Research Article

Causal Digital Twins: Real-Time Counterfactuals for Industrial Process Optimization

Authors

  • Sree Charanreddy Pothireddi Parabole Inc, USA

Abstract

Digital twin platforms have become commonplace in process industries, yet most rely on correlation-based simulators that provide limited insight into causal mechanisms. This article introduces the Causal Digital Twin (CDT), an architecture that combines structural-causal models with high-frequency sensor streams to generate counterfactual answers in near real-time. A cluster of graphics-processing units executes constraint and score-based discovery across billion-scale graphs, while a sliding-window engine keeps parameters fresh as conditions evolve. Field evaluations demonstrate substantial reductions in energy consumption and unplanned downtime, accompanied by alert latencies that approach the cadence of plant-control loops. The discussion outlines system design, governance safeguards, and empirical evidence that causal reasoning shortens operator troubleshooting cycles and strengthens trust in automated recommendations.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

7 (7)

Pages

691-697

Published

2025-07-17

How to Cite

Sree Charanreddy Pothireddi. (2025). Causal Digital Twins: Real-Time Counterfactuals for Industrial Process Optimization. Journal of Computer Science and Technology Studies, 7(7), 691-697. https://doi.org/10.32996/jcsts.2025.7.7.77

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Keywords:

Causal inference, Digital twins, Industrial automation, Counterfactual reasoning, Process optimization