Data Reconciliation

Role Of Steady State Data Reconciliation In Process Model Development

In chemical and hydrocarbon industry operational efficiency is improved by model-based solutions. Historical process data plays an important role in the identification and verification of models utilized by these tools. Since most of the used information are measured values, they are affected by errors influencing the quality of these models. Data reconciliaton aims the reduction of random errors to enhance the quality of data used for model development resulting in more reliable process simulators. This concept is applied to the development and validation of the complex process model and simulator of an industrial hydrogenation system. The results show the applicability of the proposed scheme in industrial environment.

Synergy between DR and Principal Component Analysis (PCA)

In many practical applications simulators used for planning, scheduling or operator training are often too complex for direct usage in real-time process monitoring; the structure of the related non-linear models does not support low-cost and rapid implementation of process monitoring systems. We presented a novel method that effectively utilizes these first principle models for the development, maintenance and validation of multivariate statistical models. We demonstrated that the performance of Principal Component Analysis (PCA) models used for process monitoring can be improved by models used for data reconciliation. The synergy between data reconciliation and PCA has already been realized (Amand et al. 2001): when reconciled data are used for PCA, the numbers of principal components are reduced.

The aim of our research is to develop a method that can be applied in industrial environment and includes advantages of prior knowledge based models and data-driven multivariate statistical process monitoring tools.

Data Reconciliation for iterative model development

Historical process data can be used to determine the unknown elements and parameters of first-principles based models. Data reconciliation (DR) can improve the applicability of these data. We propose an iterative model building – data reconciliation procedure that can continuously improve the quality of models and data.

Firstly, raw data is reconciled based on the draft model of the process (mainly based on mass balances). The unknown parameters of the simulator are identified based on this reconciled data. The reconciled data and estimated parameters are used as an input of the process simulator. This improved model can be used again for reconciliation of the raw data. This procedure could be repeated till there is a significant difference between reconciled and calculated process values.

We used this method to monitor a time-varying process (deactivation of catalyst).

Farsang, B., Németh, S., & Abonyi, J. (2015). Online Monitoring of Catalyst Deactivation Based on Data Reconciliation and Flowsheeting Simulator. Periodica Polytechnica Chemical Engineering, 59(2), 145-150.

Farsang B., Gomori Z., Horvath G., Nagy G., Nemeth S., Abonyi J.: Simultaneous Validation of Online Analyzers and Process Simulators by Process Data Reconciliation, Chemical Engineering Transactions, 32, 1303-1308 DOI: 10.3303/CET1332218

Online Monitoring of Catalyst Deactivation Based on Data Reconciliation and Flowsheeting Simulator

Most of chemical technologies are based on heterogeneous catalytic reactions (~80%). Model based tools used for design, control and monitoring of these processes require accurate kinetic parameters of these catalytic reactions. Laboratory measurements and on-line analysers can be used to monitor decreasing catalyst activity. However these measurements are affected by errors influencing the estimation of kinetic parameters. To increase the robustness and accuracy of the estimation we developed a method based on the integrated application of data reconciliation and flowsheeting simulation. The proposed technique is applied for an industrial hydrogenation system. The estimated reaction kinetic parameters can be utilized advanced process control of the process.

Information transfer between PCA and DR

The performance of model based process monitoring systems highly depends on the quality of the model. Good PCA based solutions require accurate and validated historical process data with high information content. However, measurements are always affected by errors. To minimize random errors a pre-processing of data is necessary. Data reconciliation (DR) technique is a useful tool, because this method uses the balance equations and physical-chemical laws so the consistency of data is provided. We analysed the projection matrices of linear DR and found a strong relationship between DR and PCA. With the use of our approach not only the projection matrix of PCA can be calculated, but the similarity to the projection matrix of DR and PCA can be also measured. Furthermore, by using total least squares regression (based on principal components) the parameters of the balance equations can also be (re)calculated.

Barbara Farsang, János Abonyi: Process development based on model mining and data reconciliationtechniques, CAPE FORUM 2014.

Farsang, Barbara, Sandor Nemeth, and Janos Abonyi. "Life-cycle Modelling for Fault Detection–Extraction of PCA Models from Flowsheeting Simulators." Computer Aided Chemical Engineering. Vol. 33. Elsevier, 2014. 421-426.

Barbara Farsang, Sándor Németh, János Abonyi: Synergy between Data Reconciliation and Principal Component Analysis in Energy Monitoring, Chemical Engineering Transactions, 39 (2014) 721-726

MATLAB Implementation