## 1 Introduction
[14] proposed two novel long-term prediction methods using MOGPR-based prediction enhancement to solve this industrial problem by learning from historical data and production plans. In terms of oxygen production scheduling, [PERSON] et al. [15] proposed an oxygen system scheduling model that considers power costs, using a particle swarm optimization algorithm to solve the model, and validated the model's effectiveness through actual field data. [PERSON] et al. [3] studied oxygen balance and scheduling under blast furnace shutdown conditions and established a scheduling model for air separation startup and shutdown based on mixed integer linear programming [16]. [PERSON] et al. [17] proposed a production scheduling optimization model that was built to minimize the oxygen demand fluctuation and the penalty cost of casts' starting time. [PERSON] et al. [18] developed a scheduling model for an oxygen system, which was tested on actual production data. Moreover, to improve the limitation of current ASU models with a single type of load adjustment, [PERSON] et al. [19] established a general framework for modeling ASU scheduling to unify multiple load-adjustment types and applied it to an oxygen system scheduling model. [PERSON] et al. [20] presented a study on a novel dynamic operation optimization method based on multi-objective deep reinforcement learning for steel-making processes. Relative to forecasting and scheduling models, few studies have investigated a two-stage model containing both forecasting and scheduling. [PERSON] et al. [21] proposed a two stage predictive scheduling method, in which a predictive model based on granular computing (GrC) and a mixed integer optimization model were established in the prediction and scheduling stages, respectively. The application results showed the accuracy and practicality of the method. [PERSON] et al. [22] investigated the problem of optimal oxygen distribution with flexible demands under uncertainty and proposed a two-stage robust optimization model with a budget-based uncertainty set that protected the initial distribution policy with low conservatism. [PERSON] et al. [23] presented a two-stage distributionally robust integrated scheduling approach for oxygen distribution and continuous casting processes in steel-making enterprises. With the rapid development of the industrial Internet and intelligent manufacturing technology, digital twins will gradually become popular in the industry. It has become possible to carry out predictions and analyses based on big data and machine learning and drive the physical world with optimal results. This study took a large-scale iron- and steel-making enterprise as a study case and developed a two-stage forecasting and scheduling model for reducing the energy consumption of its oxygen system. The novel aspect and progressiveness of this work are as follows: First, the oxygen demand forecasting model was developed based on the backpropagation neural network with genetic algorithm optimization (GABP) and is driven only by historical data. Compared with research in the literature, the accuracy of this model has been reduced, but the model does not need to consider production plans for other process steps, making it more practical and feasible. Second, different from the existing literature, an oxygen production scheduling model was developed for load-variable ASUs with an internal compression process, and both the oxygen emissions and pipeline pressure are included in the objective function. Moreover, the accuracy of the forecast model and the energy-saving effect of the scheduling model were verified through field data.