Industrial Environmental Impact Assessment Method Based on Detection of Complex Anomalies in Time Series

To minimize the environmental impact of energy enterprises, it is important to promptly identify cases of possible changes in the quality of wastewater generated at power plants, that is, cases of exceeding the maximum permissible concentrations of contamination in wastewater. The goal of the method...

Full description

Saved in:
Bibliographic Details
Published in:Applied system innovation 2024-10, Vol.7 (5), p.89
Main Authors: Safonova, Elena, Kravets, Alla, Shcherbakov, Maxim, Kizim, Alexey, Al-Gunaid, Mohammad, Echin, Alexander
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Anomalies
anomaly detection
Artificial intelligence
Chemical elements
complex anomalies
Contamination
Drinking water
Electric power-plants
Energy
Environmental impact
Environmental impact analysis
Environmental impact assessment
Industrial plant emissions
Industrial wastes
isolation forest
Laboratories
Literature reviews
Machine learning
Methods
Monitoring systems
Multidimensional methods
outlier
Petroleum products
Power plants
Production processes
Sensors
smart energy
Task complexity
Time series
Wastewater
Water quality
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To minimize the environmental impact of energy enterprises, it is important to promptly identify cases of possible changes in the quality of wastewater generated at power plants, that is, cases of exceeding the maximum permissible concentrations of contamination in wastewater. The goal of the method for detecting complex anomalies in multidimensional time series obtained from smart energy stations’ sensor channels is to improve the accuracy of detecting contamination levels in industrial wastewater. To achieve this goal, the following tasks were addressed: methods for detecting time series anomalies were analyzed, the method for detecting complex anomalies was developed, software implementation of the algorithm was carried out, and experiments were conducted. The developed method is recommended for use in a smart energy monitoring system.
ISSN:2571-5577
2571-5577
DOI:10.3390/asi7050089