Method and system for production optimization
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Solution Overview
Problem
Existing methods for optimizing liquefied natural gas (LNG) production in LNG plants fail to effectively consider multiple variables concurrently, leading to inadequate or delayed responses to changes in process variables and ambient conditions, resulting in suboptimal production.
Innovation Solution
An advanced process control system (APC) with a solution vector and production optimization (RTPO) system that adjusts manipulated variables based on dependencies indicated by models, predicting and analyzing correlations between control and manipulated variables to optimize LNG production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional control methods are used to optimize LNG production, then the control system is simple and easy to operate, but the system cannot effectively consider multiple variables concurrently, leading to inadequate or delayed responses to changes in process variables and ambient conditions
Solution Approach 1:
The control system is segmented into multiple functional modules: an Advanced Process Control (APC) module that handles real-time control decisions, a Real Time Optimization (RTO) module that performs production optimization, and a machine learning module that captures variable correlations. Each module operates with specific responsibilities, allowing the complex system to be managed through divided functionality while achieving comprehensive multi-variable optimization.
Solution Approach 2:
A gain matrix serves as an intermediary data structure that captures and quantifies the relationships between manipulated variables and control variables. This matrix acts as a mediator that translates complex process dynamics into actionable control signals, enabling the system to respond effectively to multiple variables simultaneously without requiring direct complex modeling of all interactions.
2Reliability
If traditional control methods are used, then the control system is simple, but the response to changes in process variables and ambient conditions is inadequate or delayed
Solution Approach 1:
The system performs preliminary actions by pre-calculating gain matrices that capture the relationships between variables before control decisions are needed. The APC and RTO modules pre-process data and establish control strategies in advance, enabling the system to respond rapidly to changes without delayed computation. This preliminary preparation ensures adequate response to process variations and ambient conditions.
Solution Approach 2:
The control system implements continuous feedback loops where the APC module receives real-time process data, compares actual values with target values, and adjusts manipulated variables accordingly. The RTO module provides higher-level feedback by optimizing production targets based on changing conditions. This multi-layered feedback mechanism ensures adequate and timely response to process variable changes while maintaining system reliability.
3Productivity
If multiple variables are considered concurrently for optimization, then production efficiency improves, but the control system complexity increases significantly
Solution Approach 1:
The control system is segmented into multiple functional modules: an Advanced Process Control (APC) module that handles real-time control decisions, a Real Time Optimization (RTO) module that performs production optimization, and a machine learning module that captures variable correlations. Each module operates with specific responsibilities, allowing the complex system to be managed through divided functionality while achieving comprehensive multi-variable optimization.
Solution Approach 2:
The system transforms complex multi-variable optimization into a series of parameter adjustments using gain matrices. By changing the representation of variable relationships into standardized gain parameters, the system can efficiently handle multiple variables concurrently. The APC and RTO modules adjust these parameters in real-time based on process conditions, enabling production efficiency improvement without proportionally increasing system complexity.
4Productivity
If a comprehensive optimization system is implemented to consider multiple variables, then production optimization improves, but the system requires sophisticated modeling and data analysis capabilities
Solution Approach 1:
A gain matrix serves as an intermediary data structure that captures and quantifies the relationships between manipulated variables and control variables. This matrix acts as a mediator that translates complex process dynamics into actionable control signals, enabling the system to respond effectively to multiple variables simultaneously without requiring direct complex modeling of all interactions.
Solution Approach 2:
The system replaces traditional mechanical control approaches with data-driven modeling and machine learning techniques. Instead of relying on complex physical models of process dynamics, the system uses statistical correlations captured from historical data through the gain matrix. This substitution of data analytics for mechanical modeling simplifies the overall system architecture while maintaining comprehensive optimization capabilities.
Data Source
AI summary
The disclosure provides a method and system for optimizing production of a natural gas liquefaction process, the method comprising the steps of: selecting at least one manipulated variable (MV) for controlling the liquefaction process; selecting at least one control variable (CV), the at least one control variable at least comprising liquefied natural gas (LNG) throughput; providing at least one model, each model providing a dependency of the at least one control variable (CV) on the at least one manipulated variable (MV); using the at least one model to estimate LNG throughput for at least one of the manipulated variables (MV); obtaining process data from the liquefaction process, the process data at least including observed values of LNG throughput; creating a gain matrix based on said interdependencies; and using the gain matrix to optimize a process control system of the liquefaction process.