The Experts below are selected from a list of 318393 Experts worldwide ranked by ideXlab platform
Kuang Chen - One of the best experts on this subject based on the ideXlab platform.
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Selection of Root-Cause Process Variables Based on Qualitative Trends in Historical Data Samples
IEEE Access, 2019Co-Authors: Jiandong Wang, Kuang ChenAbstract:Root cause analysis helps industrial plant operators in finding possible root causes of alarms and their associated abnormalities, where one pre-requisite information is the set of root-cause Process Variables. This paper proposes a method to determine a set of root-cause Process Variables for a primary Process Variable based on their qualitative trends. According to requirements on time durations, amplitude changes and correlation coefficients, qualitative trends of Process Variables are extracted from historical data samples via a dynamic programming approach. A set of root-cause Process Variables is selected as the one whose qualitative trend combinations are associated with the largest ratio in explaining increasing and decreasing trends of the primary Process Variable. An industrial case study is provided to illustrate the effectiveness of the proposed method.
Kimito Funatsu - One of the best experts on this subject based on the ideXlab platform.
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classification of the degradation of soft sensor models and discussion on adaptive models
Aiche Journal, 2013Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:Soft sensors are used widely to estimate a Process Variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. It is called as the degradation of soft sensor models. In this study, we attempted to classify this degradation of models in terms of changes in an explanatory Variable and an objective Variable, and the rapidity of the changes. Moreover, we discussed characteristics of adaptive soft sensor models, based on the classification results. By analyzing simulated data sets, we could obtain knowledge and information on appropriate adaptive models for each type of the degradation. Keyword: Process control, Soft sensor, Degradation, Adaptive model, Predictive ability
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a new Process Variable and dynamics selection method based on a genetic algorithm based wavelength selection method
Aiche Journal, 2012Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:Soft sensors have been used in industrial plants to estimate Process Variables that are difficult to measure online. Soft sensor models predicting an objective Variable should be constructed with only important explanatory Variables in terms of predictive ability, better interpretation of models and lower measurement costs. Besides, some Process Variables can affect an objective Variable with time-delays. Therefore, we have proposed the methods for selecting important Process Variables and optimal time-delays of each Variable simultaneously, by modifying the genetic algorithm-based wavelength selection method that is one of the wavelength selection methods in spectrum analysis. The proposed methods can select time-regions of Process Variables as a unit by using Process data that includes Process Variables that are delayed in the range from zero to a set/given maximum value. The case study with simulation data and real industrial data confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed methods. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1829–1840, 2012
Jiandong Wang - One of the best experts on this subject based on the ideXlab platform.
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Selection of Root-Cause Process Variables Based on Qualitative Trends in Historical Data Samples
IEEE Access, 2019Co-Authors: Jiandong Wang, Kuang ChenAbstract:Root cause analysis helps industrial plant operators in finding possible root causes of alarms and their associated abnormalities, where one pre-requisite information is the set of root-cause Process Variables. This paper proposes a method to determine a set of root-cause Process Variables for a primary Process Variable based on their qualitative trends. According to requirements on time durations, amplitude changes and correlation coefficients, qualitative trends of Process Variables are extracted from historical data samples via a dynamic programming approach. A set of root-cause Process Variables is selected as the one whose qualitative trend combinations are associated with the largest ratio in explaining increasing and decreasing trends of the primary Process Variable. An industrial case study is provided to illustrate the effectiveness of the proposed method.
Luciano Da Silva - One of the best experts on this subject based on the ideXlab platform.
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synthesis of 4a zeolites from kaolin for obtaining 5a zeolites through ionic exchange for adsorption of arsenic
Materials Science and Engineering B-advanced Functional Solid-state Materials, 2012Co-Authors: Carolina Resmini Melo, Humberto Gracher Riella, Nivaldo Cabral Kuhnen, Elidio Angioletto, Aline Resmini Melo, Adriano Michael Bernardin, Marcio Roberto Da Rocha, Luciano Da SilvaAbstract:Abstract The synthesis of adsorbing zeolite materials requires fine control of the Processing Variables. There are distinct Process Variable settings for obtaining specific desired types of zeolites. The intent of this study was to obtain 4A zeolites from kaolin in order to obtain 5A zeolites through ionic exchange with the previously synthesized zeolite. This zeolite 5A was used as an adsorbent for arsenic ions. The results obtained were satisfactory.
Douglas C. Montgomery - One of the best experts on this subject based on the ideXlab platform.
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Mixture-Process Variable experiments including control and noise Variables within a split-plot structure
International Journal of Quality Engineering and Technology, 2011Co-Authors: Tae Yeon Cho, Connie M. Borror, Douglas C. MontgomeryAbstract:In mixture-Process Variables experiments, it is common that the experimental runs are larger than the mixture only design or basic experimental design to estimate the increased coefficient parameters due to the mixture components, Process Variable, and interaction between mixture and Process Variables, some of which are hard to change or cannot be controlled under normal operating condition. These situations often prohibit a complete randomisation for the experimental runs due to the time or financial reason. These types of experiments can be analysed in a model for the mean response and a model for the slope of the response within a split-plot structure. When considering the experimental designs, low prediction variances for the mean and slope model are desirable. We demonstrate the methods for the mixture-Process Variable designs with noise Variables considering a restricted randomisation and evaluate some mixture-Process Variable designs that are robust to the coefficients of interaction with noise Variables using fraction of design space plots with the respect to the prediction variance properties. Finally, we create the G-optimal design that minimises the maximum prediction variance over the entire design region using a genetic algorithm.
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Graphical evaluation of mixture-Process Variable designs within a split-plot structure
International Journal of Quality Engineering and Technology, 2009Co-Authors: Tae Yeon Cho, Connie M. Borror, Douglas C. MontgomeryAbstract:In mixture-Process Variable experiments, the number of runs can become prohibitively large as the number of Variables (Process or mixture) increases. In addition, some Variables can be hard-to-change due to practical or economical considerations. In these cases, the split-plot design is often used to overcome the restricted randomisation problem. In this paper, fraction of design space (FDS) plots for a mixture-Process Variable design within a split-plot structure are developed and demonstrated. FDS plots are used to evaluate the prediction capability of various designs. Sliced FDS plots are presented to show the influence of mixture Variables and Process Variables on the prediction variance over the design space.
