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In order to decrease the pollution to sea water during the course of oil and water replacement, oil and water separating membrane will be adopted. Considering the expansion and contraction of oil and water separating membrane, transmission of wave pressure and the density difference between the oil and water, the membrane may be damaged by tension, buckling, friction, scraping and so on. Thus, the material used as oil and water separating membrane should have enough strength and flexibility. The generally used coating fabrics were discussed in this paper as per the coating agents, base cloths, and coating methods. Glass fiber cloth coated with silica gel and polyurethane coated fabrics were chosen and a series of tests were performed as per the corresponding standards. By analyzing the test results, the preferred material for oil and water separating membrane was proposed.

There is no incremental learning ability for the traditional support vector machine (SVM) and there are all kind of merits and flaws for usually used incremental learning method. Normal SVM is unable to train in large-scale samples, while the computer’s memory is limited. In order to resolve this problem and improve training speed of the SVM, we analyze essential characteristic of SVM and bring up the incremental learning algorithm of SVM based on regression of SVM related to SV (support vectors). The algorithm increases the speed of training and can be able to learning with large-scale samples while its regressive precision loses fewer. The experiments show that SVM performs effectively and practically. Its application to prediction of the transition temperature (Tg) for high molecular polymers show that this model (R2=0.9427) proved to be considerably more accurate compared to a ANNs regression model (R2=0.9269).