HydroSphere
HydroSphere is an intelligent governance framework for wastewater treatment that integrates a TCN-LSTM hybrid prediction model, a PPO reinforcement learning dosing optimization module, and a SHADE anomaly detection module. It covers a complete pipeline from real-time water quality monitoring and prediction to treatment optimization and fault recovery.
Timeline (last 90 days)
HydroSphere is a governed, self-healing wastewater infrastructure framework covering the complete chain from real-time water quality monitoring, prediction, and treatment optimization to fault recovery.
The research team evaluated the HydroSphere framework using a total of 2.82 million water quality measurements spanning 1940 to 2023.
HydroSphere's prediction layer employs a TCN-LSTM hybrid model to perform multi-step ahead prediction of seven water quality parameters.
The metrics of HydroSphere's prediction module are RMSE 0.1417, MAE 0.1047, and R² 0.3596.
HydroSphere's adaptive dosing optimization module uses the PPO reinforcement learning algorithm to dynamically adjust chemical dosing amounts.
The average per-step reward of the adaptive dosing optimization module reaches 1.059, slightly higher than the fixed-dosing baseline of 1.017.
The study found that unconstrained reward optimization leads to excessive dosing, confirming the necessity of introducing explicit operational constraints in industrial control scenarios.
The SHADE module uses a deep autoencoder to identify anomalies at the sensor and process levels, achieving an F1 score of 0.651 in controlled fault injection tests.
HydroSphere introduces three mechanisms—tiered governance, deterministic safety boundaries, and human oversight—using deterministic safety boundaries to constrain aggressive dosing recommendations from reinforcement learning.
HydroSphere explicitly aligns with United Nations Sustainable Development Goals SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action).
All Facts (10)
HydroSphere is a governed, self-healing wastewater infrastructure framework covering the complete chain from real-time water quality monitoring, prediction, and treatment optimization to fault recovery.
50%UnverifiedThe research team evaluated the HydroSphere framework using a total of 2.82 million water quality measurements spanning 1940 to 2023.
50%UnverifiedHydroSphere's prediction layer employs a TCN-LSTM hybrid model to perform multi-step ahead prediction of seven water quality parameters.
50%UnverifiedThe metrics of HydroSphere's prediction module are RMSE 0.1417, MAE 0.1047, and R² 0.3596.
50%UnverifiedHydroSphere's adaptive dosing optimization module uses the PPO reinforcement learning algorithm to dynamically adjust chemical dosing amounts.
50%UnverifiedThe average per-step reward of the adaptive dosing optimization module reaches 1.059, slightly higher than the fixed-dosing baseline of 1.017.
50%UnverifiedThe study found that unconstrained reward optimization leads to excessive dosing, confirming the necessity of introducing explicit operational constraints in industrial control scenarios.
50%UnverifiedThe SHADE module uses a deep autoencoder to identify anomalies at the sensor and process levels, achieving an F1 score of 0.651 in controlled fault injection tests.
50%UnverifiedHydroSphere introduces three mechanisms—tiered governance, deterministic safety boundaries, and human oversight—using deterministic safety boundaries to constrain aggressive dosing recommendations from reinforcement learning.
50%UnverifiedHydroSphere explicitly aligns with United Nations Sustainable Development Goals SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action).
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