Liquid-liquid equilibrium of binary ethanol–polyisobutene and ternary ethanol–water–polyisobutene systems under low- and high-pressure conditions: An assessment using the PC-SAFT equation of state

•Solubility of polyisobutene (PiB) in ethanol fuel depends on process conditions.•This phase equilibrium was investigated using the PC-SAFT equation of state.•Extrapolation strategies to estimate binary interaction parameters were developed.•Phase diagrams were calculated from 298.15 K to 350 K and from 1 bar to 200 bar.•Strategies to prevent this phase separation at high and low pressures are presented. Sugarcane bioethanol in either anhydrous or hydrous forms stands out as an economically viable bioproduct that can be used as a fuel or feedstock in various innovative applications, reducing human-generated greenhouse gas emissions. Low-molar-mass polyisobutene (PiB) is used as a lubricant in sugarcane-processing plants and is a possible contaminant of sugarcane bioethanol. Understanding the liquid–liquid equilibrium (LLE) of ethanol–PiB binary systems and ethanol–water–PiB ternary systems is essential to avoid undesired phase separation at high-pressure engine conditions and other potential innovative applications. However, the experimental investigation of these systems is significantly hampered by very low polymer solubilities in aqueous ethanol. The LLE of ethanol-PiB and ethanol–water–PiB mixtures was qualitatively investigated using the PC-SAFT equation of state through the development and implementation of extrapolation strategies allowing to estimate ethanol–PiB and water–PiB binary parameters using experimental data obtained for other systems containing lighter alkanes. Phase diagrams were calculated considering temperatures ranging from 298.15 K to 350 K and pressures ranging from 1 bar to 200 bar. It was shown that lower pressures, higher temperatures, lower PiB molar masses, and lower water contents increase the polymer solubility in the solvents. Thus, the risk of liquid–liquid separation can be mitigated by correctly managing such operational parameters. The obtained phase diagrams may present significant quantitative deviations from reality because of the model’s sensitivity to the estimated binary parameters. Thus, they should be interpreted as general qualitative guidelines to avoid and manage undesired phase separation in industrial processes and engines rather than phase equilibrium predictions.