# Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques
## 1 Introduction  
The flotation process has been used in mineral processing plants to separate valuable minerals from ore. In this process, three phases are combined in the flotation pulp: solid (mineral particles), liquid (water) and gaseous (air). Mineral particles are separated from the pulp based on the difference in their surface hydrophobicity. The ones that are easily wetted by water are called hydrophilic, while particles with a limited affinity for wetting are called hydrophobic [1, 2]. Flotation is a key process in many paper recycling plants as well and was introduced successfully to the paper recycling industry in the 1980s. Generally, the deinking process is based on the separation between hydrophobic inks and hydrophilic paper fibers. In modern paper recycling plants, the process of removing unwanted particles from the pulp can have as many as three times more steps than in the mineral processing industry due to the fact that a higher quality product is needed to compete with virgin paper [3]. Given that the heterogeneity of the feed would affect the different steps of the recycling process, and those steps can change the quality of the paper, it is necessary to control the operating parameters in all steps. A variable that can affect the final quality of the product but which will not affect the reduction of plant capacity or require significant operating costs is called a practical control variable. Variables that have a significant impact on the process but at the same time cause disturbances in the process and must also be optimized are considered non-practical controlvariables [4]. There are a number of possible control variables for deinking plants and many researchers have investigated their influence on the deinking process [3; 5; 6; 7; 8; 9; 10]. In addition to chemical additives, such as sodium hydroxide, sodium silicate, hydrogen peroxide and surfactant, the pulp temperature in the phase of disintegration, i.e., pulp formation and in the flotation phase, pulp formation time and flotation time, flotation consistency and pH value in the flotation are considered as process variables. Process variables that are often not adjustable in practice such as pulp consistency, flotation air flow rate and foam height are not considered as good modeling variables. The deinking process in industrial practice is continuous, with a constant flow of input, acceptance and rejection, and the quality of the recycled paper is the main control variable in the deinking process [3]. Predictive modeling is based on analyzing relationships between input variables to make predictions about continuous output variables. In supervised machine learning, these relationships are learned from the data, during a training process. The trained model can then be applied to previously unseen input data not used during the training process, thereby allowing the inference of implicit properties about the modeled process from the data. Recent research in the literature has focused on machine learning applications that have been developed to estimate and adjust parameters in flotation processes. Only a limited number of studies report the application of machine learning to ink removal processes. Artificial Neural Networks (ANNs) are used to model and predict flotation behavior in industrial paper recycling process [4], while [PERSON] et al., 2007 [11] propose a model to predict the deinking efficiency based on an ANN. [PERSON] et al., 2024, developed a method for monitoring ink removal based on neural network color image analysis [12]. ANNs were used by many authors to model and simulate the quality characteristics of pulp and paper [13] and the macroscopic mechanical properties of minerals [14]. Multivariate Nonlinear Regression (MNLR), Radial Basis Function Neural Networks (RBFNNs) and Recurrent Neural Networks (RNNs) were used to predict flotation performance [15]. [PERSON] et al., 2018 [16] used SVR to model coal flotation. [PERSON] et al., 2024 [17], reviewed the research over the last ten years.