使用一般回归神经网络进行混合混合的预测建模和优化
Ivan Kopal1, Ivan Labaj1, Juliána Vršková1
1Department of Numerical Methods and Computational Modelling, Faculty of Industrial Technologies in Púchov, Alexander Dubček University of Trenčín, Ivana Krasku 491/30, 020 01 Púchov, Slovakia.
Polymers
|July 12, 2025
概括
本研究介绍了一种使用一般回归神经网络 (GRNN) 进行实时混合混合控制的智能系统. 它准确地预测了工艺参数,并优化了早期的混合,提高了工业质量和生产力.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 人工智能的人工智能
背景情况:
- 混合物混合对于产品质量至关重要.
- 实时流程控制对于效率至关重要.
- 预测建模可以优化复杂的工业流程.
研究的目的:
- 开发一个智能预测系统,实时控制混合物混合.
- 准确预测关键的过程参数,如粘度,温度和能耗.
- 为了使混合进度的早期检测能够进行流程优化.
主要方法:
- 一般回归神经网络 (GRNN) 模型的实施.
- 使用来自布拉本德胎盘仪EC Plus的实验数据.
- 通过十倍交叉验证优化GRNN内核宽度 (σ).
主要成果:
- 对粘度,温度和能源消耗的高预测精度.
- 从最初的10%的数据准确评估混合进展.
- 实现了接近1的R2值和低的RMSE,证实了模型的可靠性.
结论:
- 基于GRNN的系统为混合提供了强大的和可扩展的智能控制.
- 该系统可提高工业应用中的生产率和质量保证.
- 预测方法在混合过程之外也适用.
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