title: On-line estimation of key process variables based on kernel partial least squares in an industrial cokes wastewater treatment plant.
abstract: 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.
authors.affiliation: Department of Chemical Engineering, Hanbat National University, Daejeon 305-719, Republic of Korea.
authors.forename: Seung Han
authors.initials: SH
authors.lastname: Woo
authors.forename: Che Ok
authors.initials: CO
authors.lastname: Jeon
authors.forename: Yeoung-Sang
authors.initials: YS
authors.lastname: Yun
authors.forename: Hyeoksun
authors.initials: H
authors.lastname: Choi
authors.forename: Chang-Soo
authors.initials: CS
authors.lastname: Lee
authors.forename: Dae Sung
authors.initials: DS
authors.lastname: Lee
mesh_terms: D003077:Coke / Q000032:analysis*; D016018:Least-Squares Analysis; D016014:Linear Models; D009862:Online Systems / Q000295:instrumentation*; D014865:Waste Disposal, Fluid / Q000295:instrumentation* / Q000379:methods*
chemical_list: D003077:Coke