Liu Cuiling

The Method of Soft Sensing Based on RBF Neural Network and Intelligent Control for Sewage Disposal Process

Soft sensing method was proposed for determination of effluent BOD from SBR and its principle was introduced, the method was based on the radial basic function (RBF) artificial neural network. The RBF neural network was trained and simulated by a lot of observed data, and the result showed that the RBF neural network may be used to fulfil soft sensing for effluent BOD from SBR, so as to create condition for real-time control of sewage disposal process, showing abroad perspective in application. The fuzzy control method for DO in SBR sewage disposal process was represented, and the aeration time and the energy consuming according to different DO curves were also studied. DO can be controlled in real time by the fuzzy logical to change the frequency of motor. Then we can adjust the concentration of DO according to different DO curves, and get the obvious advantages in shortening time of wastewater treatment and reducing energy consumed

Outlier Sample Analysis on Near Infrared Spectroscopy Determination for Flour Ash

The flour ash was determined by near infrared (NIR) apparatus. Mahalanobis distance method and Monte Carlo cross validation (MCCV) method for abnormal samples removed and using partial least squares (PLS) modeling, Eventually, Mahalanobis distance method for abnormal samples removed, when the weight factor of 1.5, excluding the number of samples is 3 to get the best results, the correlation coefficient (R2) is 92.67, cross validation mean square error (RMSECV) is 0.0485, MCCV method for abnormal samples removed, excluding the number of samples 3 to get the best results, R2 is 94.64, RMSECV is 0.0411. Mahalanobis distance method and MCCV method for abnormal samples removed have improved the accuracy of the model, and MCCV method for flour ash model is more stable and more to improve the accuracy.

Notice of Retraction Dynamic simulation study on fracturing fluid filtration in process of oil fracturing

This paper studies pressure liquid filtration mechanism in the process of hydraulic fracturing of oil. Combined with three kinds of control mechanism such as the viscosity of fracturing fluid, the compressibility of formation fluid and the wall building property of fracturing, we obtain integrated filtration coefficient of fracturing fluid. With numerical simulation methods of fracturing fluid filtration, we establish dynamic simulation model of fracturing fluid filtration, based on which we make simulation model with the combination of qualitative and quantitative.