Robust optimization method and robust optimization device for pollutant emissions

By using a sparse chaotic polynomial model and turbulent combustion coupling technology, the uncertainty of NOx and CO emissions in industrial pyrolysis furnaces was solved, a robust optimization model was established, and the quantification accuracy and optimization efficiency of pollutant emissions were improved.

CN116341247BActive Publication Date: 2026-05-29EAST CHINA UNIV OF SCI & TECH
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Patent Information

Application Number
CN202310309197.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-27
Publication Date
2026-05-29
Estimated Expiration
2043-03-27

AI Technical Summary

Technical Problem

Existing technologies cannot effectively address the uncertainties in NOx and CO emissions from industrial pyrolysis furnaces. Traditional CFD simulations cannot account for uncertainties in actual production, leading to unreliable design optimization. Furthermore, the classic UQ method and PCE cannot efficiently handle large-scale models.

Method used

A sparse chaotic polynomial model combined with a turbulent combustion coupling model was adopted. Data was obtained through Latin hypercube sampling, a sparse chaotic polynomial model was constructed, Legendre polynomials were introduced, and a robust optimization model was established to optimize pollutant emissions.

Benefits of technology

It improves the accuracy and efficiency of uncertainty quantification in large-scale numerical models, achieves robust optimization of pollutant emissions, and enhances the reliability and efficiency of design optimization.

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Abstract

The application discloses a robust optimization method and device for pollutant emission. The robust optimization method comprises the following steps: obtaining sampling data of uncertain input variables of a cracking furnace; performing numerical simulation on the sampling data via a turbulent combustion coupling model to obtain a training data set; constructing a sparse chaotic polynomial model according to the uncertain input variables, and training the sparse chaotic polynomial model via the training data set to obtain a first uncertain quantification model; introducing deterministic optimization variables of the cracking furnace into the first uncertain quantification model, and obtaining a second uncertain quantification model dependent on the deterministic optimization variables based on a Legendre polynomial; and performing robust optimization on the deterministic optimization variables of the pollutant emission via the second uncertain quantification model. The application can solve the uncertain quantification precision and efficiency problem of a large-scale numerical model, establish a continuous robust optimization model, and greatly improve the optimization efficiency.
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