Key Technology Of Online Raman Analyzer In Para-xylene Separation Unit And Its Application

As an important chemical product,p-xylene(PX)is widely used in polyester industry.It is significant to monitor the key components in process online.Raman spectroscopy,well known as molecular fingerprint,is a powerful process analytical technology especially in the identification and analysis of isomers.Therefore,this thesis studies the key technology of online Raman analyzer in PX separation unit and its application.The thesis specifically includes:(1)The content of non-aromatic hydrocarbon components(denoted as NA)is about 3～10%of feedstock in an adsorption tower in a PX unit.However,the specific composition of NA is unknown,so it is difficult to analyze NA in the feedstock.Therefore,a novel method based on two spectral decomposition algorithms for two Raman spectral regions is proposed.In the region 650～900 cm-1,a direct decomposition algorithm is applied to get their corresponding peak areas based on spectra of pure components ethylbenzene,PX,m-xylene and o-xylene.In the region 1350～1550 cm-1,another spectral decomposition algorithm based on Lorentzian function is adopted to obtain the spectral peak of NA which is fitted by several Lorentz peaks,then the corresponding peak area is calculated.Finally,the linear models between peak areas and concentrations of each component are built with all training samples.The experimental results show that the standard prediction error for NA is 0.242%,and the correlation coefficient reaches 0.993.(2)An analysis algorithm for impurities in PX product based on spectral decomposition is proposed.The total content of impurities in PX product is less than 0.5%,so it is difficult to quantitatively analyze each impurity.Firstly,adjust the integration time to amplify the characteristic peaks of impurities.Secondly,an impurity spectral decomposition algorithm based on Lorentz function is used to obtain the characteristic peaks of impurities which are fitted by Lorentz peaks.Then,the correspondence between characteristic peaks and impurities is determined by Raman spectra of pure components.Finally,the quantitative analysis models are established based on the four characteristic peaks and the corresponding impurity concentrations.The experimental results show that the standard prediction errors for the contents of impurities toluene,ethylbenzene,m-xylene,and o-xylene are 0.013%,0.016%,0.011%,and 0.014%respectively,and the correlation coefficients are 0.983,0.976,0.984,and 0.988.(3)According to the situation of the PX unit in a refinery plant,an online analysis system is developed to monitor feedstock and PX product in the PX unit,based on the Raman equipment developed by ourselves.The repeatability error of the system for feedstock and PX product is less than 0.02%and 0.01%while the standard prediction error is less than 0.20%and 0.05%,respectively.Application results show that the online Raman analysis system has a good long-term property and meets the requirements of real-time monitoring.