**1. Introduction**

Theblast furnace iron making process (BFIMP) represents the most relevant process on the main route for ore-based production of iron in the steelmaking industry [1]. Meanwhile, the iron and steel industry is known for having high energy consumption and high pollution [2]. Around the world approximately 5% of global energy is consumed by the iron and steel industry [3–5], its CO2 emission accounts for approximately 7% of the total anthropogenic CO2 emissions [6,7]. Therefore, many novelty methods and technologies of energy saving have emerged in manufacturing fields [8], including the iron and steel industry.

Currently, blast furnace-basic oxygen furnace (BF-BOF) is one of the major production patterns [9]. Moreover, the whole iron making system (including coking, sintering, iron making and other processes) accounts for 70–75% of the total energy consumption in the integrated steel enterprise, whereas BFIMP is more than 50% [10]. Therefore, the BFIMP is one of the most energy-intensive processes in the iron and steel industry [11–14]. RY Yin [15] pointed out that material flows and energy flows are the most

basic component. Consequently, material flows and energy flows analysis can also be applied to BFIMP. Related research mainly includes as follows:

Firstly, some optimization models were established based on material flows and energy flows analysis. The improvement of energy efficiency and energy saving is the focus of these optimization models [16]. An optimization model [17] was established based on material balance and energy balance in BFIMP. In this model, the exergy loss minimization was taken as the optimization target. Then, the measures of energy saving wereput forward. Bo Zhou [18] developed the principal component analysis, which could analyze material flows, energy flows and operation parameters in the process of blast furnace (BF) smelting. Furthermore, this model was applied to detect the early abnormality in the iron-making process. Moreover, on the foundation of material flows and energy flows of the oxygen BF with top gas recycling, and a model, which comprised the oxygen BF, the top gas removal process and the preheating units, was established [19]. Then, energy consumption and carbon emission of the integrated steel mill was analyzed based on this model. While, S.B. Kuang [20] proposed a complex function, which was integrated with HM (hot metal) yield and useful energy of the BF. Then the optimal cost distribution of raw materials (namely "generalized optimal construct") was obtained, the influence of some parameters, such as oxygen enrichment ratio, blast temperature and pulverized coal dosage, on the optimization results were further analyzed.

Secondly, the mechanism of the smelting process, which was based on the material or energy evolutionary process, was studied. Due to the complexity of the BFIMP, the numerical simulation method has been applied more widely [21–24]. Y.S. Shen [25] and Yansong Shen [26] simulated the flow and combustion of a ternary coal blend under simplified BF conditions by a three-dimensional computational fluid dynamics (CFD) model. Meanwhile, the effect of the coke reaction index on the reduction and permeability of the ore layer in the BF lumpy zone under the non-isothermal condition was analyzed through a CFD model. Then, the reasonable control of the coke reaction index, which was one of the key factors for BF low-carbon, was pointed out [27]. Moreover, José Adilson de Castro [28] focused on modeling the simultaneous injection of pulverized coal and charcoal into the BF through the tuyeres with oxygen enrichment. The results indicated that the productivity of the BF could be increased up to 25% with simultaneous injection combined with oxygen enrichment. Additionally, the means of simulation, the test procedure was also used to detect reactions in the furnace. Mineral matter of tuyere level cokes was quantified using a personal computer quantitative X-ray diffraction analysis software and examined using a scanning electron microscope in a working BF. At the same time, the apparent CO2 reaction rates were measured using a fixed bed reactor [29].

Generally, the energy saving or energy efficiency of BFIMP had been explored through material flows and energy flows in the above studies. The energy efficiency of BFIMP has been improved in a large extent. Unfortunately, there were still some deficiencies in the following two aspects. (i) The influence of operation parameters on energy consumption was not involved. (ii) The influence intensity of these parameters on energy consumption was not clear in BFIMP. Additionally, data-driven methodologies have been wildly applied to various thermal equipment in the iron and steel industry due to rapid developments of industrial automation and information systems [30]. Therefore, an all-factors analysis approach, which can analyze the influence of all parameters (material flows, energy flows and operation parameters) on the energy consumption of BFIMP, is proposed based on material balance, thermal equilibrium and data-driven methodologies in this paper. Furthermore, the key influence factors can be achieved by the application of the proposed approach. Then, the corresponding energy saving measures can be put forward effectively. Therefore, the proposed model can provide support for the formulation of the reasonable production plan and the operation management in BFIMP. In addition, the proposed model can also widely be used in various BFIMPs, too.

#### **2. Methods**

The all-factors analysis approach mainly includes (as shown in Figure 1):

**Figure 1.** The research route of the all-factors analysis approach.

(1) Data collection:

The BFIMP composition and production data can be achieved through data collection.

(2) All-factors analysis based on material flows and energy flows:

In general, energy consumption is affected by many factors in BFIMP, mainly including material flows, energy flows and operation parameters. Consequently, material flows and energy flows analysis model should be established based on material balance and thermal equilibrium. Moreover, operation parameters [31], which represent the coupling quality between material flows and energy flows, should also be listed.

(3) Influence intensity analysis on energy consumption in BFIMP:

All-factors analysis approach, which mainly includes data pre-processing, all-factors partial correlation analysis (PCA) and all-factors multivariate linear model (MLR) model, is an effective influence intensity method on energy consumption in BFIMP.

(4) Suggestion and summary:

Some suggestion and summary, which can achieve improvement of energy efficiency, should be put forward based on the influence intensity analysis.

#### *2.1. Data Collection*

Data collection mainly includes the following aspects:

(1) The BFIMP composition:

The BFIMP composition should be clarified firstly. Therefore, production process investigation should be carried out.

Generally, the BFIMP is composed of the BF body and six auxiliary equipment systems, which includes the charging system (CS), blast system (BS), gas purification system (GPS), fuel injection system (FIS), top power generation system (TPGS) and slag treatment system (STS). The TPGS and STS are subsequent processes of the by-product. The material flows and energy flows proportion of BF body, CS, BS, GPS and FIS accounts for more than 90% of the total amount for BFIMP. Consequently, the TPGS and STS will not be considered in this paper due to their seldom proportion.

(2) Production data:

As discussed in the previous section, there are three kinds of parameters (material flows, energy flows and operation parameters), which affect energy consumption in BFIMP. These data can be collected through various computer detection systems or working records in BFIMP. Especially, a computer detection system can acquire and store these kinds of parameters regularly, such as the production management system and energy management system.
