## 1 Introduction  
China is a major steel producer, and its crude steel production accounts for more than 50% of global crude steel production [1]. Steel production capacity is the embodiment of a country's industrial strength, but high output also means high energy consumption. The energy consumption of the steel industry accounts for approximately 15% of China's total energy consumption [2]. Blast furnace iron-making and converter steel-making require a large amount of oxygen, which is mainly produced by separating air in an air separation unit (ASU) using a cryogenic rectification method. Most of the iron- and steel-making enterprises in China have captive oxygen plants, which are equipped with several large-scale ASUs. The oxygen systems of iron- and steel-making enterprises are mainly composed of ASUs, oxygen compressors, and high-pressure storage vessels. Oxygen systems are energy-extensive and a large power consumer in iron and steel production enterprises because the separation process of air and the pressure delivery process of oxygen consume a lot of electric energy. In China, oxygen systems often have an imbalance between supply and demand. On the oxygen supply side, ASUs have the characteristic of stable production due to the characteristics of the equipment, and their output is relatively stable. However, on the oxygen demand side, affected by the production rhythm, the oxygen demand fluctuates greatly. At present, ASUs, blast furnaces, and converters tend to be very large, and the amount of equipment decreases when the steel output remains unchanged. This further aggravates the imbalance between the supply and demand of oxygen systems, resulting in a waste of resources and energy. To cope with the imbalance between supply and demand, the main scheduling means that can be adopted in oxygen systems are gas storage by high-pressure spherical vessels and load adjustment of ASUs. High-pressure spherical vessels can passively buffer the fluctuation of oxygen demand, while load adjustment of ASUs can be used as an active means to alleviate the fluctuation of oxygen demand, but the oxygen demand needs to be forecasted in advance. Oxygen demand forecasting and ASU variable-load operation are relatively complex. In the absence of a decision-making support system, it is difficult to predict and schedule oxygen systems via human judgment. As a compromise, at present, on-site workers generally adopt the practice of oxygen supply exceeding demand to ensure production safety, but the price paid is that the pressure of the oxygen pipeline is high and oxygen is often emitted, which results in high energy consumption for oxygen delivery and causes release loss. The optimized production and reasonable transportation of oxygen can effectively reduce the total energy consumption of steel production and play a positive role in promoting the early realization of carbon peaks in the steel industry. Therefore, it is necessary to establish a demand forecasting and optimal scheduling model for oxygen systems [3; 4; 5]. In terms of oxygen demand forecasting, most forecasting models are based on predicting the production rhythm of basic oxygen furnaces (BOFs) [6; 7; 8; 9]. [PERSON] et al. [10] presented a new model that describes the relationship between input variables such as blast temperature and blast composition and the output variable, namely, the tapped temperature of the steel. [PERSON] et al. [11] presented a study on the dynamic modeling and simulation of BOF operation with potential applications for process control and optimization in the steel industry. [PERSON] et al. [12] presented a study on a novel modeling method based on support vector domain description and LS-SVM for steel-making processes. [PERSON] et al. [13] presented a Gaussian process-based hybrid model for predicting oxygen consumption in the converter steel-making process. [PERSON] et al.