Showing papers by "Raghunathan Rengaswamy published in 2004"

A Novel Interval-Halving Framework For Automated Identification of Process Trends

Abstract: Qualitative process trend representation is an useful approach to model the temporal evolution of sensor data and has been applied in areas such as process monitoring, data compression, and fault diagnosis. However, the sheer volume of real-time sensor data that needs to be processed necessitates an automated approach for trend extraction. The step of recovering important temporal features is a difficult procedure to automate because of the absence of a priori knowledge about the sensor trend characteristics such as noise and varying scales of evolution. A novel approach is proposed to automatically identify the qualitative shapes of sensor trends using a polynomial-fit based interval-halving technique. To estimate the significance of fit-error, an estimate of the noise obtained from wavelet-based denoising is used. The procedure identifies the qualitative trend as a sequence of piecewise unimodals or quadratic segments. The least-order (among constant, first-order and quadratic) polynomial with fit-error statistically insignificant compared to noise (as dictated by F-test) is used to represent the segment. If the fit-error is large even for the quadratic polynomial, then the length is halved and the process is repeated on the first half segment until fit-error is acceptable. A constrained polynomial fit is used to ensure the continuity of the fitted data and an outlier detection methodology is used to detect any jump (step) changes in the signal. The whole procedure is recursively applied to the remaining data until the entire data record is covered. Finally, a unique assignment of qualitative shape is made to each of the identified segments. The application of the interval-halving technique for trend extraction is illustrated on a variety of both simulated and industrial data. © 2004 American Institute of Chemical Engineers AIChE J, 50: 149–162, 2004

Multi-objective input signal design for plant friendly identification of process systems

Abstract: System identification is the process of constructing an accurate and reliable dynamic mathematical model of the system from observed data and available knowledge. The choice of inputs used for perturbing the system is critical in the identification and model building exercise. One of the major objectives of system identification is accurate estimation of the system parameters. Identification of chemical process plants is carried out on running plants in real time. The practitioner would thus prefer a 'plant friendly' input signal. We propose unified multi-objective formulations and solution methods for the input design for two particular cases. The input can be evaluated as a solution to a multi-objective optimization problem.

Recursive state estimation in nonlinear processes

Abstract: The task of improving the quality of the data so that it is consistent with material and energy balances is called reconciliation. Since chemical processes often operate dynamically in nonlinear regimes, techniques like extended Kalman filter (EKF) and nonlinear dynamic data reconciliation (NDDR) have been developed. There are various issues that arise with the use of either of these techniques: EKF cannot handle inequality or equality constraints, while the NDDR has high computational cost. In this paper, a recursive nonlinear dynamic data reconciliation (RNDDR) formulation is presented. The RNDDR formulation extends the capability of the EKF by allowing for incorporation of algebraic constraints and bounds. The RNDDR is evaluated with four case studies that have been previously studied by Haseltine and Rawlings. It has been shown that the EKF fails in constructing reliable state estimates in all the four cases due to the inability in handling algebraic constraints. Reliable state estimates are achieved by the RNDDR formulation in all the cases in presence of large initialization errors.

INDUSTRIAL EXPERIENCE WITH OBJECT-ORIENTED MODELLING FCC Case Study

Abstract: Process modelling and simulation have emerged as important tools for detailed study and analysis of chemical processes. In activities such as design, optimization and control of processes, realistic process models, which incorporate physics and chemistry of the process in adequate detail, are becoming almost indispensable. Simulation studies also provide guidance in the development of new processes and can reduce both time and capital investment. A difficulty with process models is that they are based on the state of knowledge and simulation objectives defined at the time of their formulation. In addition, it is not easy to modify process models to incorporate new knowledge as it becomes available and as new needs arise. There is a need, therefore, to use advanced modelling and simulation strategies such that refinements and additional capabilities can be incorporated in the model without disproportionate additional effort. This work presents the framework of one such multipurpose process simulator, MPROSIM, an object-oriented process modelling and simulation environment. Though considerable literature is available on process modelling from a subjective or theoretical viewpoint, very little has been published on application of these ideas on complex industrial-scale processes. This being the focus of the paper, a case study of an object-oriented model for automatic generation of a fluid catalytic cracking unit (FCCU) reactor/regenerator is presented. The utility of the framework is illustrated by demonstrating how the model for FCCU could be fine-tuned both structurally and parametrically to represent the behaviour under changing process operating conditions.