Hydrocracker Abnormal Operation Detection via Regression Models
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Solution Overview
Problem
Existing process control systems in hydrocrackers often fail to detect abnormal operations in a timely manner, leading to suboptimal performance and potential significant costs or damage due to delayed detection of issues such as temperature runaway, which can cause equipment damage, raw material loss, and unexpected downtime.
Innovation Solution
A method and system that utilize configurable regression models to predict abnormal operations in hydrocrackers by analyzing temperature difference variables and load variables, generating predictions, and detecting deviations from these predictions to alert operators before significant issues arise, thereby preventing abnormal situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional process control systems are used to monitor hydrocrackers, then the system structure is simple and easy to operate, but abnormal operations are not detected in a timely manner leading to delayed response
Solution Approach 1:
The system performs preliminary actions by collecting historical process data and generating prediction models before abnormal conditions occur. The prediction models are trained on normal operating data to establish baseline expectations, enabling the system to detect deviations before they become significant problems.
Solution Approach 2:
The patent introduces prediction models as intermediary elements between raw process data and abnormal condition detection. These models act as mediators that translate complex process variables into predictive indicators, improving detection reliability without requiring direct complex monitoring of all process parameters.
2Measurement precision
If prediction models are implemented to detect abnormal operations, then detection accuracy improves, but the complexity of the system increases
Solution Approach 1:
The system changes parameters by transforming raw process data into predicted values using multiple regression models. Different prediction models are applied to different process variables, and the system monitors deviations between actual and predicted values to detect abnormalities with high precision.
Solution Approach 2:
The patent segments the monitoring system into multiple independent prediction models, each handling specific process variables. This segmentation allows the system to achieve high detection accuracy for different parameters independently while maintaining manageable system complexity through modular architecture.
3Reliability
If multiple regression models are used to predict process variables, then detection reliability improves, but computational requirements and system complexity increase
Solution Approach 1:
The system applies partial action by using multiple regression models selectively for different process variables rather than applying complex models to all parameters. This approach achieves sufficient prediction reliability for critical variables while minimizing unnecessary computational energy consumption on less critical parameters.
Data Source
AI summary
A system and method for detecting abnormal operation of a hydrocracker includes a hydrocracker model. The model may be configurable to include one or more regression models corresponding to different operating regions of the portion of the hydrocracker. The system and method may also determine if a monitored temperature difference variable deviates significantly from the temperature difference variable predicted by the model. If there is a significant deviation, this may indicate an abnormal operation.