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Modeling and Optimization of a Roll-Type Electrostatic Separation Process Using Artificial Neural Networks

机译:人工神经网络建模与优化辊式静电分离过程

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The aim of this work is the development of a procedure for the optimization of electrostatic separation processes using artificial neural networks (ANN) in association with genetic algorithms. The objective was to maximize the insulation product, the control variables being the high-voltage that supplies the electrodes system and the rotation speed of the roll electrode. The ANN model is compared with that obtained using the classical experimental design methodology. The predicted optimum is confirmed by experiment.
机译:这项工作的目的是开发使用与遗传算法相关的人工神经网络(ANN)优化静电分离过程的程序。 目的是最大化绝缘产品,控制变量是提供电极系统的高压和辊电极的旋转速度。 将ANN模型与使用经典实验设计方法获得的型号进行比较。 通过实验确认预测的最佳。

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