Real-time monitoring control of sequencing batch anammox process

Jia, Fangxu; Liu, Chenyu; Zhao, Xingcheng; Chen, Jiayi; Zhang, Ziyan; Yao, Hong

doi:10.1007/s11356-022-23233-z

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…A method for analyzing and identifying the electricity consumption behavior of large users based on the fusion of multi-source data is proposed, which utilizes multi-source data such as the electricity consumption data, electricity consumption contract, and electricity consumption equipment of large users, and provides an effective means for the supervision and service of the electricity consumption of large users [20]. It realizes the dynamic prediction of the electricity consumption cost of large users, and provides a reference basis for the decision-making and optimization of electricity consumption of large users [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud-Enabled Real-Time Monitoring and Alert System for Primary Network Resource Scheduling and Large-Scale Users

Zhang,

Shu,

et al. 2024

ijacsa

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This paper innovatively combines cloud computing with Bayesian networks, aiming to provide an efficient and realtime prediction and scheduling platform for power main network scheduling and large-scale user monitoring. The core of the research lies in the development of a set of novel intelligent scheduling algorithms, which integrates multi-objective optimization theory and deep reinforcement learning technology to achieve dynamic and optimal allocation of power grid resources in the cloud environment. By constructing a comprehensive evaluation system, this study verifies the advancement of the proposed model in multiple dimensions: not only does it make breakthroughs in the in-depth parsing and accurate prediction of electric power data, but it also significantly improves the prediction accuracy of the main grid load changes, tariff dynamic adjustments, grid security posture, and power consumption patterns of large users. The empirical study shows that compared with the existing methods, the model proposed in this study effectively reduces energy consumption and operation costs while improving prediction accuracy and dispatching efficiency, demonstrating its significant innovative value and practical significance in the field of intelligent grid management. The innovation of this paper lies in the development of a composite prediction model that integrates the powerful classification and prediction capabilities of Bayesian networks and the efficient learning mechanism of deep reinforcement learning in complex decision-making scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Cloud-Enabled Real-Time Monitoring and Alert System for Primary Network Resource Scheduling and Large-Scale Users

Zhang,

Shu,

et al. 2024

ijacsa

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Real-time control suitable for feed distribution strategy based on ammonia introduced in SBR sub-cycle: Field experience and validation for pilot-scale single-stage deammonification

Yun,

Chun,

Jung

et al. 2023

Journal of Water Process Engineering

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Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach

Guo,

Kim,

Park

et al. 2024

Water

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The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH3-N, and NO3-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.

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Real-time monitoring control of sequencing batch anammox process

Cited by 3 publications

References 27 publications

Cloud-Enabled Real-Time Monitoring and Alert System for Primary Network Resource Scheduling and Large-Scale Users

Cloud-Enabled Real-Time Monitoring and Alert System for Primary Network Resource Scheduling and Large-Scale Users

Real-time control suitable for feed distribution strategy based on ammonia introduced in SBR sub-cycle: Field experience and validation for pilot-scale single-stage deammonification

Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach

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