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Palm Oil Extraction Line Automation Upgrade Case Study: 20% Output Increase & 15% Energy Reduction Proven
2025-11-12
QI ' E Group
Industry Research
A Nigerian oilseed processing plant achieved a 20% production increase and 15% energy reduction after implementing PLC-based automation and an industrial sensor network across its palm oil extraction line. This case study details how logic control optimization in feeding, pressing, and oil-water separation stages improved operational efficiency. It also demonstrates how pressure, temperature, and flow sensors enable predictive maintenance and remote monitoring—offering actionable insights for mid-sized oil producers on technology selection, training, and system integration. Real-time data analytics and future-ready trends like edge computing and AI-driven predictive maintenance are explored to guide digital transformation in the edible oils industry.
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How Automation Transformed a Nigerian Palm Oil Mill: 20% More Output, 15% Less Energy

In the competitive palm oil processing industry, efficiency isn’t just a goal—it’s survival. A recent case study from a mid-sized refinery in Nigeria demonstrates how smart automation can turn operational challenges into measurable gains.

The Problem: Manual Control = Inefficiency

The facility, producing approximately 8 tons of crude palm oil per day, struggled with inconsistent throughput and high energy consumption. Operators relied on manual adjustments for feed rate, temperature, and pressure across three key stages: pre-heating, pressing, and oil-water separation. This led to frequent downtime due to overheating, clogged filters, and suboptimal extraction yields.

The Solution: PLC + Industrial Sensors

By integrating a programmable logic controller (PLC) system with strategically placed industrial sensors—pressure transducers at the press chamber, thermocouples in the heating zone, and flow meters on the oil discharge line—the mill achieved real-time monitoring and automated control. The PLC adjusted parameters based on live data, reducing human error and enabling predictive maintenance.

Key improvements:

Process Stage Before Automation After Automation
Feed Rate Control Manual, inconsistent Auto-adjusted via flow sensor feedback
Press Temperature Frequent overheat alarms Stable ±2°C deviation
Oil Yield ~68% 72.5% (an increase of 6.6%)

These changes translated into tangible business outcomes: 20% higher daily output and 15% reduction in kWh/ton of oil produced. The ROI was realized within 14 months through lower labor costs, fewer breakdowns, and improved product consistency.

PLC control panel integrated with industrial sensors in a palm oil processing plant

Why This Matters for Smaller Mills

Many small-to-midsize palm oil producers hesitate to adopt automation due to perceived complexity or cost. But this case shows that even modest investments—such as a basic PLC setup with 5–8 sensors—can yield significant returns. Critical success factors include:

  • Start with one process loop (e.g., press section) before scaling
  • Train operators on data interpretation, not just button pushing
  • Use cloud-based dashboards for remote monitoring and alerts

Looking ahead, edge computing and AI-driven predictive maintenance will further reduce unplanned downtime—especially valuable in regions where spare parts delivery takes weeks.

Ready to Transform Your Palm Oil Line?

Our team has helped over 40 mills across Africa, Southeast Asia, and Latin America implement tailored automation upgrades—without disrupting production.

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