## 5 Results and Discussion
### Analysis of the Forecasting Results of the Oxygen Demand
By comparing the real data with the scheduling results in Figure 6, the oxygen output decrease rate at each time point could be calculated. To assess the situation over the entire period, a weighted average of all of the decrease rates was calculated, and the results indicate that the oxygen output of the ASUs after scheduling was approximately 1.38% lower than the field data on average. Figure 7 compares the field data and scheduling results in terms of oxygen emissions, which shows that the oxygen emissions decreased after scheduling. By comparing the real data with the scheduling results in Figure 7, the oxygen emission decrease rate at each time point could be calculated. To assess the situation over the entire period, a weighted average of all of the decrease rates was calculated, and the results indicate that within the time frame of the case study, the oxygen emissions could be reduced by approximately 6.32% through scheduling on average. Figure 8 shows a comparison between the scheduling results and the field data in terms of pipeline pressure. The calculation results show that, within the time range of the case study, the pipeline pressure after scheduling was approximately 0.61% lower than the field data. The high-pressure oxygen in the pipeline has pressure energy, and the energy consumption required by the system is not only the energy consumption of oxygen separation (the oxygen separation energy consumption of ASUs is usually 0.4 kWh\(\cdot\)m\({}^{-3}\)) but also the energy consumption of oxygen compression. The compression energy input by the system for the produced oxygen per unit of time is the compression power of oxygen, which is related to the pipeline pressure, as well as the oxygen output. For the convenience of comparison, the compression power can be quantified according to the energy consumption equation of multi-stage adiabatic compression, namely:  
\[W_{t}=m\frac{n}{n-1}RT\left(\pi^{\frac{n-1}{n}}-1\right)\delta\sum_{i=1}^{2} Ago_{i,t},\ \pi=\sqrt[n]{\frac{P_{t}}{P_{0}}} \tag{8}\]  
where \(W_{t}\) is the compression power of oxygen, \(m\) is the number of compression stages (in this paper, \(m=3\)), \(n\) is the adiabatic coefficient, \(\pi\) is the single-stage pressure ratio, \(P_{0}\) is the ambient pressure, and \(\delta\) is the conversion coefficient (\(\delta=0.0000124008\)). According to the pressure shown in Figure 8, the calculated oxygen compression power is shown in Figure 9. Since the compression power is affected by both the oxygen output and the pipeline pressure, the scheduled compression power experienced a significant  
Figure 9: Compressor energy consumption of the oxygen system: Scheduling results vs. field data. decrease compared to the unscheduled field data. The calculation results show that, within the time range of the case study, the energy consumption of the oxygen compression after the schedule decreased by approximately 1.60% compared to the field data. Considering the loss of oxygen emissions, as well as the compression energy consumption, the total power consumption of the oxygen system after scheduling was reduced by approximately 1.38% compared to the field data. The total annual power consumption of the oxygen system of the case enterprise was approximately 120 million kWh, so approximately 16.42 million kWh/year of electricity can be saved through scheduling. Converted according to the electricity price of 0.55 RMB/kWh, optimal scheduling could bring economic benefits of 9.03 million RMB/year to the studied enterprise.