Air-Cooled Heat Exchanger Group Layout Using Pareto Optimization
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Solution Overview
Problem
The design of air-cooled heat exchanger groups in processing plants, such as LNG and petroleum refining plants, is complex due to numerous design variables and requires a trial-and-error approach, lacking an optimized method to determine the number and arrangement of heat exchangers and fans for efficient cooling and power consumption.
Innovation Solution
A method using multi-objective genetic algorithms or particle swarm optimization to calculate Pareto solutions for installation length, total heat transfer area, and total power consumption of air-cooled heat exchangers, considering design variables like tube bundle and fan parameters, to achieve optimal design values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If trial-and-error process is used to design ACHE group, then design flexibility is maintained, but design time and effort increase significantly
Solution Approach 1:
The patent replaces the manual trial-and-error mechanical design process with computer-based automated optimization algorithms. The system uses software to automatically evaluate multiple design scenarios and determine optimal configurations, substituting human iterative work with computational automation while maintaining design flexibility through programmable parameters.
Solution Approach 2:
The invention systematically varies key design parameters such as the number of ACHEs, installation area, and cooling capability to explore the design space. By programmatically changing these parameters and evaluating their effects on multiple objectives, the system efficiently identifies optimal configurations without manual trial-and-error.
2Manufacturing precision
If multiple design variables are considered for ACHE structure, then design optimization is improved, but design complexity increases
Solution Approach 1:
The patent segments the complex design problem into distinct modules: number of ACHEs determination, installation area calculation, cooling capability assessment, and fan configuration. Each module can be independently optimized and evaluated, making the overall complex problem manageable through systematic decomposition.
Solution Approach 2:
The system manages design complexity by systematically varying and evaluating multiple parameters simultaneously using computational methods. Rather than manually managing each parameter, the computer automatically explores parameter spaces and identifies optimal combinations, transforming complexity into a structured computational process.
3Productivity
If computer-based optimization is used for ACHE group design, then design efficiency is improved, but calculation complexity increases
Solution Approach 1:
The patent replaces manual design calculations with computer-based automated optimization algorithms. The system uses software to automatically evaluate multiple design scenarios and determine optimal configurations, substituting human iterative work with computational automation while maintaining design flexibility through programmable parameters.
Solution Approach 2:
The invention introduces an intermediary computational system that acts as a mediator between design requirements and optimal solutions. This software intermediary automatically processes design parameters, evaluates performance metrics, and generates optimized configurations, simplifying the overall process despite internal computational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enables the calculation of optimal design values for air-cooled heat exchanger groups, reducing the complexity of design and improving efficiency by determining the best combinations of installation length, heat transfer area, and power consumption.
Implementation Method 1
The Pareto solutions are calculated by a multi-objective genetic algorithm or a multi-objective particle swarm optimization method.
Implementation Method 2
The Pareto solutions are calculated by a multi-objective genetic algorithm or a multi-objective particle swarm optimization method.
Implementation Method 3
an air cooled heat exchanger (ACHE) is a kind of heat exchanger, which is configured to supply cooling air to a plurality of tubes (heat transfer tubes) so as to cool fluid to be cooled flowing through the tubes
Data Source
AI summary
Provided is a method of designing a heat exchanger group being installed in a processing plant and having multiple ACHEs. In a first step, at least one design variable relating to ACHE design and the number of installed ACHEs are set as variable parameters, and a variable range and a change unit of each of the variable parameters are set. In a second step, a design value of the ACHE, which includes a value of a design variable non-selected as the variable parameter, is set. In a third step, Pareto solutions for at least two objective functions selected from an objective function group consisting of an installation length of the heat exchanger group, a total heat transfer area of heat transfer tubes, and total power consumption of fans are calculated by using a computer while the variable parameter are changed.