A Novel Series-Concatenation Hybrid Prediction Model of Energy Consumption in Hot Strip Roughing Process With Multi-Step Rolling

The steel industry has received serious attention under the background of carbon neutralization and carbon peaking. However, the traditional end-to-end energy consumption (EC) prediction method does not consider the effects of multi-step rolling, multi-time series, and error accumulation. To this end, a series-concatenation model based on a two-stage hybrid network is proposed to achieve EC high-precision prediction in multi-step continuous rolling. Specifically. First, this paper analyzed the mechanism between rolling EC and multiple variables. Second, a two-stage hybrid network with a deep neural network block, convolutional neural network block, long short-term memory block, and SoftBoost block (DCLS-Net) is established for EC prediction of single-step rolling. SoftBoost block is a double-layer structure method proposed in this paper for multi-block precision improvement based on two kinds of boosting strategies. Last, according to the error mechanism and multi-time series characteristics in the multi-step rolling, a series-concatenation EC prediction model is designed to suppress errors and achieve high-precision prediction. The experimental results show that the SoftBoost can effectively improve prediction performance. And the prediction precision of the two-stage DCLS-Net for single-step rolling is improved by 9.43% on average compared with the end-to-end machine learning algorithm. Furthermore, the precision of the series-concatenation model for multi-step rolling is improved by 4.96% compared with the traditional series model, which can satisfy the requirements of high accuracy and positive error in strip rolling production. Note to Practitioners—The inspiration for this paper mainly comes from the multi-objective process planning problem in the hot multi-step continuous roughing process, especially the energy consumption target. This method is also applicable to other variable prediction problems in the continuous multi-step process industry. The common method of energy consumption prediction is end-to-end machine learning. However, the characteristics of multi-step rolling and multi-time series, as well as the influence of intermediate process variables on the results, are not considered, which makes the precision and reliability of prediction unable to satisfy the requirements of industrial production. In this paper, the series-concatenation model structure is adopted to realize the high-precision energy consumption prediction and error suppression of multi-step rolling of strip steel. Such research is helpful for engineers to understand the energy consumption mechanism of strip rolling. It provides guidance for multi-objective roughing process planning. In future research, we will study the influence of more process variables on energy consumption.