## 3 Research Results
### CO\({}_{2}\) Reduction Effects due to Operation Shift  
To analyze the elements related to the current operation plan, a multiple regression analysis was performed on the amount of waste carried in and the weekly operation plan for the operation plan line data. The analysis revealed a positive linear correlation (Figures 16 and 17). This result agrees with the results of the field survey. Therefore, it was evident that the import quantity is one of the important elements of the operation plan. In addition, a similar multiple regression analysis revealed a positive linear correlation between the operating time and electric energy (Figures 18 and 19). These collective findings indicate that the operating time can be calculated from the weekly carry-in amount, and the weekly electric energy can be predicted from the operating time. Figure 16: Relationship between the amount of crushing facility A and solidification facility input and operating time. Figure 17: Relationship between the carry-in/out volume and operating time of facilities other than crushing facility A and solidification facility. Figure 15: Relationship between solar power generation and CO\({}_{2}\) emission factor. Minimizing the amount of CO2 reduction by altering the operation plan of the operation plan line was explored by mathematical optimization. The model equation for optimizing calculation is shown in Equation (6). From Equation (6), the operation judgment of each processing facility is binary, so it is non-linear. Therefore, the calculation was performed using the generalized reduction gradient method and the genetic algorithm. The constraints are shown in the following Table 9. The operation plan line comprises seven more facilities than the always in operation line (Roaster, Roaster pretreatment, Water treatment, New water treatment, office, Incinerator B (operation power), Incinerator A (operation power)), and the operating power uses the setting values obtained in Table 4. In addition, the actual measurement value is used for the operating time of each processing facility. \[EM_{(x_{i},CEF_{t})}=\left(\sum_{i=1}^{8}A_{i}\cdot x_{i}-EP\right)\times CEF _{t} \tag{6}\]  
where \(EM\) is the CO2 emission amount (kg-CO2/W), \(A_{i}\) is the operating power of each treatment facility (kWh/W), \(x_{i}\) is the operation decision of each treatment facility (0 or 1), and \(EP\) is the power generation amount (kWh/W). Figure 20 illustrates how to create an operational plan that minimizes CO2 emissions. In this study, actual data are used in some calculation processes because the verification does not include understanding operating hours and operating schedules. Figure 21 shows the results of the operation shift from 13 April (Monday) to 18 April (Saturday). Before the shift to operation, the amount of power generated on Monday was small, which was below the power demand of the processing equipment. The difference between power generation and power demand was relatively low from Tuesday to Friday. Approximately 500 kW of surplus electricity was generated on Saturday. When the operation shift was performed, the operation on Monday turned to Saturday, and the power usage from the  
Figure 19: Relationship between operating time and weekly operating power. Figure 18: Relationship between operating time and weekly operating power. grid on Monday was significantly reduced. The amount of reduction was 2520 kg-CO\({}_{2}\) before and after the shift, and the reduction rate was 20.95%. Figure 22 shows the results of the operation shift from 15 to 20 June. The weather was cloudy throughout that week, and the fluctuation in the amount of solar power generation  
Figure 21: Results of weekly operational shifts where surplus power is generated.