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

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.