OBJECTIVE

Predictive strategy for operational efficiency at Bunge plants

Bunge International Limited is a global leader in the agri-food sector and a major producer, handler, processor and trader of soybeans, canola, sunflower, corn, wheat and other crops.

Preditec has been collaborating with Bunge since 2012 in the implementation and operation of the predictive strategy within maintenance. The plan includes Bunge’s assets in four of its plants in Spain, covering about 1300 rotating assets. The implementation phase begins with the analysis of criticality and failure modes to define the techniques to be used and the frequency with which they should be measured. This work allows establishing routes and setting initial alert and alarm levels, which are adjusted over time according to the history generated, thus increasing its reliability.

Execution Date:

2012

Location:

Spain

Sector:

Agri-food

HOW WE MADE IT HAPPEN

Strategic application of predictive techniques to maximize asset reliability

Vibration measurement and visual inspection, ultrasonic inspection, ESA (Electric Signature Analysis) and MCA (Motor Circuit Analysis) are performed as routine tasks in the plant. Ultrasonic inspection is used for machines with slow speeds, as a complement to vibration measurement, and electrical measurement techniques are used on the most critical motors. ESA tests are performed with the motor energized and MCA tests with the motor de-energized. Another fundamental service is the detection of hexane leaks, a flammable and explosive compound that is the main production consumption for oil extraction.

With the correct application and combination of predictive techniques, we achieve a reliable diagnosis and an improvement in the monitoring of KPIs. We anticipate failures in order to plan corrective works at the next shutdown, which results in fewer unplanned shutdowns, lower cost and duration of repairs. Thus, we have a holistic view of the mechanical and electrical condition of the equipment, generating a history of asset behavior and being able to cross-reference data between similar machines to make decisions based on continuously updated real data. Thanks to these inspections, resources are invested in machines that really need it.

Although this is a variable figure, we can say that within an inspection, 5-10% of the assets require exhaustive follow-up and 10-15% require less follow-up. The rest of the machines, which represent the highest percentage, have a good condition (Green Asset) and can wait until the next inspection.

OUR CONTRIBUTION

What products and services we applied to the project

  • Thermography

  • Vibration transmitters

    Detect anomalies early, improving predictive maintenance and operational efficiency.

  • Accessories and cables

    Our range of accessories and cables is designed to provide you with everything you need for an efficient, safe and durable installation of your predictive maintenance and vibration monitoring systems. Whether you are setting up a new system or need specific spare parts, here you will find high quality and compatible solutions that guarantee the optimal performance of your equipment.

  • UAV components

  • Asset Reliability Practitioner (ARP-L) Reliability Leader

    The Asset Reliability Specialist [ARP-L] "RELIABILITY PROGRAM LEADER" course encompasses not only the technical knowledge, but also the roles necessary to be able to lead teams, communicate frequently and clearly, and have strong budget and project management skills.

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RESULTS AND ACHIEVEMENTS

Definition of criticality criteria

The inspections are documented in the Preconcerto web platform, where the database is generated with the relevant asset information, the status of the plant is updated, KPI’s are tracked, bi-directional plant-analyst communication is enabled and customized reports are generated.

Apart from these inspections with portable equipment, there are assets that require continuous monitoring due to their criticality, the nature of the failures or
their location in inaccessible areas. These are digitization projects where the machine is instrumented (with wired or wireless sensors) and the signals are taken to the monitoring hardware, managing the information remotely from the Asset Web Monitor (AWM) web platform. This tool allows us to centralize information from various sources, parameterize different failure modes, generate machine status with different alarms, plot multivariable trends for supervision and analyze both scalar data and associated graphs.

5-10%

of the assets requires exhaustive monitoring

10-15%

of assets requires less monitoring