Continuous-time block-oriented nonlinear modeling with complex input noise structure

Zhai, Dongmei

doi:10.31274/rtd-180813-15344

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Study 1 demonstrated that the use of sample moments to estimate the model parameters can be accurate and have sound statistical properties. Other parameter estimation methods were applied in this study, and the details can be found in Zhai . These include the methods of Bellach and Chen and Kozin for estimating τ and the method of Sorensen, Pedersen's maximum likelihood estimation (MLE), and Pedersen's quasi-MLE for estimating α.…”

Section: Studiesmentioning

confidence: 99%

Dynamic Predictive Modeling Under Measured and Unmeasured Continuous-Time Stochastic Input Behavior

Rollins¹,

Zhai²,

Bhandari³

et al. 2012

Ind. Eng. Chem. Res.

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Many input variables of chemical processes have a continuous-time stochastic (CTS) behavior. The nature of these variables is a persistent, time-correlated variation that manifests as process variation as the variables deviate in time from their nominal levels. This work introduces methodologies in process identification for improving the modeling of process outputs by exploiting CTS input modeling under cases where the input is measured and unmeasured. In the measured input case, the output variable is measured offline, infrequently, and at a varying sampling rate. A method is proposed for estimating CTS parameters from the measured input by exploiting statistical properties of its CTS model. The proposed approach is evaluated based on both output accuracy and predictive ability several steps ahead of the current input measurement. Two parameter estimation techniques are proposed when the input is unmeasured. The first is a derivative-free approach that uses sample moments and analytical expressions for population moments to estimate the CTS model parameters. The second exploits the CTS input model and uses the analytical solution of the dynamic model to estimate these parameters. The predictive ability of the latter approach is evaluated in the same way as the measured input case. All of the data in this work were artificially generated under the probabilistic CTS model. ABSTRACT: Many input variables of chemical processes have a continuous-time stochastic (CTS) behavior. The nature of these variables is a persistent, time-correlated variation that manifests as process variation as the variables deviate in time from their nominal levels. This work introduces methodologies in process identification for improving the modeling of process outputs by exploiting CTS input modeling under cases where the input is measured and unmeasured. In the measured input case, the output variable is measured offline, infrequently, and at a varying sampling rate. A method is proposed for estimating CTS parameters from the measured input by exploiting statistical properties of its CTS model. The proposed approach is evaluated based on both output accuracy and predictive ability several steps ahead of the current input measurement. Two parameter estimation techniques are proposed when the input is unmeasured. The first is a derivative-free approach that uses sample moments and analytical expressions for population moments to estimate the CTS model parameters. The second exploits the CTS input model and uses the analytical solution of the dynamic model to estimate these parameters. The predictive ability of the latter approach is evaluated in the same way as the measured input case. All of the data in this work were artificially generated under the probabilistic CTS model.

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Section: Studiesmentioning

confidence: 99%

Dynamic Predictive Modeling Under Measured and Unmeasured Continuous-Time Stochastic Input Behavior

Rollins¹,

Zhai²,

Bhandari³

et al. 2012

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In real engineering applications, it is difficult to achieve step inputs especially if the sampling time is slow and periodic inputs can better represent such condition. Therefore Zhai et al 27,28 extended H-and W-BEST and successfully predicted process responses with sinusoidal input sequences. Further, Rollins et al 29 address serially correlated noise, which is commonly found in chemical processes.…”

Section: The Block-oriented Exact Solution Technique (Best)mentioning

confidence: 99%

Advances in model identification using the block-oriented exact solution technique in a predictive modeling framework

Chin¹

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CHAPTER 1. INTRODUCTION 1. General Background and Organization 2. References CHAPTER 2. LITERATURE REVIEW 1. Modeling Approaches 2. Modeling Nonlinear Processes 3. Block-oriented Modeling 4. The Block-oriented Exact Solution Technique (BEST) 5. Application of Experimental Design in Dynamic Modeling 6. Motivations and Thesis Organization 7. References CHAPTER 3. ACCURATE MODEL IDENTIFICATION FOR MULTIPLE INPUT NON-INVERTIBLE HAMMERSTEIN-WIENER PROCESSES Abstract 1. Introduction 2. The Proposed Method 3. Process Examples 3.1 Example 1 3.2 Example 2 3.3 Example 3 3.4 Example 4 4. Closing Remarks 5. Nomenclature 6. References

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Advancing block-oriented modeling in process control

Loveland

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The increasing pressure in industry to maintain tight control over processes has led to the development of many advanced control algorithms. Many of these algorithms are modelbased control schemes, which require an accurate predictive model of the process to achieve good controller performance. Because of this, research in the fields of nonlinear process modeling and predictive control has advanced over the past several decades.

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Continuous-time block-oriented nonlinear modeling with complex input noise structure

Cited by 3 publications

References 34 publications

Dynamic Predictive Modeling Under Measured and Unmeasured Continuous-Time Stochastic Input Behavior

Dynamic Predictive Modeling Under Measured and Unmeasured Continuous-Time Stochastic Input Behavior

Advances in model identification using the block-oriented exact solution technique in a predictive modeling framework

Advancing block-oriented modeling in process control

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