Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment

2026-04-24 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Operations & Process Management · Depth: Expert, extended

Summary

Aalborg University's Department of Energy presents CCSS-RS, a novel data-driven open-loop simulator designed for digital-twin operator decision support in wastewater treatment plants (WWTPs). This controlled continuous-time state-space model addresses challenges like irregular and missing sensor data, and the need for 12-36 hour planning horizons. CCSS-RS integrates typed context encoding, gain-weighted forcing of prescribed and forecast drivers, semigroup-consistent rollouts, and Student-$t$ plus hurdle outputs for heavy-tailed and zero-inflated WWTP sensor data. Evaluated on the public Avedøre full-scale benchmark, with 906,815 timesteps and 43% missingness, CCSS-RS achieved an RMSE of 0.696 and CRPS of 0.349 at $H=1000$ across 10,000 test windows. This represents a 40–46% reduction in RMSE compared to Neural CDE baselines and a 31–35% reduction over simplified internal variants, establishing its practical value for offline scenario screening.

Key takeaway

For AI Scientists and Machine Learning Engineers developing industrial control systems, CCSS-RS offers a robust framework for open-loop simulation in complex environments like WWTPs. You should prioritize architectural designs that explicitly separate control inputs from state variables, natively handle irregular and missing data, and incorporate mechanisms like semigroup consistency for long-horizon stability. This approach enables effective scenario screening and decision support, even with observational data, by focusing on relative plan comparison rather than absolute prediction accuracy.

Key insights

CCSS-RS provides a robust, data-driven simulator for WWTPs, enabling long-horizon scenario screening despite challenging data conditions.

Principles

• Model future controls as privileged drivers.
• Handle irregular sampling and missingness natively.
• Long-horizon stability requires specific architectural components.

Method

CCSS-RS uses a two-phase architecture: context encoding via TCN and attention pooling, followed by a rollout phase with parallel TCN encoders, gain-weighted forcing, four parallel affine scans, sticky regime switching, and semigroup consistency.

In practice

• Use CCSS-RS for comparing alternative control plans.
• Screen candidate plans with configurable criteria.
• Assess robustness under sensor outages.

Topics

• Wastewater Treatment
• Digital Twin
• Open-Loop Simulation
• CCSS-RS Model
• Irregular Time Series

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.