Abstract
In air cushion furnace, the floating height is a key process parameter which greatly affects the quality and production efficiency of high quality metal strips. However, the floating height is hard to be collected in the complex and abominable industry environment. Furthermore, due to the flow field characteristics, some important process variables are difficult to accurately calculate by traditional mechanism modeling methods. In order to accurately predict the floating height, firstly, a low discrepancy heuristic evolution ELM and ground effect theory based serial hybrid soft sensor model is proposed, which constituted by a mechanism model and two data driven models. Secondly, based on the force equilibrium equation and ground effect theory, the mechanism model is constructed, which describes the relationship between the floating height and the process variables including the jet impact angle. Thirdly, a low discrepancy heuristic evolution ELM is proposed as the data driven model to predict the jet impinging angle. In the data driven model, the novel dual mutation strategies collaboration differential evolution is proposed to guarantee the low discrepancy and physical applicability of data driven model. The effectiveness of the proposed method was validated on the self-developed air cushion experiment platform and got desirable experimental results. The research lays an important foundation for the successful implementation of monitoring and control of the strip floating process.
Highlights
The high quality mental strips, such as aluminum alloy strip, electronic copper strip and thin silicon steel strip, are extensively used in automobile industry, national defense industry and electric power industry [1]
The metal strip is suspended in the air and the desirable quality and high heating efficiency is guaranteed by this special work mode [2]
A low discrepancy heuristic evolution extreme learning machine (ELM) and ground effect theory based serial hybrid soft sensor model is proposed to accurately predict the floating height, which is constituted by a mechanism model and two data driven models
Summary
The high quality mental strips, such as aluminum alloy strip, electronic copper strip and thin silicon steel strip, are extensively used in automobile industry, national defense industry and electric power industry [1]. S. Hou et al.: Low Discrepancy Heuristic Evolution ELM and Ground Effect Theory-Based Serial Hybrid Soft Sensor Model and working safety [3]. The jet impinging angle is a key variable for the prediction of floating height and is too difficult to be described by mechanism modeling method In such cases, the serial hybrid model is suitable for the prediction of the floating height in the air cushion furnace. A serial hybrid soft sensor model is proposed for predicting the strips floating height in air cushion furnace. A low discrepancy heuristic evolution ELM and ground effect theory based serial hybrid soft sensor model is proposed to accurately predict the floating height, which is constituted by a mechanism model and two data driven models.
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