Water-energy-carbon nexus assessment of China’s iron and steel industry: Case study from plant level

Journal Pre-proof Water-energy-carbon nexus assessment of China’s iron and steel industry: case study from plant level

Xiaozhuang Wang, Qi Zhang, Lisong Xu, Yongjuan Tong, Xiaoping Jia, Hong Tian PII:

S0959-6526(19)34780-8

DOI:

https://doi.org/10.1016/j.jclepro.2019.119910

Reference:

JCLP 119910

To appear in:

Journal of Cleaner Production

Received Date:

11 September 2019

Accepted Date:

27 December 2019

Please cite this article as: Xiaozhuang Wang, Qi Zhang, Lisong Xu, Yongjuan Tong, Xiaoping Jia, Hong Tian, Water-energy-carbon nexus assessment of China’s iron and steel industry: case study from plant level, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.119910

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Water-energy-carbon nexus assessment of China’s iron and steel industry: case study from plant level Xiaozhuang Wanga, Qi Zhanga,*, Lisong Xua, Yongjuan Tongb, Xiaoping Jiac, Hong Tiand a.

State Environmental Protection Key Laboratory of Eco-Industry, Northeastern University, Shenyang 110819, PR China

b.

The State Key Laboratory of Refractories and Metallurgy, Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, PR China

c.

School of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China

d.

School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, PR China

*Corresponding author: E-mail address: [email protected] (Q. Zhang).

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1. Introduction The prosperity of the industrial sector has brought about environmental problems and pressure on resources. Sustainable development has become a concern of governments and researchers around the world. Green development in the industrial sector is of great significance to environmental sustainability and the realization of the 2 °C target. As commonly known, China is the country with the largest steel output in the world. Statistical data show that 831.7 Mt of crude steel were produced in China, accounting for 49% of the world's total output in 2017 (World Steel Association (WSA), 2018). As a pillar industry of the national economy, the success of the steel industry is achieved at the cost of intensive water and energy consumption. However, China's water resources and energy resources are not rich. China's per capita oil and gas reserves are only 2.56 t and 3971 m3, and the water resources per capita are about 2068 m3, comprising 7.8%, 15.3%, and 34% of the world average, respectively (Jiang, 2015; Wang, 2018). Moreover, the conflict between crude steel production and geographical distribution of water resources has increased the pressure on water resources. The three provinces of Hebei, Jiangsu, and Shandong have the largest crude steel output, while their water resources account for a relatively small proportion. (China Iron and Steel Industry Association, 2017; Ministry of water resources of the People's Republic of China, 2018). In order to facilitate the sustainable development of steel industry, a series of measures have been formulated in China’s national development strategy plan (The State Council, 2016; Ministry of Industry and Information Technology (MIIT), 2010, 2011, 2016, 2018). Previous studies have also addressed how to reduce energy consumption and the environmental impact of the steel industry (Hasanbeigi et al., 2013; Zhang et al., 2013; Li and Zhu, 2014; Zhang et al., 2014; Ma et al., 2016; Tan et al., 2019; Zhang, 2019b). From the energy-saving stage of individual equipment or terminal technology to the system energy-saving stage, the energy savings and consumption reduction effort of the steel industry has achieved significant results. The energy consumption of the steel industry has decreased by 24.8% over the past ten years, and the consumption of specific fresh water has also decreased by about 68.7% (Tong et al., 2016; Wang, 2017). However, with the passage of time, the cost of energy conservation in the iron and steel industry is ever increasing, and further energy savings and water conservation has become one of the major challenges facing the future iron and steel industry (MIIT, 2016; Gao et al., 2019a). In this case, some scholars have pointed out that many

Journal Pre-proof environmental and sustainability issues are actually interrelated, and a new perspective can be provided for sustainable development through a nexus method (Howells et al., 2013; Liu et al., 2015). As a typical example, the idea of a water-energy nexus has become a hot topic, attracting the attention of a large number of researchers (Hellegers et al., 2008; Perrone et al., 2011; Scott et al., 2011; Wang et al., 2012; Lazarova, 2015; Fang and Chen, 2017). The water-energy nexus can be expressed as the extraction, transportation, and utilization of water, and treatment of waste water require energy consumption; meanwhile, the extraction and utilization of energy, and the recovery of waste heat require water consumption. The energy sector consumes 15% of the global annual water use, while the water sector consumes 1.7–2.7% of the global annual primary energy use (Rothausen and Conway, 2011;Wang et al., 2017b). As the largest developing country in the world, China's water use and energy use are more closely linked (Duan and Chen, 2016). Therefore, the nexus between water utilization and energy consumption has been introduced into China's practice. Feng et al., (2019) established an accounting framework to assess the synergy between energy conservation and regional water resources and water quality. Qin et al., (2015) analyzed water use in different energy processes and compliance with water policies across industries, and recommended a comprehensive review of energy and water resources. Li et al., (2012) combined input-output analysis with a life-cycle analysis to assess water consumption and CO2 emission from wind power in China. Based on the recursive multi-sector dynamic CGE model, Zhou et al., (2018) comprehensively evaluated the environmental and water-saving benefits of long-term energy conservation. Gu et al., (2014) established an input-output model to assess the waterenergy relationship in Chinese industry, and they found that by achieving energy-saving goals during the 12th Five-Year Plan, the achievement of water use goals would also be promoted. In the comprehensive management of water and energy needs, not only do the above-mentioned synergy issues need to be considered but also the trade-offs. In the field of power generation, the use of new energy sources such as solar energy, wind energy, and biomass energy for power generation leads to less coal consumption. Nevertheless, when considering water resources, wind power generation is more environmentally friendly than biomass power generation (Gerbens-Leenes et al., 2009; Nanduri and Saavedra-Antolínez, 2013). Sewage treatment is also a typical case that needs to be weighed. Sewage treatment is an indispensable process to reduce the environmental pollution of industrial production water and human domestic water. However, handling 1 m3 of sewage requires 0.4–0.5 kWh of electricity in China’s sewage treatment plant (Gu et al., 2016). The consumption of electricity to treat sewage indirectly has led to other environmental problems, owing to the fact that China's power

Journal Pre-proof generation methods are dominated by coal (National Bureau of Statistics of China, 2018). The concept of water-energy nexus can be traced back to the 1990s. After more than twenty years of development, the framework of the water-energy nexus was extended to the water-energy-environment nexus (Nanduri and Saavedra-Antolínez, 2013; Pereira-Cardenal et al., 2014; Tidwell and Pebbles, 2015) , water-energy-food nexus (Daher and Mohtar, 2015; Li et al., 2016; Kaddoura and El Khatib, 2017), and water-energy-land-climate nexus (Popp et al., 2011; Kraucunas et al., 2015; Mouratiadou et al., 2016). Recently, the water-energy nexus method has been applied to the steel industry. Wang et al., (2017a) developed a technology-based approach to study the relationship between water, energy and emissions in the steel industry by building a bottom-up model. Wang et al., (2017b) used a mixed input-output model to study the water impact associated with energy conservation measures, and found that there was a water-energy trade-off in production process adjustment. Zhang et al., (2019c) integrated the material/energy/water flow analysis and nexus method into the MESSAGEix model to fully assess the resource-energy-environment relationship of China’s iron and steel industry (CISI). Broberg Viklund and Karlsson, (2015) studied examples of waste heat recovery in the electric furnace steelmaking industry and assessed the impact of feasible interventions on primary energy and water consumption and CO2 emission. Taking China's steel production as the research object, Tong et al., (2019) established a material flow analysis-input-output model to analyze the indirect water use and wastewater discharges of energy and material consumption in different processes. Through the analysis of 21 energy-saving technologies and 9 water-saving technologies, Gao et al., (2019b) developed 5 schemes to evaluate the energy saving and water-saving potential of China's steel industry. The results showed that by 2025, the energy saving and water saving potential of China's steel industry are 15.01-15.59 GJ/t and 54.13-58.77 m3/t, respectively. Existing studies have made a commendable contribution to the sustainable development of steel industry. In past studies, input-output analysis, life-cycle assessment, ecological network analysis, a computable general equilibrium model, and a hybrid model composed of multiple methods were often used to analyze the relationship between water and energy. However, these methods were all based on the macro-level to analyze current or future situations, and the main research objects were one or more industries in a country or region. The water-energy nexus at the plant and process level were often ignored. Past studies have not been able to determine from the plant level how water and energy are related in the actual production process of a specific steel company. Moreover, few studies have been conducted on greenhouse gas impact when expanding the water-energy framework of the steel industry. When energy-saving technologies (ESTs) were applied to the steel industry to achieve energysaving effects, synergistic water-saving effects could also be brought about. Although

Journal Pre-proof ESTs were a good indication of the water-energy nexus in the steel industry, this is not sufficient. In order to fill these gaps, a plant-level water-energy-carbon nexus model was established from a microscopic perspective in this study. The integrated material and energy flow analysis method was used to analyze the operational level of the case company. The main indicators include energy and water consumption and CO2 emission. A water-CO2 energy conservation supply curve (ECSC) evaluation method was also used to analyze the impact of energy conservation measures on the case company. The cost-effectiveness of 31 ESTs, the benefits of energy and water savings and emission reduction, as well as investment information were also evaluated by the ECSC model. In addition, the water-energy nexus in steel production was also quantified by water footprint in this study.

2. Methods In this study, an integrated material and energy flow model and a water-CO2 ECSC model were established to analyze the water-energy-carbon nexus in a typical iron and steel works. A water footprint model was also established to quantify the water-energy nexus. In addition, a process-level nexus model was established based on the types of water used in the case study.

2.1 Integrated material-energy flow model Material flow analysis is an effective method to study the industrial metabolism of certain substances in a country or region. The core of material flow analysis is the law of conservation of mass (Vander, 2002; Yellishetty and Mudd, 2014; Liu et al., 2019). In this study, the integrated material and energy flow model included energy flow, carbon flow and water flow, and the modeling process was based on the principle of mass balance. Energy is an important part of the water-energy nexus, and the level of energy consumption directly or indirectly affects water consumption. Energy intensity is widely used in various articles evaluating the energy consumption level. In this study, it was evaluated by process energy consumption and comprehensive energy consumption per t of steel (Lu and Cai, 2010). The calculation method is shown in Eq. (1) and Eq. (2). EC𝑖 =

∑ (Q𝑖𝑛, 𝑖, 𝑟 × 𝑆𝐶𝐹𝑟 ― Q𝑜𝑢𝑡, 𝑖, 𝑟 × 𝑆𝐶𝐹𝑟) r 𝑃𝑖

SEC𝑐𝑠 = ∑iEC𝑖 × 𝑝𝑖 where: i = production process of the case company; r = resource;

(1) (2)

Journal Pre-proof EC𝑖 = energy consumption of process i, in kgce/t-product; Q = quantity of the input or output energy and resource; P𝑖 = production of process i, in t; SCF = standard coal factor; SEC = specific energy consumption, in kgce/t-cs; 𝑝𝑖 = steel ratio coefficient of process i. After the application of advanced energy conservation and emission reduction technologies, the energy consumption level of process i is as follows: 𝐸𝐶'𝑖 = EC𝑖 × ∏𝑗(1 ― 𝐸𝑆𝑅𝑖, 𝑗)

(3)

where: 𝐸𝐶'𝑖 = energy consumption after the application of EST j in process i; 𝐸𝑆𝑅𝑖, 𝑗 = energy-saving rate of EST j in process i. CO2 is a major contributor to global warming; achieving a 2 °C target requires strict limits on CO2 emission (Meinshausen et al., 2009; Liu et al., 2017). In the steel industry, large amounts of energy consumption result in large amounts of CO2 emission. Water consumption indirectly leads to CO2 emission through energy consumption. Therefore, carbon flow, as a non-negligible environmental impact in steel production, was incorporated into the water-energy nexus framework. According to the WSA, CO2 emission measurement is divided into direct emission and total emission. The total emission needs to consider both indirect emissions and emission credits (WSA, 2016). In this study, total emission was used to describe CO2 emission. Similar to the energy flow model, CO2 emission from process i is calculated by Eq. (4). CE𝑖 =

∑𝑟(𝑄𝑖𝑛, 𝑖, 𝑟 × 𝐶𝐸𝐹𝑟 ― 𝑄𝑜𝑢𝑡, 𝑖, 𝑟 × 𝐶𝐸𝐹𝑟) 𝑃𝑖

(4)

The comprehensive emission per t of steel in the case company can be calculated by Eq. (5). SCE𝑐𝑠 = ∑𝑖CE𝑖 × 𝑝𝑖

(5)

where: CE𝑖 = CO2 emission in process i, in kg/t; CEF = CO2 emission factor; SCE𝑐𝑠 = specific CO2 emission, in kg/t-cs. CO2 emission is generated from the consumption of carbon-containing energy. Therefore, the application of EST has a positive impact on CO2 emission reduction, which is calculated by Eq. (6). 𝐶𝐸'𝑖 = 𝐶𝐸𝑖 × ∏𝑗(1 ― 𝐶𝑀𝑅𝑖, 𝑗)

(6)

𝐶𝐸'𝑖 = CO2 emission after the application of EST j in process i; 𝐶𝑀𝑅𝑖, 𝑗 = CO2 emission mitigation rate of EST j in process i. Within a steel company, the water usage for any system or process can be

Journal Pre-proof represented by Fig. 1. The water input to process i includes industrial fresh water and self-circulating water. Then, the sewage is discharged, inducing water loss in the process. The dashed lines indicate the cascading water and reclaimed water that may exist between the processes. The total water consumption of the process is the sum of fresh water and circulating water, and the relationship between these water flows is given by Eqs. (7) and (8). Industrial fresh water

Wc, i-1

Process i-1

Wc, i

Wc, i+1

Process i

Wl, i-1

Process i+1

Wl, i

Wd, i-1

Wd, i

Wl, i+1 Wd, i+1

Reuse water and cascade water

Fig. 1. Schematic diagram of water use system of the case company.

𝑊𝐶i = 𝐹𝑊𝐶i + 𝐶𝑊𝐶i

(7)

𝐶𝑊𝐶i = 𝑊𝑜, 𝑖 ― 𝑊𝑑, 𝑖 ― 𝑊𝑙, 𝑖

(8)

where: 𝑊𝐶𝑖 = water consumption in process i, in m³/t; 𝐹𝑊𝐶𝑖 = fresh water consumption in process i, in m³/t; 𝐶𝑊𝐶𝑖 = circulating water consumption in process i, in m³/t; 𝑊𝑜, 𝑖 = water obtained in process i, in m³/t; 𝑊𝑑, 𝑖 = water discharge in process i, in m³/t; 𝑊𝑙, 𝑖 = water loss in process i, in m³/t. The water consumption of the case company is divided into direct water use and indirect water use in this study. The direct water consumption refers to fresh water and circulating water used for production, cooling, and waste heat recovery. Indirect water consumption refers to the water footprint caused by consumption of various resources and energy. The direct water use of the process i can be expressed in terms of fresh water consumption, which is also an evaluation index widely used by Chinese steel companies, calculated by Eq. (9). For the consumption of fresh water per t of steel, the calculation method is consistent with the comprehensive energy consumption per t of steel. 𝐹𝑊𝐶𝐼𝑖 =

𝑄𝑓𝑤, i 𝑃𝑖

(9)

where: 𝐹𝑊𝐶𝐼𝑖 = fresh water consumption intensity of process i, in m³/t; The water footprint per t of steel was used in this study to assess the environmental impact of resources and energy consumption. For any energy or resource consumed by

Journal Pre-proof steel production, the water footprint can be calculated by Eq. (10) (10) 𝑊𝐹 = 𝑄 × 𝑊𝐹𝐹 The water footprint per t of product for process i can be calculated by Eq. (11). 𝑊𝐹𝑝𝑟𝑜𝑑𝑢𝑐𝑡, 𝑖 =

∑𝑟(𝑄𝑖𝑛, 𝑖, 𝑟 × 𝑊𝐹𝐹𝑟 ― 𝑄𝑏𝑦𝑝𝑟𝑜𝑑𝑢𝑐𝑡, 𝑖, 𝑟 × 𝑊𝐹𝐹𝑟) 𝑃𝑖

(11)

where: WF = water footprint, in m³; WFF = water footprint factor. In addition to the energy-saving and carbon-reduction effects, ESTs also have a water-saving effect. For example, 0.46 GJ/t of energy can be saved, carbon emission can be reduced by 50.05 kg/t, and synergistically, 0.19 m3/t of water can be saved through the replacement of traditional water quenching with T1(MIIT, 2012). Moreover, the application of ESTs not only impact direct water use, but can also indirectly reduce the water footprint through energy conservation. The direct and indirect water consumption of process i after the application of ESTs are calculated by Eqs. (12) and (13), respectively. 𝐹𝑊𝐶𝐼'𝑖 = 𝐹𝑊𝐶𝐼𝑖 × ∏𝑗(1 ― 𝐹𝑊𝑆𝑅𝑖, j)

(12)

𝑆𝑊𝐹'𝑖 = 𝑊𝐹𝑖 ― ∑𝑗(𝐸𝑆𝑃𝑖, 𝑗 × 𝑊𝐹𝐹)

(13)

where: 𝐹𝑊𝐶𝐼'𝑖 = fresh water consumption intensity of process i after the application of EST j in process i, in m³/t; 𝐹𝑊𝑆𝑅𝑖, 𝑗 = fresh water-saving rate of EST j in process i; 𝑆𝑊𝐹'𝑖 = specific water footprint after the application of EST j in process i, in m³/t ; 𝐸𝑆𝑃𝑖, 𝑗 = energy-saving potential of EST j in process i.

2.2 Water-energy-carbon nexus from the process level For the case company, various types of water are used, consuming energy in each process, e.g., production, transportation, while simultaneously generating CO2 and a water footprint. A process-level nexus model was established to describe this relationship, calculated by Eqs. (14)–(16). From these equations we can find that energy consumption is the core of the water-energy-carbon nexus of water use in various processes. The CO2 emission and water footprint generated by water utilization are closely related to energy consumption. When other parameters are fixed, the CO2 emission and water footprint of all types of water are directly proportional to the energy consumption, that is, there is a synergistic effect of water-energy-carbon nexus. In other words, by optimizing the water supply network and other measures to reduce energy consumption due to long transport distances, CO2 emission and water footprint due to

Journal Pre-proof water use will also decrease. 𝑆𝐸𝐶𝑖, 𝑘 =

𝐸𝐶𝑖, 𝑘 𝑄𝑖, 𝑘

(14)

𝑆𝐶𝐸𝑖, 𝑘 = (𝑚 × 𝐶𝐸𝐹𝑝 +(1 ― 𝑚) × 𝐶𝐸𝐹𝑠) × 𝑆𝐸𝐶𝑖, 𝑘

(15)

𝑆𝑊𝐹𝑖, 𝑘 = (𝑚 × 𝑊𝐹𝐹𝑝 +(1 ― 𝑚) × 𝑊𝐹𝐹𝑠) × 𝑆𝐸𝐶𝑖, 𝑘

(16)

where: k = different types of water; m = proportion of purchased electricity, in %; p = purchased electricity; s = self-produced electricity; 𝑄𝑖, 𝑘 = quantity of flow of water k in process i, in m3; 𝐸𝐶𝑖, 𝑘 = energy consumption of water k in process i, in kWh; 𝑆𝐸𝐶𝑖, 𝑘 = specific energy consumption of water k in process i, in kWh/t; 𝑆𝐶𝐸𝑖, 𝑘 = specific carbon emission of water k in process i, in kg/t; 𝑆𝑊𝐹𝑖, 𝑘= specific water footprint of water k in process i, in L/t.

2.3 Water-CO2 ECSC model The ECSC model is widely used in the economic feasibility analysis of EST (Hasanbeigi et al., 2013; Zhang et al., 2014). From the model, we can know the investment, operating cost, energy-saving potential and benefits, investment payback period, and other information of a technology, which can provide reference for policy makers and business decision-makers to decide whether to apply a certain technology. Unlike the traditional energy-saving supply curve model, the benefits of water-saving and CO2-reduction are included in the cost-effectiveness evaluation. As shown in Eq. (17), two items are added, namely, direct water-saving benefits and carbon reduction benefits from ESTs. That is to say, when considering the unit energy-saving cost of a technology, besides the fuel cost savings, it is also necessary to deduct the saved water fee and carbon tax. The water fee is easy to understand. The industrial fresh water consumed by the case company comes from the local water group, and the water fee is reduced as the application of ESTs reduces the demand for water. Carbon tax and carbon trading markets are effective means of reducing CO2 emissions in a region or country (da Silva Freitas et al., 2016; H. Zhang et al., 2019a). The Chinese government promised at the Paris Climate Change Conference that CO2 emissions would peak by 2030, and the feasibility of the carbon tax has received a great deal of attention from the Chinese government and many scholars.(The State Council, 2011; Wu et al., 2016; Zhang et al., 2017b). 𝐶𝐶𝐸𝑗 =

𝑃𝑖 × (𝐴𝐶𝐶𝑗 + ∆𝑂&𝑀𝑗) ― 𝐴𝐸𝐵𝑗 × 𝑃𝐸 ― 𝐷𝑊𝑆𝑗 × 𝑃𝑊 ― 𝐶𝐸𝑀𝑗 × 𝐶𝑇 𝐴𝐸𝐵𝑗 𝑑

(17)

𝐴𝐶𝐶 = 𝐼 × (1 ― (1 + 𝑑) ―𝑛)

(18)

𝐴𝐸𝐵𝑗 = 𝑃𝑖 × 𝑇𝐸𝑆𝑗

(19)

Journal Pre-proof where: 𝐶𝐶𝐸𝑗 = cost of conserved energy of an energy efficiency measure j, in CNY/GJ; 𝐴𝐶𝐶𝑗 = annualized capital costs of technology j, in CNY/t; 𝐴𝐸𝐵𝑗 = annual energy benefits of technology j, in GJ; 𝐷𝑊𝑆𝑗 = direct water-saving potential of technology j, in m³/t; 𝐶𝐸𝑀𝑗 = CO2 emission mitigation of technology j, in tCO2/t; 𝑇𝐸𝑆𝑗 = typical energy-saving of technology j, in GJ/t; ∆𝑂&𝑀𝑗 = annual change in operation and maintenance cost of technology j; in CNY/t; 𝑃𝐸 = price of energy, in CNY/GJ; 𝑃𝑊 = price of water, in CNY/m³; 𝐶𝑇 = carbon emission tax, in CNY/t. 𝐼 = investment of the energy efficiency measures, in CNY; 𝑑 = discount rate, in %; 𝑛 = lifetime of energy efficiency measures.

3. Case study The case study is a typical BF-BOF production route with complete production processes, including five main processes of coking, sintering (contains pelleting), iron making, steel making, and steel rolling, as well as auxiliary processes, such as oxygen production and power generation. The case company is located in northeast China and has an annual production capacity of 5.5 Mt of crude steel. The average monthly actual production capacity is 465.7 Kt of crude steel, which requires 206.2 Kt of coke, 625.6 Kt of sinter, and produces 502.2 Kt of finished steel. Therefore, the steel ratio coefficients of each process are 0.43, 1.34, 0.96, 1, and 1.08 for coking, sintering, iron making, steelmaking, and rolling process, respectively. As shown in Fig. 2, the system boundary is limited to five major production processes and power generation process. The pre-iron processes (coking, sintering, iron making) provide raw materials for the iron making process—coke, sinter, and pellet. After pretreatment, the hot metal is smelted into molten steel in the steelmaking process and then processed through a refining and continuous casting process; finally, it is sent to a rolling process for final processing. Power generation provides electricity to each process. In the entire steel production process, a large amount of substances, energy, and water are consumed, and a large amount of CO2 is emitted. Fig. 3 is a production flow chart drawn by e!Sankey software. The data are from the energy balance table and expert consultation of the case company. Different colors represent various substances, different widths represent relative sizes, and arrows and tails represent the direction of material flows.

Journal Pre-proof Energy for extraction and utilization of material

Energy for extraction and treatment of water

Energy

The source and carrier of energy

Material

Coal

Onsite power plant Coking

Sintering

Pelletizing

Coke

Sinter

Pellet

Flux Scrap

Iron Making

Iron ore

Hot metal

Fresh water Reuse water

Liquid steel

Steel Rolling

Emission credit Indirect emission of carbon free materials

Direct and indirect emission

CO2

Fig.2. System boundary in this study.

Indirect emission through power consumption

Steel Making

Water

Direct emission of carbon containing materials

Oil

Water for production and heat recovery of energy

Purchased electricity

Fig. 3. Production flow chart of the case company

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4. Results 4.1 The plant-level water-energy-carbon nexus 4.1.1 Water consumption for energy Water consumption for energy refers to the water footprint generated by the various resources during energy consumption. The water footprint factors for these substances were taken from the previous literatures (Rydh and Karlström, 2002; Classen et al., 2009; Zhang and Anadon, 2013; Gao et al., 2019a). Based on the abovementioned indirect water use model, the water footprint of each process was calculated by combining the water footprint factor with the consumption of resources and energy in the case company. Table 1 shows the water footprint of the main products of the steel-making process, which is the calculation result of the water footprint per t of steel in this study. The water footprint of crude steel in the case company is 9.48 m3/t, which is larger than that of previous studies (Gu et al., 2015; Ma et al., 2018). This is due to the inconsistency in the selection of water footprint factors and the large differences in the research objects, scale, and production level, and technology penetration rate of different steel plants. Table 1 Water footprint calculation of main products in steelmaking process Substances Quantities Unit WFFa Unit WFb(m³) Hot metal 44.76 104 t 5.68 m³/t-hot metal 2540165 COG 41772.00 GJ 0.0007 m³/m³-gas 1709 BFG 3380.00 GJ 0.0007 m³/m³-gas 727 LDG 14963.00 GJ 0.0007 m³/m³-gas 1265 Electricity 3313.15 104 kWh 0.02 m³/kWh 506912 Steam 56520.00 GJ 2.80 m³/t-steam 41646 4 O2 2641.50 10 m³ 0.01 m³/m³-O2 296723 Softened water 10.83 104 m³ 1.12 m³/t-water 121712 Freshwater 16.63 104 m³ 1.00 m³/t-water 166922 4 Air 3204.60 10 m³ 0.0026 m³/m³-air 83593 4 N2 3216.90 10 m³ 0.0025 m³/m³-N2 79216 4 Ar 65.90 10 m³ 0.05 m³/m³-Ar 30829 Scrap 54666.66 t 11.32 m³/t-steel 618827 Flux 106977.50 t 0.51 m³/t-flux 54374 Total 4544620 Crude steel 465673.00 t 9.48 m³/t-cs 4413906 LDG 312370.00 GJ 0.0007 m³/m³-gas 26403 Steam 37253.84 t 2.80 m³/kWh 104311 Total 4544620 a

WFF=Water footprint factor. WF= Water footprint. Fig. 4 shows the composition and proportion of the water footprint in each process. It can be seen that the direct water withdrawal in the power generation and coking processes is relatively large, accounting for 27% and 24%, respectively, while that of sintering process is only 4%. The indirect water withdrawal of the steel-making and rolling processes account for the highest proportion, 25% and 32% respectively. b

Journal Pre-proof Because of the gradual cumulative calculation method, as the steel production process tends to the final product, the water footprint accumulated in the upstream product gradually increases. For the total water footprint of the processes, owing to the indirect water withdrawal, the steel-rolling and steel-making processes account for the largest proportion, followed by the iron-making, sintering, and coking processes

8%

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CO SI IM Fig. 4. The composition and proportion of water footprint in each process

The water footprint of the various types of fuels and power used by the case company is shown in Fig. 5. Electricity consumption is a significantly contributor to the water footprint. The greater the electricity consumption is, the greater is the negative impact on water resources in the natural environment. The dominant position of thermal power generation leads to a higher water footprint in electricity production. Linz— Donawitz Process Gas (LDG) has the smallest water footprint due to the negligible usage. 1% 0% 7% 7%

11% 48% 11%

15% Electricity

Coke Power Coal BFG Steam COG Fig. 5. Water footprint of different fuels and power

LDG

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4.1.2 Energy consumption for water As the steel industry consumes a large amount of water, water is known as the blood of steel production. The extraction, transportation, and utilization of water are often done by pump work. Therefore, energy consumption for water in this study was defined as the power consumption generated by the water pump. Table 2 shows the energy consumption of the three types of water in each process, as well as the CO2 emission and water footprint due to energy consumption. The power generation process and sewage treatment are not included owing to data limitations. For the water types, the power consumption of circulating water is the main energy consumption of the case company, which is 7.64 kWh/t, accounting for 94% of the total energy consumption of the three types of water. In other words, the electricity consumption of circulating water required per t of crude steel is 7.64 kWh. For the processes, the energy consumption of water used in rolling process is 4.28 kwh/t, accounting for 53% of the total, and that of sintering process is the smallest. The CO2 emission and water footprint generated by water are measured by electricity consumption, and the calculation results are based on Eqs. (15) and (16). It is worth noting that the CO2 emission factor and water footprint factor of electricity are calculated by weighting the ratio of self-generated and purchased electricity. The calculation results are based on energy consumption, resulting in the maximum CO2 emission and water footprint of the recycled water, which are 6.14 kg CO2/t and 70.19 L/t, respectively. Similarly, the CO2 emission and water footprint of the water used in the sintering process are the smallest, 0.03 kg CO2/t and 0.34 L/t, respectively. The difference in the amount of water in the different water types and different processes is the main cause of these results. Table 2 Energy consumption, CO2 emission and water footprint of the three types of water Kinds Items Units CO SI IM SM RO Energy consumption kWh/t 0.18 0.01 0.04 0.12 0.08 Fresh kg/t 0.15 0.004 0.03 0.09 0.06 CO2 emission water Water footprint L/t 1.71 0.04 0.32 1.08 0.68 Energy consumption kWh/t 1.17 0.03 0.49 1.74 4.20 Recycled kg/t 0.94 0.03 0.39 1.40 3.38 CO emission 2 water Water footprint L/t 10.77 0.30 4.48 16.03 38.61 Energy consumption kWh/t 0.04 0.0002 0.003 0.01 0.01 Wastewater kg/t 0.03 0.0001 0.002 0.01 0.005 CO2 emission discharge Water footprint L/t 0.35 0.002 0.03 0.11 0.05

4.1.3 Energy and water consumption as well as CO2 emission Based on the integrated material and energy flow model, the energy consumption, water consumption, and CO2 emission of the case company were calculated and shown in Fig. 6. The energy consumption was based on the actual input-output substances provided by the case company, and the standard coal factors originate from the General Principles for Comprehensive Energy Consumption Calculation (GB/T, 2008). The CO2 emission factor of self-generated electricity was calculated by the method given in our previous study (Zhang et al., 2018a), and other emission factors come from the World Steel Association (WSA, 2016). The fresh water consumption intensity was calculated based on the total fresh water flow and the process product yield given in the energy balance sheet.

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Fig. 6 Results of the integrated material-energy flow model

It can be found from the calculation results that the comprehensive energy consumption per t steel of the case company is 590.16 kgce/t. The iron-making process accounts for a maximum of 59.16%, which is determined by the characteristics of the production process. Many previous studies have neglected the energy consumption of the power process in iron and steel production (Guo and Fu, 2010; Chen et al., 2014). The power process of the case company consumes 13% of the total energy owing to power generation. If the power process is not considered, the energy consumption of the iron making process reaches 68%, which is consistent with the research result of (Wang et al., 2007). The fresh water consumption per t of steel in the case company is 3.12 m3/t. The power process is the largest contributor to fresh water consumption, accounting for 27.59% consumption, followed by the coking process, while that of the sintering process accounts for only 2.96%. The CO2 emission per t of steel in the case company reached 2437.45 kg/t. The result is similar to enterprise B in our previous study (Zhang et al., 2018a), and the result of (Yu et al., 2015). For the specific process, the pre-iron processes are the main contributors to CO2 emission, accounting for 61% among the six processes. The iron-making process and coking process account for the largest amounts of CO2 emission, accounting for 24% and 21%, respectively.. According to the above research results, 590.16 kgce of fuel and power and 3.17 m³ of industrial fresh water are required to produce one t of crude steel by the case company, while generating 2437.45 kg of CO2 emissions and 9.48 m³ of water footprint.

Journal Pre-proof 4.2 Effectiveness evaluation of ESTs 4.2.1 Selection of ESTs Energy efficiency is praised as the first fuel (Yang and Yu, 2015). The adoption of ESTs can significantly improve the energy efficiency of steel companies and the entire industry, while bringing about synergistic water-saving and emission-reduction effects. As shown in Tables 3 and 4, most of the information regarding the selected technologies is obtained from our previous studies (Zhang et al., 2017a; Zhang et al., 2018b; Zhang et al., 2019b); the water price was obtained from the local water group. Considering the variation in the carbon trading price from CNY 23.8 per t to CNY 68.9 per t and the different price scenario analysis of previous studies (Wang et al., 2009; Yang et al., 2014; X. Wu et al., 2016), CNY 50 per t was set as the carbon tax value of this study. The payback period for different technologies varies considerably, and a discount rate of 15% was adopted in this study, which is acceptable to both business decision-makers and government policy-makers. Table 3 ESTs in each process. Coking Steel making 1 Coke dry quenching (CDQ) 16 Flue gas waste heat recovery 2 Coal moisture control(CMC) 17 LT-PR of converter gas Sintering 18 Slag heat recovery Heat recovery from sintering and sinter 3 19 Continuous casting cooler 4 Increasing bed depth 20 Efficient ladle preheating 5 Reduction of air leakage Rolling Integrated casting and rolling (strip 6 Low-temperature sintering tech. 21 casting) Iron making 22 Recuperative or regenerative burner 7 Pulverized coal injection (130kg/t) 23 Process control in hot strip mill 8 Top-pressure recovery turbines (TRT) 24 Waste heat recovery from cooling water Preheating of fuel and air for hot blast Hot delivery and hot charging of casting 9 25 stove billet 10 Recovery of blast furnace gas 26 Heat recovery on the annealing line Automated monitoring and targeting 11 Slag heat recovery 27 systems 12 Pulverized coke oven gas injection General technologies 13 Pulverized waste plastic injection 28 Preventative maintenance Energy monitoring and management Steel making 29 systems 14 Recovery of BOF gas and sensible heat 30 Cogeneration 15 Dry gas cleaning system (wet to dry ) 31 Combined cycle power plant-CCPP

Table 4 Details of 31 technologies in the CISI. No.

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24 T25 T26 T27 T28 T29 T30 T31

Typical energy saving (GJ/t)

Cost of Conserved Energy yuan/GJ

0.46 0.07 0.35 0.08 0.17 0.35 0.74 0.12 0.26 0.01 0.19 0.41 0.11 0.13 0.15 0.10 0.76 0.07 0.42 0.02 0.30 0.16 0.30 0.04 0.25 0.12 0.22 0.45 0.12 0.38 0.51

-12.50 217.29 -35.04 -26.28 -30.50 -31.37 -56.49 -45.81 -29.85 96.37 101.64 -30.73 -23.84 -34.51 -17.62 0.63 -27.85 271.10 -46.49 50.44 310.14 -33.13 32.17 592.19 -28.51 416.38 64.60 -27.27 -29.33 109.79 -37.24

Cost of Saved Energy (yuan/t)

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Direct water saving

Water saving benefits

Carbon reduction

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(yuan/t)

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10.00 14.60 2.39 0.48 0.16 0.24 1.70 0.96 1.69 1.52 26.63 2.59 1.32 1.99 4.15 4.32 0.24 27.32 2.26 1.66 253.11 2.03 20.32 23.09 0.42 55.25 21.92 4.28 2.06 57.51 0.21

1.87 4.40 0.00 0.00 0.00 0.00 -17.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.65 3.50 -4.85 -6.80 0.00 -148.93 0.00 0.00 2.00 1.95 0.00 0.00 0.00 0.00 0.00 0.27

14.54 2.29 10.91 2.52 5.45 10.91 23.14 3.91 8.24 0.30 6.01 12.83 3.31 4.07 4.70 3.13 23.86 2.25 13.15 0.63 9.39 5.10 9.39 1.25 7.83 3.76 6.76 14.09 3.77 11.90 15.97

62.57 85.36 12.00 2.40 0.78 1.20 10.63 5.61 10.55 9.50 166.67 16.23 8.29 10.00 24.24 21.67 1.40 170.98 14.13 10.40 1661.91 10.18 102.00 135.00 2.12 277.30 110.00 25.00 10.34 360.00 1.25

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2.50 0.82 3.69 0.07 0.01 0.26 3.02 1.37 1.30 0.29 0.95 2.36 0.54 2.41 1.44 0.96 0.20 0.69 1.83 0.03 1.75 2.33 1.28 0.15 1.67 1.53 1.20 2.46 1.82 3.90 2.73

0.66 0.02 0.41 0.06 0.03 0.02 0.30 0.44 0.10 1.15 0.40 1.21 0.14 0.06 0.08 0.25 0.17 0.13 0.32 0.00 0.12 0.16 0.13 0.06 0.04 0.09 0.01 0.20 0.19 0.36 0.22

Annualized capital costs

Journal Pre-proof 4.2.2 Cost-effectiveness evaluation of ESTs under different scenarios Fig. 7 shows the energy-saving potential and cost of 31 ESTs. According to the ECSC model, the total cumulative energy-saving potential of energy efficiency measures is 7.84 GJ/t, and the energy-saving cost is CNY 1924.76 per GJ. T17 has the highest energy-saving potential, accounting for 9.72% of the total potential. There are 19 cost-effective technologies, accounting for 76.74% of the total energy-saving potential. For the processes, iron-making and steel-making have the greatest energysaving potential, which are 1.64 GJ/t and 1.46 GJ/t, respectively. The comprehensive energy consumption per t of steel in the case company is 17.3 GJ/t, and the total energysaving potential of 31 ESTs accounts for 45%. It is worth noting that the energy consumption of the case company will not reduce to 9.46 GJ/t after the implementation of all ESTs. This is because some technologies have been applied to the case company, with unknown application levels, and hence, energy consumption will not reduce to 9.46 GJ/t. 600

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Energy efficiency measures play a key role in reducing the present and future energy consumption in CISI. In addition, the adoption of energy efficiency measures also brings about direct synergistic water-saving effects (MIIT, 2012). As shown in Fig. 8, eight of the 31 ESTs have direct water-saving effects, and the cumulative watersaving potential is 1.65 m3/t. The water-saving potential of the steel-making process is 0.70 m3/t, which accounts for 44.38% of the total water-saving potential. T8 has the largest direct water-saving capacity, reaching 0.43 m3/t.

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Fig. 8. Direct water-saving potential of ESTs.

In Fig. 9, the black dashed line represents the energy-saving effect of the ESTs, and the red dashed line represents the direct water-saving ECSC of the ESTs. Compared with only considering the energy-saving effect, the cumulative energy-saving cost is reduced by 1.49% by incorporating the direct water-saving effect into the costeffectiveness evaluation. The water-saving benefit of T8 has the greatest impact on energy-saving cost, from CNY-23.62 per GJ to CNY-34.87 per GJ.

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Apart from the direct water-saving effect, the indirect water-saving effect can also be brought about owing to the adoption of ESTs. Energy-saving methods include reducing the energy input from the source or increasing energy recovery at the end. The types of energy savings include electricity, steam, gas, coal, and coke. Detailed calculation results are listed in Table 4, which are derived from (MIIT, 2012; Zhang and Wang, 2013). The cumulative indirect water savings of 31 ESTs is 7.54 m3/t, of which the ironmaking process accounts for a maximum of 49.73%. It can be found that T12 and T10 have the greatest indirect water-saving potential. Injecting COG to replace coke can effectively reduce the water footprint because COG has a water footprint factor much smaller than coke. Although the water footprint factor of BFG is small with a large quantity, if it is not recycled, much energy waste and indirect water waste will result. The adoption of ESTs can also lead to synergistic carbon reduction. As shown in Fig. 10, the total emission reduction potential of 31 ESTs is 911.26 kg/t, of which general technologies have the largest emission reduction potential, accounting for 24%, followed by the steel-rolling and iron-making processes, accounting for 22% and 21%, respectively. Among the individual technologies, T30 has the largest emission reduction potential, at 77.99 kg/t.

Fig. 10. CO2 emission reduction potential of ESTs. The black dashed line in Fig. 11 is the ECSC of the ESTs, and the red dashed line

Journal Pre-proof incorporates carbon-reduction benefits into cost assessment. It can be seen that the red line as a whole is below the black line. In other words, compared with considering only the energy conservation benefit, the total energy conservation cost can be effectively reduced while incorporating carbon reduction benefits into the cost-effectiveness evaluation. Considering the benefit of carbon reduction leads to a 12.35% decrease in the total energy-saving cost. T10 is the most affected technology, and its energy-saving cost dropped by CNY 30.42 per GJ. The 19 ESTs are cost-effective, accounting for 76.74% and 70.27% of the total energy savings and emission reduction, respectively. 600

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The direct water-saving benefits and carbon-reduction benefits are included in the cost-effectiveness evaluation, as shown in Fig. 12. The black dashed line only considers the energy-saving benefits, while the red dashed line considers all three benefits simultaneously. Compared with the single energy-saving benefits, when the benefits of direct water-saving and carbon reduction are included in the cost-effectiveness evaluation, the energy conservation cost per unit energy is reduced to CNY 1658.37 per GJ, a decrease of 13.84%. Considering the water-saving and carbon-reduction benefits, the impact on the energy-saving cost of T10 and T8 is the largest, which are reductions by CNY 30.42 per GJ and CNY 22.19 per GJ, respectively.

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Fig. 12. ECSC with energy and water-saving potential and CO2 emission abatement potential.

5. Discussion 5.1 Sensitivity analysis The cost-effectiveness, energy efficiency improvement potential, and synergistic water-saving and carbon-reduction effects of the selected 31 ESTs were described in detail. Because the discount rate and price are important parameters in the costeffectiveness evaluation, it is necessary to determine how the discount rate and price changes affect the cost-effectiveness of the technologies. 5.1.1 Impact of discount rate Based on the above results, the impact of discount rate was first analyzed. Because policy-makers and business decision-makers have different starting points for considering issues, the latter tend to prefer a high discount rate, while the former usually use a lower discount rate. Therefore, a 5% and 25% discount rate for sensitivity analysis were used in this section. The impact of the discount rate is shown in Fig. 13. As the discount rate increases, the energy-saving cost increases simultaneously. When the lower discount rate of 5% is chosen, a total of 21 technologies are cost-effective, with an accumulated energy savings of 6.41 GJ/t. Compared with the benchmark discount rate of 15%, the cumulative energy-saving potential of cost-effective ESTs increases by 6.65%, the water-saving potential increases by 18.58%, and the carbon reduction potential increases by 8.46%. When the higher discount rate of 25% is chosen, the cost-effective ESTs are reduced to 18, and the cumulative energy savings is 5.55 GJ/t. Compared with the benchmark discount rate of 15%, the energy-saving potential of cost-effective ESTs

Journal Pre-proof is reduced by 7.72%, the water-saving potential is reduced by 14.38%, and the carbonreduction potential is reduced by 7.82%. As a typical case affected by the discount rate, T21 has the largest change in costeffectiveness; in this case, cost-effectiveness is only demonstrated in the low discount rate scenario but not in the benchmark discount rate scenario nor high discount rate scenario. At the same time, T17 is the least affected by the change in discount rate, with only a change of 0.49–0.58%. In addition, it is noteworthy that the total energy-saving, water-saving, and emission-reduction potential of each technology remains unchanged at different discount rates. 1000.0000

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Fig. 13. Sensitivity analysis on ECSC with different discount rates.

5.1.2 Impact of price The impact of energy price has been analyzed in our previous studies, and this paper focuses on CO2, namely, the impact of carbon tax changes on the costeffectiveness of ESTs. Since CNY 50 per t is a low carbon tax value, two higher values of CNY 150 per t and CNY 300 per t were set for sensitivity analysis. As can be seen from Fig. 14, with the increases of carbon tax, the energy-saving cost of individual ESTs shows a downward trend. Compared to the baseline scenario of CNY 50 per t, when the carbon tax is increased to CNY 150 per t, a total of 20 ESTs are cost-effective, with T16 added as a result of the increase in carbon tax. The cumulative energy-saving potential, water-saving potential, and carbon-reduction potential increase by 1.66%, 18.58%, and 3%, respectively. When the carbon tax increases to CNY 300 per t, the energy cost of T10 is reduced from CNY 96.37 per t to CNY -55.71 per t, and the costeffective ESTs increase to 21. The energy-saving potential and carbon-reduction potential increase by 1.82% and 3.92%, respectively. Because the T10 does not have a direct water-saving effect, the water-saving potential still maintains an 18.58% increase

Journal Pre-proof compared to the baseline scenario. 600.0000

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Fig. 14. Sensitivity analysis on ECSC with different carbon tax.

5.2 Factor analysis of electricity consumption structure Electricity consumption plays a key role in environmental impact in this study. As a non-main production process, CO2 emission and water footprint generated by power consumption are 261 kg/t and 13 L/kWh, accounting for 11% and 10% of the total, respectively. The carbon emission factor and water footprint factor of the electricity generated by the case company and that purchased from power grids are quite different. The power consumption structure of the case company consists of 40% purchased electricity and 60% self-generated electricity. Therefore, it is particularly important to analyze the impact of changes in the power consumption structure.

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Fig. 15. Impact of changes in power consumption structure on water footprint and CO2 emission.

Fig. 15 shows the effect of changes in the power consumption structure of the case company on CO2 emission and water footprint per t of steel, expressed by the amount of change. Because the carbon emission factor of the self-generated electricity is larger than that of the purchased electricity, and the water footprint factor is smaller than that of the purchased electricity, the variation of CO2 emission and water footprint per t of steel show opposite trends. With the change in power consumption structure from completely outsourced to completely self-produced, the CO2 emission per t of steel increases, and the water footprint per t of steel decreases. Compared with the baseline scenario, when the power consumption structure of the case company completely turns to self-produced electricity, the CO2 emission per t of steel increases by 728.96 kg/t, and the water footprint of the steel per t decreases by 0.87 m3/t.

5.3 Recommendation for steel works from a nexus point of view The use of energy at the cost of water consumption, reducing energy consumption directly or indirectly decreased the use of water resources. For any iron and steel company, the adoption of EST is one of the most effective way to save energy when economic conditions permit, especially considering the synergistic effects of watersaving and carbon reduction, as well as pollutant emission reduction. In this study, T1, T16, T17, and T31 have both large energy-saving potential and water-saving potential, and such technologies should be implemented preferentially. Owing to the imbalance of regional steel production and water resource distribution in China, technologies with small energy-saving potential but high water-saving potential, such as T8 and T15, are still worthy for adoption in areas with water shortages or high water prices. For provinces with less water distribution and higher steel production, policy-makers should take into account the intrinsic nexus in these technologies. Similarly, technologies with high energy-saving potential and emission reduction potential, such as T3 and T30, have the highest application priority. For companies with large carbon emission or small carbon emission quota, technologies such as T14 and T22, which have small energy-saving potential but large emission reduction potential,

Journal Pre-proof are still worth adopting. Water comes at the cost of energy consumption. In addition to the energy used in water extraction, transportation, and utilization, wastewater treatment also requires energy consumption. Therefore, the reduction of water input directly reduces the energy consumption and, indirectly, reduces the energy consumption of wastewater treatment. Gao et al., (2011) found that by optimizing the water system of a steel company, sewage discharge can be reduced by 0.429 m3/t. Gu et al., (2016) pointed out that 0.4–0.5 kWh of electricity is required for 1 m³ of sewage treatment. Therefore, by optimizing the water system, water savings of 0.429 m3 and energy savings of 0.17–0.21 kWh per t of steel can be achieved. We have found that steel plants in the coastal areas of China have access to fresh water through desalination. For example, 50% of the industrial fresh water in Shougang Jingtang Company comes from desalinated seawater; however, this is achieved at the expense of greater energy consumption and CO2 emission (Tong et al., 2018; Jia et al., 2019). From the perspective of saving fresh water resources, coastal cities with less fresh water resources can replace river water and groundwater with desalinated seawater. However, from the perspective of the water-energy-carbon nexus, this is not conducive for energy conservation and CO2 emission reduction. Generally speaking, although there are certain differences between different steel companies, the analysis ideas and analysis methods of the case company can still be used for other companies. The water footprint analysis method and conservation supply curve analysis method selected in this paper are universal. And the original data is calculated from the energy balance sheet of the case study which also used in other steel plants. According to their own same original data and provided method, the researcher can find ways to reduce the consumption of water and energy, as well as the generation of CO2 and water footprint, so as to jointly contribute to the energy and water conservation and emission reduction of China's iron and steel industry.

6. Conclusions An integrated material-energy flow model and a water-CO2 ECSC model were systematically established to analyze the water-energy-carbon nexus of a typical steel company from the plant level. The results show that 590.16 kgce of fuel and power and 3.17 m³ of industrial fresh water are required to produce one t of crude steel in the case company, while generating 2437.45 kg of CO2 emissions and 9.48 m³ of water footprint. The application of ESTs has a significant effect on reducing energy consumption, and there are also synergistic effects of water savings and carbon reduction. The 31 ESTs have accumulated energy savings of 7.84 GJ/t, direct water savings of 1.65 m3/t, indirect water footprint reduction of 7.54 m3/t, CO2 emission reduction of 911.26 kg/t, and the total energy-saving cost is CNY 1658.37 per GJ. The benefits of water savings and carbon reduction change the economic efficiency of technology, changing the priority of technology choice. Under the company economic condition permits, T1, T16, T17, and T31 should be popularized preferentially for companies with high pressure on water resources. As for energy consumption of the three types of water in the case company, the use of recycled water consumes most of the electricity, and the related CO2 emission and water footprint are also the largest, 6.14 kgCO2/t and 70.19 L/t, respectively. In addition to the synergy of technologies, there are also trade-offs in the case company, especially for the electricity consumption structure. For the business

Journal Pre-proof decision-makers, the synergy effect brought by a technology is worth adoption, and the trade-off effects remain to be discussed. Although the results of this study are based on the plant level, they can still be extended to the industry level. The conclusions can provide a reference for other business operators and government policy makers, as well as provide theoretical basis for other fields. It also has practical significance for guiding the production, energy-saving and emission reduction works of steel companies. The research is based on a typical Chinese steel company. There may be a certain gap between the actual situation of the company and the selected reference data. Therefore, further efforts are needed to eliminate this difference, such as establishing a database of carbon emission factors in line with China's actual situation. Future work can be comprehensively reviewed in conjunction with pollutants, such as wastewater and waste gas. Relevant direct economic losses (emissions taxes), abatement costs, and indirect losses due to human health impacts can be included in the cost-effectiveness assessment. Furthermore, owing to data limitations, only the energy consumption of water utilization has been considered. The next step is to establish a life-cycle energy consumption model of water for steel companies from water extraction to sewage treatment. Through these efforts, the water-energy nexus model can be further improved.

Journal Pre-proof Acknowledgment This work was supported by the National Natural Science Foundation of China (No. 51874095) and by the National Key Research and Development Program (Project No. 2016YFB0601305). The authors gratefully acknowledge the reviewers and editors for their fruitful comments. Nomenclature Abbreviation ACC AEB BF BFG BOF CCE CCPP CDQ CE CEF CEM CISI CMC CMR CNY CO COG CT CWC DWS EC ECSC ESP ESR EST FWC FWCI FWSR IM LDG LT-PR MIIT OPP PE PW RO SCE SCF SEC SI SM SWF

Annualized capital cost Annual energy benefit Blast furnace Blast furnace gas Basic oxygen furnace Cost of conserved energy Combined cycle power plant Coke dry quenching CO2 emission CO2 emission factor CO2 emission mitigation China’s iron and steel industry Coal moisture control CO2 emission mitigation rate China Yuan Coking Coke oven gas Carbon emission tax Circulating water consumption Direct water saving potential Energy consumption Energy conservation supply curve Energy-saving potential Energy-saving rate Energy saving technology Fresh water consumption Fresh water consumption intensity Fresh water-saving rate Iron making Linz—Donawitz Process Gas LT process for purification and recovery Ministry of Industry and Information Technology Onsite power plant Price of energy Price of water Rolling Specific CO2 emission Standard coal factor Specific energy consumption Sintering Steel making Specific water footprint

Journal Pre-proof TRT WC WF WFF WSA Symbols d i I in j k m n O&M out p pi P Q r s Wo Wd Wl

Top-pressure recovery turbines Water consumption Water footprint Water footprint factor World Steel Association Discount rate Production process Investment Input Technology Different types of water Proportion of purchased electricity Lifetime of the energy efficiency measures Operating and maintenance Output Purchased electricity Steel ratio coefficient Production Quantity resource Self-produced electricity Water obtained Water discharge Water loss

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Journal Pre-proof Wang Xiaozhuang: Methodology, Formal analysis, Writing - Original Draft Zhang Qi: Conceptualization, Resources, Writing - Review & Editing, Supervision, Funding acquisition Xu Lisong: Investigation, Data Curation Tong Yongjuan: Writing Review & Editing Jia Xiaoping: Writing - Review & Editing Tian Hong: Writing Review & Editing

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Journal Pre-proof Highlights: 1) A plant-level water-energy-carbon nexus was developed. 2) Models for water footprint and integrated material-energy flow were developed. 3) Synergy and trade-offs exist in the production process and technology selection. 4) Energy conservation supply curve model evaluates cost-effectiveness of technologies.