Multiclass moisture classification in woodchips using IIoT Wi-Fi and machine learning techniques
Multiclass moisture classification in woodchips using IIoT Wi-Fi and machine learning techniques
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DOI：
10.1016/j.compchemeng.2021.107445
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发表时间：
2021-07-31
影响因子：
4.3
通讯作者：
He, Q. Peter
中科院分区：
文献类型：
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作者：
Suthar, Kerul;He, Q. Peter
对于以木片为原料的制浆造纸厂的制浆过程，木片的水分含量 (MC) 是影响产品质量以及能源、水和化学品消耗的主要过程干扰。由于性能不可靠和/或在制造环境中难以满足的高维护要求，现有的木片 MC 传感技术尚未被业界广泛采用。为了解决这些限制，我们提出了一种无损、经济且稳健的木片 MC 传感方法，利用基于 Wi-Fi 的工业物联网 (IIoT) 的通道状态信息 (CSI)。虽然这些 IIoT 设备体积小、成本低且坚固耐用，可以承受恶劣的环境，但它们也有其局限性，例如原始 CSI 数据通常噪声很大且对木片包装敏感。为了解决这个问题，利用统计模式分析 (SPA) 从原始 CSI 数据中提取物理和/或统计上有意义的特征，这些特征对木片 MC 敏感，但对包装不敏感。然后，SPA 功能用于使用各种线性和非线性机器学习技术开发多类分类模型，为纸浆和造纸行业的木片 MC 估计提供潜在的解决方案。这项工作还表明，单独的分类精度并不是工业应用的良好性能指标，并且还必须考虑错误分类的实际影响。 (c) 2021 Elsevier Ltd. 保留所有权利。
For the pulping process in a pulp & paper plant that uses woodchips as raw material, the moisture content (MC) of the woodchips is a major process disturbance that affects product quality and con-sumption of energy, water and chemicals. Existing woodchip MC sensing technologies have not been widely adopted by the industry due to unreliable performance and/or high maintenance requirements that can hardly be met in a manufacturing environment. To address these limitations, we propose a non-destructive, economic, and robust woodchip MC sensing approach utilizing channel state information (CSI) from industrial Internet-of-Things (IIoT) based Wi-Fi. While these IIoT devices are small, low-cost, and rugged to stand for harsh environment, they do have their limitations such as the raw CSI data are often very noisy and sensitive to woodchip packing. To address this, statistics pattern analysis (SPA) is utilized to extract physically and/or statistically meaningful features from the raw CSI data, which are sensitive to woodchip MC but not to packing. The SPA features are then used for developing multiclass classification models using various linear and nonlinear machine learning techniques to provide poten-tial solutions to woodchip MC estimation for the pulp and paper industry. This work also demonstrates that classification accuracy alone is not a good performance metric for industrial applications, and the practical implications of misclassification must also be considered. (c) 2021 Elsevier Ltd. All rights reserved.