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Research on Environmental Hazards of Urban Gas Pipeline Rupture

Lei ZHOU, Yan XU, Hao PENG

Abstract （42）

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The study of the environmental hazards of chemical gas pipeline rupture is of great significance to the design of explosion prevention measures and the development of emergency response programs. Taking a typical overhead gas pipeline as the research object, the environmental hazards of the whole process of natural gas leakage diffusion, jet fire, vapor cloud flash fire and explosion accident development were analyzed. The results show that when designing explosion-proof measures and formulating emergency plans, the explosion risk area should be calculated according to the low wind speed. Natural gas leakage mainly affects the chemical park B area, office buildings A， B， C, street stores A. The downwind direction of the occurrence of jet fire accident 8.7~76.1 m is a high-risk area, affecting the area of the chemical park B area, office buildings B and the street stores A. The area affected by the vapor cloud flash fire is Chemical Park B, Chemical Park A, Office Buildings A, B, C, D, Street Shops A, Residential Area A, the main road and the edge of the Science and Technology Park, so the area should be evacuated in advance of the fire. Natural gas explosion damage area is downwind -47.1~67.2 m, mainly affecting the chemical park B, chemical park A, office buildings B, street stores A, residential neighborhoods A.

2025, 38 (1): 19-25. DOI: 10.12422/j.issn.1006-396X.2025.01.003

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Analyze the Effect of PCA Whitening on Chemical Process Monitoring Based on PCA-ICA

JIANG Qing-chao, YAN Xue-feng

Abstract （780）

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Principal component analysis (PCA) was an important and most widely used data whitening approach in process monitoring based on independent component analysis (ICA) due to its effectivity in reducing dimensions of objects. PCA model was generated based on sample data of normal process, and the first several PCs which contain the most variance information of normal process were employed for ICA and process noise was eliminated. In PCA model, T2 statistic of each principal component has the property that it can measure variation along direction of the component. By researching the T2 statistic of sample data of fault process, it is found that information of some faults are mostly reflected on the components corresponding to smaller variance contribution, which are regarded as process noise and eliminated, and thus missed detection problem is happened. At last, the effect of PCA whitening on chemical process monitoring based on PCA-ICA is illustrated through simulations of both a simple process and TE process, and the results prove the proposed opinion.

2012, 25 (1): 71-75. DOI: 10.3969/j.issn.1006-396X.2012.01.015

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Differential Evolution Algorithm With Control Parameter Co-Evolution and Its Application

FAN Qin-qin, YAN Xue-feng

Abstract （860）

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In order to implement dynamic and self-adaptive adjustment of control parameter with population evolution, a novel differential evolution algorithm with control parameter co-evolution (DE-CPCE) was proposed. In DE-CPCE, control parameter were designed as the symbiotic individual of original individual, and each original individual had its own symbiotic individual. Differential evolution operator was applied to search the global optimization solution of problem; meanwhile, it was also employed to co-evolve the population consisting of symbiotic individuals according to the evolution efficiencies of original individuals. Thus, with the evolution of the population consisting of original individuals in DE-CPCE, control parameter were dynamically and self-adaptively adjusted and the real-time optimum control parameter were obtained. The results of the experiments show that control parameter of DE-CPCE have dynamic and self-adaptive property; the probability and precision of obtaining the global optimization solution, and the convergence are all better than those of DE/rand/1, DE/best/1, DE/rand-to-best/1, DE/rand/2, DE/best/2, self-adaptive Pareto DE and self-adaptive DE. Further, DE-CPCE is applied to estimate the kinetic model parameter of oxidation, and the result is better than that of the reference.

2010, 23 (1): 93-98. DOI: 10.3696/j.issn.1006-396X.2010.01.022

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Process Modeling Based on SOM-PCA-RVM and Its Application

LI Xin, YAN Xue-feng

Abstract （2144）

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A hybrid modeling method for the complex process was proposed．The process，which was highly nonlinearity，had interacting independent variables， and had process data belonged to different categories． This method combined self－organizing map network （SOM network），principal component analysis （PCA），and relevance vector machine （RVM）．First，
divided the sample datum into several subspaces which had the same patterns with the utilizing of SOM，the pattern space separation was realized．Then derived the principal components based on the modeling samples of each subspace，defined the optimal principal component numbers based on prediction ability，and filtered the redundancy．At last，the principal component
of each subspace was applied as the input of RVM，building the models individually ，and the classifying modeling was realized based on the separated of sample pattern spaces．The simulation results and the application in 4－carboxybenzaldehyde content soft sensor of pureed terephthalic acid production show that the accuracies of regressing and predicting of SOM－PCA －RVM model is better than both RVM model and PCA － RVM model．

2009, 22 (4): 89-94. DOI: 10.3696/j.issn.1006-396X.2009.04.022

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Adaptive Weighted Least Square Support Vector Machine Regression and Its Application

CUI Wen-tong,LIN Wen-cai,YAN Xue-feng

Abstract （1026）

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In order to eliminate the influence of unavoidable outliers in soft sensor sample data on model's performance,a novel adaptive weighted least square support vector machine regression method(AWLS-SVM) was proposed.Firstly,in AWLS-SVM,least square support vector machine regression was employed for the sample data to develop model and obtain the sample datum fitting error.Then,according to the fitting error,the initial error weight was calculated.Secondly,the lever weight was defined according to the space distribution of the sample data.Finally,combining the error weights and lever weights,the adaptive sample weights were obtained via the proposed adaptive iterative method.Thus,the model was developed by AWLS-SVM with the adaptive sample weights.The simulation experiment results show that the outliers influence on the model's performance is eliminated in AWLS-SVM,and that the prediction performance is better than those of LS-SVM and radial basis function network method.Further,AWLS-SVM was applied to develop the soft sensor model for 4-carboxybenzaldehyde concentration in terephthalic acid,which is the most important intermediate product of p-xylene oxidation to terephthalic acid reaction.The satisfied result is obtained.onclick="SelectDisplayDiv('EnDivSummaryMore','EnDivSummaryHuanYuan');HideSpanDiv('EnDivSummaryMoreSummary')">

2009, 22 (4): 84-88. DOI: 10.3696/j.issn.1006-396X.2009.04.021

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KPCA － RVM Modeling Method and Its Application for Soft Sensor

YAN Xue－feng, CHEN Jia, HU Chun－ping, QIAN Feng

Abstract （1895）

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A novel modeling method integrated KPCA with RVM was proposed． The kernel primary component analysis （KPCA） was employed to identify the principal components from the nonlinear transform data of independent variables，which were regarded as character variables．Regression between character variables and dependent variables was done based on RVM，and the optimal number of the character variables was adaptively determined according to the generalization performance of the regression model．Thus ，KPCA－RVM method could eliminate the disturbance of redundant information and achieve the best nonlinear model with good generalization performance．The method of KPCA－RVM was demonstrated by a 4－CBA’s content soft －sensing of PTA．Simulation results show this method is effective and the performance is better than those of PCA－RVM and RVM．

2009, 22 (1): 82-85.

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Outlier Detection of High Dimensional Chemical Engineering Process Data Based on Self-Organizing Map

YAN Xue-feng， TU Xiao-zhi， QIAN Feng

Abstract （255）

PDF （351KB）（190）

For the high dimensional chemical engineering processing data, an outlier detection method based on self-organizing map (SOM)networks and its visualization methods was proposed. Practically, it was applied for the observed data of preflash tower and the satisfactory result was obtained. Firstly, SOM was applied to obtain the topology-preserving plane for the high dimensional data. Then, based on the mapping plane and its visualization methods, the outliers were visualized clearly and easily. The results show that the proposed method does not need complex calculation, and the outliers in high dimensional data are effectively detected and eliminated.

2008, 21 (4): 84-86.