# Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques
## 2 Materials and Methods
### Dataset  
Experimental specimens were obtained using IQ ECONOMY+ A4, 80 g/m\({}^{2}\) white paper and the HP LaserJet Q2610A toner. The paper was mechanically cut in a paper shredder, soaked in distilled water, and mixed to obtain cellulose fiber specimens. The toner was printed on precoated Q CONNECT A4 universal laser transparency film with polyvinyl alcohol [26] and disintegrated in a mechanical stirrer to obtain plate-shaped particles for toner specimens. The specimens of cellulose fibers and toner particles were further mixed to form a pulp, transferred to the Denver 1,6-L flotation cell and floated at the conditions specified below in Tables 1 and 2. The parameters which may have a significant effect on the deinking process, but are not used as the practical control variables, are summarized in Table 2.  
The concentration of oleic acid, with or without CaCl\({}_{2}\), as a surfactant in flotation, pH value and retention time in flotation were used as parameters of the input model. The pH value is an important control parameter because it affects the function of surfactants in deinking flotation, particularly fatty acid soaps. The calcium concentration has been shown to affect the performance of deinking systems. The recovery of cellulose fibers and optical properties represents the trade-off between economy and quality that must be reconciled in any deinking operation, and flotation retention time is a consistent determinant of deinking performance [4; 6]. The recovery of toner and the recovery of cellulose fibers were used as parameters of the output model [6]. Samples were extracted from the foam at 1, 2, 4, 10 and 20 min, and in order to calculate toner recovery in the rejected stream (E\({}_{t}\)) and cellulose  
\begin{table}
\begin{tabular}{c c} \hline
**Process Control Variables** & **Range of Process Control Variables** \\ \hline Flotation pH & 3–12 \\ \hline Surfactant in flotation cell: & \\ Oleic acid & 0.1–7 kg/t \\ Oleic acid + CaCl\({}_{2}\) & 0.125–1.5 kg/t + 30 kg/t \\ \hline Flotation time & 1–20 min \\ \hline \end{tabular}
\end{table}
Table 1: Ranges of deinking parameters used as the input model variables.  
fiber recovery in the acceptable stream (E\({}_{\text{m}}\)), float and sink products were filtered through the Buchner funnel, dried at room temperature and weighed, while the dried froth filter pads were then heated at 550 \({}^{\circ}\)C in a muffle furnace to determine the ash content by x-ray fluorescence (XRF), for E\({}_{\text{t}}\) calculation. The printability of the prepared sink filter pads and hand sheet after deinking was checked via printing in a controlled environment using the monochrome laser-jet printer, HP 1018.  
For each experiment, 100 experiments were performed, i.e., 100 pairs of input/output model parameters were created. Since a limited data set is used, 90% of all data were randomly selected for training the model and the remaining 10% were used for testing the prediction ability of the created model [44]. This ensures that the model has enough data to learn from, and using a cross-validation approach will allow a small amount of data to be used to train/test the model [10]. The data that were used for testing were not included in the training dataset [44].