Health Prediction

Reliable. Efficient. Optimized.

Health Prediction

Lummus Digital enables organizations: to predict and detect process upset or unit shutdown; forecast catalyst residual life and performance, asset performance, and corrosion detection. Consequently, the life of the unit, as a whole, is enhanced.

Click and Explore our Innovative Solutions

Process Upset/ Shutdown/ Off-spec Prediction & Detection

Solution Capability

Prediction, anomaly detection, and avoidance of process system upset/ plant shutdown, product off-spec.

Value Proposition

Catalyst Residual Life & Performance Forecasting

Solution Capability

To predict the residual life of a catalyst; detect anomalies due to poisoning/ variation in the plant; enhance catalyst lifetime; and avoid the reduction in reactor conversion/ unplanned shutdown for catalyst replacement.

Value Proposition

Asset Performance (Rotating & Static)

Solution Capability

Includes predictions, anomaly detection, cause identification, failure management/ shutdown avoidance, and machine life extension of equipment.

Value Proposition

Asset Performance (Control Valves & Instruments)

Solution Capability

It helps in the prediction; anomaly detection/malfunction; and cause identification of control valves and instruments’ health conditions, which include internal parts, using data science, and advanced analytics.

Value Proposition

Corrosion Detection & Image Analytics

Solution Capability

Includes predictions, anomaly detection, cause identification, and failure management/ shutdown avoidance due to leakage and fatigue.

Value Proposition

Asset Performance Prediction Solution employed to Increase Uptime of an Offgas Compressor at a Major Petrochemical Group in India

Business Objective

To increase Uptime of an Offgas Compressor and derive more value from existing expensive assets at a Major Petrochemical Group in India A large Petrochemical Group in India turned to Lummus Digital for solving the various problems associated with its Offgas Compressors and wanted to increase their uptime. By doing so, the Petrochemical Group wanted to derive more value out of their existing expensive assets, improve on reliability, efficiency, and minimize the costs associated with the running of their equipment. The business objective included:
  • Frequent failure of Offgas Compressors leading to unplanned downtime and production loss
  • Costly and time-consuming maintenance activities
  • Hard to identify root cause of the problem – whether process or equipment

Key Solution

  • Anomaly Detection: Using supervised and unsupervised machine learning, anomalies are detected along with other contributing factors that are responsible for the anomalous behavior of the equipment
  • Root Cause Identification: Perform root cause analysis of the contributing factors that lead up to the equipment part/ system and generate corresponding alerts. Further, controlled variable in a data driven Failure Mode and Effect Analysis (FMEA) of equipment and application of Data Science are utilized to ascertain the same.
  • Predicting Failures Ahead of Occurrence: By using deep learning Long Short-Term Memory (LSTM) of the networks, prediction of anomalies and remaining time to failure of equipment is made possible.
  • Best Optimal Solution Recommendation: Conducting ‘What-if analysis’ for failure mitigation/repair management results in recommending the most appropriate solution.

Business Impacts & Outcomes

  • By predicting an early compressor failure, way ahead of its occurrence, the Solution helped the Petrochemical Group steer clear of unplanned shutdown and avoid production loss amounting to an Avg. $1.2 Million per year
  • Reduction in maintenance costs : Avg. $0.25 million per year
  • Reduced cost of safety incidents
  • Improved life of equipment as well as reduced labor and spare parts opex

CONTACT US






    Please prove you are human by selecting the Flag.

    * Fields are mandatory