Nanoprecise Helps Chemical Manufacturer Avoid €50,000 Equipment Loss & Reduce CO2 Emissions 17,71kg/Year


Summary

A leader in chemical manufacturing, renowned for its commitment to sustainability, partnered with Nanoprecise to gain real-time visibility into equipment health at one of its strategic plants in Spain.

Given the plant’s critical role in European polymer production, even minor equipment inefficiencies could significantly impact profitability, energy use, and sustainability targets.

Within six weeks of deploying Nanoprecise’s Energy-Centric Maintenance (ECM) solution, the system detected abnormal vibration and energy consumption patterns in two refrigeration pump motors — one consuming 20% more energy than expected.

By identifying faults early, the team was able to act proactively, avoiding a €50,000 equipment replacement, cutting €9,422 in annual energy waste, and reducing CO₂ emissions by 17,071 kg per year!

Result: Lower energy waste, extended equipment life, reduced unplanned downtime and a measurable environmental impact.

A technician inspects chemical vials in a lab.

Chemical Manufacturer Profile

A leading European chemical producer specializing in high-quality polymers and advanced materials for global markets. The Spanish facility plays a central role in the company’s European operations, adhering to strict safety, reliability, and sustainability standards.

Maintaining optimal equipment performance is essential to minimize energy use and ensure continuous production across processes such as polymer synthesis, blending, and packaging.

The Challenge

The facility’s reliability team faced recurring challenges in ensuring sustained efficiency and reliability of rotating assets, including refrigeration motors, agitators, filler motors, and vertical pumps.

Unmonitored variations in vibration or energy consumption could:

  • Escalate into unplanned downtime or catastrophic equipment failures.
  • Increase operational costs through excessive energy consumption.
  • Disrupt production schedules and supply chain commitments.

The maintenance team needed a proactive, data-driven approach to detect faults early, optimize maintenance schedules, and align operations with sustainability goals.


Solution Deployed

Nanoprecise implemented it’s Energy-Centric Predictive Maintenance solution, across its most critical assets, including:

  • Refrigeration Motors
  • Agitators
  • Filler Motors
  • Vertical Pumps
  • Post-maintenance diagnostics for structural and alignment issues

The implementation included MachineDoctor™ sensors, with NrgMonitor™ feature for continuous health and energy monitoring, offering:

  • 6-in-1 condition tracking, including triaxial vibration, acoustic emission, temperature, magnetic flux, humidity, and RPM.
  • Automated alerts for early-, mid-, and late-stage fault conditions.
  • Energy monitoring to correlate mechanical faults with energy inefficiency.
  • Cloud-based dashboards for real-time insight and remote diagnostics.

Within six weeks, the system identified two refrigeration pump motors exhibiting abnormal vibrations. One (Pump Motor 410) was consuming 20% more power than normal due to mechanical and hydraulic issues.

Observation & Analysis

Pump Motor Observations:

  • Elevated vibration levels in both velocity and acceleration exceeded standard limits for the motor and pump (Figure 1).
  • Real-time energy data from NrgMonitor confirmed a 20% increase in power consumption and associated CO₂ emissions (Figure 2).
Figure 1: RMS Trend for Pump Motor indicating elevated vibration levels
Figure 2: X Axis Spectrum Motor DE – After Motor Replacement with speed indication
Figure 2: X Axis Spectrum Motor DE – After Motor Replacement with speed indication

Analytical Insights

  • Vibration Spectrum Analysis: Identified a dominant frequency and harmonics for both the motor and pump, along with a secondary frequency linked to vane pass.
  • Acceleration Analysis: Detected a modulated high-frequency sideband, indicating possible rotor bar damage.
  • Acoustic Analysis: Elevated bearing noise levels suggested cavitation within the pump.
  • Time Waveform Analysis: Showed irregular impacts with high acceleration values, confirming mechanical stress and imbalance.

Maintenance Recommendations

  • Schedule immediate inspection and maintenance.
  • Inspect pump internals and vane conditions.
  • Relubricate pump bearings to reduce friction and vibration amplitude.

Fault Modes Identified

  • Schedule immediate inspection and maintenance.
  • Inspect pump internals and vane conditions.
  • Relubricate pump bearings to reduce friction and vibration amplitude.

Key Takeaways:

  • What appeared as a motor imbalance was actually a mounting and structural resonance issue.
  • A system-level, data-driven approach was critical for identifying and correcting the true root cause.
  • Early detection with Nanoprecise allowed for planned maintenance, avoided emergency repairs, and extended equipment life.

Cost Avoidance

  • Prevented a €50,000 motor replacement through early fault detection and timely intervention.

Energy Savings

  • Faulty motor consumed 20% more energy, equating to €1.1/hour in energy loss.
  • Annualized savings of €9,422 for just one motor.
  • ROI exceeded total solution cost within six weeks.

Environmental Impact

  • Reduced 17,071 kg of CO₂ emissions annually, based on Spain’s emission factor of 0.174 kg/kWh.
  • Supported the company’s broader sustainability and carbon reduction goals.

Operational Efficiency

  • Enhanced maintenance planning and reduced reliance on emergency repairs.
  • Automated alerts enabled timely maintenance without disrupting production.
  • Improved reliability of critical equipment supporting continuous operations.

Conclusion

Nanoprecise’s Energy-Centric Predictive Maintenance solution provided the Spanish chemical plant with comprehensive visibility into both mechanical health and energy performance.

By detecting an impending failure in the Pump Motor, the facility:

  • Avoided €50,000 in equipment losses.
  • Recovered €9,422 in annual energy waste.
  • Reduced CO₂ emissions by over 17 tonnes per year.
  • Achieved ROI within six weeks.

The combination of predictive analytics and energy monitoring delivered a rapid ROI, empowering the maintenance team to take proactive, informed action — ensuring long-term operational reliability and sustainability.