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<p>Improving the efficiency and sustainability of wastewater treatment plants (WWTPs) is essential for protecting the environment and ensuring energy-conscious operations. This research introduces an advanced AI-integrated framework tailored to accurately predict water quality indicators during the aeration stage—one of the most critical phases in the treatment process. At the heart of this approach lies a Parallel Adaptive Neuro-Fuzzy Inference System (PANFIS), which orchestrates multiple ANFIS models running concurrently. This parallelism boosts the system’s scalability, resilience, and predictive accuracy. The architecture follows a structured, multi-step pipeline. The process starts with a Autoencoder-Generative Adversarial Network (AutoGAN) that selects key features and fixes data anomalies, ensuring cleaner inputs. Then, Self-Organizing Maps (SOM) is used to smartly initialize ANFIS membership functions, improving the model’s ability to learn underlying patterns. To further refine model performance, the framework integrates metaheuristic optimization techniques—Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)—that fine-tune the hybrid SOM-PANFIS setup. A defining innovation of this work is the introduction of a three-tier attention mechanism within the PANFIS structure. Attention is strategically applied at the feature level, membership function level, and rule level, enabling the system to focus adaptively on the most critical information at each stage of reasoning. Evaluation across standard performance metrics—Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R<sup>2</sup>—demonstrates the effectiveness of the proposed method. The SOM-PSO-PANFIS model achieved an MSE of 0.0005, RMSE of 0.0233, and R<sup>2</sup> of 0.9751. After integrating attention mechanisms, the SOM-PSO-PANFIS-Attention configuration significantly improved performance, reaching an MSE of 0.0003, RMSE of 0.0183, and R<sup>2</sup> of 0.9866. These results affirm the model’s capability to handle the complex and nonlinear behavior inherent in wastewater data, delivering a solution that is not only highly accurate but also scalable for real-world WWTP deployment. This study ultimately showcases the transformative potential of AI-driven hybrid models in modernizing wastewater treatment.</p>

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An attention infused multi-stage parallel adaptive neuro fuzzy systems framework with metaheuristic optimization for accurate water quality prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • S. Ramya,
  • S. Srinath,
  • Pushpa Tuppad,
  • V. Chandan

摘要

Improving the efficiency and sustainability of wastewater treatment plants (WWTPs) is essential for protecting the environment and ensuring energy-conscious operations. This research introduces an advanced AI-integrated framework tailored to accurately predict water quality indicators during the aeration stage—one of the most critical phases in the treatment process. At the heart of this approach lies a Parallel Adaptive Neuro-Fuzzy Inference System (PANFIS), which orchestrates multiple ANFIS models running concurrently. This parallelism boosts the system’s scalability, resilience, and predictive accuracy. The architecture follows a structured, multi-step pipeline. The process starts with a Autoencoder-Generative Adversarial Network (AutoGAN) that selects key features and fixes data anomalies, ensuring cleaner inputs. Then, Self-Organizing Maps (SOM) is used to smartly initialize ANFIS membership functions, improving the model’s ability to learn underlying patterns. To further refine model performance, the framework integrates metaheuristic optimization techniques—Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)—that fine-tune the hybrid SOM-PANFIS setup. A defining innovation of this work is the introduction of a three-tier attention mechanism within the PANFIS structure. Attention is strategically applied at the feature level, membership function level, and rule level, enabling the system to focus adaptively on the most critical information at each stage of reasoning. Evaluation across standard performance metrics—Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2—demonstrates the effectiveness of the proposed method. The SOM-PSO-PANFIS model achieved an MSE of 0.0005, RMSE of 0.0233, and R2 of 0.9751. After integrating attention mechanisms, the SOM-PSO-PANFIS-Attention configuration significantly improved performance, reaching an MSE of 0.0003, RMSE of 0.0183, and R2 of 0.9866. These results affirm the model’s capability to handle the complex and nonlinear behavior inherent in wastewater data, delivering a solution that is not only highly accurate but also scalable for real-world WWTP deployment. This study ultimately showcases the transformative potential of AI-driven hybrid models in modernizing wastewater treatment.