“A Review on Applications of Artificial Neural Network Approach for Troubleshooting of Sewage Treatment Plant Process"

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D.K. Khopade, S. G. Ban

Abstract

The paper "Artificial Neural Network Approach for Troubleshooting of Sewage Treatment Plant Process" proposes an ANN-based troubleshooting model to optimize the operation and maintenance of sewage treatment plants (STPs). The model uses historical plant data to detect, predict, and resolve process anomalies, identifying potential problems before they escalate. It also suggests corrective actions to restore optimal plant performance. The model uses data from multiple sensors across different stages of the sewage treatment process, identifying correlations between variables and providing real-time insights. The results show that the ANN approach significantly improves troubleshooting accuracy and response time, reducing reliance on manual labor and expert intervention. This approach contributes to environmental sustainability by ensuring high-quality effluent discharge.


The methodology involves data collection from multiple sensors across different stages of the sewage treatment process. These data points include parameters such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), pH levels, turbidity, and flow rates. The ANN model processes these inputs, identifies correlations between variables, and provides real-time insights into plant operations. The model’s predictive capabilities allow operators to anticipate issues like aeration system failures, chemical imbalances, or sludge settling problems, and make timely adjustments.


The results of the study demonstrate that the ANN approach significantly improves troubleshooting accuracy and response time compared to conventional methods. By automating the detection and resolution of process anomalies, the ANN-based system reduces reliance on manual labor and expert intervention, leading to more consistent treatment outcomes and lower operational costs. Additionally, the system’s ability to learn from new data enables continuous optimization and adaptation to changing environmental conditions.


In conclusion, the application of Artificial Neural Networks in the troubleshooting of sewage treatment plants represents a major advancement in wastewater management. This approach not only enhances the operational efficiency of STPs but also contributes to environmental sustainability by ensuring high-quality effluent discharge. The proposed ANN-based troubleshooting system offers a proactive and scalable solution that can be integrated into existing plant infrastructures, paving the way for smarter and more resilient sewage treatment operations in the future.

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How to Cite
D.K. Khopade, S. G. Ban. (2023). “A Review on Applications of Artificial Neural Network Approach for Troubleshooting of Sewage Treatment Plant Process". International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 5643–5654. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/11327
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