Shankar Narasimhan S

A reliable dynamic model is essential for model–based control, monitoring, and optimization of reaction systems. Hence, a change in a part or whole of the reaction kinetics of these systems leads to poor performance. In this work, the problem of model–plant mismatch in open reaction system is studied. We propose an online fault diagnosis and rectification framework for solving the problem of model–plant mismatch for open reaction systems. The framework combines the concept of the extents of reaction and flowrate in reaction systems and incremental model identification approach for isolation and rectification of the deficient part of the model. The proposed framework will be demonstrated via a simulation example of the acetoacetylation of pyrrole in a semi–batch reactor for two scenarios: (i) shift in the change of one of the reaction rates, and (ii) change in the inlet flowrate.

Data reconciliation (DR) and principal component analysis (PCA) are two popular data analysis techniques in process industries. Data reconciliation is used to obtain accurate and consistent estimates of variables and parameters from erroneous measurements. PCA is primarily used as a method for reducing the dimensionality of high dimensional data and as a preprocessing technique for denoising measurements. These techniques have been developed and deployed independently of each other. The primary purpose of this article is to elucidate the close relationship between these two seemingly disparate techniques. This leads to a unified framework for applying PCA and DR. Further, we show how the two techniques can be deployed together in a collaborative and consistent manner to process data. The framework has been extended to deal with partially measured systems and to incorporate partial knowledge available about the process model.

The performance of model-based control and optimization depends on the accuracy of process models. However, changes in physiochemical and operational conditions can result in a mismatch between the process and its model. This model-plant mismatch (MPM) must be detected and rectified quickly to achieve the desired performance. In this work, we consider model-plant mismatch due to structural and parametric changes in the underlying process model of reactor systems. We formulate MPM detection as a fault detection and identification problem. We propose an online model-plant mismatch detection and model re-identification framework using the concept of the reaction extents and incremental model identification for detecting and isolating the faults and appropriately re-identifying the faulty part of the model. The proposed approach is illustrated through simulation studies of acetoacetylation of pyrrole in a batch, semi-batch and continuous stirred tank reactor configuration for different fault scenarios.

Data Reconciliation is a technique that has been developed more than fifty years ago and has been refined over the past few decades and is now being deployed routinely in several process industries such as refining, petrochemicals, mineral processing and hydro-metallurgy. The main aim of data reconciliation is to obtain accurate estimates of all process variables and parameters using plant measurements that are invariably corrupted by random errors and may also contain biases and other gross errors. These reconciled estimates are subsequently used either in offline exercises such as simulation, retrofitting, and de-bottlenecking or in online optimization and control applications. In this presentation, a brief history of data reconciliation, its assumptions, and uses are reviewed. New developments in this area are highlighted such as robust data reconciliation to handle model uncertainties, and reconciliation using identified models derived purely from operating data. Several case studies in the mineral processing industries reported in the literature are also highlighted to indicate the potential benefits of this technique.