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Technology research and analysis of industrial process data correction

Author: LiJiuLong
Tutor: ZhouLingKe
School: Nanjing University of Technology and Engineering
Course: Control Theory and Control Engineering
Keywords: Data correction Robust Estimation Robust Adaptive NT-GLR method
CLC: TP273
Type: Master's thesis
Year: 2012
Downloads: 22
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Abstract


Modern industrial process, accurate and reliable measurement data production and decision-making, process control and optimization, operations analysis and improvement of the basis, however, in the actual production process, due to the measurement of the deviation, sensor or instrument failure as a result of pipeline leaks and other reasons. resulting in the presence of random errors or significant error seriously affect the measurement data accuracy and reliability of the measurement data. Data correction algorithm for processing the measurement data, and to avoid the impact of a variety of errors, get a group of chemical process not only satisfy the material balance, energy balance, physical or chemical laws and reasonable and close to the true value of the coordination of value. In this paper, data correction the robust correction algorithm and significant error detection algorithm research and analysis, and improved their limitations, the main contents are as follows: (1) M estimates: robust estimation characteristics The data correction robust estimation, maximum likelihood estimation of the structural conditions, a new robust estimation function LnRLS robust estimation function not only meet the characteristics of the maximum likelihood estimation , but also has strong robustness and stability. Of the error distribution (2): Analysis of the Tjoa and Biegler proposed, contaminated normal distribution model robustness characteristics, its application in the process of bilinear constraint, this model, however, there are certain deficiencies in the practical application: Requires The estimated significant error variance and contamination rate correction range is limited, and is less stable. Gao Qian robust adaptive distribution model, by improving a new robust adaptive distribution model, the new model overcomes the defects of contaminated normal distribution model, but also for a variety of significant magnitude error have good inhibition. (3) leakage error: analysis the NT and GLR two significant advantages and disadvantages of the error detection algorithm from each other, \easy to misjudge the shortcomings, but also can detect a significant error caused due to reasons such as plumbing fixtures leak instance validation. (4) industrial instance: a the coking company's methanol process modeling and analysis, the robustness of LnRLS use text estimated, based on the idea of ??the two-step method, the total flow rate of the industrial collect data good data coordination and constituents.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Automatic control,automatic control system
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