Enhancing Effluent Quality Predictions in Wastewater Treatment with LSTM Neural Network

This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate t...

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Bibliographic Details
Published in:International Conference on System Theory, Control and Computing pp. 83 - 88
Main Authors: Voipan, Daniel, Vasiliev, Iulian, Diaconu, Larisa, Voipan, Andreea-Elena, Barbu, Marian
Format: Conference Proceeding
Language:English
Published: IEEE 10.10.2024
Subjects:
Accuracy
Adaptation models
Artificial Neural Networks
BSM2
Effluent Quality
Effluents
Long short term memory
LSTM
Predictive models
Real-time systems
Stochastic processes
Time series analysis
Wastewater
Wastewater treatment
Wastewater Treatment Plants
ISSN:2473-5698
Online Access:Get full text
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Summary:This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate the dynamics of wastewater treatment more accurately and adjust operational strategies dynamically. By integrating LSTM networks, the research enables continuous prediction of Effluent Quality Index (EQI) variables under stochastic and deterministic scenarios, thereby improving the accuracy and efficiency of predicting pollutant levels. The research uses an LSTM model to learn from a comprehensive dataset derived from historical simulations of BSM2, where key parameters such as the oxygen transfer coefficient (K L a) are systematically varied to measure their impact on effluent quality. The LSTM's capability to handle complex, nonlinear data and its adaptability to time series forecasting significantly enhances model performance, offering a robust tool for real-time decision-making and process optimization in wastewater treatment facilities. This approach not only improves the accuracy and efficiency of predicting pollutant levels but also supports environmental compliance and operational sustainability, making it a valuable tool for environmental engineers and professionals in the field of wastewater treatment.
ISSN:2473-5698
DOI:10.1109/ICSTCC62912.2024.10744668