Hydrocracker Feed Nitrogen Estimation for Early Temperature Optimization
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Solution Overview
Problem
Current methods for nitrogen measurement in hydrocracker reactor feeds are inadequate, leading to delayed optimization, increased costs, and reduced refinery capacity due to nitrogen poisoning, as they rely on labor-intensive laboratory analyses and real-time measurement devices that are costly and prone to errors.
Innovation Solution
A system that uses machine learning and artificial intelligence to estimate potential nitrogen amounts in the reactor feed by analyzing process variables and refinery raw material properties, allowing for pre-optimization and suggesting optimum temperature values at least 5 hours in advance, thereby preventing catalyzer poisoning and reducing production losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laboratory analysis methods are used to measure nitrogen in hydrocracker reactor feed, then measurement accuracy is improved, but measurement time delay increases and productivity decreases
Solution Approach 1:
The system performs preliminary nitrogen estimation using machine learning models trained on historical data and process variables. By continuously analyzing feedstock properties and operational parameters, the system predicts nitrogen content in advance, enabling proactive reactor optimization before nitrogen poisoning occurs, thus eliminating the time delay inherent in traditional laboratory analysis methods
Solution Approach 2:
The patent replaces the mechanical/chemical laboratory analysis system with an information-processing system based on machine learning and artificial intelligence. The neural network model processes operational data and process variables to estimate nitrogen content, substituting physical laboratory procedures with computational analysis that provides rapid, real-time predictions without sacrificing accuracy
2Productivity
If real-time nitrogen measurement devices are deployed, then productivity is improved, but device complexity and cost increase
Solution Approach 1:
The machine learning system serves multiple functions: it estimates nitrogen content, predicts potential nitrogen poisoning events, suggests optimization strategies, and provides early warnings. This multi-functional approach replaces the need for dedicated real-time measurement hardware, achieving high productivity benefits while avoiding the complexity and cost of specialized measurement devices
Solution Approach 2:
The system uses existing process variables and operational data as intermediaries to infer nitrogen content indirectly. Rather than deploying complex measurement devices that directly measure nitrogen, the machine learning model uses readily available data from the control system as intermediaries to calculate nitrogen estimates, thereby achieving real-time monitoring without complex hardware
3Device complexity
If traditional nitrogen monitoring methods are used, then device complexity is reduced, but harmful effects from nitrogen poisoning increase
Solution Approach 1:
The system applies preliminary anti-action by continuously monitoring process variables and predicting nitrogen content before nitrogen poisoning occurs. The machine learning model provides early warnings and suggests preventive optimization measures, allowing operators to take corrective action in advance to prevent the harmful effects of nitrogen poisoning on catalyst activity and refinery capacity
Solution Approach 2:
The system implements continuous feedback by analyzing operational data, comparing predicted nitrogen levels against safe thresholds, and providing real-time recommendations for reactor optimization. This closed-loop feedback mechanism enables proactive management of nitrogen content, preventing harmful effects before they manifest, while maintaining simple device architecture based on existing sensors and computational models
Data Source
AI summary
The present invention relates to an estimation system and method of nitrogen that enables estimation of the potential nitrogen amount present in reactor feed minimum 5 hours beforehand by analyzing the process variables and refinery raw material properties. The invention is particularly related to a nitrogen estimation method that provides the required pre-optimization in the reactor according to the pre-estimated nitrogen values and suggests optimum temperature values for reactor optimization by analyzing the correlation between nitrogen and the reactor variables.