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Tag: Predictive AI

  • Predictive AI Model For Crop Nutrient Needs

    Predictive AI Model For Crop Nutrient Needs

    Predictive AI Model For Crop Nutrient Needs

    Predictive AI Model For Crop Nutrient Needs

    Overview

    We worked with Wesfarmers Chemicals, Energy & Fertilisers to predict crop nutrient uptake needs so fertilisation can be scheduled promptly. Our predictive regression models achieved over 85 percent accuracy, meeting the 80 percent deployment threshold and aligning with lab result accuracy ranges of 80 to 90 percent.

    Problem

    Lab analyses of plant and soil samples did not always arrive in time to act optimally, given changing weather and varying crop needs.

    Solution

    • Predictive models: Developed regression models that estimate nutrient uptake needs with more than 85 percent accuracy.
    • Deployment-ready: Designed to meet a minimum 80 percent accuracy requirement, comparable to lab result accuracy of 80 to 90 percent, so estimates are available as soon as samples are taken.

    Result

    • Model accuracy: Over 85 percent for nutrient uptake estimates.
    • Benchmark: Within the 80 to 90 percent lab result accuracy range and above the 80 percent deployment threshold.
    • Operational impact: Earlier estimates enable timely fertilisation aligned to conditions, reducing risk of over or under fertilisation.

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  • Proactive Centrifuge Maintenance

    Proactive Centrifuge Maintenance

    Proactive Centrifuge Maintenance

    Proactive Centrifuge Maintenance

    Overview

    We worked with Wesfarmers Chemicals, Energy & Fertilisers to predict upcoming centrifuge vibration levels using a machine learning model trained on historical sensor data, enabling timely washes and reducing wear.

    Problem

    Material build-up on centrifuge walls increases vibration and accelerates equipment wear. Scheduled periodic washes could not always occur in time to prevent prolonged high vibration, and were wasteful when no spike was imminent.

    Solution

    • Predictive model: Trained on historical data from several centrifuges to forecast near-term vibration levels.
    • Actionable timing: Designed to trigger targeted washes and maintenance when risk is rising, and avoid unnecessary cleans when it is not.

    Result

    • Early warning: Predicted when centrifuges would consistently exceed safe vibration levels up to 1 hour in advance, with over 90% accuracy.
    • Operational gains: Significant reduction in production downtime and water usage from unnecessary washes.

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