Sustainability gains using machine learning to optimize fired heater power plant operations.
By BKO AI Engineer RANSAC.
Strategy Proposal: Use ML to optimize fired heater operations.
Energy Consumption: Fired heaters account for ~33% of refinery energy use.
ML vs. CFD Models: ML adapts in real-time, considers more variables.
Variables for ML Modeling: Sensor data, ambient conditions, air quality, air dampers, burner configurations, fuel compositions, etc.
You'll learn how to choose the right database solutions for your tasks, and how to use your new knowledge to build agile, flexible, and high-performing graph-powered applications!
- Predict coking events.
- Ensure NOx and SOx emissions compliance.
- Forecasts wear and potential failures.
Improve energy efficiency, reduce costs, support environmental compliance.
Fired heaters account for approximately 33% of our refinery's total energy consumption. This substantial share underscores the critical importance of optimizing their operation for energy efficiency and cost reduction. ML can dynamically adapt to changing conditions, offering real-time optimization versus the static nature of traditional Computational Fluid Dynamics (CFD) models. Also, incorporates a broader range of variables, including ambient conditions, air quality, burner configurations, and fuel compositions, providing a more holistic approach to optimization. Machine learning models are capable of learning from historical data, leading to continuously improving performance predictions and operational recommendations.
Utilize Databricks for Data Management and Analytics:
Centralized Data Hub: Leverage Databricks to aggregate, process, and analyze data from diverse sources, including sensor data from fired heaters, operational logs, ambient conditions, and fuel characteristics.
Advanced Analytics: Use Databricks’ ML capabilities to develop, train, and refine predictive models for optimizing fired heater operations.
Real-time Data Processing: Implement Databricks’ real-time analytics to dynamically adjust operational parameters, enhancing responsiveness to changing conditions.
Adopt Digital Twins for Simulation and Prediction:
Virtual Replication: Develop Digital Twins of the fired heaters to simulate their physical counterparts in a virtual environment. This allows for detailed analysis of how different variables affect performance without impacting actual operations.
Predictive Modeling: Use Digital Twins to test the efficacy of different operational strategies under various scenarios, including changes in fuel composition, burner configurations, and environmental conditions.
Operational Optimization: Integrate Digital Twins with ML models developed in Databricks to predict outcomes like energy efficiency, emission levels, and the likelihood of coking events. This integration enables the identification of the most efficient operational settings.
Maintenance and Downtime Reduction: Utilize Digital Twins for predictive maintenance, forecasting wear and potential failures before they occur, thereby reducing unscheduled downtime and extending equipment lifespan.
Integration for Enhanced Decision-Making:
Feedback Loop: Establish a continuous feedback loop between the Digital Twins and Databricks’ ML models to constantly update and refine predictions based on new data and outcomes. This ensures that the optimization strategies evolve with changing operational realities.
Dashboard and Reporting: Implement a comprehensive dashboard within Databricks to visualize key performance indicators (KPIs), operational metrics, and predictions from Digital Twins. This aids in making informed decisions quickly.