One of the most common processes to produce polymer nanoparticles is to induce self-assembly by using the solvent-displacement method, in which the polymer is dissolved in a “good” solvent and the solution is then mixed with an “anti-solvent”. The polymer processability and its ability to self-assemble in solution is therefore determined by its structural and transport properties in solutions of the pure solvents and at the intermediate compositions. In this work, we focus on poly-ε-caprolactone (PCL) which is a biocompatible polymer that finds widespread application in the pharmaceutical and biomedical fields, performing simulation at three different scales using three different computational tools: full atomistic molecular dynamics (MD), population balance model (PBM) and computational fluid dynamics (CFD). Simulations consider PCL chains of different molecular weight in solution of pure acetone (good solvent), of pure water (antisolvent), and their mixtures, mixing at different rates and concentration in a confined impinging jets mixer (CIJM). Our MD simulations reveal that the nanostructuring of one of the solvents in the mixture leads to an unexpected identical polymer structure irrespectively of the concentration of the two solvents. In particular, although in pure solvents the behavior of the polymer is, as expected, very different, at intermediate compositions, the PCL chain shows properties very similar to those found in pure acetone as a result of the clustering of the acetone molecules in the vicinity of the polymer chain. We derive an analytical expression to predict the polymer structural properties in solution at different solvent compositions and use it to formulate an aggregation kernel to describe the self-assembly in the CIJM via PBM and CFD. Simulations are eventually validated against experiments.