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Multi-objective optimization support tool Product List

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[Case Study] Optimization of Hydrocarbon Reforming Reaction Process

[Case Study] Optimization of Hydrocarbon Reforming Reaction Process

Introducing an optimization case that performed comprehensive result evaluation with rich post-processing features!

We will introduce a case where our "modeFRONTIER" was applied to optimize the hydrocarbon reforming reaction process. By coupling with the detailed chemical reaction analysis program CHEMKIN, we varied the pressure P, preheating temperature Tin, amount of oxygen, and amount of water vapor to explore the operating point where the H2 concentration is maximized and the CH4 and CO concentrations are minimized. This problem is highly nonlinear, and as a result of multi-objective optimization, it was found that the set of optimal solutions becomes discrete between CO and CH4. Even in such cases, by utilizing the rich post-processing capabilities of modeFRONTIER, comprehensive result evaluation becomes possible. 【Case Study】 ■ Software Used: modeFRONTIER, CHEMKIN ■ Objective: To explore the operating point where the H2 concentration is maximized and the CH4 and CO concentrations are minimized ■ Result: It was found that the set of optimal solutions becomes discrete between CO and CH4 ■ Effect: Comprehensive result evaluation is possible by using the rich post-processing capabilities *For more details, please refer to the external link or feel free to contact us.

  • Company：IDAJ
  • Price：Other
  • Other analyses
  • simulator

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[Case Study] Automatic Fitting of 1D Engine Analysis Tool

[Case Study] Automatic Fitting of 1D Engine Analysis Tool

Strict fitting completed in a short time! Introducing a case of multi-purpose optimization.

We would like to introduce a case study where we performed automatic fitting of a one-dimensional engine analysis tool using our "modeFRONTIER." We set design variables for parts that could not be accurately read from 2D drawings into 3D shapes, as well as parameters that are difficult to measure in experiments (a total of 14 variables). We established two objective functions: one for minimizing the squared difference between measured and calculated values, and another for expressing variability as standard deviation, with the goal of minimizing both. As a result, a fitting that is difficult to achieve through experience alone was completed in a short time. 【Case Study】 ■ Software Used: modeFRONTIER, GT-POWER ■ Challenge: Fitting between measured and calculated values ■ Method: Executed multi-objective optimization with a total of two objective functions ■ Result: Precise fitting completed in a short time *For more details, please refer to the PDF document or feel free to contact us.

  • Company：IDAJ
  • Price：Other
  • Other analyses
  • simulator

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