Foam formation in distillation and separation units reduces effective tray capacity, compromises product quality, and forces conservative operating limits. Broad-spectrum antifoams may suppress foam but often introduce downstream fouling or contamination risks.

Targeted antifoam programmes focus on understanding foam drivers—such as feed composition, contaminants, and operating conditions—before selecting treatment chemistry. This approach allows lower dosing rates and more consistent foam control.

By stabilising vapour-liquid separation, refiners can operate closer to design limits with fewer disruptions. Improved throughput is achieved not by pushing equipment harder, but by reducing inefficiencies that previously constrained performance.

Targeted programmes also reduce unintended impacts on heat exchangers, catalysts, and wastewater systems—supporting overall refinery reliability.

Data once followed the experiment. Now, it defines the hypothesis. Machine learning models can simulate outcomes before physical trials begin, compressing what once took months into days. This shift doesn't just increase speed—it alters the nature of discovery itself.