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Improving Refinery Safety and Efficiency with AI at the Edge

Historically, Texmark's operations and maintenance teams monitored equipment manually and through wired sensors at a significant cost of dollars and man-hours each year in inspecting equipment on site.

Wanting to act quickly and execute on this multi-year plan, Texmark turned to Deloitte to deliver an end-to-end IIoT implementation in late 2017. Over the course of the next two years, Deloitte coordinated the following partners for the project:

1. National Instruments to outfit pumps with sensors that collect operational data

2. Hewlett Packard Enterprises to enable deployment at the edge

3. OSIsoft PI to aggregate the sensor data from the NI sources

4. SparkCognition™ to predict impending pump failures

5.Flowserve to help develop the model and data flow architecture 6. PTC ThingWorx to create a mobile-supported interface

  • SparkCognition
    SparkCognition is a global leader in cognitive computing analytics. A highly awarded company recognized for cutting-edge technology, SparkCognition develops AI-Powered cyber-physical software for the safety, security, and reliability of IT, OT, and the IoT.The company's technology is capable of harnessing real-time sensor data and learning from it continuously, allowing for more accurate risk mitigation and prevention policies to intervene and avert disasters. SparkCognition???s cognitive software, DeepArmor, is utilized to prevent and detect Malware with a high level of accuracy and efficacy by using advanced Machine Learning and Artificial Intelligence.
  • Chemicals
  • Maintenance
  • Predictive Maintenance
    Predictive maintenance is a technique that uses condition-monitoring sensors and Machine Learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive Maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on Predictive Maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using Predictive Maintenance and Predictive Maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.
  • Texmark Chemicals Inc., a leading contract manufacturer (toller) of speciality petrochemicals that owns and operates a plant near Houston, Texas.

  • Installation of the data acquisition hardware and edge infrastructure was completed in spring of 2018. For the first time, the Texmark operations team was able to monitor live pump behaviour from wireless sensors and react to impending failures. SparkCognitionTM could not model the asset behaviour in its SparkPredict® solution immediately, as machine learning models depend on rich, historical data, but within two months of implementation, SparkPredict was able to create accurate asset models.

    Once enough data was collected, SparkCognitionTM data scientists built a model of the pump operating behaviour based on features derived from accelerometer, pressure, and temperature data. They initially began with 125 features, and with input from Texmark’s operations and maintenance team, reduced them down to the 70 features most relevant to the model’s performance. With a still-limited data set and no labelled failure data, the data scientists used an unsupervised approach to train the model. Unsupervised learning finds patterns in unlabeled data so that a subject matter expert can interpret and label them appropriately. In total, the data cleansing, feature engineering, model building, and deployment took just over one month to complete. Another SparkCognition partner, leading pump manufacturer Flowserve, was consulted for validation of the approach.

  • Mature (technology has been on the market for > 5 years)
  • Impact #1
    [Process Optimization - Predictive Maintenance]

    New solution identifies precursors to pump failure, allowing operators to avert accidents and plan repairs ahead of time.

    Impact #2
    [Data Management - Data Analysis]

    For each operating state that was identified, SparkPredict surfaced the main contributing features that were most highly correlated. This way, SMEs can better understand why and how the behavior has changed and take steps to minimize the negative impacts on flow and overall efficiency.

    Impact #3
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