一种改进OSELM算法在片烟复烤过程水分在线检测中的应用
Application of an improved OSELM algorithm in online detection of moisture content in the tobacco strip redrying process
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摘要: 针对片烟复烤过程中关键质量指标出口烟叶含水率难以直接在线检测,且离线化验滞后严重的问题,提出一种改进在线序列极限学习机(Online Sequential Extreme Learning Machine,OSELM)的复烤干燥过程自适应建模方法,实时在线检测干燥区出口烟叶的含水率。首先,采用专家知识与互信息方法选择与烟叶含水率相关性最强的辅助变量,增强模型的泛化能力并降低复杂度。然后,针对复烤过程的强非线性和显著时变特性,提出一种基于自适应遗忘因子的OSELM建模方法,设计的自适应遗忘因子策略能够根据复烤工况的变化动态迭代更新,以此增强软测量模型对复杂工况的在线跟踪能力。最后,基于某复烤厂的实际生产数据进行实验,结果表明,相较于传统软测量建模方法,本文方法具有较高的在线检测精度和响应速度,证明了该算法的有效性和优越性。Abstract: To address the challenges in directly detecting the moisture content of tobacco strips (a key quality indicator) and the significant delays in offline moisture measurements during the redrying process, this study proposes an adaptive modeling method using an improved Online Sequential Extreme Learning Machine (OSELM) for real-time online monitoring of moisture content at the drying zone exit. First, domain-specific expert knowledge and mutual information analysis were combined to select auxiliary variables most relevant to moisture content, thereby improving model generalization while maintaining predictive accuracy and reducing computational complexity. Subsequently, an OSELM-based modeling approach with adaptive forgetting factors (AFFs) was developed to address the strong nonlinearity and time-varying dynamics. The AFF strategy dynamically adjusted according to process variations, significantly enhancing the soft sensor’s online tracking performance under complex operational conditions. Finally, validation using real-world production data from an industrial redrying facility showed that the proposed method outperforms traditional soft sensing approached in both detection accuracy and response time, thereby confirming its superior effectiveness.
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