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Combustion performance prediction in oil and gas plants using integrated neural network models and SAP S4HANA sensor analytics
Keshireddy, S.R. Jamithireddy, N.H. Sappa, Ankita Vandrangi, S.K. Emani, S. Gurunadh, G. Jamithireddy, N.S.
Keshireddy, S.R.
Jamithireddy, N.H.
Sappa, Ankita
Vandrangi, S.K.
Emani, S.
Gurunadh, G.
Jamithireddy, N.S.
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2026-01-12
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Article
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Keywords
Combustion forecasting,Industrial process optimization,Neural network integration,SAP S4HANA sensor analytics
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Citation
Keshireddy, S.R., Jamithireddy, N.H., Jamithireddy, N.S. et al. Combustion performance prediction in oil and gas plants using integrated neural network models and SAP S4HANA sensor analytics. Sci Rep 16, 5069 (2026). https://doi.org/10.1038/s41598-026-35364-1
Abstract
The efficiency of oil combusting and gas facilities deals with energy expenses as well as operational costs and the overall carbon footprint. Other approaches use simplistic dynamic control and process automation, but these approaches are neither flexible enough to account for drifts and dynamics in real-time, nor performant enough for process deviation in real-time to respond. This paper describes a transformer internal combustion engine with a scalable, platform agnostic AI architecture for combustion IT forecasting combined with real-time hardware integration from enterprise systems like SAP S/4HANA, Oracle, and Siemens MindSphere, using hybrid GRU machine learning with dense GRU neural networks. The process unites level balance thermodynamic models with sequential learning to physically integrated geo-temporal coupling learning. Operational data spanning 6 months and 3 plants (6.5 million samples) was used to train the system. The system beat conventional machine learning approaches with RMSE 2.1–2.4, MAE 1.7–1.9, and R2 > 0.91 for combustion-efficiency forecasting. The models were robust as they kept the mean +/- SD and 5% confidence intervals on all metrics with temporally disjoint folds. Real-time availability of the entire network was confirmed with mean latency of 0.1 s and availability metrics of 99.7%. Using SHAP-based feature attribution for interpretability allowed operators to identify the sensors that impacted gaps in the driven efficiencies, thus improving trust in the AI-based control. From the sustainability perspective, the recorded 2–5% efficiency improvements directly translate to fuel savings and lowered CO₂ emissions authoritative to the ISO 50,001 energy efficiency guidelines. This research provides a trustworthy and detailed explainable AI foundation for combustion systems that combines data-centric modeling and process engineering. Upcoming advances will add reinforcement-learning controllers and edge-only inference units for fully autonomous, transparent, and compliant autonomous optimization of industrial systems. © The Author(s) 2026.
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This is an open access article under the CC BY license.
Publisher
Nature Research
Journal
Scientific Reports