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Rougher flotation process bauxite slurry pH value measurement model and application software

Author: DuJinFang
Tutor: TangChaoZuo
School: Central South University
Course: Control Science and Engineering
Keywords: froth flotation digital image process soft sensor model pH prediction
CLC: TP274
Type: Master's thesis
Year: 2011
Downloads: 103
Quote: 1
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Abstract

Flotation is an effective method for separating various fine grained mineral particles based on the difference of the surface physicochemical properties. Rougher flotation is the beginning of flotation. The pH value of its slurry directly reflects sodium carbonate addition in mill. At the same time, it has significant impact on reagents addition of other processes, concentrate and tailings grade. Present pH determinator wastes time and human resource and has time delay. Furthermore, the result is easily affected by external conditions. It can not be used continuously and stably.The pH value of rougher flotation is affected by many factors and the change of pH value is a nonlinear process. It is hard to build a soft measurement model for slurry pH value based on chemical reaction mechanism. Froth image includes much information related to production indexes. Its features can reflect the changes of slurry pH value. Therefore, the real time pH value of rougher flotation slurry can be obtained by froth image features.In this paper, the static features such as color, size and texture as well as dynamic features like velocity and stability are extracted by digital image process technology. At the same time, because BP neural network has good function of approximation ability and RBF neural network can trace the function quickly, a hybrid neural network model based on BP and RBF neural network is proposed. Due to the strong correlation between the froth image features, it may lead a large amout of calculation and a long training time if the features are used in the soft measurement model directly. So the features’dimension is reduced by principal component analysis before they are used as instrumental variable of soft sensor model. Owing to the node number of hidden layer in neural network and hybrid coefficient of soft measurement model are not determined, they are optimized by genetic algorithm. Meanwhile, aiming at the disadvantage of traditional genetic algorithm such as local optimum, oscillation around the optimal value and slow convergence speed, an adaptive genetic algorithm is proposed based on the improvement of calibration of fitness, diversity of population and crossover and mutation operator. In addtion, in order to improve the robustness of the model, Levenberg-Marquardt algorithm is used to modify the weights and threshold of neural network on-line when measurement error exceeds the set value.Running results show that the soft sensor model has advantages of high precision, fast response and strong robustness. It can replace the offline manual measurement, reduce labor intensity and guide workers to adjust the sodium carbonate addition in the mill. Moreover, it can be popularized in other industrial process.

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