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To make an accurate prediction about the amount of equipment maintenance materials consumption (EMMC), which plays an important role of equipment maintenance materials support, precondition and management, an LM algorithm prediction model of EMMC established based on the improved BP neural network algorithm by means of history data processing, and which has been discussed and verified through example...
Two-input layer wavelet neural network prediction model is established, through the analysis of the smelting process of submerged arc furnace and combining of wavelet analysis and neural network theory, used to predict the power consumption of the submerged arc furnace timely. The input variables are not input in one layer, but in different layers according to their action sequences,thereby reducing...
As the technics of Grate-Kiln is complicated, for which it's difficult to establish accurate model, a black-box prediction model of quality is established in this paper with compression strength, drum index and screening index being the output based on the method of BP Neural Network optimized by Genetic Algorithm. And then the parameters of model are identified by MATLAB using the real data of some...
This paper presented a new prediction model for Pressure-Volume-Temperature (PVT) properties based on the recently introduced learning algorithm called Sensitivity Based Linear Learning Method (SBLLM) for two-layer feedforward neural networks. PVT properties are very important in the reservoir engineering computations. The accurate determination of these properties such as bubble-point pressure and...
Water content in crude oil is a very important data in oilfield production logging system. It is also an indispensable parameter for the research of its development prospect. During the process of exploitation, storage and transportation of oilfield, high accuracy measuring of water content in crude oil can optimize production parameters and improve oil recovery rate. The GRNN (general regression...
Radial basis function (RBF) neural network is used to predict the blast furnace hot metal based on its characteristics such as fast convergence and global optimization. As hot metal silicon content had close relationship with furnace temperature, the change of temperature in furnace was reflected indirectly by hot metal silicon content. Newrbe function in Matlab was applied for function approximation...
Accurate prediction of the end-point temperature and carbon content of AOD furnace is of great significance to raise the hitting rate of the end-point. Based on AOD refining practice, the predictive model of end-point temperature and carbon content of AOD furnace low carbon Chromium iron making based on BP neural network was put forward. The results showed that the model is much accurate and applicable.
The measurement of water content in crude oil based on method of dielectric coefficient is affected by multi-factor, including temperature, salinity content, flow states of oil/water mixture and output characteristic of measuring-sensor, is regarded as a multi-input and single-output nonlinear system with time-variance, strong uncertainty and randomicity. In this paper, a measuring system based on...
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