Study On The Application Of Chemometrics Methods In Petroleum Products Quality Analysis

As the quality requirements of petroleum products in the process of refining and chemical industry become higher,it is necessary to evaluate the performance and quality of oil products in a low-cost,short time and online monitoring way to achieve the purpose of fully and effectively using precious petroleum resources.The application of chemometrics method and technology in oil refining and chemical analysis can make full use of chemical measurement data obtained by near infrared spectroscopy,online chromatography,online thickness measurement system,etc.,to realize the rapid determination of other related properties(through laboratory measurement,time-consuming and laborious,difficult to achieve online measurement),so as to meet the actual needs of production.This dissertation mainly studies the application of near infrared spectroscopy in oil refining and chemical production process analysis and the application expansion of online monitoring analysis.Chapter 1 IntroductionThe present analysis status and application of chemometrics in refining and chemical production process are summarized.Chapter 2 An effective and rapid approach to predict molecular composition of naphtha based on raw NIR spectraMolecular management has become an important trend in petroleum refining,which relies on the information of petroleum composition.In this contribution,a simple and effective analytical approach is proposed for the rapid prediction of the more detailed molecular composition of naphtha samples based on raw near infrared(NIR)spectroscopy for the first time.The 101 samples of reformed naphtha were collected and determined,and Tchebichef curve moments(TCMs)were calculated directly from the raw NIR spectra and employed to establish linear models for the quantitative analysis of 26 hydrocarbons(PIONA)with different carbon numbers and components.For the obtained models,the average of RMSE of prediction is 0.10.According to the ratio of performance to deviation(SD/RMSE_p),the 23 obtained TCM models achieved“excellent”predictive quality.By means of the conventional PLS method with spectral pretreatment,there were only 15 models with“excellent”predictive quality,which indicated that TCM method without any spectral preprocessing could provide more simple,accurate and reliable analytical results,and meet the requirements of fast assessment.This work suggests the feasibility of the proposed method for the rapid and non-destructive analysis of molecular composition in naphtha,which is significant in the determination of refinery operating conditions.Chapter 3 Rapid determination of the key temperatures in diesel distillation process based on near-infrared spectroscopyThe distillation temperature of petroleum is the significant information for the determination of refinery operating conditions.As the standard laboratory test method,ASTM D86 is often cost,time-consuming and not well suitable for on-line determination.In this paper,we proposed a simple approach to the prediction the key temperatures in diesel distillation process based on the raw near infrared(NIR)spectra of samples.After the NIR spectrum was decomposed by discrete wavelet transform to get the different NIR sub-signals,the selected sub-signals by genetic algorithm(GA)were superposed to form the new effective signal.Then stepwise regression was employed to build the linear prediction models.The proposed strategy was applied to predict the five distillation temperatures of diesel simultaneously,and the obtained R_p~2values of independent external test set were more than 0.96 as well as the average relative errors(ARE)were lower than 1%,which showed that the predicted values were well correlated with the reference values.Compared with the other conventional methods,such as PLS,i PLS,si PLS and stepwise,the proposed approach could obtain more accurate and reliable prediction models.This study not only indicated the validity of the new approach,but also provided an important support for the further realization of on-line NIR detection to predict the distillation temperatures.Chapter 4 Rapid determination of control properties in jet coal hydrogenation production by gas chromatographyBy collecting the gas chromatographic simulation distillation data of the raw material of jet coal hydrogenation unit and associating the control properties in the production process,the properties of the raw material in the later processing were predicted and evaluated from the source,which greatly improved the analysis efficiency,saved manpower and material resources.According to the change of the nature of raw materials,the process of production can be properly adjusted to improve the yield of the final product.Chapter 5 Online prediction of corrosive components in the top of atmospheric tower and its condensation systemIn the process of petroleum refining,the occurrence of corrosions not only affects the normal operation of equipment and increases consumption,but also increases the risk of accidents.Therefore,the rapid and accurate prediction for corrosions is of great significance.In this paper,an online detection method combined with hybrid intelligent algorithm was presented to monitor the corrosive components of the condensed water in the top of atmospheric tower and its condensation system.Principal component analysis(PCA)combined with back-propagation neural network(BP)was proposed.The correlation coefficients of the obtained five models for test sets(R_p~2)were greater than 0.97.Compared with other classical methods(PLS-BP,PCA-SVR and PLS-SVR),the proposed method was relatively simple and the obtained models had higher prediction accuracy,which could be used in the prediction and content control the corrosive components of the condensed water.Chapter 6 Investigation on the partition of samples in modeling analysisIn the study of some actual sample data,it is found that the conventional sample division is difficult to establish a satisfactory model.Therefore,a new strategy is proposed in this study:through the iteration of modeling and partitioning,the space composed of characteristic variables derived from the obtained model is divided into different subsets to improve the performance of the established model.This strategy has been successfully applied to the modeling and prediction of five distillation temperatures of diesel samples by near infrared(NIR)spectroscopy,while the traditional KS and SPXY partitioning methods based on initial variable space are difficult to obtain reliable results.The applicability of the proposed strategy was also validated on the reported IRIV-PLS approach for the two published datasets.This work demonstrates that the partition of samples based on the feature variable space could improve the homogeneity of the distribution of samples in different subsets,and establish more reliable prediction model.