Adaptive control applications in pulp and paper

Cunningham, D. C.

doi:10.1109/ccece.1993.332453

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…As organic foods cost more than conventional foods, purchasing decisions are influenced by income. In addition to studies reporting the relationship between income and organic food demand [61,72], there are also those claiming that there is no specific determinant for organic demand [66]. Conversely, in Türkiye, Oraman [73] noted that organic product consumers have above-average incomes.…”

Section: Incomementioning

confidence: 99%

Results from Türkiye: Which Factors Drive Consumers to Buy Organic Food?

Baş,

Kahriman,

Çakir Biçer

et al. 2024

Foods

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The trend towards organic foods as an alternative has recently increased. Several individual, environmental, and behavioural factors can affect this situation. This study was conducted with 1417 participants to examine the factors affecting attitudes, purchase intention (PI), and actual purchasing behaviour towards organic foods. Consequently, a two-part questionnaire was used to query participants’ sociodemographic information and their attitudes and preferences towards organic foods. Data were analysed using multiple regression analysis, Pearson’s correlation, and structural equation modelling. Our findings confirmed that health consciousness, the knowledge of organic foods, subjective norms, perceived price, values (health and safety), nutritional content, naturalness, availability, monetary barriers, risk barriers, and trust affect attitudes towards organic products. These findings indicate that increasing consumers’ knowledge and awareness about organic foods, encouraging their consumption by society, accessibility them in the food market and making them affordable can affect the attitude towards these products. Furthermore, we determined the direct effect of the attitude on actual buying behaviour with the mediating role of PI. Additionally, we noted that marital status, employment status, disease diagnosis in the last 12 months, and the presence of a baby at home affect actual buying behaviour. In conclusion, they can help food marketers target consumers to their sociodemographic status and develop new sales strategies.

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Section: Incomementioning

confidence: 99%

Results from Türkiye: Which Factors Drive Consumers to Buy Organic Food?

Baş,

Kahriman,

Çakir Biçer

et al. 2024

Foods

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“…Compensated brightness control regulates the dosage of the chlorine mixture to achieve target brightness after a one minute reaction time, leaving a residual amount of chemical to complete the reaction in the bleaching tower. The total applied chemical is used with a regression model for the calculation (Rankin and Bialkowski 1984;Corbi et al 1986;Cunningham 1993). The compensated brightness value is often fed to a conventional PI controller to maintain a constant brightness, which in turns cascades down to either the chlorine and or the chlorine dioxide flow controller (Van Fleet 1998) The essential aspect of the compensated brightness control is how to adjust the target brightness set point, which could be determined by pulp brightness after tower and/or reset by a feed-forward model for predicting incoming brown stock kappa number (Rankin and Bialkowski 1984;Corbi et al 1986;Cunningham 1993).…”

Section: Generalization Of a Human-supervised Dcs-based Processmentioning

confidence: 99%

Improved Model for Brightness Optimization Control in the First (C95/D5) Bleaching Stage

Zhang¹,

Li²,

Liu³

2016

BioResources

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In the first stage of pulp bleaching, the quantity of added chemicals (ClO2 and/or Cl2) is commonly controlled by kappa factor, based on a kappa number online analyzer together with a compensated brightness control scheme as a feedback strategy. However, a kappa number analyzer is not always available, so the bleaching quality relies heavily on the chemical dosage set-point chosen by the operators. In this study, an improved model for the chlorination stage brightness optimization was proposed, based on brightness and residual chemicals before pulp enters the bleaching tower. Additionally, the experience of operators of (C95/D5) bleaching was employed in order to find an optimum chemical dosage setpoint quickly. The golden section search algorithm (i.e., '0.618 method') was used to find the optimum chemical dosage in this paper. After applying the proposed method in a pulp mill (C95/D5) bleaching stage, the chlorination stage brightness shifted from 62.9% ISO to the target value 60.7% ISO. Meanwhile, the standard deviation was reduced from 3.0 to 2.5.

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Nonintrusive PID auto-tuning using dynamic modeling technology

Hassell

Harper

Proceedings of IEEE Pulp and Paper Industry Conference

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lEEEMEMBER 900 -1441 Creekside Drive Vancuuver, British Columbia V6J 4V3 Caneda Uni~DynamiCsLimited &&& -Dyoamic Modeling Technology uses a model beaed pnaictive and adaptive algorithm for caatinuous pfoccm control. The technology was dcdaped two yeers ago and is available in at least o I I L ! c 4 m " d yavailableproduct. Thisproduct bas banused succe&uy on S e V d difficult industrial cmtrol applications. This paper covers a recat qplicatia of the Dynamic Modeling Technology (DMT) algorithm for timing proportional, Integral and Daivative 0 ) controllers.

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Adaptive control applications in pulp and paper

Cited by 3 publications

References 1 publication

Results from Türkiye: Which Factors Drive Consumers to Buy Organic Food?

Results from Türkiye: Which Factors Drive Consumers to Buy Organic Food?

Improved Model for Brightness Optimization Control in the First (C95/D5) Bleaching Stage

Nonintrusive PID auto-tuning using dynamic modeling technology

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