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Process Input (process + input)

Distribution by Scientific Domains

Engineering	50%

Selected Abstracts

Improved Fourier transform for processes with initial cyclic-steady-state

AICHE JOURNAL, Issue 6 2010
Yu Jin Cheon
Abstract A new process identification method is proposed to estimate the frequency responses of the process from the activated process input and output. It can extract many more frequency responses and guarantees better accuracy than the previous describing function analysis algorithm. In addition, the proposed method can be applied to the case that the initial part of the activated process data is periodic (cyclic-steady-state), which is not possible with any previous nonparametric identification methods using the modified Fourier transform or Fourier analysis. Furthermore, it can incorporate all the cases in which either the initial part is steady-state and the final part is cyclic-steady-state or both the initial and final parts are steady-state. © 2010 American Institute of Chemical Engineers AIChE J, 2010 [source]

Identification and fine tuning of closed-loop processes under discrete EWMA and PI adjustments

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 6 2001
Rong Pan
Abstract Conventional process identification techniques of a open-loop process use the cross-correlation function between historical values of the process input and of the process output. If the process is operated under a linear feedback controller, however, the cross-correlation function has no information on the process transfer function because of the linear dependency of the process input on the output. In this paper, several circumstances where a closed-loop system can be identified by the autocorrelation function of the output are discussed. It is assumed that a proportional integral controller with known parameters is acting on the process while the output data were collected. The disturbance is assumed to be a member of a simple yet useful family of stochastic models, which is able to represent drift. It is shown that, with these general assumptions, it is possible to identify some dynamic process models commonly encountered in manufacturing. After identification, our approach suggests to tune the controller to a near-optimal setting according to a well-known performance criterion. Copyright © 2001 John Wiley & Sons, Ltd. [source]

An integrated approach to optimization of Escherichia coli fermentations using historical data

BIOTECHNOLOGY & BIOENGINEERING, Issue 3 2003
Matthew C. Coleman
Abstract Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input,output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%. © 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 84: 274,285, 2003. [source]

Analysis of multivariable controllers using degree of freedom data

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7-9 2003
T. J. Harris
Abstract Most approaches for monitoring, diagnosis and performance analysis of multivariable control loops employ time series methods and use non-parametric statistics to analyse the process inputs and outputs. In this paper, we explore the use of a discrete variable that summarizes the status of the constraint set of the controller to analyse the long run behaviour of control systems. We introduce a number of waiting and holding time statistics that describe the status of this data, which we call the degree of freedom data. We demonstrate how Markov Chains might be used to model the status of the degree of freedom data. This model-based approach has the potential to provide considerable insight into the behaviour of a model based control scheme with relative ease. We demonstrate the methodologies on simulated and industrial data. Copyright © 2003 John Wiley & Sons, Ltd. [source]