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Application of Visual Recognition Based on BP Neural Network in Architectural Design Optimization. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3351196. [PMID: 36211004 PMCID: PMC9546661 DOI: 10.1155/2022/3351196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/30/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022]
Abstract
In order to establish the mapping relationship between architectural design parameters and building performance and optimize architectural design parameters, an architectural design optimization method based on BP neural network is proposed. The selected main design parameters of building ventilation include spacing coefficient, air outlet area, and height from the bottom of the window sill to the ground. Take the comprehensive performance of building ventilation design as the main optimization objective to optimize the building design. First, nine groups of samples of building optimization design are obtained through uniform experimental design. Then, based on the architectural design sample data obtained by BP neural network training, the mapping relationship between architectural design parameters and building performance is established, and based on this mapping, the optimal design parameters of the building are calculated. The research results have a certain reference value for architectural design optimization.
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Tang C, Zhang J, Wang W, Sun X, Pan W. Experimental investigation of the strength characteristics of low‐temperature fouling containing ammonium bisulfate in boilers. ASIA-PAC J CHEM ENG 2022. [DOI: 10.1002/apj.2826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Congwei Tang
- College of Energy and Mechanical Engineering Shanghai University of Electric Power Shanghai China
| | - Jiakai Zhang
- College of Energy and Mechanical Engineering Shanghai University of Electric Power Shanghai China
- Key Laboratory of Clean Power Generation and Environmental Protection Technology in Mechanical Industry Shanghai China
- Shanghai Engineering Research Center of Power Generation Environment Protection Shanghai China
| | - Wenhuan Wang
- College of Energy and Mechanical Engineering Shanghai University of Electric Power Shanghai China
- Key Laboratory of Clean Power Generation and Environmental Protection Technology in Mechanical Industry Shanghai China
- Shanghai Engineering Research Center of Power Generation Environment Protection Shanghai China
| | - Xiaoli Sun
- College of Energy and Mechanical Engineering Shanghai University of Electric Power Shanghai China
| | - Weiguo Pan
- College of Energy and Mechanical Engineering Shanghai University of Electric Power Shanghai China
- Key Laboratory of Clean Power Generation and Environmental Protection Technology in Mechanical Industry Shanghai China
- Shanghai Engineering Research Center of Power Generation Environment Protection Shanghai China
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