Application of Least Square Support Vector Machine with Adaptive Particle Swarm Parameter Optimization in Grate Pressure Optimization Setting of Grate Cooler

机译：采用自适应粒子群参数优化应用最小二乘支持向量机的应用炉篦冷却器的炉排压力优化设置

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Grate cooler is an important equipment in the process of new dry process cement production, The grate down pressure of the grate cooler is an important indicator reflecting the clinker cooling effect of the grate cooler. When the pressure under the grate of the grate cooler is stable within a reasonable range, on the one hand, the grate cooler can maintain the highest cooling effect, and on the other hand, the maximum waste heat can be recovered to ensure the normal operation of the kiln system. Therefore, a method of optimizing the set value of the grate cooler pressure based on the least squares support vector machine algorithm based on adaptive particle swarm parameter optimization is proposed, through the adaptive particle swarm optimization algorithm to achieve the least squares support vector machine model parameters and optimization calculation, Solve the limitation of the prediction accuracy of the least squares support vector machine by the value of the model parameters, furthermore, the optimal setting value of the grate down pressure of the grate cooler is reasonably given to ensure the cooling effect and clinker quality of the grate cooler.

机译：Grate Cooler是一种重要的设备，在新的干燥工艺水泥生产过程中，炉排冷却器的炉排压力是反映炉渣冷却器的熟料冷却效果的重要指标。当炉渣冷却器的炉排下的压力在合理的范围内稳定时，一方面，炉篦冷却器可以保持最高的冷却效果，另一方面，可以回收最大的废物以确保正常操作窑系统。因此，提出了一种基于基于自适应粒子群参数优化的最小二乘支持向量机算法优化炉渣冷却器压力设定值的方法，通过自适应粒子群优化算法实现最小二乘支持向量机模型参数和优化计算，解决了通过模型参数的值的最小二乘的预测精度的限制，此外，格栅冷却器的格栅下压力的最佳设定值合理地给出了冷却效果和炉排冷却器的熟料质量。

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《Youth Academic Annual Conference of Chinese Association of Automation》|2020年|632-637|共6页
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Shuo Wang; Hongliang Yu; Shizeng Lu; Xiaohong Wang; Huaguo Liu;
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Support vector machines; Adaptation models; Cooling; Predictive models; Prediction algorithms; Particle swarm optimization; Optimization;

机译：支持向量机;适配模型;冷却;预测模型;预测算法;粒子群优化;优化;

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Application of Least Square Support Vector Machine with Adaptive Particle Swarm Parameter Optimization in Grate Pressure Optimization Setting of Grate Cooler

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