Application of neural network model coupling with the partial least-squares method for forecasting watre yield of mine

Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.

A data-derived soft-sensor method for monitoring effluent total phosphorus

The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.

Voidage Measurement of Gas-Oil Two-phase Flow

A new method for the voidage measurement of gas-oil two-phase flow was proposed.The voidage measurement was implemented by the identification of flow pattern and a flow pattern specific voidage measure- ment model.The flow pattern identification was achieved by combining the fuzzy pattern recognition technique and the crude cross-sectional image reconstructed by the simple back projection algorithm.The genetic algorithm and the partial least square method were applied to develop the voidage measurement models.Experimental results show that the proposed method is effective.It can overcome the influence of flow pattern on the voidage measure- ment,and also has the advantages of simplicity and speediness.

Modeling and Optimization for Piercing Energy Consumption

Energy consumption is an important quality index in the production of seamless tubes. The complex factors affecting energy consumption make it difficult to build its mechanism model, and optimization is also very difficult, if not impossible. The piercing process was divided into three parts based on the production process, and an energy consumption prediction model was proposed based on the step mean value staged multiway partial least square meth- od. On the basis of the batch process prediction model, a genetic algorithm was adopted to calculate the optimum mean value of each process parameter and the minimum piercing energy consumption. Simulation proves that the op- timization method based on the energy consumption prediction model can obtain the optimum process parameters effectively and also provide reliable evidences for practical production.