Dynamic Data Reconciliation in Industrial Processes

Dynamic data reconciliation (DDR) represents a cornerstone in the modern control and monitoring of continuous and batch chemical processes. By integrating temporal process models with on-line measurement data, DDR continuously adjusts sensor readings to satisfy mass, energy and momentum balances while accounting for process dynamics. This approach extends traditional steady‐state reconciliation by incorporating rate equations and dynamic constraints, thereby enabling more accurate estimation of time-varying process variables. Through the application of optimised numerical solvers, DDR reduces the impact of random noise, identifies gross measurement errors and improves the reliability of soft sensors. It underpins real‐time optimisation and model predictive control systems by supplying consistent, high‐fidelity data to decision‐support tools, facilitating tighter operating margins, reduced energy consumption and enhanced product quality. Industrial implementations range from petrochemical hydrogen networks, where reconciled flows and compositions guide economic dispatch, to polymerisation reactors, where evolving heat‐transfer coefficients are inferred online. Recent advances in computational power and algorithmic robustness have further accelerated the adoption of DDR in digital-twin frameworks, offering a unified platform for virtual sensing, anomaly detection and predictive maintenance. As industries strive for greater sustainability and digital integration, DDR serves as a critical enabler of smart manufacturing, allowing operators and automated systems to respond confidently to disturbances, feedstock variability and shifting market demands.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Research from all publishers

Recent work has extended DDR to the field of waste-heat recovery in gas-turbine plants operating on supercritical CO₂ cycles. A dynamic reconciliation model was shown to reduce parameter deviations to below 1 per cent while automatically detecting and eliminating gross errors, thereby delivering high-precision estimates of mass flow and power output. Another study has proposed a modified expectation–maximisation algorithm that overcomes the limitations of persistent measurement biases. By fixing the variance of random errors, this approach achieves faster convergence and more accurate rectification of noisy process data. Earlier foundational research in batch suspension polymerisation has demonstrated the integration of phenomenological reaction models within DDR procedures, revealing significant batch-to-batch variations in heat-transfer coefficients and enabling reliable real-time monitoring of reactor temperature and composition.

Dynamic Data Reconciliation in Industrial Processes publication trend

The graph below shows the total number of articles in dynamic data reconciliation in industrial processes across all publications each year (not limited to Nature Index journals).

Technical terms

Dynamic data reconciliation: A method that uses time‐dependent process models to adjust noisy measurements so that they satisfy physical and chemical balances.

Real-time optimisation (RTO): An on‐line decision‐support tool that continuously recalculates optimal operating setpoints to improve process economics and safety.

Expectation–maximisation algorithm: A two‐step statistical technique for estimating true values from noisy or incomplete data by iteratively maximising likelihood functions.

Soft sensor: A virtual sensor that derives unmeasured process variables from reconciled data and mathematical models.

Gross error: A large, systematic deviation in measurement data often caused by sensor faults or calibration drift.

References

1. Implementation of RTO in a large hydrogen network considering uncertainty. Optimization and Engineering (2019).
2. On-Line Dynamic Data Reconciliation in Batch Suspension Polymerizations of Methyl Methacrylate. Processes (2017).
3. A Modified Expectation Maximization Approach for Process Data Rectification. Processes (2021).
4. Parameter Correction on Waste Heat Recovery System of a Gas Turbine Using Supercritical CO2 Based on Data Reconciliation. Applied Sciences (2024).