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Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

机译：基于核极限学习机的数据驱动建模用于甘蔗汁澄清

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摘要

Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data‐driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model.
机译：澄清甘蔗汁是制糖工业生产过程中的重要操作。果汁的重力纯度和色值是使用硫酸盐澄清法生产蔗糖时最重要的两个评价指标。但是，在实际操作中，这两个指标的测量通常是通过离线实验滴定获得的，因此无法及时调整系统指标。提出了一种基于核极限学习机的数据驱动模型，以预测果汁的重力纯度和纯果汁的色值。通过粒子群优化对模型参数进行优化。进行实验以验证建模方法的有效性和优越性。与BP神经网络，径向基神经网络和支持向量机相比，该模型具有良好的性能，证明了该模型的可靠性。

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