A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

Xu, Ouguan; Fu, Yuxi; Su, Hongye; Li, Lijuan

doi:10.1016/j.cjche.2014.05.012

Cited by 11 publications

(13 citation statements)

References 15 publications

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“…There are multiple collinearity between variables. The main methods to eliminate multicollinearity are partial least squares [16], principal component regression [17], and ridge regression [18]. In this study, principal component regression is used.…”

Section: Results Of the Regression Modelmentioning

confidence: 99%

Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Shao

2019

Energies

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With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

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Section: Results Of the Regression Modelmentioning

confidence: 99%

Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Shao

2019

Energies

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“…ACCEPTED MANUSCRIPT 6 For a time-invariant system, it is preferred to take R = 1. Note that  min is a lower limit of the VFF to avoid over sensitivity in the presence of measurement outlier.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Since time-delay is usually associated with industrial applications, model-based control design needs a plant model with a time delay parameter to be identified as precise as possible, in order to obtain superior control performance [4][5][6][7]. It is quite challenging to identify a transfer function model with time delay due to the nonlinear relationship between the delay and other model parameters, especially in the presence of measurement noise.…”

Section: Introductionmentioning

confidence: 99%

Iterative identification of output error model for industrial processes with time delay subject to colored noise

Dong

Liu

et al. 2015

Chinese Journal of Chemical Engineering

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“…A fast recursive exponentially weighted PLS algorithm is proposed by [26], where the adaptation to new data is in the covariance matrices instead of the input and output data matrices. Some applications of the different RPLS algorithms can be found in [27,28,29,30], but all these recursive algorithms are based on linear PLS models.…”

Section: Introductionmentioning

confidence: 99%

Knowledge based recursive non-linear partial least squares (RNPLS)

et al. 2020

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Soft sensors driven by data are very common in modern industry to predict critical variables which are difficult to measure using other variables that are relatively easier to obtain. The use of soft sensors implies some challenges, such as the predictor variables colinearity, the time-varying and possible non-linear nature of the industrial process. To deal with the first challenge, the partial least square (PLS) regression has been employed for many applications to deal with the linear variable relationships, with noisy and highly correlated data. However, the PLS model needs to deal with the other two issues: the non-linear and the time-varying side behaviour of the processes. In this work, a new knowledge based methodology for a recursive non-linear PLS algorithm (RNPLS) is systematized to deal with these issues. Here, the non-linear PLS algorithm is made by carrying out the PLS regression over the augmented matrix of input, which includes knowledge based non-linear transformations of some of the variables. This transformation depends on the type of the system, and takes into account the available knowledge about the process, which is provided by expert knowledge or em-

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A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

Cited by 11 publications

References 15 publications

Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Iterative identification of output error model for industrial processes with time delay subject to colored noise

Knowledge based recursive non-linear partial least squares (RNPLS)

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