Automated Plant Tuning System for Refinery Performance Optimization
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Solution Overview
Problem
Refineries face challenges in optimizing operations due to increasingly complex technologies, reduced workforce experience levels, and changing environmental regulations, leading to performance gaps and increased operational risks, necessitating an improved automated tuning system for monitoring and bridging performance gaps.
Innovation Solution
A method that involves obtaining plant operation information, generating a process model, and using a simulation engine to monitor and optimize performance, with key parameters defined and reconciled to assess fitness, and a threshold value to determine the need for additional tuning, implemented through a web-based computer system utilizing various sensors and laboratory measurements to identify and resolve operational discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tuning systems are implemented to monitor and optimize plant performance, then productivity and efficiency are improved, but device complexity increases
Solution Approach 1:
The system performs self-tuning by automatically comparing simulation model predictions with actual plant measurements and adjusting model parameters without requiring manual intervention from operators. This automation resolves the contradiction by eliminating the need for highly skilled personnel while maintaining optimization capabilities.
Solution Approach 2:
The system continuously monitors plant performance measurements, compares them against simulation model predictions, and uses the discrepancies to automatically adjust model parameters. This closed-loop feedback mechanism enables the system to adapt and optimize plant performance autonomously, improving productivity while managing complexity through algorithmic control.
2Productivity
If complex technologies are deployed to increase production and efficiency, then productivity is improved, but reliability decreases due to increased operational risks
Solution Approach 1:
The system replaces manual operator judgment and experience with automated computational algorithms that objectively compare simulation predictions with actual measurements. This substitution eliminates human error and subjectivity, improving reliability while maintaining the ability to optimize complex production processes.
Solution Approach 2:
The system uses simulation models to predict plant performance under various operating conditions before actual changes are made. By pre-evaluating potential outcomes and identifying optimal settings in advance, the system enables informed decision-making that improves reliability while pursuing productivity gains.
3Device complexity
If manual monitoring and tuning methods are used, then device complexity is reduced, but loss of time increases due to delayed identification of performance gaps
Solution Approach 1:
The system continuously monitors plant performance measurements and automatically compares them against simulation model predictions in real-time, rather than relying on periodic manual reviews. This continuous operation eliminates delays in identifying performance gaps, enabling immediate detection and correction of deviations while keeping the overall system architecture relatively simple.
Data Source
AI summary
A refinery or petrochemical plant may include a fractionation column and related equipment, such as one or more condensers, receivers, reboilers, feed exchangers, and pumps. The equipment may have boundaries or thresholds of operating parameters based on existing limits and/or operating conditions. Illustrative existing limits may include mechanical pressures, temperature limits, hydraulic pressure limits, and operating lives of various components. There may also be relationships between operational parameters related to particular processes. For example, the boundaries on a naphtha reforming reactor inlet temperature may be dependent on a regenerator capacity and hydrogen-to-hydrocarbon ratio, which in turn may be dependent on a recycle compressor capacity. Operational parameters of a final product may be determined based on actual current or historical operation, and implemented in one or more models to determine adjustments for enhanced operational efficiency.