Predictive Pressure Protection for Flare and Relief Capacity
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Solution Overview
Problem
Current predictive pressure protection systems for petroleum facilities lack a systematic approach to model dynamic interactions between process equipment and disposal systems, making it difficult to predict the ability of relief systems to accommodate emergency releases effectively.
Innovation Solution
A predictive pressure protection system is developed using a computer-implemented method that sectionalizes emergency events, models each part hydraulically, and integrates data from flare and relief valves to simulate and analyze potential impacts on the passive protection layer, linking the basic process control layer with the passive layer for improved prediction and capacity assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a systematic approach to model dynamic interactions between process equipment and disposal systems is implemented, then the ability to predict relief system performance is improved, but the device complexity increases
Solution Approach 1:
The emergency event is divided into multiple time slices or temporal segments, allowing the dynamic modeling process to be broken down into manageable discrete intervals. This segmentation enables the complex continuous problem to be solved through a series of simpler discrete steps, improving prediction accuracy without overwhelming system complexity
Solution Approach 2:
The system performs preliminary data collection and event characterization before the actual emergency occurs. By pre-establishing the modeling framework, collecting baseline data from basic process control systems, and preparing the hydraulic model structure in advance, the system reduces the complexity burden during actual emergency prediction while maintaining high reliability
2Measurement precision
If dynamic load modeling with actual process equipment changes is performed, then the prediction of passive layer performance is improved, but the loss of time increases due to cumbersome exercise
Solution Approach 1:
The system employs periodic or repeated modeling cycles where the hydraulic model is executed multiple times with updated parameters representing different stages of the emergency event. This periodic execution allows the system to capture dynamic changes in process equipment while using efficient computational methods that reduce the time penalty compared to single comprehensive simulations
Solution Approach 2:
The system creates simplified digital representations or copies of the actual process equipment and disposal systems. These virtual models replicate the essential hydraulic characteristics without requiring full physical complexity, enabling rapid prediction of passive layer performance under various emergency scenarios with minimal time investment
3Loss of information
If information from basic process control layer is integrated with passive layer data, then the predictive capability is improved, but the device complexity increases
Solution Approach 1:
The system merges data streams from the basic process control layer with passive layer protection system data into a unified predictive model. By combining active control information (valve positions, flow rates) with passive system characteristics (flare capacity, relief valve settings), the system achieves comprehensive predictive capability while using integration architectures that manage complexity through standardized data interfaces and unified modeling frameworks
Data Source
AI summary
Systems and methods include a computer-implemented method for providing a predictive pressure protection system. Flare sources, performance limits, and relationships between control valves and relief valves are established. A flare simulator is generated using piping isometric drawings. An emergency event is monitored, and information for the emergency event is filtered based on a control valve limit breach. Event start and finish time periods are divided into cases representing smaller time frames. Source max loads are determined for each case, and each case is run through the flare simulator. Flare/relief valve performance indicators are determined based on the source max loads after running each case.