Optimization of a wood chip refining process based on fuzzy relational models

Qian, Yu; Liu, Huanbing; Zhang, Xiaoping; Tessier, Patrick

doi:10.1016/s0098-1354(97)87655-7

Cited by 3 publications

(1 citation statement)

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“…Regarding the estimation of pulp quality characteristics, chip impregnation and refining is a multivariable and nonlinear process (Qian et al 1997), and there is a resulting difficulty in establishing a satisfying mechanism model for pulp property estimation. A data-driven method is a good choice for realizing optimization and control of the refining process via modeling (Qian et al 1997;Thibault et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Soft Sensors for Pulp Freeness and Outlet Consistency Estimation in the Alkaline Peroxide Mechanical Pulping (APMP) High-Consistency Refining Process

Zhang¹,

Li²,

Liu³

et al. 2016

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In the mechanical pulping process, some process state and product quality variables are difficult to measure on-line. In this paper, soft sensors were used to estimate Canadian Standard Freeness (CSF) and outlet consistency (Cout) after the high consistency refining stage of the alkaline peroxide mechanical pulping (APMP) process. After the secondary variables for modeling that are readily available processed measurements in pre-treatment and the HC refining stage was selected, models based on the case-based reasoning (CBR) method were developed to estimate CSF and Cout. The ability of CBR soft sensors to predict CSF and Cout was tested using data collected from an APMP mill, and the results were satisfactory. Additionally, two typical soft sensor methods that back propagation network (BP) algorithms and support vector regression algorithms (SVR) were employed to predict CSF and Cout and evaluate the performance of the CBR soft sensor. As a result, the proposed soft sensor demonstrated a better performance than the BP method and can be regarded as of comparable quality to the SVR method.

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Section: Introductionmentioning

confidence: 99%