Assessment of Solubility Behavior of a Copolymer in Supercritical CO2 and Organic Solvents: Neural Network Prediction and Statistical Analysis

In the industrial sector, understanding the behavior of block copolymers in supercritical solvents is crucial. While qualitative agreement with polymer solubility curves has been evaluated using complex theoretical models in many cases, quantitative predictions remain challenging. This study aimed to create a rapid and accurate artificial neural network (ANN) model to predict the lower critical solubility and upper critical solubility space of an atypical block copolymer, poly(styrene-co-octafluoropentyl methacrylate) (PSOM), in different supercritical solvent systems over a wide range of temperatures (51.75–182.05 °C) and high pressure (3.28–200.86 MPa). The experimental data set used in this study included one copolymer, five supercritical solvents, one cosolvent, and one initiator. It consisted of seven unique copolymer–solvent combinations (252 cloud point pressures) used to predict the model quantitatively and qualitatively. To predict the PSOM–solvent interactions, the study considered two different input systems: a six-variable system, a five-variable system, and one target output. Initially, we used a three-layer feed-forward neural network to select the best learning algorithm (Levenberg–Marquardt) from 14 different algorithms, considering one sample PSOM–solvent system. Then, the network topology was optimized by varying hidden neuron numbers from 2 to 80 for all seven PSOM–solvent combination systems. The predicted cloud point pressures were in excellent agreement with the experimental cloud point pressures, confirming the model’s accuracy. It is clear from the results of a minimum mean square error (≤1.90 × 10^–27) and maximum linear regression R² (≥0.99) during training, validation, and testing of all the data sets. Further, the ANN model accuracy was tested by statistical analysis, confirming the model’s ability to accurately capture the miscibility regions of polymers, enabling efficient processing of various polymer materials. This data-driven approach facilitates the prediction of coexistence curves for other polymers and complex macromolecular systems.