Method of constructing a processing plant
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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 and often requires a trial-and-error approach due to numerous design variables and combinations, lacking efficient optimization methods.
Innovation Solution
A method utilizing multi-objective genetic algorithms and particle swarm optimization to calculate Pareto solutions for design variables like tube bundles, heat transfer tubes, and fan parameters, optimizing installation length, heat transfer area, and power consumption using dynamic programming and computer simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If trial-and-error process is used for designing 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 a computer-based automated optimization system. The computer executes algorithms that automatically evaluate multiple design combinations and identify optimal solutions, substituting human iterative experimentation with computational automation.
Solution Approach 2:
The design system performs self-optimization by automatically evaluating design parameters and selecting optimal configurations without requiring manual intervention. The computer-based system independently conducts the optimization process, reducing reliance on designer experience and manual trial-and-error efforts.
2Manufacturing precision
If multiple design variables are considered for ACHE structure, then design optimization capability is improved, but design complexity increases
Solution Approach 1:
The patent substitutes complex manual analysis of multiple design variables with a computer-based automated optimization system. The computer efficiently handles the complexity of evaluating numerous design parameters simultaneously, transforming a manually intractable problem into a computationally manageable task.
Solution Approach 2:
The system systematically varies multiple design parameters (number of ACHEs, installation area, tube bundle configurations, fan specifications) to explore the design space. By automating parameter variation and evaluation, the system manages complexity while achieving comprehensive optimization across all variables.
3Reliability
If number of ACHEs and installation area are increased, then cooling capability is improved, but plant cost and space occupation increase
Solution Approach 1:
The patent uses computer-based optimization to determine the minimum necessary number of ACHEs and installation area required to achieve the specified cooling capability. The automated system calculates cost-effective configurations by evaluating the relationship between cooling performance and investment costs, identifying optimal points that avoid excessive spending.
Solution Approach 2:
The system optimizes key parameters including the number of ACHE units, installation area, and their spatial arrangement to achieve the required cooling capability at minimum cost. By systematically varying these parameters and evaluating their impact on both performance and cost, the system identifies the most economical configuration that meets cooling requirements.
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 approach enables the determination of optimal design values for air-cooled heat exchanger groups, reducing installation area and cost while maintaining efficient cooling performance, thus streamlining the design process.
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
Implementation Method 4
a fan configured to supply the cooling air
Data Source
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AI summary
Provided is a technology of efficiently designing a heat exchanger group to be installed in a processing plant configured to process fluid to be processed. In a method of designing a heat exchanger group, which is installed in a processing plant (1) configured to process the fluid to be processed and includes a plurality of ACHEs (2), 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 when the plurality of ACHEs are arranged in one row, a total heat transfer area of heat transfer tubes to be included in the heat exchanger group, and total power consumption of fans included in the heat exchanger group are calculated with use of a computer while the variable parameters are being changed.