Combined with synchronized monitoring techniques, purification processes for active constituent of alkylbenzene sulfonate have been optimized, and the total alkylbenzene sulfonate, mono-alkylbenzene sulfonate and di-alkylbenzene sulfonate can be obtained with purity over 90%. With the above active constituents as standard substances respectively, the determination of alkylbenzene sulfonate content in the original samples was carried out by high performance liquid chromatograph. The results showed the sample contained mono- and di-alkylbenzene sulfonate simultaneously, and the content of mono-alkylbenzene sulfonate was much higher than that of di-alkylbenzene sulfonate. They had maximum absorption at the wavelength of 224 nm, and the absorption coefficients of total alkylbenzene sulfonate and mono-alkylbenzene sulfonate were similar, higher than that of di-alkylbenzene sulfonate. With total, mono- and di-alkylbenzene sulfonate as standard substances respectively, the limits of quantitation were 5 mg/L, 5 mg/L and 10 mg/L, respectively. The determination result of active constituent was about 3% lower with total alkylbenzene sulfonate as standard substance than that with mono- and di-alkylbenzene sulfonate as standard substances simultaneously, owing to the weaker absorption coefficient of di-alkylbenzene sulfonate.

The security condition of oil storage tank can be assessed without opening pot by acoustic emission technology, and acoustic emission signal recognition is the basis of analysis of the corrosion status for oil storage tanks. Against deficiencies of the analysis method by parameters, a new acoustic emission signal recognition method was proposed based on wavelet transform and RBF neural networks. Acoustic emission signal was decomposed to 6 layer by db2 wavelet, and feature vector from acoustic emission signal was composed of the space energy based on 6 layer detail feature. RBF neural network was designed combining the characteristics of acoustic emission signal. The RBF network was trained by using of the acoustic emission signal which pattern has been known.Corrosion, crack and condensation acoustic emission signal were studied by RBF neural network, respectively.The results showed that the recognition rate of RBF neural network reached 93.3%, and the RBF network displayed superiority in identification of the acoustic emission signal, which had a certain significance for quantitative analysis on oil storage tank safety situation.