Refiner quality control in a CTMP plant

Tervaskanto, Manne; Ikonen, Enso; Ahvenlampi, Timo

doi:10.1109/icca.2009.5410443

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…The accuracy and capability of the developed model was compared to alternative models in the study and outperformed them. Tervaskanto et al (2009) modelled the pulp properties of a CTMP refining process and compared the results with an online pulp quality analyzer. The study included a nonlinear static modelling approach for freeness, and one of the objectives with the model was to address the time delay of the freeness measurements after the latency chest; the idea with the model was to avoid waiting for actual freeness measurements.…”

Section: Previous Studies On Mechanical Pulp Modellingmentioning

confidence: 99%

Time series analysis of refining conditions and estimated pulp properties in a chemi-thermomechanical pulp process

Ekbåge

Nilsson

Håkansson

2019

BioRes

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Frequently sampled process data from a conical disc refiner and infrequently sampled pulp data from a full scale chemi-thermomechanical pulp (CTMP) mill were evaluated to study autocovariance with aspects of potential dynamic modelling applicability. Two trial measurements with an online pulp analyzer at decreased sampling intervals were performed. For variability analysis, time-series containing up to one day of operational data were used. At the chip refiner, the clearest significant autocovariance was identified for the specific electricity consumption, based on the longer sequences. Most of the estimated pulp properties indicated low or non-significant autocovariance, limiting applicability of a specific dynamic model. A mill trial was conducted to investigate the impact from an increase in the conical disc gap on the specific electricity consumption and the resulting freeness. The response time from the gap change in the refiner to measured change in freeness was estimated at 19 min, which was approximately the hydraulic residence time in the latency chest. The relevance of this study lies in applicability of mill-data-driven modelling to capture the dynamics of a specific refining process. Through mill trials the sampling speed of pulp properties was more than doubled to gain insights into short term systematic variations by applying time-series-analysis.

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Section: Previous Studies On Mechanical Pulp Modellingmentioning

confidence: 99%

Time series analysis of refining conditions and estimated pulp properties in a chemi-thermomechanical pulp process

Ekbåge

Nilsson

Håkansson

2019

BioRes

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“…Thus, empirical models and regression models seem to be inevitable. Recently, Tervaskanto presented a steady‐state empirical model on the CSF estimation in [25]. First, based on the force balance inside the refiner, a simplified residence time calculation model as shown in (1) was developed

T_{sr} = \frac{μ_{normalr}}{μ_{normalt}} \times \frac{4 S_{ec} C_{I} L_{S}}{ω_{r}^{3} [L_{s} (R_{1}^{2} - R_{2}^{2}) + C_{I} S_{ec} R_{1}^{2}]} \times [\ln \frac{R_{2}}{R_{1}} - 0.5 \ln (\frac{L_{s} - C_{I} S_{ec}}{L_{s}})]

where T sr represents the slurry residence time ( s ), μ r and μ t are the radial and tangential friction coefficients, respectively, ω r is the refiner's rotation speed (rad/s), L S is the latent heat of steam (kJ/kg), R 1 and R 2 are the inlet and output radius of refining zone, respectively (mm), C I is the inlet consistency (%), and S ec is the specific energy consumption (kJ/kg).…”

Section: Hcr System Descriptionmentioning

confidence: 99%

“…The refining intensity I R (kJ/kg) is calculated as follows [25]:

I_{normalR} = \frac{S_{ec}}{n_{normalb} ω_{normalr} (false(R_{1} + R_{2} false) / 2) T_{sr}}

where n b is the number of bars per unit length of arc.…”

Section: Hcr System Descriptionmentioning

confidence: 99%

“…This means that to apply these empirical models requires a lot of assumptions. Suppose ω , R 1 , R 2 , and n b are determined by the disc type, and their values are assumed to be constant; L S is determined by the output temperature of refiner; The output pressure of refiner is controlled, leading to a basically unchanged latent heat value in the freeness model [25]. Consequently, the steady‐state CSF model has the following simple form [25]:

CSF = λ_{1} S_{ec} + λ_{2} I_{normalR} + λ_{3}

where λ i , i = 1, 2, 3 are the model coefficients that to be identified.…”

Section: Hcr System Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Data‐driven ALS‐SVR‐ARMA _2K modelling with AMPSO parameter optimisation for a high consistency refining system in papermaking

Zhou

Wang

et al. 2016

IET Control Theory & Applications

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“…Qian and Tessier (1995) proposed a mathematical model to predict the pulp properties based on Miles's theory. Mathematical models that describe the operating variables and process conditions on pulp quality were employed using model-based control and optimization of the refining process (Broderick et al 1996;Strand 1996;Tessier et al 1997;Runkler et al 2003;Tervaskanto et al 2009;Harinath et al 2011).…”

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

BioResources

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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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Refiner quality control in a CTMP plant

Cited by 9 publications

References 3 publications

Time series analysis of refining conditions and estimated pulp properties in a chemi-thermomechanical pulp process

Time series analysis of refining conditions and estimated pulp properties in a chemi-thermomechanical pulp process

Data‐driven ALS‐SVR‐ARMA _2K modelling with AMPSO parameter optimisation for a high consistency refining system in papermaking

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

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Refiner quality control in a CTMP plant

Cited by 9 publications

References 3 publications

Time series analysis of refining conditions and estimated pulp properties in a chemi-thermomechanical pulp process

Time series analysis of refining conditions and estimated pulp properties in a chemi-thermomechanical pulp process

Data‐driven ALS‐SVR‐ARMA 2K modelling with AMPSO parameter optimisation for a high consistency refining system in papermaking

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

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Data‐driven ALS‐SVR‐ARMA _2K modelling with AMPSO parameter optimisation for a high consistency refining system in papermaking