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

Zhou, Ping; Wang, Hong; Li, Mingjie; Zhao, Zhuofeng; Chai, Tianyou

doi:10.1049/iet-cta.2015.0850

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Compared with the cogeneration technologies (Shabbir & Mirzaeian, 2017) and carbon dioxide absorption technology (Nwaoha & Tontiwachwuthikul, 2019) proposed, the method proposed in this paper does not require the pulp mill to invest new funds for process improvement. The CO 2 intensity dropped, which reflected the improvement of energy efficiency in the pulp and paper industry (Zhou et al, 2016).…”

Section: Carbon Emissionmentioning

confidence: 99%

“…Some researchers have used pulping process parameters to predict pulp quality through machine learning methods. For example, Iglesias et al (2017) established a Kappa value prediction model, Zhou et al (2016) proposed a DOF (Degrees of freedom) prediction model, and Li et al (2017) proposed a pulp quality prediction model. The above prediction model proves that there is a nonlinear relationship between pulp quality and process parameters.…”

Section: Special Issue On Waste Valorisation For Sustainable Production Process and Productsmentioning

confidence: 99%

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Analysis of Energy Saving and Emission Reduction of Secondary Fiber Mill Based on Data Mining

Hong

et al. 2021

Journal of Environmental Engineering and Landscape Management

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Waste paper recycling is an important way to realize the environmental protection development of the papermaking industry. The quality of the pulp will affect the pulp sales of the secondary fiber paper mills. The waste paper pulp can be adjusted by controlling the pulping process working conditions, but the working conditions of the waste paper pulping process have too many parameters. And the parameters are coupled with each other, it is difficult to control. In order to find the best working conditions and improve the quality of the pulp, this study uses the association rules algorithm to optimize the parameters for the waste paper pulping process. These parameters are power of refiner, waste paper concentration of refiner, the volume of slurry that enters deinked process, deinking agent amount, deinking time, deinking temperature, bleaching agent amount, bleaching time, and bleaching temperature. The test results show that the qualified rate of the pulp produced under the improved working conditions is 92.56%, an increase of 6.93%, and the average electricity consumption per ton of pulp is reduced by 5.76 kWh/t. In addition to potential economic benefits, this method can reduce carbon emissions.

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Section: Carbon Emissionmentioning

confidence: 99%

Section: Special Issue On Waste Valorisation For Sustainable Production Process and Productsmentioning

confidence: 99%

Analysis of Energy Saving and Emission Reduction of Secondary Fiber Mill Based on Data Mining

Hong

et al. 2021

Journal of Environmental Engineering and Landscape Management

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“…To address the time delay, Zhou et al (2016) studied a modelling strategy involving an Auto Regressive Moving Average (ARMA) model to make an online estimation of freeness. The accuracy and capability of the developed model was compared to alternative models in the study and outperformed them.…”

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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Analytical design based hierarchical control for non-square MIMO wood-chip refining process

Zhou

2019

ISA Transactions

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

Cited by 4 publications

References 20 publications

Analysis of Energy Saving and Emission Reduction of Secondary Fiber Mill Based on Data Mining

Analysis of Energy Saving and Emission Reduction of Secondary Fiber Mill Based on Data Mining

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

Analytical design based hierarchical control for non-square MIMO wood-chip 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

Cited by 4 publications

References 20 publications

Analysis of Energy Saving and Emission Reduction of Secondary Fiber Mill Based on Data Mining

Analysis of Energy Saving and Emission Reduction of Secondary Fiber Mill Based on Data Mining

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

Analytical design based hierarchical control for non-square MIMO wood-chip refining process

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Product

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