of sewage sludge. However, the drainage efficiency depends on a number of factors, from the type and dose of the conditioning agent to the parameters of the drainage device. The selection of appropriate methods and parameters of conditioning and dewatering of sewage sludge is the task of laboratory work. This work can be accelerated through the use of artificial neural network (ANNs). The paper discusses the possibilities of using ANNs in predicting the dewatering efficiency of physically conditioned sludge. The input variables were only four parameters characterizing the conditioning methods and the dewatering method by centrifugation. These were the dose of the sludge skeleton builders (cement, gypsum, fly ash, and liquid glass), sonication parameters (sonication amplitude and time), and relative centrifugal force. Dewatering efficiency parameters such as sludge hydration and separation factor were output variables. Due to the nature of the research problem, two nonlinear networks were selected: a multilayer perceptron and a radial neural network. Based on the results of the prediction of artificial neural networks, it was found that these networks can be used to forecast the effectiveness of municipal sludge dewatering. The prediction error did not exceed 1.0% of the real value. ANN can therefore be useful in optimizing the dewatering process. In the case of the conducted research, it was the optimization of the sludge dewatering efficiency as a function of the type and parameters of conditioning factors. Therefore, it is possible to predict the dewatering efficiency of sludge that has not been tested in the laboratory, for example, with the use of other doses of physical conditioner. However, the condition for correct prediction and optimization was the use of a large dataset in the neural network training process.https://www.mdpi.com/1996-1073/14/6/1552sewage sludgeconditioningdewateringartificial neural networks |
| spellingShingle | Mariusz Kowalczyk Tomasz Kamizela Artificial Neural Networks in Modeling of Dewaterability of Sewage Sludge Energies sewage sludge conditioning dewatering artificial neural networks |
| title | Artificial Neural Networks in Modeling of Dewaterability of Sewage Sludge |
| title_full | Artificial Neural Networks in Modeling of Dewaterability of Sewage Sludge |
| title_fullStr | Artificial Neural Networks in Modeling of Dewaterability of Sewage Sludge |
| title_full_unstemmed | Artificial Neural Networks in Modeling of Dewaterability of Sewage Sludge |
| title_short | Artificial Neural Networks in Modeling of Dewaterability of Sewage Sludge |
| title_sort | artificial neural networks in modeling of dewaterability of sewage sludge |
| topic | sewage sludge conditioning dewatering artificial neural networks |
| url | https://www.mdpi.com/1996-1073/14/6/1552 |
| work_keys_str_mv | AT mariuszkowalczyk artificialneuralnetworksinmodelingofdewaterabilityofsewagesludge AT tomaszkamizela artificialneuralnetworksinmodelingofdewaterabilityofsewagesludge |