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2 Ecological Chemistry and Engineering. S

2 2023

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Remote sensing detection and resource utilisation of urban sewage sludge based on mobile edge computing

Hu Weijie, Zhou Youfei, Lu Junying, Sheng Jun, Jin Zechen

Ecological Chemistry and Engineering. S

2023

Vol. 30, nr 2

275--282

Improper disposal of municipal sewage sludge poses a significant threat to effective environmental protection. With the continuous advancement of artificial intelligence technology and the Internet of Things (IoT), remote sensing detection technology is emerging as a promising research avenue to address this issue. However, the current state of real-time detection technology is inadequate, hindering comprehensive and stable monitoring operation. Additionally, the rational use of network resources remains suboptimal. To address this challenge, this study proposes a resource optimisation technology for the current insufficient intelligent monitoring system of urban sewage sludge. By leveraging IoT and wireless technology, water meter data can be collected with minimal earth construction compared to traditional PLC collection. This is followed by utilising Faster R-CNN to plan the network transmission of sewage remote sensing information resources. Finally, the architecture collection module’s scalability is enhanced by incorporating edge computing and reserving sensor ports to meet future plant expansion demands. The experiment demonstrates the significant potential of this technology in application and resource optimisation. In actual parameter tracking tests, the proposed method effectively monitors sewage sludge, providing policy guidance and measure optimisation for relevant authorities, ultimately contributing to pollution-free urban development.

Design of urban sludge emission reduction optimisation strategy based on fuzzy neural network

Zhou You-Fei, Hu Wei-Jie, Sheng Jun, Zhou Juan-Juan, Zou Wei-Guo

Ecological Chemistry and Engineering. S

2023

Vol. 30, nr 2

243--250

Urban sewage sludge treatment is important for sustainable utilisation and virtuous cycle of freshwater resources. However, with the improvement of sewage discharge standards, ensuring stable operation of sewage sludge treatment plants is becoming an urgent problem to be solved in the sewage treatment industry. This paper proposes a FNN control framework based on different working conditions to optimise the whole process of municipal sewage sludge treatment and discharge. The framework first divides the working conditions according to the weather, forming a separate feature and an input vector together with the typical indicators of other sewage treatment plants. Then the FNN is used to complete the control and optimisation of various indicators, achieving the dual objectives of reducing energy consumption and optimising water quality. Finally, the model is tested for the tracking index of sewage flow. The results demonstrate that the FNN control method used has significantly lower MAE than the single method in the two indexes of energy consumption and water quality evaluation. This provides new ideas for the optimisation of urban sewage sludge treatment process in the future. Overall, the paper effectively highlights the importance of urban sewage sludge treatment and presents a well-designed FNN control framework for optimising the treatment process. Additionally, the paper could benefit from further elaboration on the significance of the results obtained, and suggestions for future research in this area.