Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1)

Author:

Khurshid Amir,Pani Ajaya Kumar

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Reference76 articles.

1. Alauddin, M., Khan, F., Imtiaz, S., & Ahmed, S. (2018). A bibliometric review and analysis of data-driven fault detection and diagnosis methods for process systems. Industrial & Engineering Chemistry Research, 57(32), 10719–10735.

2. Alex, J., Rieger, L., Winkler, S., & Siegrist, H. (2003). Progress in sensor technology-progress in process control? Part II: Results from a simulation benchmark study. Water Science and Technology, 47(2), 113–120.

3. Alex, J., Benedetti, L., Copp, J., Gernaey, K. V., Jeppsson, U., Nopens, I., Pons, M-N., Steyer, J-P., & Vanrolleghem, P. A. (2008). Benchmark Simulation Model no. 1 (BSM1). Report by the IWA Taskgroup on benchmarking of control strategies for WWTPs, 1. Lund University.

4. Anter, A. M., Gupta, D., & Castillo, O. (2020). A novel parameter estimation in dynamic model via fuzzy swarm intelligence and chaos theory for faults in wastewater treatment plant. Soft Computing, 24(1), 111–129.

5. Arpitha, V., & Pani, A. K. (2022). Machine learning approaches for fault detection in semiconductor manufacturing process: A critical review of recent applications and future perspectives. Chemical and Biochemical Engineering Quarterly, 36(1), 1–16.

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1. Fault Diagnosis of Wastewater Treatment Processes Based on CPSO-DKPCA;International Journal of Computational Intelligence Systems;2024-02-01

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