1. |
Multivariate Model Identification and Stochastic Control of a Chemical Reactor |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 453-464
JohnF. MacGregor,
A.K. L. Wong,
Preview
|
PDF (1201KB)
|
|
摘要:
Multivariate time series and process identification methods are used to develop a dynamicstochastic model for a packed bed tubular reactor carrying out highly exothermic hydrogenolysis reactions. A canonical analysis procedure is used on the data collected from the reactor to first reduce the dimensionality of the identification and control problems. The identified transfer function-ARIMA model is transformed into a state space model form and used to develop a multivariable optimal stochastic controller for the reactor. The controlled variables are inferred production rates reconstructed from temperature and flow measurements. The parameters of the inferential equation are updated recursively using measurements of actual concentrations available periodically. The controller is implemented using a process minicomputer, and is shown to perform very well.
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486192
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
2. |
Discussion |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 465-467
DavidW. Bacon,
Preview
|
PDF (315KB)
|
|
摘要:
Multivariable process control problems contain many difficult challenges for both statisticians and control engineers. The approach presented in this paper includes a number of novel features that resolve some troublesome practical problems in process control applications. These contributions and the success of the control algorithm for the reactor example described in the paper should encourage statisticians to become more closely involved in the important area of process control. Unfortunately some confusion exists in the descriptions of the control objectives and the chemical reactions in the example with the result that the example is not as effective as it might have been in illustrating the significant statistical contributions ofthe paper.
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486193
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
3. |
Response |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 468-468
J.F. MacGregor,
Preview
|
PDF (138KB)
|
|
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486194
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
4. |
Extreme Values from a Nonstationary Stochastic Process: An Application to Air Quality Analysis |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 469-478
Joel Horowitz,
Preview
|
PDF (845KB)
|
|
摘要:
A procedure for using air quality data to estimate the mean value of the maximum concentration in a year-long sequence of lognormally distributed air pollutant concentrations has been described by Larsen. This procedure and analogous procedures for non-lognormal concentrations implicitly assume that sequences of pollutant concentrations are stationary. However, air pollutant concentrations often vary systematically in response to seasonal and other factors and, therefore, are nonstationary. In this paper it is shown that application of procedures, such as Larsen's, that assume stationarity to a nonstationary sequence of concentrations can produce seriously erroneous results. Two methods for using air quality data to estimate the distributional properties of maxima of nonstationary sequences of concentrations are illustrated. One method involves identifying a nonstationary stochastic process that explains the data and computing the probability distributions of maxima of sequences generated by this stochastic process. The other involves applying the Larsen procedure to a suitably selected subsequence of the data.
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486195
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
5. |
Discussion |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 479-481
WilliamS. Cleveland,
Preview
|
PDF (238KB)
|
|
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486196
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
6. |
Response |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 482-482
Joel Horowitz,
Preview
|
PDF (73KB)
|
|
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486197
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
7. |
Determining Statistical Characteristics of a Vehicle Emissions Audit Procedure |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 483-493
ThomasJ. Lorenzen,
Preview
|
PDF (1011KB)
|
|
摘要:
The Environmental Protection Agency currently audits automobiles for emissions compliance at assembly plants with multiple attribute multiple staged sampling plans. This paper summarizes the auditing procedure and develops methods for determining its statistical characteristics. Specifically, we consider how to evaluate a multiple attribute multiple staged sampling plan, give a procedure that efficiently estimates attribute-type probabilities from multivariate variables-type data, and quantify the effect of tightening the acceptable quality level of the sampling plan. A key technique for this analysis is the multivariate version of the Box-Cox power transformation. All procedures are illustrated with data from a typical vehicle configuration.
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486198
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
8. |
Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 495-508
R.Dennis Cook,
Sanford Weisberg,
Preview
|
PDF (1281KB)
|
|
摘要:
Traditionally, most of the effort in fitting full rank linear regression models has centered on the study of the presence, strength and form of relationships between the measured variables. As is now well known, least squares regression computations can be strongly influenced by a few cases, and a fitted model may more accurately reflect unusual features of those cases than the overall relationships between the variables. It is of interest, therefore, for an analyst to be able to find influential cases and, based on them, make decisions concerning their usefulness in a problem at hand.
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486199
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
9. |
Detection of Unsuspected Feedback in linear Dynamic Systems |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 509-516
H.Y. Tsang,
D.W. Bacon,
Preview
|
PDF (673KB)
|
|
摘要:
Three simple methods for the detection of unsuspected feedback effects in a linear dynamic system are evaluated. Two of these methods, involving cross correlations of prewhitened versions of the observed input and output series, can be carried out prior to fitting a transfer function model. These detection tools are shown to be potentially deficient. A more sensitive indicator of feedback is the cross correlation function between the prewhitened input and the residuals from the best fitted open loop model. Application of the three methods is illustrated using operating data from an industrial petroleum fractionation process.
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486200
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|
10. |
Suspension Systems for Small Sample Inspections |
|
Technometrics,
Volume 22,
Issue 4,
1980,
Page 517-533
JosephR. Troxell,
Preview
|
PDF (1358KB)
|
|
摘要:
In this paper rules are discussed for suspending inspection on the basis of unfavorable lot history, when small sample sampling plans are specified. The decision to suspend, which is based on cumulative lot dispositions overkconsecutive lots, occurs ifjlots are rejected. The expected time to suspension is given forj= 2 andj= 3. The user is offered the choice of employing a designed plan of suspension systems based on MIL-STD-105D or designing a specific system using the notion of a reference quality level. Several methods of choosing a suspension system are discussed.
ISSN:0040-1706
DOI:10.1080/00401706.1980.10486201
出版商:Taylor & Francis Group
年代:1980
数据来源: Taylor
|