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In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). Typically, it considers regressing the outcome (also known as the response or the dependent variable) on a set of covariates (also known as predictors, or explanatory variables, or independent variables) based on a standard linear regression model, but uses PCA for estimating the unknown regression coefficients in the model.
Oil and gas two-phase flow gas holdup detection device and method using halogen lamp light source
A device and method for detecting the gas content of a two-phase flow of oil and gas using a halogen lamp light source are disclosed. The method includes the following steps: Step 1: Turn on the halogen lamp light source and inject the prepared two-phase flow of oil and gas to be tested into the pipeline through a three-way valve according to the pre-calibrated gas content. Step 2: Turn on the peristaltic pump to uniformly fill the pipeline with the two-phase flow of oil and gas to be tested, then turn off the peristaltic pump. A first collimating lens is connected to the halogen lamp light source via a first optical fiber to receive the incident light emitted by the halogen lamp light source. A spectrometer is connected to the second collimating lens via a second optical fiber to receive the outgoing light of the two-phase flow of oil and gas passing through the flow cell. The spectrometer records the spectral information in a computer. Step 3: Repeat steps 1 and 2 until the spectral information of all calibrated gas contents is obtained. Step 4: Establish a prediction model using the spectral information. Specifically, a principal component regression (PCR) model for detecting the gas content of the two-phase flow of oil and gas is established to predict the gas content of the two-phase flow.
Owner:XI AN JIAOTONG UNIV +1