A method for optimal design of structural parameters of biomass boiler economizer
A technology for biomass boilers and structural parameters, applied in the field of big data learning models, can solve problems such as high cost and parameter optimization process assistance, and achieve the effects of reducing optimization costs, optimizing heat exchange efficiency, and improving modeling accuracy.
Active Publication Date: 2022-03-18
ZHEJIANG UNIV +1
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Problems solved by technology
[0005] The purpose of the present invention is to solve the disadvantages of auxiliary and high cost in the optimization process of biomass boiler economizer parameters in the prior art, and to provide a method for optimizing the structural parameters of biomass boiler economizer design
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[0062] In this example, the pillow-shaped plate heat exchanger is proposed as the economizer of the biomass boiler, and the structural parameters of the heat exchanger are optimally designed based on the structural parameter optimization design method of the economizer of the biomass boiler in the aforementioned S1~S3. The specific implementation process is described in detail below.
[0063] like figure 1 As shown, the present invention provides an intelligent optimization method for economizer structural parameters applied to biomass boilers. The steps of the technical solution adopted by the present invention are as follows:
[0064] Step 1. Establish a sample database of heat exchanger operation: obtain historical data on the operation of different heat exchangers in different biomass boiler units under different working conditions, and construct a sample database; each sample in the sample database corresponds to a The input of the sample is the structural parameters of...
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The invention discloses a structural parameter optimization design method of a biomass boiler economizer, which belongs to the field of big data learning models. This invention uses the historical operation data of the biomass boiler economizer to construct a sample database, and establishes a heat exchanger residual self-attention convolution model based on CNN and self-attention mechanism, and realizes multiple optimization tasks through machine learning. The rapid prediction of target parameters, combined with the iterative optimization algorithm, can perform multi-objective optimization of the structural parameters to be optimized in the economizer. Compared with the traditional optimization of all variables of the biomass boiler economizer, the self-attention mechanism can automatically focus on the features of high importance, thereby better optimizing the variables of high importance, making subsequent optimization adjustments convenient and fast. , significantly reducing optimization costs.
Description
Technical field [0001] The invention belongs to the field of big data learning models, and specifically relates to a method for parameter optimization of the economizer structure of a biomass boiler. Background technique [0002] Biomass boilers have developed rapidly in the boiler field in recent years due to their low operating costs, high thermal efficiency and renewable energy. The economizer is a necessary component in the biomass boiler, which can realize the utilization of waste heat of the boiler and save energy. However, in existing biomass boilers, traditional economizers often have performance defects. In particular, the high-concentration flue gas generated during the biomass combustion process contains highly corrosive chemical components, resulting in economizers in the tail flue gas. The coal device produces ash, slagging and even corrosion, which greatly reduces the heat transfer efficiency. Therefore, there is a technical need for improved economizers in b...
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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F111/06
CPCG06F30/27G06N3/08G06F2111/06G06N3/045G06F30/17
Inventor 童水光王海丹童哲铭赵剑云何伟校陈伟
Owner ZHEJIANG UNIV