Nonlinear Process Systems Research Articles

Adaptation of output controllability index within dynamic operability framework

This paper reports. the adaptation of the Output Controllability Index (OCl) approach for the regulatory case Within the Dynamic Operability Framework to facilitate controllability assessment m process system design. The original framework requires the solution of Nonlinear programning problem on two optimization levels. This study replaces the multiple maximation problems in inner level and the whole inequalities in outer-level by a single geometnc calculattion, and prodes a controllability measurement involving all of the output vanables. The applicability of the proposed approach is demonstrated on a nonlinear process system.

A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems

Dynamic data reconciliation and parameter estimation are challenging problems for large, nonlinear process systems due to problem size and complexity, and the effects of nonlinearities. Recently, an elegant nonlinear optimization formulation has been proposed in the literature. In this work, we extend the nonlinear reconciliation problem to include the detection of the biased parameters. The central idea in this framework is the recognition that the biased parameter identification problem can be viewed as a diagnostic problem, and methods from fault diagnosis literature may be brought in to improve the performance. Once the biased parameter is identified, then the estimation of the bias is performed using nonlinear optimization methods. Using several case studies, this framework is shown to both, detect and produce acceptable estimates of the biased parameters. Since, the bias detection and estimation are decoupled, this framework is shown to provide faster and more accurate estimates for real-time applications.

A 'Model-on-Demand' identification methodology for non-linear process systems

An identification methodology based on multi-level pseudo-random sequence (multi-level PRS) input signals and 'Model-on-Demand' (MoD) estimation is presented for single-input, single-output non-linear process applications. 'Model-on-Demand' estimation allows for accurate prediction of non-linear systems while requiring few user choices and without solving a non-convex optimization problem, as is usually the case with global modelling techniques. By allowing the user to incorporate a priori information into the specification of design variables for multi-level PRS input signals, a sufficiently informative input-output dataset for MoD estimation is generated in a 'plant-friendly' manner. The usefulness of the methodology is demonstrated in case studies involving the identification of a simulated rapid thermal processing (RTP) reactor and a pilot-scale brine-water mixing tank. On the resulting datasets, MoD estimation displays performance comparable to that achieved via semi-physical modelling and semi-physical modelling combined with neural networks. The MoD estimator, however, achieves this level of performance with substantially lower engineering effort.

Design of Minimum Crest Factor Multisinusoidal Signals for "Plant-Friendly" Identification of Nonlinear Process Systems

Guidelines for specifying design parameters for minimum crest factor multisine signals per the approach of Guillaume et al. are presented. These guidelines are evaluated for the identification of nonlinear process systems. The minimum crest factor multisine signals offer some distinct advantages over both Schroeder phased multisine signals and m-level Pseudo-Random Sequence (m-level PRS) signals with respect to "plant-friendliness" considerations. These signals can be used to reduce the effects of nonlinearity in obtaining an empirical transfer function estimate (ETFE). As an example, the ETFE of a Rapid Thermal Processing (RTP) reactor simulation is constructed. "Plant-friendly" issues are also discussed and illustrated in the identification and control of a CSTR simulation via "Model-on-Demand" estimation. This provides a compelling example, since the "Model-on-Demand" estimator is a data-driven nonlinear identification approach.

Issues in Design of Input Signals for the Identification of Nonlinear Models of Process Systems

Abstract This paper addresses the design of input signals for the identification of Volterra models for nonlinear process systems. In this work, we review and compare deterministic and stochastic signals for the identification of Volterra models. We also discuss the various issues involved in the design of input signals. Accurate identification of Volterra kernels through the suppression of higher order nonlinearities is also discussed.

On the Dynamics of Nonlinear Process Systems with Input Constraints

This work deals with the dynamical analysis of nonlinear process systems with input constraints. In particular, a characterization of the “domain of attraction” of an equilibrium point under bounded control is provided and the notion of regions of invariance within these domains of attraction is introduced and characterized. A case study on a two-dimensional Gray and Scott model of an isothermal, auto-catalytic reaction occurring in a CSTR is also carried out.

Describing functions for nonlinear optical systems

The concept of describing functions is useful for analyzing and designing nonlinear systems. A proposal for using the idea of describing functions for studying the behavior of a nonlinear optical processing system is given. The describing function can be used in the same way that a coherent transfer function or optical transfer function is used to characterize linear, shift-invariant optical processors. Two coherent optical systems for measuring the magnitude of the describing function of nonlinear optical processors are suggested.

Nonlinear dynamics in telecommunication systems: complex behaviour in nonlinear processing systems

Nonlinear dynamic techniques are used to create a theoretical framework, from which the development, analysis and evaluation of a nonlinear model of a generalised switching element in a telecommunications network is investigated. The model represents the data flow through nonpreemptive multiprocess data processor systems found in communication nodes. A numerical solution to the model is presented, and is used in computer simulations to analyse the behaviour of both a single processing element and a multiprocessor array of switching nodes, in response to different input data streams. Presentation of resulting data is done using time domain and multidimensional phase space techniques which identify transitions between behaviour as the system parameter values are varied. A range of behaviour including stable, unstable chaotic and unbounded modes is identified, and the impact on network performance is assessed.

Controller design for nonlinear process systems via variable transformations

Controller design for nonlinear process systems via variable transformations

Intensity Dependent Index Of Refraction In Organic Materials

Because of the large variety of materials and material forms available, organic materials are attractive candidates for ultrafast nonlinear optical signal processing systems based on the intensity-dependent index of refraction, i.e., the change of index of refraction with a change in applied optical intensity. As an example, we consider an organic polymeric system, the polydi-acetylenes. Through molecular engineering (organic synthesis and Langmuir-Blodgett film growth combined with monolayer deposition techniques), the polydiacetylene was prepared in the technologically important format of a planar waveguide on a grating. By measuring the change in the angle that the incident laser radiation couples into the guided-wave mode (via the grating) with the change in intensity of the laser radiation, we determined the intensity-dependent index of refraction to be 10-6 (MW/cm2)-1 in the transparent region of the material. Implications of this reseach for future work related to signal processing applications are discussed.

Pat-hological dynamic behavior of forced periodic chemical processes

Nonlinear chemical process systems which are periodically forced do not necessarily have unique, stable periodic responses with the same period as the input, although such behavior has been widely assumed previously. Theory for the as symptotic case of very fast forced cycling indicates that systems which possess multiple steady-states or self-oscillations under unforced conditions may exhibit unusual dynamic phenomena under forced periodic operation. Simulations of a periodically forced CSTR reveal several jumps in forced system qualitative behavior as the forcing frequency is altered. Among the pathological responses obtained for the CSTR example are multiple stable forced states, subharmonic oscillations, and almost periodic outputs.

Non-monotonic non-linear image processing using halftone techniques

A method is described of combining specially designed halftone screens with a spatial filtering process to obtain a non-monotonic, non-linear image processing system. As an example results are shown for a halftone mask that produces three isophotes simultaneously at predetermined intensity intervals. Application to A/D conversion is briefly discussed.

Monte-carlo simulation of an active sonar

This paper describes the analysis of a non-linear sonar processing system by simple Monte-Carlo methods. The target detection capability with isotropic background noise and spatially coherent reverberation is found with attention being paid to the effects of frequency filters in the input channels from the transducers.The resolution and detection of multiple and diffuse targets is assessed under noise free conditions and with the assumption of target scintillation due to forward scattering or medium inhomogeneity.