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<p>There is a pressing need in terms of developing efficient and adaptive wastewater treatment systems, especially for dairy wastewaters. Current treatment systems frequently experience challenges in dynamic multi-parametric environments, and their performance is notably suboptimal compared with expectations on the parameters of BOD, COD, and turbidity. Traditional models fail during real-time adaptation and multi-objective optimization, particularly if diverse operational conditions and media configurations are included. In further efforts to enhance the efficiency in overcoming the hurdles, this study proposes an advanced deep learning framework for the optimization of multiple-parameter effects in rotating biological contactor (RBC) systems. Here, it proposes integration with several high-rated techniques in order to increase the performance levels of the system. To start with, a hybrid model combining convolutional neural networks with a deep Q- network model for optimizing the operational parameters along with media type and rotational speed performs image-based turbidity analysis. Transfer learning using a pre-trained ResNet50 model improves turbidity classification accuracy over different media types, such as jute, scouring sheet, and aquarium sponge, with minimal data requirements. The XGBoost algorithm is applied for predictive modeling for predicting water quality parameters by historical samples of operational data. The GWO approach is used to attain a balance between conflicting objectives, like efficiency in treatment, energy consumption, and cost. Finally, a long short-term memory (LSTM) network predicts maintenance needs based on trends of system performance forecasting. The framework considered shows fairly significant improvements and encompasses 10–15% greater efficiency in BOD/COD removal; classification accuracy about turbidity at around 93–96%, and energy consumption cut down by 10–12%. There is also a case of reducing unplanned failures by 15–20% with the new predictive model of maintenance. This approach of deep learning provides a strong adaptable as well as an efficient solution to optimize wastewater treatment systems of RBC-based solutions.</p>

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Enhanced media-based solutions for dairy wastewater treatment in rotating biological contactor system using deep learning optimization technique

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Pallavi Chakole,
  • Ajay Gajbhiye

摘要

There is a pressing need in terms of developing efficient and adaptive wastewater treatment systems, especially for dairy wastewaters. Current treatment systems frequently experience challenges in dynamic multi-parametric environments, and their performance is notably suboptimal compared with expectations on the parameters of BOD, COD, and turbidity. Traditional models fail during real-time adaptation and multi-objective optimization, particularly if diverse operational conditions and media configurations are included. In further efforts to enhance the efficiency in overcoming the hurdles, this study proposes an advanced deep learning framework for the optimization of multiple-parameter effects in rotating biological contactor (RBC) systems. Here, it proposes integration with several high-rated techniques in order to increase the performance levels of the system. To start with, a hybrid model combining convolutional neural networks with a deep Q- network model for optimizing the operational parameters along with media type and rotational speed performs image-based turbidity analysis. Transfer learning using a pre-trained ResNet50 model improves turbidity classification accuracy over different media types, such as jute, scouring sheet, and aquarium sponge, with minimal data requirements. The XGBoost algorithm is applied for predictive modeling for predicting water quality parameters by historical samples of operational data. The GWO approach is used to attain a balance between conflicting objectives, like efficiency in treatment, energy consumption, and cost. Finally, a long short-term memory (LSTM) network predicts maintenance needs based on trends of system performance forecasting. The framework considered shows fairly significant improvements and encompasses 10–15% greater efficiency in BOD/COD removal; classification accuracy about turbidity at around 93–96%, and energy consumption cut down by 10–12%. There is also a case of reducing unplanned failures by 15–20% with the new predictive model of maintenance. This approach of deep learning provides a strong adaptable as well as an efficient solution to optimize wastewater treatment systems of RBC-based solutions.