Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface

Efficiency of a system in a sewage treatment plant (STP) is significant in providing high quality of treated water to be discharged for the usage of surrounding neighborhood. However, the problems in measuring and monitoring the water quality in the treated wastewater or effluent water in real time...

Full description

Saved in:
Bibliographic Details
Main Authors: Rizal N.N.M., Hayder G.
Other Authors: 57654708600
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2024
Subjects:
GUI
IoT
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-34413
record_format dspace
spelling my.uniten.dspace-344132024-10-14T11:19:36Z Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface Rizal N.N.M. Hayder G. 57654708600 56239664100 Domestic wastewater Efficiency Energy GUI IoT Sewage treatment plant Supervised machine learning artificial neural network biochemical oxygen demand domestic waste effluent energy efficiency forecasting method machine learning real time sewage treatment water quality Efficiency of a system in a sewage treatment plant (STP) is significant in providing high quality of treated water to be discharged for the usage of surrounding neighborhood. However, the problems in measuring and monitoring the water quality in the treated wastewater or effluent water in real time has made it difficult to maintain the efficiency and preserve the energy of the STP. Therefore, this study purposes a graphical user interface (GUI) that has been embedded with a machine learning model to predict effluent parameters in real time. In this study, artificial neural network (ANN) and support vector machine were developed to predict biochemical oxygen demand (BODeff) using several effluent variables. Both models were evaluated using correlation coefficient (R), root mean square error (RMSE), determination of coefficient (R2), and mean square error (MSE). Based on the results, ANN model has outperformed SVM model by achieving the value of MSE and RMSE < 0.02 while R and R2 near to the value of 1.00. Therefore, the ANN model has been inserted into the GUI as ANN model is the most optimum model to predict BODeff in real time. The GUI-based app also performed well and able to predict the parameter with great accuracy. � 2022, University of Tehran. Final 2024-10-14T03:19:36Z 2024-10-14T03:19:36Z 2023 Article 10.1007/s41742-022-00493-8 2-s2.0-85142227241 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142227241&doi=10.1007%2fs41742-022-00493-8&partnerID=40&md5=95646dbe89f33ebab6e9078643a4af64 https://irepository.uniten.edu.my/handle/123456789/34413 17 1 4 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Domestic wastewater
Efficiency
Energy
GUI
IoT
Sewage treatment plant
Supervised machine learning
artificial neural network
biochemical oxygen demand
domestic waste
effluent
energy efficiency
forecasting method
machine learning
real time
sewage treatment
water quality
spellingShingle Domestic wastewater
Efficiency
Energy
GUI
IoT
Sewage treatment plant
Supervised machine learning
artificial neural network
biochemical oxygen demand
domestic waste
effluent
energy efficiency
forecasting method
machine learning
real time
sewage treatment
water quality
Rizal N.N.M.
Hayder G.
Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface
description Efficiency of a system in a sewage treatment plant (STP) is significant in providing high quality of treated water to be discharged for the usage of surrounding neighborhood. However, the problems in measuring and monitoring the water quality in the treated wastewater or effluent water in real time has made it difficult to maintain the efficiency and preserve the energy of the STP. Therefore, this study purposes a graphical user interface (GUI) that has been embedded with a machine learning model to predict effluent parameters in real time. In this study, artificial neural network (ANN) and support vector machine were developed to predict biochemical oxygen demand (BODeff) using several effluent variables. Both models were evaluated using correlation coefficient (R), root mean square error (RMSE), determination of coefficient (R2), and mean square error (MSE). Based on the results, ANN model has outperformed SVM model by achieving the value of MSE and RMSE < 0.02 while R and R2 near to the value of 1.00. Therefore, the ANN model has been inserted into the GUI as ANN model is the most optimum model to predict BODeff in real time. The GUI-based app also performed well and able to predict the parameter with great accuracy. � 2022, University of Tehran.
author2 57654708600
author_facet 57654708600
Rizal N.N.M.
Hayder G.
format Article
author Rizal N.N.M.
Hayder G.
author_sort Rizal N.N.M.
title Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface
title_short Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface
title_full Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface
title_fullStr Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface
title_full_unstemmed Forecasting Effluent Biochemical Oxygen Demand in Sewage Treatment Plants Using Machine Learning and User-Friendly Interface
title_sort forecasting effluent biochemical oxygen demand in sewage treatment plants using machine learning and user-friendly interface
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814061179075559424
score 13.214268