Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support

2026-05-19

AI Summary

This study addresses the challenge of simultaneously ensuring safety and energy efficiency in aeration and chemical dosing control at wastewater treatment plants, where conventional approaches often lead to effluent violations and surges in nitrous oxide emissions. To this end, the authors propose an interpretable digital twin system built upon a context-aware structured state-space model that integrates continuous-time regime switching, local linear experts, and a context-gated network. The framework further incorporates conformal risk control to establish a self-falsifying decision layer with finite-sample coverage guarantees. Experimental results demonstrate that both static and adaptive variants of the model achieve RMSE below 0.78% and 1.08%, respectively, on real-world and benchmark datasets. The proposed restart rule reduces cumulative regret by 43.6%, eliminates unsafe actions in the BSM2 primary scenario, and avoids 93 erroneous safety decisions, thereby significantly enhancing operational safety and energy efficiency.

Abstract

Operators of safety-critical industrial processes increasingly rely on digital twins to screen control interventions, but such simulators rarely carry certified safety guarantees. Wastewater treatment plants exemplify the gap: operators face a daily safety-efficiency trade-off where aerating too little risks effluent violations and nitrous-oxide (N2O) spikes, and aerating too much wastes energy. We develop an explainable digital twin for aeration and dosing setpoints. CCSS-IX, the simulator, is a bank of interpretable locally linear state-space "experts" adaptively mixed by a context-aware gating network, building on a continuous-time regime-switching scaffold. A runtime decision layer applies conformal risk control to abstain, reopen, or return a falsifying temporal witness for any operator-proposed action that cannot be statistically certified. The artificial-intelligence contribution is twofold: an identifiable, context-conditioned structured surrogate that retains operator-readable dynamics, and a self-falsifying decision rule with finite-sample coverage guarantees. The engineering contribution is a validated, end-to-end decision-support pipeline, tested on a 1000-step slice of the Avedøre full-scale plant (42.6% sensor missingness, 2-minute sampling), the Agtrup/BlueKolding full-scale plant in Denmark, and the Benchmark Simulation Model No. 2 (BSM2) international benchmark, under a matched ten-seed protocol. The static structured ensemble lies within 0.78% root-mean-square error of an unconstrained black-box reference, and the adaptive variant within 1.08%. The calibrated reopen rule cuts aggregate two-plant regret by 43.6% at an unsafe-action cost weight of 4 and eliminates unsafe chosen actions on the BSM2 main slice. Event-aligned temporal witnesses prevent 93 of 187 false-safe N2O approvals, about 4.65x the dyadic baseline (paired McNemar p < 1e-21).

Problem

• wast日晚间 treatment
• digital twins
• safety-efficiency trade-off
• aeration control
• unsafe actions

Innovation

• Explainable Digital Twin
• Context-Conditioned Structured Simulator
• Self-Falsifying Decision Support
• Conformal Risk Control
• Temporal Witness