A variety of factors affect air quality, making it a difficult issue. The level of clean air in a certain area is referred to as air quality. It is challenging for conventional approaches to correctly discover aberran...A variety of factors affect air quality, making it a difficult issue. The level of clean air in a certain area is referred to as air quality. It is challenging for conventional approaches to correctly discover aberrant values or outliers due to the significant fluctuation of this sort of data, which is influenced by Climate change and the environment. With accelerating industrial expansion and rising population density in Kolkata City, air pollution is continuously rising. This study involves two phases, in the first phase imputation of missing values and second detection of outliers using Statistical Process Control (SPC), and Functional Data Analysis (FDA), studies to achieve the efficacy of the outlier identification methodology proposed with working days and Nonworking days of the variables NO<sub>2</sub>, SO<sub>2</sub>, and O<sub>3</sub>, which were used for a year in a row in Kolkata, India. The results show how the functional data approach outshines traditional outlier detection methods. The outcomes show that functional data analysis vibrates more than the other two approaches after imputation, and the suggested outlier detector is absolutely appropriate for the precise detection of outliers in highly variable data.展开更多
Multivariate statistical process monitoring and control (MSPM& C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares...Multivariate statistical process monitoring and control (MSPM& C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper,The four-step procedure of performing MSPM &C for chemical process ,modeling of processes ,detecting abnormal events or faults,identifying the variable(s) responible for the faults and diagnosing the source cause for the abnormal behavior,is analyzed,Several main research directions of MSPM&C reported in the literature are discussed,such as multi-way principal component analysis (MPCA) for batch process ,statistical monitoring and control for nonlinear process,dynamic PCA and dynamic PLS,and on -line quality control by infer-ential models,Industrial applications of MSPM&C to several typical chemical processes ,such as chemical reactor,distillation column,polymeriztion process ,petroleum refinery units,are summarized,Finally,some concluding remarks and future considerations are made.展开更多
文摘A variety of factors affect air quality, making it a difficult issue. The level of clean air in a certain area is referred to as air quality. It is challenging for conventional approaches to correctly discover aberrant values or outliers due to the significant fluctuation of this sort of data, which is influenced by Climate change and the environment. With accelerating industrial expansion and rising population density in Kolkata City, air pollution is continuously rising. This study involves two phases, in the first phase imputation of missing values and second detection of outliers using Statistical Process Control (SPC), and Functional Data Analysis (FDA), studies to achieve the efficacy of the outlier identification methodology proposed with working days and Nonworking days of the variables NO<sub>2</sub>, SO<sub>2</sub>, and O<sub>3</sub>, which were used for a year in a row in Kolkata, India. The results show how the functional data approach outshines traditional outlier detection methods. The outcomes show that functional data analysis vibrates more than the other two approaches after imputation, and the suggested outlier detector is absolutely appropriate for the precise detection of outliers in highly variable data.
基金Supported by the National High-Tech Development Program of China(No.863-511-920-011,2001AA411230).
文摘Multivariate statistical process monitoring and control (MSPM& C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper,The four-step procedure of performing MSPM &C for chemical process ,modeling of processes ,detecting abnormal events or faults,identifying the variable(s) responible for the faults and diagnosing the source cause for the abnormal behavior,is analyzed,Several main research directions of MSPM&C reported in the literature are discussed,such as multi-way principal component analysis (MPCA) for batch process ,statistical monitoring and control for nonlinear process,dynamic PCA and dynamic PLS,and on -line quality control by infer-ential models,Industrial applications of MSPM&C to several typical chemical processes ,such as chemical reactor,distillation column,polymeriztion process ,petroleum refinery units,are summarized,Finally,some concluding remarks and future considerations are made.
