Steam and Condensate Balance Optimization Using GAMS Set-Points
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Solution Overview
Problem
Existing energy monitoring systems for facilities, such as those using natural gas, incur high maintenance and project costs due to inefficiencies in real-time energy use optimization, particularly in balancing steam and condensate operations.
Innovation Solution
A computer-implemented method utilizing a General Algebraic Modeling System (GAMS) optimizer, integrated with OSI PI systems, to optimize real-time energy use by executing steam and condensate balancing models and set-point optimization models, reducing costs and improving energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If commercial energy monitoring products are used to monitor equipment at a facility, then monitoring capability is provided, but project and annual maintenance costs are high
Solution Approach 1:
The patent replaces expensive commercial energy monitoring products with a cost-effective software-based optimization platform that uses open-source or internally developed optimization algorithms. This software solution provides the necessary monitoring and optimization capabilities without the high project implementation and annual maintenance costs associated with commercial hardware products.
2Adaptability or versatility
If third-party development environment tools are included with GAMS optimizer, then development flexibility is improved, but system performance and cost efficiency deteriorate
Solution Approach 1:
The patent extracts and removes third-party development environment tools from the GAMS optimizer system, retaining only the essential optimization engine. This streamlined approach eliminates unnecessary bloatware and licensing costs while maintaining core optimization functionality, thereby improving system performance and cost efficiency without significantly compromising development flexibility.
Solution Approach 2:
The patent creates a customized, lightweight version of the GAMS optimizer that copies only the necessary core functionality required for energy optimization. This custom-built optimizer engine replicates the essential mathematical optimization capabilities without including extraneous third-party development tools, achieving better performance and lower costs.
3Adaptability or versatility
If manual optimization implementation is performed, then customization is possible, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements an automated energy optimization platform that performs steam and condensate balance optimization automatically without requiring manual intervention. The system self-adjusts operational parameters, generates optimization recommendations, and manages the optimization process autonomously, eliminating time-consuming manual calculations and reducing operational complexity while maintaining full customization capability through configurable parameters.
Solution Approach 2:
The patent pre-configures the optimization platform with facility-specific parameters, constraints, and objective functions during the implementation phase. This preliminary setup includes defining steam balances, condensate balances, equipment capabilities, and optimization goals, so that once configured, the system can automatically perform real-time optimization without requiring manual intervention for each optimization cycle.
Data Source
AI summary
Systems and methods include a method for optimizing real-time energy use, including balancing steam and condensate. Real-time equipment readings are received from plural pieces of equipment at a facility. A steam and condensate balancing model is executed using the real-time equipment readings. The steam and condensate balancing model uses specialized optimizer engine code to balance steam output and condensate output. A set-point optimization model is executed with selection optimization turned off to identify optimum values for boilers, STGs, letdowns, and deaerators. The set-point optimization model is executed with selection optimization turned on to identify optimized settings for drivers of turbines and motors. Setting updates are provided to the plural pieces of equipment based on the executing.