## 1 Introduction  
The steel industry is a resource- and energy-intensive industry, and is also a high-pollution, high-energy, and high-emission industry. In 2015, the energy consumption of the steel industry accounted for 26.1% of the total industrial energy consumption, which accounted for 14.9% of the total energy consumption in China [1]. The integrated steel enterprises include main steel production systems, energy systems, and auxiliary systems. The main steel production systems include sintering, pellet production, blast furnace (BF), coke oven, converting, refining, continuous casting, hot rolling, cold rolling, and so on. The energy systems include power and steam production systems, chemical product manufacturing systems, and technical gas production systems. The auxiliary systems include transportation equipment and warehousing processes. As large material energy input and output systems, steel companies include complex energy distribution networks that use multiple energy sources. Among them, 34% of the primary energy (cleaned coal and anthracic coal) is transformed to byproduct gases during the iron and steel production process [2; 3]. As the Chinese steel industry pays increasing attention to environmental protection [4], people are paying increasing attention to the recycling, conversion, and utilization of secondary energy. In the iron and steel production process,there is often a surplus or shortage of secondary energy, which will lead to an increase in operating costs and environmental pollution costs. Therefore, optimizing the utilization of secondary energy and improving the conversion efficiency of secondary energy are of great significance to the steel industry [5]. Therefore, the design and optimization of energy systems in steel enterprises are receiving increasing attention. The secondary energy utilization of iron and steel enterprises has been widely studied by researchers [6; 7; 8]. Previous studies have focused on the production or consumption prediction and optimal scheduling of byproduct gases, steam, and electricity. In terms of energy forecasting, [PERSON] et al. [9] forecasted the surplus gas in the iron and steel industry to make full use of gas resources and stabilize the pipe network pressure. [PERSON] et al. [10] made comprehensive use of the grey model and back-propagation (BP) neural network to establish a prediction model of the blast furnace gas (BFG) system. In addition, several researchers, based on the ARIMA model, neural network, and least-squares support vector machine (LSSVM), have established composite prediction models to predict the production and consumption levels of energy source in steel enterprises [11; 12; 13; 14]. In terms of energy scheduling, [PERSON] et al. [15] proposed an optimal distribution model of the gas supply and demand by considering constraints such as boiler fuel fluctuations, boiler efficiencies, and gasholder capacities. [PERSON] et al. [16] used a dynamic mixed-integer linear programming (MILP) model to optimize the multi-cycling of byproduct gas. [PERSON] et al. [17; 18; 19] used MILP to establish an optimal distribution model for surplus gas and considered the influences of peak and valley electricity prices on the optimization results, thereby making the model closer to reality. Based on analysis of the supplies and demands of gas, steam, and electricity in the production cycle, [PERSON] et al. [20; 21; 22] established a multi-cycle optimal distribution model of the surplus gas, steam, and electric power. The model considered the impacts of polluting gases on the environment. The above research aims to predict and optimize the distribution of one or several secondary energy sources (gas, steam, electricity, etc.) under the premise of determining the production processes and energy conversion devices. There are few types of energy sources involved in the above studies, and there is no comprehensive consideration of the interactions and the different uses of multiple energy sources.