Intelligent modeling and optimization of emulsion aggregation method for producing green printing ink

Abstract: In this study, Artificial intelligence method was used as a new approach in modelling and optimization of printing toners with appropriate requirements. Toner fine powder is made up of resin, colorant and additives. This composite has been utilized in electrophotographic digital printing. The optimization approach has been considered for optimizing of toner production process and to produce printing toners with an appropriate physical and color properties (particle size (PS), particle size distribution (PSD), L*, a*, b*) by an environmental friendly method which is emulsion aggregation (EA). The EA is a green technology that provides many advantages for toner production pathway and lead to high quality product and printing. The effect of heating rate (R), time of mixing (T), and mixing rate (S) on PS, PSD, and L*, a*, b* has been studied. An in-home code was established to optimize the architecture of artificial neural network (ANN) with two hidden layers, by which an accurate model was developed for the prediction of toner properties. The best process conditions with acceptable characteristics of manufacturing toners was obtained by multi-objective optimization in specified amounts of heating rate, mixing time, and mixing rate

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
Intelligent modeling and optimization of emulsion aggregation method for producing green printing ink ; volume:8 ; number:1 ; year:2019 ; pages:703-718 ; extent:16
Green processing & synthesis ; 8, Heft 1 (2019), 703-718 (gesamt 16)

Urheber
Ataeefard, Maryam
Sadati Tilebon, Seyyed Mohamad
Reza Saeb, Mohammad

DOI
10.1515/gps-2019-0041
URN
urn:nbn:de:101:1-2501120449194.147331792382
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
15.08.2025, 07:29 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Beteiligte

  • Ataeefard, Maryam
  • Sadati Tilebon, Seyyed Mohamad
  • Reza Saeb, Mohammad

Ähnliche Objekte (12)