Why Is Your Palm Kernel Oil Losing Quality During Distillation?

You're not alone if your palm kernel oil has a dull color or an off-putting odor after distillation. This isn’t just about aesthetics—it’s a clear sign that your process parameters may be out of balance. In fact, studies show that improper temperature gradients in vacuum distillation can increase free fatty acid (FFA) levels by up to 30%, directly impacting shelf life and market value.

Understanding the Core Difference: Vacuum vs Molecular Distillation

For palm kernel oil refining, two primary methods dominate: vacuum distillation and molecular distillation. While both remove volatile compounds like free fatty acids and odors, they differ significantly in how they handle heat-sensitive components.

• Vacuum Distillation: Operates at 0.1–0.5 mbar pressure with temperatures between 160°C–190°C. Ideal for removing bulk impurities but risks thermal degradation if not carefully controlled.
• Molecular Distillation: Uses ultra-high vacuum (≤0.001 mbar) and lower temps (120°C–150°C). More effective at preserving polyphenols and tocopherols—key for premium-grade oils.

According to AOCS Cd 20-91, proper control of residence time and vapor velocity is critical in avoiding recondensation of undesirable volatiles—a common issue in poorly tuned vacuum systems.

Key Parameters That Make or Break Your Output

Temperature gradient, vacuum level, and retention time are the three pillars of successful distillation. Here’s what you need to watch:

Common Mistakes & How to Fix Them

Many operators assume higher vacuum always equals better quality—but this isn't true. A frequent error is setting the inlet temperature too high (>190°C), which leads to polymerization and darkening. Another is neglecting real-time monitoring of vapor flow rate, often resulting in carryover of non-volatile residues.

Corrective action? Use automated PID controllers for precise temperature regulation, and install inline FTIR analyzers to monitor FFA content continuously. These tools reduce trial-and-error and boost consistency across batches.