Depth Drying Model And Applied Research, Based On The Incremental Improved Bp Neural Network Microwave

As an important branch of artificial intelligence research area, Back-Propagation (BP) neural network has favourable nonlinear mapping and high parallel information processing capabilities, and it is widely used in technology areas of multi-disciplinary subjects.Microwave drying technology, is different from the traditional drying methods, has characteristics of instantaneity, integrity, efficiency, safety, non-pollution, easy to realize the automatic control and improving the products quality, the direction of heat conduction is the same as the direction of moisture diffusion. But in the industrial process of microwave deep drying of selenium-rich slag, the influencing factors include the microwave input power, the acting time, the initial moisture content, the average material mass, the average material surface area and the rotational frequency. The affecting degrees of these factors are different, so that it can be guaranteed the testing cycle longer, the testing quantity larger and the parameters difficult to be optimized. The BP neural network, which has the ability of non-linear mapping, is chosen to build up the simulation model to predict the experimental process results.However, the traditional BP algorithm based on gradient descent method revises the network weights through computing the network weights'and thresholds'values of the objective functions, needs more convergent time and sometimes the convergent results can not be obtained in local minimum areas. Therefore, the BP neural network algorithm should be improved.In present thesis, the prediction model and the Smith compensation Proportion> Integration Differentiation (PID) control model based on incremental improved BP neural network are built up taking the industrial process of microwave deep drying of selenium-rich slag as the research target, and the universality of prediction model in the field of microwave calcination is researched. The main contributions are summarized as follows:1) The improved BP neural network based on Levenberg-Marquardt (L-M) algorithm overcomes these limitations. In the process of training the network, many training data probably are offered by the way of increment batch and the limitation of the system memory can make the training data infeasible when the sample scale is large, the incremental learning is implemented by adjusting the weights of the BP neural network as demonstrated.The incremental learning is realized by setting up the knowledge effective extent in single neural network and increasing the number of the nodes in hidden layers. When the new sample knowledge closes to the prior sample knowledge, the weights' and thresholds'value can be changed in the effective extent, and the number of the nodes in hidden layers is fixed flexibly. In the process of training neural network using the training sample, the number of the nodes in hidden layers is increased and the output error is calculated. If the indication value approaches to the target value, the training is ending and the number of the nodes in hidden layers is the optimization. By using the method, the network can not only learn the new sample knowledge but sustain the original knowledge.The incremental improved BP neural network has the faster convergence, better prediction accuracy, better fitting results and can avoid the error sum squares no longer be updated, the phenomenon of network paralysis and the network not be trained, be out of the local minimum when adjusting the network parameters, make the network be converged rapidly, can effectively solve the problem such as the training data can not be provided for one-time, choose the representative samples to train the network in the case of occupying less memory source.2) The incremental improved BP neural network non-linear prediction model is built up during the industrial process of microwave deep drying of selenium-rich slag. Taking the microwave input power, the acting time, the initial moisture content, the average material mass, the average material surface area and the rotational frequency as input variables, the model is used to predict the experimental results.3) The incremental improved BP neural network prediction model for industrial electricity energy consumption is built up. Taking the microwave input power, the acting time, the initial moisture content, the final moisture content and the average material mass as input variables, the model is used to predict the experimental electricity energy consumption.4) The conventional PID control model is widely applied in the fields of industry process control because of its simple structure, robustness and easily operation, such as metallurgy, chemical engineering and electric power and mechanics. With the development of industry, the complexity of the controlled plant is serious, especially for the time-delaying, time-varying and non-liner complex systems, the conventional PID control model can not be met the control accuracy of the target.The industrial Smith compensation PID control model using incremental improved BP neural network is put forward. The parameters of controlled plant are evaluated in the experimental process and identified the neural network Smith compensation PID control off-line. Using the simplified control model of microwave deep drying selenium-rich slag to research the Smith compensation PID controller, the PID control parameters are tuned on-line.5) The industrial anti-prediction model using incremental improved BP neural network is proposed in microwave deep drying of selenium-rich slag processing. Taking the final material mass, the final temperature and the final relative dehydration rate as input variables, anti-prediction model is used to predict the acting time, the initial dehydration rate and the electricity energy consumption.6) The industrial prediction model of microwave deep drying of selenium-rich slag using incremental improved BP neural network is used for the microwave calcination of Ammonium Diuranate (ADU) and Ammonium Uranyl Carbonate (AUC) in order to investigate the universality of the prediction model. The model is used to predict the experimental processing results of microwave calcination.