*3.2. Material Flows Analysis and Energy Flows Analysis Results*

Material flows analysis model and energy flows analysis model could be achieved by the modeling method and sample data, which was mentioned in Sections 2.2.1 and 2.2.2. The analysis results are shown as follow.

## 3.2.1. Material Flows Analysis Results

Material flows analysis is based on the material balance between raw materials and output products. Since the amount of the gas mud, which is produced by the circulation cooling, is seldom, this part could be ignored. Then, the analytical results are shown in Figure 4.

**Figure 4.** Material flows analysis results in blast furnace iron making process (BFIMP).

(i) The material flows (including input items and output items) are all listed out through the material balance in BFIMP.

(ii) The proportions of material flow input items and output items are all clearly indicated. For example, the amount of sinter (*Px*,1), coke (*Px*,4) and oxygen-enriched air (*Pa*) accounted for about 83% in all material flow input items. Meanwhile, the purification gas (*Ppg*) and hot metal (*Phot*) accounted for about 93% in all material flow output items. Therefore, these items should be given more attention.

3.2.2. Energy Flows Analysis Results

(1) Energy flows analysis of the BS:

The energy flows analysis results of the BS are shown in Figure 5.

**Figure 5.** Energy flows analysis results in the blast system (BS).

(i) The energy flow input items and output items are all listed out through thermal equilibrium in BS.

(ii) The proportions of energy flow input items and output items are all clearly indicated in BS. For example, chemical heat of gas combustion (*Qs*,1) is the main input item (accounted for about 87%) among all energy flow input items in BS. The 75% amount of heat quantity has carried out by hot air carrying heat (*Qr*,1) in all energy flow output items.

(2) Energy flows analysis of the BF body:

The energy flows analysis results of the BF body are shown in Figure 6.

**Figure 6.** Energy flows analysis results in the blast furnace (BF) body.

(i) The energy flow input items and output items are all listed out through thermal equilibrium in the BF body.

(ii) The proportions of energy flow input items and output items are all clearly indicated in the BF body. For example, the amount of hot air physical heat (*Qr*,1) and carbon oxidation heat (*Qg*,1) accounted for 93% in all energy flow input items. The oxide decomposition heat (*Qh*,1) and hot metal carrying heat (*Qh*,6) accounted for 81% in all energy flow output items.

#### *3.3. All-Factors Analysis on Energy Consumption in BFIMP*

#### 3.3.1. PCA

As shown in Figure 6, the carbon oxidation heat (*Qg*,1), which accounted for 77.51% of the total heat consumption, is the main energy source in the BF body. Coke and pulverized coal injection are the main carriers of carbon oxidation heat [33–35]. Therefore, these two parameters can reflect the energy consumption for BFIMP.

Usually, the percentage of coke in total material consumption is called CR, the percentage of pulverized coal injection in total material consumption is called PCIR. It has been proved that that the PCIR improvement and CR reduction are the most effective energy saving measures in BFIMP [36]. Therefore, the influence factors analysis on PCIR and CR will be carried out in this paper. According to the material flows analysis results (as shown in Figure 4) and energy flows analysis results (as shown in Figure 5; Figure 6), the influence factors on CR and PCIR can be achieved. In addition, operation parameters have an important impact on CR and PCIR, too. Then, three kinds of parameters are shown in Table 1.


**Table 1.** Influence factors analysis on coke ratio (CR) and pulverized coal injection ratio (PCIR) in BFIMP by partial correlation analysis (PCA).

Noted: M40, resistance to crushing of coke; M10, abrasion strength of coke.

As shown in Table 1, the significant influence factors on the CR mainly included: Sinter size, ore grade, sinter alkalinity, sinter tumbler index, slag ratio, PCIR and blast temperature, due to their lower *p* values (≤0.05), whereas the PCIR is the best influence factors among them. The other influence factors were weakly correlated with CR due to their higher *p* values (>0.05). Moreover, the significant influence factors on the PCIR mainly included: Sinter grade, ore grade, sinter alkalinity, M10, blast volume, blast temperature, oxygen enrichment ratio and top temperature, due to their lower *p* values (≤0.05).

#### 3.3.2. MLR Models on CR and PCIR

The CR and the PCIR prediction models could be established through MLR models, based on the high correlation influence factors (as shown in Table 1). On the one hand, these prediction models have higher precision. On the other hand, this method could reduce the prediction models complexity due to a reduction in the number of variables.

Then, the fitting degrees of the MLR models were 95% and 94% respectively. In addition, these models were validated by actual production data, too. The fitting coefficients are shown in Table 2. Standardized coefficients (as shown in Table 2), which were calculated through the normalization method, eliminated the influence of dimensional differences among various parameters. Therefore, standardized coefficients could qualitatively reflect the influence intensity of each parameter on CR and PCIR. As shown in Table 2, the PCIR had the highest correlation with the CR among the main factors. Meanwhile, sinter grade had the highest correlation with the PCIR.


**Table 2.** Multivariate linear model (MLR) modelson CR and PCIR.

#### **4. Discussion**

Furthermore, a quantitative analysis was adopted to evaluate the influence intensity of the factors (independent variables) on the dependent variables (CR or PCIR). Generally, every independent variable was divided into 100 parts between minimum and maximum, which was achieved using historical production data (as shown in Table 3). Then, the variation of the dependent variable, which was caused by the change of independent variable 1%, could be calculated through MLR models. Quantitative influence intensity of significant factors on CR and PCIR are shown in Figures 7 and 8.


**Table 3.** The range of the influence factors (independent variable).

**Figure 7.** The influence intensity of significant factors on CR.

**Figure 8.** The influence intensity of significant factors on PCIR.

Slag ratio, sinter alkalinity and blast temperature had a positive influence on CR (as shown in Figure 7), the other factors had a negative influence. Meanwhile, the influence intensity of PCIR was the greatest among them on CR. CR would reduce by 0.507 kg/t when PCIR increased by 1% (0.84 kg/t), whereassinter size was the weakest.

As shown in Figure 8, M10 had a negative influence on PCIR among the main factors. The other factors had a positive influence. The influence intensity of sinter alkalinity was the greatest among them on PCIR. PCIR would increase by 0.483 kg/t when sinter alkalinity was promoted to 1% (0.005%), whereas M10 was the weakest.

As shown previously, some factors not only affected the CR, but also affected the PCIR, such as ore grade, sinter alkalinity and blast temperature. For example, CR would decrease and PCIR would increase with ore grade increasing. Furthermore, CR would continue to fall due to the improvement of PCIR (as shown in Figures 7 and 8). Therefore, ore grade had a comprehensive effect on CR. Meanwhile, it was the same for sinter alkalinity and blast temperature. In order to further analyze this problem, the influence factors, which affected PCIR, were converted to CR. Consequently, the comprehensive influence intensity on CR could be achieved (as shown in Figure 9).

**Figure 9.** The comprehensive influence intensity of significant factors on CR.

In general, the slag ratio and M10 had a positive influence on CR among influence factors, whereas the rest of the factors had a negative influence (as shown in Figure 9). Meanwhile, the influence intensity of the ore grade was the strongest; the sinter size was the weakest among the material parameters. CR would reduce by 0.471 kg/t, when the ore grade increased by 1% (0.48 kg/t). CR would only reduce by 0.054 kg/t, when the sinter size increased by 1% (0.053 mm). It is worth mentioning that ore grade and sinter alkalinity had a positive influence not only on PCIR, but also on CR. Although CR would increase with the improvement of ore grade and sinter alkalinity, CR would reduce with the improvement of PCIR, which was also caused by increasement of ore grade and sinter alkalinity. Therefore, ore grade and sinter alkalinity had a negative influence on CR finally after conversion calculation.

M10, which belongs to the unique energy flows parameter, had an impact on CR. CR would increase by 0.094kg/t when the M10 increased by 1% (0.021%).

Among the operation parameters, the influence intensity of blast volume was the strongest. CR would reduce by 0.289 kg/t when blast volume increased by 1% (7.09 m3/min), whereas blast temperature was the weakest. As before, blast temperature had also a positive influence on the CR and PCIR (as show in Figures 7 and 8). Eventually, blast temperature had a negative influence on CR after conversion calculation (as shown in Figure 9). CR would reduce by 0.077 kg/t when blast temperature increased by 1% (1.64◦C).

In summary, there were many influence factors, which determined the amount of PCIR and CR. Therefore, it is a very important energy saving direction to determine how to improve these influence factors in BFIMP. Then, three kinds of parameters (material flows, energy flows and operation parameters) will be further discussed.

(1) Material flows improvement:

The improvement of sinter size, ore grade, sinter grade, sinter tumbler index and sinter alkalinity could improve the permeability in the BF body. Moreover, the production status could be more stable. Therefore, the improvement of these factors provides favorable conditions for increasing the PCIR. Meanwhile, the improvement of ore grade and sinter grade is conducive to a reduction of the slag ratio. Moreover, the amount of PCIR and CR will also drop.

(2) Energy flows improvement:

M10 is a physical performance index, which can reflect the coke abrasion strength. The decrease of M10 is beneficial to charge column permeability for gas in the BF body, too. Therefore, if the amount of pulverized coal injection is further increased, the quantity of coke could be dropped.

(3) Operation parameters improvement:

The increase of blast volume, blast temperature and oxygen enrichment ratio can effectively maintain a high temperature in the combustion zone. Moreover, they are also conducive to accelerating the pulverized coal decomposition and combustion. In addition, the improvement of blast volume and blast temperature is also favorable to blow the hearth center of the BF body (especially for pyknic-type BF). Furthermore, the increase of blast temperature, which can improve the heat input of the BF body, can reduce the demand for coke or pulverized coal. Meanwhile, the combustion efficiency of coke and pulverized coal are improved due to the increase of the oxygen enrichment ratio. Then, the amount of flue gas is further reduced. Therefore, the improvements of blast volume, blast temperature and oxygen enrichment ratio are very effective measures of energy saving in BFIMP. Additionally, top temperature, which directly can reflect gas distribution and blast furnace status, is determined by heat exchange between blast furnace gas and furnace charge. CR will drop with top temperature increasing. Therefore, top temperature, which is controlled in higher areas within allowable range, is also an effective energy saving measure.

The analysis indicates the following findings.

