Woo Seung Han, Jeon Che Ok, Yun Yeoung-Sang, Choi Hyeoksun, Lee Chang-Soo, Lee Dae Sung
Department of Chemical Engineering, Hanbat National University, Daejeon 305-719, Republic of Korea.
J Hazard Mater. 2009 Jan 15;161(1):538-44. doi: 10.1016/j.jhazmat.2008.04.004. Epub 2008 Apr 6.
A kernel-based algorithm is potentially very efficient for predicting key quality variables of nonlinear chemical and biological processes by mapping an original input space into a high-dimensional feature space. Nonlinear data structure in the original space is most likely to be linear at the high-dimensional feature space. In this work, kernel partial least squares (PLS) was applied to predict inferentially key process variables in an industrial cokes wastewater treatment plant. The primary motive was to give operators and process engineers a reliable and accurate estimation of key process variables such as chemical oxygen demand, total nitrogen, and cyanides concentrations in real time. This would allow them to arrive at the optimum operational strategy in an early stage and minimize damage to the operating units as shock loadings of toxic compounds in the influent often cause process instability. The proposed kernel-based algorithm could effectively capture the nonlinear relationship in the process variables and show far better performance in prediction of the quality variables compared to the conventional linear PLS and other nonlinear PLS method.
基于核的算法通过将原始输入空间映射到高维特征空间，在预测非线性化学和生物过程的关键质量变量方面可能非常有效。原始空间中的非线性数据结构在高维特征空间中很可能是线性的。在这项工作中，核偏最小二乘法（PLS）被应用于推断工业焦化废水处理厂中的关键过程变量。主要目的是为操作人员和工艺工程师提供关键过程变量的可靠且准确的实时估计，如化学需氧量、总氮和氰化物浓度。这将使他们能够在早期阶段制定出最佳操作策略，并在进水有毒化合物的冲击负荷经常导致过程不稳定时，将对操作单元的损害降至最低。与传统线性PLS和其他非线性PLS方法相比，所提出的基于核的算法能够有效地捕捉过程变量中的非线性关系，并在质量变量预测方面表现出更好的性能。
