Supercritical water (SCW) is a novel thermal agent that has been recently utilized for the production of heavy oil. However, a lack of knowledge about its recovery mechanisms limits the application of SCW. In this study, pyrolysis and sandpack flooding experiments were performed to investigate the mechanisms and viability of SCW flooding. Then an innovative simulation model was developed for SCW flooding. Finally, sensitivity studies on SCW flooding were conducted by the developed model. The results showed that SCW flooding yielded a 13.99% increase in oil recovery in comparison to steam flooding, indicating that SCW flooding is technically applicable to offshore heavy oil reservoirs. Heavy oil upgrading in SCW can suppress coke formation and plays an important role in oil recovery. A novel numerical model for SCW flooding was established based on a history match of experiments. The simulation results suggested that during SCW flooding, SCW could induce heavy oil upgrading to increase oil mobility, and long-term injection of SCW may cause the formation of coke deposits. Higher injection temperatures and pressures would benefit the production performance of SCW flooding. However, an unlimited increase in temperature would damage formations by significant coke deposits.
A new method using reflection NIR technology was developed to determine the alcoholysis degree and volatile matter of Poly-vinyl alcohol (PVA). 120 samples were used in this research. NIR spectra of the sample were scanned by the spectrometer from 1 000 to 1 800 nm. The alcoholysis degree and volatile matter were determined by the national standard method of volumetric and gravimetric method respectivily. Partial least squares (PLS1) was used to establish the quantitative correction model of alcoholysis degree and volatile matter of PVA. The corrected relationship (Rc) of alcoholysis degree and volatile matter was 0.976 and 0.981 respectively. The corrected standard deviation(SEC) was 0.176 and 0.197. The predicted relationship (R(p)) was 0.967 and 0.969. The predicted deviation(SEP) was 0.202 and 0.193. The test for actual samples showed that the NIR method was fitted for the requirement of PVA analysis.
In the present paper, the distribution of sugar level within the mini-watermelon was studied, a new sugar characterization method of mini-watermelon using average sugar level, the highest sugar level and the lowest sugar level index is proposed. Feasibility of nondestructive determination of mini-watermenlon sugar level using diffuse reflectance spectroscopy information was investigated by an experiment. PLS models for measuring the 3 sugar levels were established. The results obtained by near infrared spectroscopy agreed with that of the new method established above.
A new method is proposed for the fast determination of the induction period of gasoline using Fourier transform attenuated total reflection infrared spectroscopy (ATR-FTIR). A dedicated analysis system with the function of spectral measurement, data processing, display and storage was designed and integrated using a Fourier transform infrared spectrometer module and chemometric software. The sample presentation accessory designed which has advantages of constant optical path, convenient sample injection and cleaning is composed of a nine times reflection attenuated total reflectance (ATR) crystal of zinc selenide (ZnSe) coated with a diamond film and a stainless steel lid with sealing device. The influence of spectral scanning number and repeated sample loading times on the spectral signal-to-noise ratio was studied. The optimum spectral scanning number is 15 times and the optimum sample loading number is 4 times. Sixty four different gasoline samples were collected from the Beijing-Tianjin area and the induction period values were determined as reference data by standard method GB/T 8018-87. The infrared spectra of these samples were collected in the operating condition mentioned above using the dedicated fast analysis system. Spectra were pretreated using mean centering and 1st derivative to reduce the influence of spectral noise and baseline shift A PLS calibration model for the induction period was established by correlating the known induction period values of the samples with their spectra. The correlation coefficient (R2), standard error of calibration (SEC) and standard error of prediction (SEP) of the model are 0.897, 68.3 and 91.9 minutes, respectively. The relative deviation of the model for gasoline induction period prediction is less than 5%, which meets the requirements of repeatability tolerance in GB method. The new method is simple and fast. It takes no more than 3 minutes to detect one sample. Therefore, the method is feasible for implementing fast determination of gasoline induction period, and of a positive meaning in the evaluation of fuel quality.
The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA-SVM model. The results indicate that combining the semi-transmission hyperspectral imaging technology with CARS-SPA and AFSA-SVM can accurately detect hollow heart of potato, and also provide technical support for rapid non-destructive detecting of hollow heart of potato.
A new rapid quantitative method for the determination of oxygenates and the compounds not included in the national standard in gasoline using near-infrared spectroscopy is raised by this paper. This method combine near-infrared spectroscopy with oblique projection. This experiment choose four different types of gasoline, including reconcile gasoline, FCC refined gasoline, reformed gasoline and desulfurizing gasoline. Prepare series gasoline samples containing different concentrations and different types of compounds. Using FTIR spectrometer to measure those samples and got transmission spectrums. Oblique projection method could separate quantity spectral signal from mixed spectrum signal, and using projection to calculate and analyze the separated signal to obtain the content of measured component. The deviation between this method and the real content is low, the absolute error is less than 0.8 and the relative error is less than 8%. For the actual gasoline samples, compare results of this method with gas chromatography, the absolute error are less than 0.85 and the relative error are less than 6.85%. This method solves the problem of general multivariate calibration methods. It is very significant for the development of rapid detection technology using NIR suitable for on-site and the improvement of the quality of gasoline.
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