Showing papers in "Journal of Process Control in 1995"

TL;DR: Powerful procedures for diagnosing assignable causes for the occurrence of a fault by interrogating the underlying latent variable model for the contributions of the variables to the observed deviation are presented.

TL;DR: A novel method for the on-line identification of steady state in noisy processes is developed using critical values of an F-like statistic, and its computational efficiency and robustness to process noise distribution and non-noise patterns provide advantages over existing methods.

TL;DR: In this article, the authors give an overview of some of the key issues in empirical nonlinear modeling for chemical process applications, focusing on specific sub-classes of nonlinear models that have analytically useful structural characteristics.

TL;DR: In this paper, the development of a multivariable controller for the FCC Kellog Orthoflow F reactor/regenerator unit is discussed. And the proposed control structure is simulated for a particular set of manipulated and controlled variables of the Kellog FCC converter and the results indicate good potential for the application to the real system.

TL;DR: In this paper, a polynomial type nonlinear autoregressive models with exogenous inputs (NARX) are used to identify and control of highly nonlinear processes.

TL;DR: In this paper, a general formulation of least squares estimation is given, and an algorithm with a fixed-size moving estimation window and constraints on states, disturbances and measurement noise is developed through a probabilistic interpretation of least square estimation.

TL;DR: Reference LA-ARTICLE-1995-002View record in Web of Science Record created on 2004-11-26, modified on 2017-05-10 as mentioned in this paper, created on 2003

TL;DR: In this paper, a multi-pass packed bed reactor temperature profile is modelled via recurrent neural networks using the backpropagation through time training algorithm, which is then used in conjunction with an optimizer to build a nonlinear model predictive controller.

TL;DR: The neural network as a universal approximator is used to good effect in this nonlinear problem, as is shown in the simulation results.

TL;DR: A novel approach for process control that uses neural networks to model the steady-state inverse of a process which is then coupled with a simple reference system synthesis to generate a multivariable controller that shows robust performance for both setpoints and disturbances.

TL;DR: In this article, a linearizing feedback adaptive control structure was proposed to guarantee high quality regulation of the output error in the face of unknown parameters, and the effectiveness of this control structure is demonstrated on a continuous stirred tank reactor in two instances.

TL;DR: In this paper, a frequency domain expression for the asymptotic prediction error sum of squares is used to reinterpret some of these results, and to analyse the role of the noise model and data prefilters on the identifiability of the process dynamic model.

TL;DR: This method does allow for the detection of a single bias in a nonlinear dynamic process whether or not the exact model equations are known, and is an important step toward the ultimate goal of reconciling 'raw' process data that may contain bias and gross errors in addition to small random errors.

TL;DR: In this article, a control-relevant parameter estimation problem (CRPEP) is proposed to capture the interplay that occurs between controller sophistication, speed and shape of the closed-loop response, and set-point/disturbance directions affecting the closedloop system.

TL;DR: In this paper, the optimal tracking control of linear discrete time delay systems and its application to an industrial electric heater with pure delays is discussed, and the conditions for stability and exact tracking of closed-loop systems are given.

TL;DR: An online identification technique where a process is identified in terms of pseudo impulse response coefficients and subsequently used to update convolution type models to accommodate process-model mismatch is presented.

TL;DR: In this paper, an internal model control strategy employing a fuzzy neural network is proposed for SISO nonlinear process, where the control-affine model is identified from both steady state and transient data using back-propagation.

TL;DR: An application of state and parameter estimation techniques in an altering activated sludge process with regard to biological phosphorus removal and a simplified model describing the phosphorus dynamics in an alternating activatedSludge process is proposed.

TL;DR: In this article, a joint university-industry study was conducted to control a fatty acid distillation sequence, which is plagued with severe disturbance problems. And the results of the study showed that a model predictive control algorithm was modified in terms of disturbance prediction.

TL;DR: In this paper, the MPC approach is combined with the H x μ framework in order to obtain a robust design for multivariable 5 × 5 distillation control, i.e., control of levels, pressure and compositions by one multi-controller, provides opportunities to improve the control performance as compared to decentralized control.

TL;DR: The results of the workshop on process identification at the 42nd Canadian Chemical Engineering Conference in October 1992 are described in this paper, where participants were asked to identify process models for an industrial distillation column.

TL;DR: In this paper, a quick methodology is presented for examining the likely economic impact of disturbances and model uncertainty on the achievable optimal performance of a process, based on a consideration of the amount the optimal operating point has to be backed-off from the active constraint set to ensure no operational constraints are violated.

TL;DR: In this paper, three nonlinear control strategies, including input-output linearization, generic model control and Su-Hunt-Meyer transformation, were studied on a simulation of the evaporation stage of the liquor burning process associated with the Bayer process for the production of alumina.

TL;DR: In this article, the estimation and control of a nonlinear industrial polymerization process is addressed. But the majority of the available estimation techniques are not easy to implement on-line due to the intensive computations involved.

TL;DR: The workshop reaffirmed that data prefiltering is a necessary step in the model building process in order to ensure nonstationary disturbances are not modelled as part of the process and confirmed that improvements in the steady-state gain are seen if input test signals are carefully designed using the disturbance dynamics.

TL;DR: In this article, a self-tuning regulator was developed for temperature control in a food extrusion process, which was designed in an explicit formulation based on feedforward and internal model-based predictive control strategies.

TL;DR: The proposed combination of a feedback neural network and the available partial process model satisfactorily solves this realistic prediction problem, for which the previously available methods prove inadequate.

TL;DR: The presented method is readily extended to enable use in recursive identification of parameters in a chosen model structure and as an improvement of tracking ability of an ordinary recursive routine.

TL;DR: In this article, a self-contained module for dynamics estimation is developed which is based on an integration of procedures for frequency response estimation, algorithms for conversion from frequency response to transfer function models and suitable heuristics.

TL;DR: In this article, the authors proposed a model-based control algorithm called Elementary Nonlinear Decoupling (END) to decouple and linearize a nonlinear multivariable process in order to achieve better control than can be obtained by conventional decentralized linear feedback control.