Combine machine learning, first-principles engineering, and physics-based modeling to characterize system behavior, improve process understanding, and enable more accurate, reliable operational decision-making.
Industrial systems are governed by complex physical, chemical, and operational relationships that evolve over time. While data-driven models provide powerful predictive capabilities, relying solely on historical data can sometimes limit reliability under changing operating conditions.
At nilmuda, we combine predictive analytics with physics-based, hybrid and reduced-order modeling approaches to develop solutions that are both data-informed and grounded in real-world engineering principles.
Our models leverage:
This integrated approach enables more accurate predictions, deeper process understanding, and more robust decision support across industrial operations.
Develop advanced forecasting and predictive models for:
Build virtual sensors that estimate difficult-to-measure variables using existing process data.
Soft sensors can help infer:
without requiring additional physical instrumentation.
Develop models grounded in engineering fundamentals, including:
These models provide deeper process insight and improved interpretability.
Combine machine learning with first-principles engineering to create hybrid systems that balance:
Hybrid models are particularly valuable in environments where operational conditions frequently change or where process understanding is critical.
Develop computationally efficient reduced-order models (ROMs) that simplify complex physical systems while preserving the key dynamics and behaviors of the process.
Reduced-order models enable:
These models are especially valuable in industrial environments where full-scale physics simulations may be too computationally expensive for real-time operational use.
Unlike purely statistical models, physics-based approaches incorporate the actual scientific behavior of industrial systems.
This provides several important advantages:
Models remain grounded in physical laws and engineering constraints, improving robustness under changing conditions.
Predictions align with known process behavior, making results easier for engineers and operators to trust and validate.
Physics-informed approaches can still perform effectively even when historical data is incomplete or sparse.
Gain deeper insight into how variables interact across unit operations and process systems.
Operational recommendations can incorporate physical and operational constraints to reduce risk.
At nilmuda, we combine engineering expertise with advanced analytics to ensure models are not only mathematically accurate, but operationally meaningful.
We work closely with client teams to:
This collaborative approach ensures solutions are practical, scalable, and grounded in real industrial environments.