Real-Time Plant Diagnostics for Refinery Process Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional diagnostic systems for monitoring refinery operations lack the ability to provide real-time, direct, and specific analysis, making it difficult for plant operators to promptly identify and correct faulty conditions or compositional measurements outside predetermined ranges, leading to increased operational expenses and time consumption.
Innovation Solution
A method and system that utilize real-time plant operation information to generate a plant process model, enabling direct and specific analysis and optimization of process units, using web-based platforms for data collection and processing, and automatic generation of diagnostic reports to identify and bridge performance gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional diagnostic systems are used to monitor refinery operations, then the system structure is simple, but the diagnostic speed and real-time analysis capability are slow
Solution Approach 1:
The diagnostic system is segmented into multiple functional modules including data collection module, data processing module, diagnostic analysis module, and report generation module. Each module handles specific tasks independently, enabling parallel processing and faster diagnostic speed without overwhelming system complexity.
Solution Approach 2:
A web-based platform serves as an intermediary between the complex diagnostic processing system and the user interface. This intermediary layer handles data transmission, processing coordination, and presentation, allowing the backend to operate at high speed while maintaining a simple user-facing interface.
2Productivity
If real-time data collection and processing is implemented, then diagnostic efficiency is improved, but the complexity of data management increases
Solution Approach 1:
The web-based platform performs multiple functions including data collection from various sources, data validation, processing coordination, diagnostic result aggregation, and report generation. This multi-functional approach consolidates data management complexity into a single universal system rather than requiring separate complex systems for each function.
Solution Approach 2:
The system automatically collects data from plant operations, processes it through predefined algorithms, generates diagnostic reports, and delivers them to operators without manual intervention. This self-service capability maintains high diagnostic efficiency while reducing the operational complexity of data management.
3Loss of time
If conventional periodic data review is used, then the system complexity is low, but the time consumption and operational expenses increase
Solution Approach 1:
The system implements continuous real-time data collection and processing instead of periodic reviews. Data is continuously monitored, analyzed, and processed as it becomes available, eliminating gaps in diagnostic capability and reducing the time required to identify and respond to operational issues.
Solution Approach 2:
The system provides immediate feedback to operators through automated diagnostic reports and alerts when anomalies are detected. This continuous feedback loop enables rapid response to operational issues, significantly reducing the time loss associated with delayed detection and manual analysis.
4Measurement precision
If direct and specific diagnostic analysis is implemented, then the ability to identify root causes is improved, but the complexity of analysis mechanisms increases
Solution Approach 1:
The diagnostic analysis focuses on specific local conditions and parameters relevant to each process unit rather than attempting comprehensive analysis of the entire plant simultaneously. Each diagnostic module is specialized for its specific function, providing high precision for local conditions without requiring equally complex mechanisms across the entire system.
Data Source
AI summary
A plant diagnostic system and method for plant process control and analysis comprising one or more sensors configured to collect and report compositional operation information of the equipment in the plant or refinery in real-time. At least one of the one or more sensors may be selected from a group of GC, GCxGC, micro GC, micro GCxGC, or combinations thereof. The diagnostic system may comprise a detection platform, an analysis platform, a visualization platform, and/or an alert platform.