Machine Learning Applied in Wastewater Treatment

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Environmental and Green Processes".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2081

Special Issue Editors

School of Chemical Engineering, Yeungnam University, Gyeongsan 712749, Republic of Korea
Interests: SCADA anammox WWTP; exergy analysis; design optimization and LNG process optimization; simulation-optimization framework developer
School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: hydrogen; process systems engineering; hydrogen liquefaction; energy conversion and management; sustainability; cogeneration systems; machine learning
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Special Issue Information

Dear Colleagues,

Machine learning techniques can be applied in wastewater treatment to improve the efficiency, reliability, and cost effectiveness of the treatment process. Here are some ways in which machine learning can be applied in wastewater treatment:

  1. Prediction of Wastewater Characteristics: Machine learning algorithms can be used to predict the characteristics of incoming wastewater, such as its flow rate, chemical oxygen demand (COD), biological oxygen demand (BOD), and total suspended solids (TSS). These predictions can help operators adjust the treatment process in real time to optimize treatment efficiency and reduce costs.
  2. Fault Detection and Diagnosis: Machine learning algorithms can be used to detect and diagnose faults in the treatment process, such as equipment failures, clogs, or leaks. These algorithms can analyze data from sensors and other sources to identify patterns that indicate a problem and provide recommendations for corrective actions.
  3. Optimization of Treatment Process: Machine learning algorithms can be used to optimize the treatment process by adjusting the dosage of chemicals, aeration rate, and other parameters. This can help improve treatment efficiency, reduce energy consumption, and decrease the amount of chemicals used.
  4. Monitoring and Control of Treatment Plant: Machine learning algorithms can be used to monitor and control the treatment plant's operations in real time. This can help ensure that the treatment process is running smoothly and that all equipment is functioning correctly.

Overall, machine learning can play a significant role in improving the efficiency and effectiveness of wastewater treatment. By analyzing large amounts of data and providing real-time insights, machine learning algorithms can help operators make better decisions, optimize the treatment process, and reduce costs.

Dr. Alam Nawaz
Dr. Amjad Riaz
Guest Editors

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Keywords

  • predictive maintenance
  • real-time control
  • anomaly detection
  • optimization
  • fault detection and diagnosis
  • wastewater treatment processes
  • resource sustainability
  • data analytics
  • principal component analysis
  • clustering

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Published Papers (1 paper)

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Research

19 pages, 5100 KiB  
Article
Assessment of Blast Furnace Slags as a Potential Catalyst in Ozonation to Degrade Bezafibrate: Degradation Study and Kinetic Study via Non-Parametric Modeling
by Alexandra Galina-Licea, Mariel Alfaro-Ponce, Isaac Chairez, Elizabeth Reyes and Arizbeth Perez-Martínez
Processes 2024, 12(9), 1998; https://doi.org/10.3390/pr12091998 - 17 Sep 2024
Viewed by 702
Abstract
This study investigates the effectiveness of blast furnace slags (BFSs) as catalysts in the ozonation process to degrade complex contaminants such as bezafibrate (BFZ) at different pH levels. The findings reveal that the presence of BFS enhances degradation efficiency, achieving a 10% improvement [...] Read more.
This study investigates the effectiveness of blast furnace slags (BFSs) as catalysts in the ozonation process to degrade complex contaminants such as bezafibrate (BFZ) at different pH levels. The findings reveal that the presence of BFS enhances degradation efficiency, achieving a 10% improvement at pH 10 and a 30% improvement at pH 5.5 compared to simple ozonation. The highest degradation efficiency was observed in the Ozonation–BFS system at pH 10, with 90% decomposition of BFZ. These results were corroborated through ozone consumption analysis, BOD5 measurements, and the identification of oxalic acid as the final decomposition product. Due to the complexity of the reaction system, kinetic characterization was performed using non-parametric modeling based on differential neural networks. The model indicated that the observed reaction rate for BFZ degradation in the presence of ozone and BFS was 4.12 times higher at pH 5.0 and 1.08 times higher at pH 10.0 compared to simple ozonation. These results underscore the potential of using BFS in catalytic ozonation processes for the effective treatment of recalcitrant contaminants in wastewater. Full article
(This article belongs to the Special Issue Machine Learning Applied in Wastewater Treatment)
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