LNG Cascade Refrigeration Load Optimization Using Model-Based Control
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Solution Overview
Problem
Liquefied natural gas facilities face challenges in optimizing operations due to numerous process variables, changing ambient conditions, and feed gas composition, leading to sub-optimal performance and monetary losses.
Innovation Solution
An apparatus and method utilizing a processor and memory to adjust manipulated variables in a cascade liquefied natural gas facility, optimizing refrigeration system loads and processing rates through models and economic optimization techniques to maintain controlled variables within defined limits, thereby maximizing LNG and NGL production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human operators manually adjust operating variables periodically, then operational simplicity is maintained, but production efficiency deteriorates due to sub-optimal operation
Solution Approach 1:
The control system performs self-adjustment by automatically monitoring process variables and manipulating control variables to optimize LNG production without requiring continuous human intervention. The system serves itself by making real-time decisions based on measured process conditions.
Solution Approach 2:
The patent replaces manual mechanical adjustment operations with an automated control system that uses sensors, processors, and actuators. The control system substitutes human operators by automatically determining optimal operating conditions and adjusting process parameters in real-time.
2Loss of information
If the number of monitored process variables increases to capture all facility conditions, then measurement completeness improves, but operator ability to respond deteriorates due to information overload
Solution Approach 1:
The control system extracts and isolates the critical control variables from the large set of monitored process variables. By identifying and focusing on key manipulated variables that directly impact LNG production, the system separates essential control information from redundant data, enabling effective automated control without operator overload.
Solution Approach 2:
The control system performs multiple functions simultaneously: it monitors all process variables, identifies optimal operating conditions, determines control variable adjustments, and executes control actions. This multi-functional approach consolidates numerous operator tasks into a single automated system.
3Device complexity
If manual periodic adjustments are used to simplify control, then system complexity is reduced, but production loss increases due to sub-optimal operation
Solution Approach 1:
The control system dynamically adjusts operating conditions in real-time based on changing process variables and ambient conditions. Rather than static periodic adjustments, the system continuously adapts control variables to maintain optimal LNG production, transforming the control approach from static to dynamic operation.
Solution Approach 2:
The system implements closed-loop feedback by continuously measuring process variables, comparing actual performance against optimal conditions, and automatically adjusting control variables to correct deviations. This feedback mechanism ensures optimal operation by responding to real-time process changes without requiring complex manual intervention.
Data Source
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Figure 3A
AI summary
At least one model (214) is associated with one or more manipulated variables and one or more controlled variables, which are associated with a cascade liquefied natural gas facility (100). Adjustments to the one or more manipulated variables are made using the at least one model (214) to maintain the one or more controlled variables within defined limits. For example, a controlled variable may identify an overall load placed on multiple refrigeration systems in the facility. The one or more manipulated variables may be adjusted to increase the overall load placed on the refrigeration systems. The overall load can be determined by identifying a maximum of: a projected feed gas rate to operate a propane refrigeration system (118-128) at maximum load, a projected feed gas rate to operate an ethylene or ethane refrigeration system (130-140) at maximum load, and a projected feed gas rate to operate a methane refrigeration system (144-150) at maximum load.