Innovative Brain-Inspired Model Enhances Wastewater Treatment Predictions

Newswise — Wastewater treatment plants (WWTPs) play a crucial role in environmental protection by mitigating risks to public health and aquatic ecosystems through the prevention of pollutant release. Accurately predicting effluent quality, especially levels of ammonia nitrogen (NH₃) and chemical oxygen demand (COD), is essential for ensuring water safety and enhancing the efficiency of WWTPs. Despite advances in data-driven methods, persistent challenges arise from the complexity of wastewater data.

A groundbreaking study, published in Frontiers of Environmental Science & Engineering on 10 November 2023, has explored brain-inspired hybrid models that merge CNN and LSTM to enhance prediction accuracy by leveraging multimodal data fusion techniques that mimic the human brain's processing capabilities. These innovative approaches aim to improve the accuracy and efficiency of predictive models, marking a significant stride in effluent quality prediction.

In this study, researchers developed a Brain-Inspired Image and Temporal Fusion (BITF) model with a CNN-LSTM network, designed to enhance effluent quality prediction by analyzing wastewater surface images and water quality data. Utilizing an overhead camera and water quality sensor arrays, the study captured synchronized high-resolution images and monitored crucial water quality indicators such as COD and NH₃. At the core of this study, the BITF module emulates the human brain's data processing capabilities, integrating image and water quality data through a self-attention mechanism. Coupled with the VGG11 network for image feature extraction and an adaptive feature fusion method, this mechanism effectively prioritizes and fuses multimodal data, significantly improving prediction accuracy. The model's effectiveness was demonstrated through superior performance in benchmarks against traditional models and evaluated using metrics like Root Mean Square Error, Coefficient of Determination, and Mean Absolute Percentage Error. The study's innovative approach, which combines artificial intelligence with environmental engineering, not only sets a new standard in effluent quality prediction but also paves the way for future advanced wastewater management research.

Highlights
● A novel brain-inspired network accurately predicts sewage effluent quality.
● Sewage-surface images are utilized in data analysis by the model.
● The developed method outperforms traditional ones by reducing error by 23%.
● The model offers the potential for costeffective monitoring.

Yongzhen Peng, one of the corresponding authors, stated, "This research marks a pivotal advancement. By emulating the brain's data processing prowess, we've developed a system that significantly outperforms traditional methods, promising a new era in effluent quality management."

The BITF-CL model demonstrated remarkable prediction accuracy, surpassing existing models in benchmark tests. Such precision in effluent quality prediction aids not only in regulatory compliance but also reduces dependency on expensive sensors, potentially lowering operational costs. This approach could revolutionize wastewater treatment monitoring and management by offering more precise, cost-effective, and efficient solutions.

Reference
Funding information: The National Key R&D Program of China (2021YFC1809001).
DOI: 10.1007/s11783-024-1791-x
Original URL: https://doi.org/10.1007/s11783-024-1791-x
About Frontiers of Environmental Science & Engineering
Frontiers of Environmental Science & Engineering (FESE) is the leading edge forum for peer-reviewed original submissions in English on all main branches of environmental disciplines. FESE welcomes original research papers, review articles, short communications, and views & comments. All the papers will be published within 6 months since they are submitted. The Editors-in-Chief are Academician Jiuhui Qu from Tsinghua University, and Prof. John C. Crittenden from Georgia Institute of Technology, USA. The journal has been indexed by almost all the authoritative databases such as SCI, Ei, INSPEC, SCOPUS, CSCD, etc.