An Investigation into the Effects of Coke Dry Quenching Waste Heat Production on the Cost of the Steel Manufacturing Process

Abstract

It is essential to evaluate the prospective development trends of coke dry quenching (CDQ) waste heat power generation, to reduce the comprehensive cost of the steelmaking system. Based on TIMES energy system optimization model, this study develops a model of China’s iron and steel production. Three scenarios are established, predictions and comparisons are conducted regarding the iron and steel production structure, total CDQ quantity, CO₂ and pollutant emissions under these scenarios. The findings indicate that: (1) The advancement of hydrogen metallurgy and EAF scrap smelting facilitates a reduction in the quantity of BF-BOF steelmaking and total CDQ consumption. (2) The decreasing demand for CDQ shows that the shift to clean production alters process pathways and compels the energy system from scale-driven to flexibility-focused. (3) The marginal value of the CDQ system is contingent upon the targeted policy support for multi-energy co-generation systems and their deep integration with hydrogen infrastructure. Accordingly, the utilization of CDQ waste heat power generation should be considered as a transitional strategy, it will be imperative to implement a reduction in capacity.

1. Introduction

The continuous advancement of dual-carbon standard construction has prompted the iron and steel industry (ISI) to evolve towards sustainable development [1]. The ISI’s two primary methods of production, i.e., blast furnace–basic oxygen furnace (BF-BOF) and electric arc furnace (EAF), contribute to over 99.5% of total production [2]. Currently, BF-BOF technology relies primarily on coke for heat generation and as a reducing agent. In total, 800 kg of coal are consumed and approximately 1.9 metric tons (tons) of CO₂ are emitted to produce 1 ton of iron and steel [3]. As a critical component of the BF-BOF steelmaking process, the energy consumption of and pollution emissions resulting from coke dry quenching (CDQ) account for approximately 8.9% of the total energy used for and emissions from steelmaking [4]. To achieve the dual-carbon development objective, hydrogen can potentially replace coke as the reducing agent in BF-BOF; initial trials are anticipated to substitute about 30% of fossil fuels with pure hydrogen. However, the cost of hydrogen raw materials is higher than that of coke, and making advances in hydrogen storage technology also remains challenging. This has become a limiting factor for the transition of the ISI from BF-BOF steelmaking to hydrogen metallurgy [5]. As such, further research and development are required for process and product quality improvements. Although the energy consumption and carbon emissions of EAF scrap smelting are only one-third of those of BF-BOF steelmaking, the cost of electricity is high. In addition, the production efficiency of EAF is lower than that of converters, and the technical level of EAF equipment in China is relatively underdeveloped [6]. Consequently, the proportion of EAF short-process steelmaking to China’s total steelmaking remains low [7].

In the face of complex developments, over the next decade, the BF-BOF steelmaking process is likely to remain dominant in the ISI as a cost-effective option [3]. The ISI includes various low-carbon transformation strategies, including enhancing energy efficiency [8], integrating renewable energy and hydrogen energy [9,10], substituting zero-carbon electricity [11], promoting scrap-based EAF production [12,13], and carbon capture, utilization, and storage (CCUS) transformation [14,15]. In this context, the effect of persistent CDQ waste heat power generation on the production structure of the ISI deserves thorough exploration. In addition, the effects of pollutant emissions and strategies to control the installed scale of CDQ waste heat power generation to minimize costs are also topics that need to be comprehensively discussed [16].

Existing studies have conducted comprehensive investigations into the energy efficiency of CDQ, waste heat power generation, hydrogen metallurgy, and EAF short-process steelmaking. These studies have thoroughly examined the strategic approaches adopted by the ISI to reduce production costs, along with the progressive development of innovative steelmaking technologies. This paper provides a systematic and in-depth analysis of these critical aspects.

Improving energy efficiency can effectively reduce energy consumption in CDQ waste heat power generation. As a result, CDQ energy efficiency has become a hot topic among numerous scholars [17]. Some researchers have suggested that energy efficiency should be enhanced through the development and measurement of novel technologies. For instance, Wang, et al. [4] proposed utilizing soft sensors to predict the energy economic efficiency of the CDQ process in real-time. Utlu and Onal [18] applied thermo-photoelectric (TPV) systems to the waste heat potential of Turkey’s ISI. Fumoto, et al. [19] examined heat transfer devices for the efficient utilization of unused heat energy in Japan. Li, et al. [20,21] proposed a novel process of coking wastewater gasification quenching in shaft furnaces to enhance the energy efficiency of quenching coke. Other scholars, such as Qin, et al. [22] and Sun, et al. [23], suggested ways to improve energy efficiency by investigating methods to optimize the efficiency of CDQ waste heat recovery. In relation to pollution discharge from CDQ, Yang, et al. [24] determined that the annual waste heat generation from CDQ is equal to 163,200 MWh, an amount that can potentially decrease the emission of approximately 168,145 tons of CO₂ equivalent annually. However, limited scholarly attention has been devoted to the integration of CDQ waste heat generation into a comprehensive analytical framework. Additionally, extant research methodologies have exhibited significant limitations regarding the temporal parameterization and associated variable configurations. This investigation would facilitate the exploration of potential improvements in systemic energy efficiency and operational cost optimization.

Numerous scholars have dedicated extensive efforts to diminishing the final cost of steelmaking. Waste heat power generation is recognized as an efficient approach to reducing pollutant emissions in China’s ISI, thereby diminishing environmental impacts and economic expenses [25]. Several researchers have examined the use of organic ranking cycle (ORC) networks to collect waste heat, estimating also the technical and economic potential of waste heat recovery [26,27,28]. Some scholars have applied the maximum theoretical work exergy to the waste heat recovery process [29,30,31]. In terms of thermal economy, Wang, et al. [32] implemented a soft sensing approach based on deep learning to optimize the CDQ process operation, utilizing economically efficient data for the modeling and optimization of CDQ flow supplementary air. Besevli, et al. [33] examined the waste heat recovery from integrated ISI plants to conduct a thermal economic analysis of oxygen-supported combined systems. Numerous investigations have proven that enhancements in the production process can entail cost reductions and augmented benefits [34,35,36,37,38]. Cost savings can also be achieved through innovations in management approaches, increases in organizational efficiency, upgrades in granular management levels [39], and the integration of time management practices [40]. Through the development of optimization models, extensive research has been carried out to assess decision-making in steel production under optimal cost frameworks [41,42,43,44]. Additionally, some studies have conducted a comparative analysis of the efficacy of pollutant control strategies in mitigating environmental costs [45]. However, this study identifies a significant gap in the literature, specifically that there is a lack of comprehensive studies evaluating the systemic costs of CDQ waste heat power generation within the steelmaking system, which consequently impedes the determination of the optimal scale for CDQ waste heat power generation. Nonetheless, existing research on the costs associated with alternative steelmaking technologies offers pertinent insights for this investigation.

As an energy-efficient reducing agent, hydrogen has multiple sources, a high calorific value, good thermal conductivity, and a high reaction rate [46]. Currently, hydrogen is recognized as the cleanest energy source with the greatest development potential [47]. Currently, hydrogen’s application in the ISI can be primarily categorized into two aspects: as a reducing agent for iron oxide reduction and as a fuel for heating purposes. The former primarily involves BF production and the gas-based direct reduced iron (DRI) process. Some researchers have suggested that hydrogen-enriched shaft furnaces are well suited for the development of China’s ISI [48], also proposing possible operation strategies for hydrogen-enriched BF utilizing hydrogen co-injection [49]. Another perspective maintains that the application of the direct reduction route utilizing hydrogen-based shaft furnaces results in lower CO₂ emissions. This approach also has room for further optimization. These researchers contend that the technical parameters, including the properties of Fe₂O₃ powder [50], the dynamics of reduction gas, and pellet size [51], are being continuously improved. As for hydrogen’s use as a fuel for heating, the optimization of the thermodynamic conditions for the reduction of molten wüstite by hydrogen is of paramount importance for the optimization of ironmaking processes, energy conservation, and emission reduction [52]. The hydrogen-rich, pure oxygen–carbon cycle reduction smelting furnace ironmaking process is also a technological innovation [53]. Current research has predominantly focused on the process characteristics, energy efficiency, and environmental impacts of various hydrogen metallurgy technologies while neglecting to analyze the implications of developing hydrogen metallurgy technologies on the scale control of CDQ waste heat power plants. Furthermore, although previous studies have reached a consensus regarding the emission reduction effects of different hydrogen metallurgy methods, this aspect has not been integrated into the cost evaluation of the comprehensive economic and environmental impacts of hydrogen metallurgy technologies on CDQ waste heat power generation systems.

In EAF scrap smelting, scrap steel is utilized as a raw material, and electric energy is the primary energy source. This method offers a number of advantages, such as a brief process, minimal pollution, and reduced investment [13]. In recent times, government policies have called for a reduction in ISI production capacity, control over energy consumption and carbon emissions, and an increase in the proportion of EAF scrap smelting [54]. Consequently, various technical studies have been conducted, seeking ways to counteract the adverse effects of high scrap recycling prices and elevated industrial electricity prices. These studies mainly cover three aspects. The first is the issue of clean energy generation. The utilization of an all-waste smelting model with moderate toner and natural gas, low-carbon materials, or the solar-powered electric arc furnace steelmaking process [55] will be the optimal choices for future EAF scrap smelting processes. Secondly, there is the question of how to enhance energy efficiency. The deployment of coherent jet technology [56,57], the utilization of exhaust gases from a rotary hearth furnace–electric arc furnace for indoor power generation [58], and the application of control vector parameterization technique [59] could facilitate the optimization of batch trajectories in EAF. This would both enhance energy efficiency and diminish CO₂ emissions in the produced materials. Thirdly, an academic team conducted an evaluation of the economic and technical viability of integrating EAF technology [60]. Energy systems modeling was utilized to evaluate the potential for fuel conservation, CO₂ emissions reduction, and the utilization of waste heat within EAF steel manufacturing [61]. These studies are instrumental in incorporating CDQ waste heat power generation into the energy systems model, thereby exploring the trajectory of CDQ waste heat power generation in conjunction with the concurrent advancement of EAF smelting and hydrogen metallurgy.

In summary, the majority of scholars’ research has predominantly focused on the impacts of advanced technologies, including waste heat power generation, hydrogen metallurgy, and EAF scrap smelting, on critical factors such as feedstock utilization, energy efficiency, and environmental sustainability. However, there has been insufficient analysis regarding potential transformations in steelmaking infrastructure, pollutant emission profiles, and technology-specific installed capacities under conditions where the total costs of hydrogen metallurgy, EAF scrap smelting, and BF-BOF steelmaking are optimized. Furthermore, previous studies did not address the impact of CDQ on the overall cost of the ISI in the BF-BOF process, nor did they provide guidelines for a reasonable determination of the scale of CDQ development. Therefore, this study employs The Integrated MARKAL-EFOM System (TIMES) model to (a) simulate the effects of CDQ waste heat power generation on steelmaking structure and pollutant emissions in the ISI under three scenarios, namely the baseline, hydrogen metallurgy development, and EAF scrap smelting development; and (b) discuss the installed capacity of CDQ waste heat power generation corresponding to the achievement of minimum cost under each of these three scenarios. The novelty of this study lies in the development of a comprehensive simulation model for the steelmaking process. The proposed model integrates multiple interconnected subsystems, including CDQ waste heat power generation, hydrogen metallurgy, and EAF scrap smelting. The aim of this model is to optimize the processing capacity of CDQ waste heat power generation by taking into account a dynamic cost analysis of the influences of technological advancements. Moreover, this study further analyzes the potential development trajectory up to the year 2060. The findings of this research are instrumental in contemplating the repercussions of novel technological breakthroughs within the ISI on the economic and environmental expenses associated with CDQ waste heat power generation. Furthermore, they serve as valuable references for advancement, strategic planning, and policy-making relative to the sustainable growth of integrated ISI. Additionally, these results contribute to fostering a systematic transition of the ISI from conventional BF ironmaking practices towards more low-carbon, cutting-edge technologies, such as hydrogen metallurgy.

2. Materials and Methods

This section presents the selection and construction of the energy system simulation model, followed by a detailed explanation of the model’s mathematical principles and module composition. Subsequently, based on existing policies and technical measures, a potential scheme for ISI production is proposed. Lastly, the data sources of the model are provided.

2.1. Model Selection and Construction

2.1.1. Model Selection

The energy system simulation model includes the energy system’s primary energy supply, energy conversion, and final energy consumption, and is divided into an “up-bottom” and a “bottom-up” approach.

The “up-bottom” modeling approach simulates comprehensive economic systems by examining the feedback effects of policy-induced price and income fluctuations across various markets [62]. This approach encompasses macroeconomic models, input–output models, and computable general equilibrium (CGE) models. Macroeconomic models are grounded in the expenditure and production approaches to GDP, where aggregate supply is determined by primary, secondary, and tertiary industries, while aggregate demand is influenced by government consumption, household consumption, gross capital formation, and net exports [63]. Discrepancies between supply and demand can impact consumer prices and other macroeconomic indicators. However, predictions may be biased due to stringent historical data requirements and the insufficient marketization of energy prices. Input–output models, which are linear general equilibrium frameworks, are employed for the static and dynamic analyses of economic interdependencies, yet their applicability is limited when assessing the macroeconomic impacts of energy policies [64]. CGE models, rooted in general equilibrium theory, integrate the strengths of input–output models and linear programming models, enabling the analysis of external shocks on economic variables [65]. Nevertheless, their policy interpretation is constrained by idealized assumptions. While top-down models strive to optimize the utility of supply and demand within energy systems and elucidate policy mechanisms and impacts, they face challenges in incorporating future discrete energy technology and cost assumptions. Consequently, these models are primarily utilized for evaluating the economic and environmental implications of policies and are deemed unsuitable for this particular study.

The “bottom-up” modeling approach, grounded in engineering technology, delineates the processes of energy consumption and production while projecting future energy demand, supply, and environmental impacts [66]. This approach encompasses three principal model categories: energy system simulation models, sectoral forecasting models, and dynamic energy optimization models. Energy system simulation models characterize energy supply and demand technologies through methodological approaches, including mechanism-based modeling and field experimental data-driven modeling, with the primary objective of minimizing system-wide comprehensive costs. These models facilitate the analysis of technological development potential under diverse policy frameworks and are extensively employed in energy system optimization and pollution emission studies. Sectoral forecasting models project energy demand at both sectoral and national levels by leveraging historical data on terminal energy consumption across various sectors and inter-sectoral development relationships. However, these models exhibit limitations in predictive accuracy, with estimation errors frequently surpassing 10%. Dynamic energy optimization models articulate the technical processes spanning energy resource exploration, processing, conversion, and terminal utilization, with the dual objectives of minimizing energy system costs and optimizing energy technology configurations [67]. These models are instrumental in exploring the prospects and optimization pathways of energy technologies within the context of coordinated energy, economic, and environmental development.

This study investigates the impact of CDQ waste heat power generation on the cost of the ISI from a technical perspective and examines the influence of CDQ technology on the environment, the economic cost, and BF-BOF steelmaking quantity. As such, this approach is akin to a “bottom-up” energy system model analysis. The TIMES model was predicated on a referenced energy system that delineates the multifaceted aspects of energy extraction, processing, conversion, distribution, and end use. This facilitated the selection of the most cost-effective technology configuration while at the same time satisfying the national energy demand. By employing the TIMES model, it is feasible to optimize the cost efficiency of the CDQ waste heat power generation system in China while simultaneously considering the investment and operational decisions within the steel production process. Consequently, the present study utilized the TIMES energy system model as the foundational research framework.

2.1.2. Model Construction

The TIMES model is a linear representation of partial equilibrium, which is capable of computing within the energy market domain. This illustrates the model’s capacity to quantify the energy flow and logistics predicated on price mechanisms [68]. Consequently, at each computed price level, this model ensures that the energy provider offers precisely the amount of goods that the consumers are inclined to purchase. This equilibrium is maintained across all the stages of the energy supply chain and encompasses primary energy provision, secondary energy conversion, and final energy services. The architecture of the TIMES model is contingent on the interplay between variables and their associated equations, as encoded in the input data. These data constitute the fundamental underpinning of each TIMES model, incorporating both qualitative and quantitative datasets. These datasets can be further distinguished based on their distinct characteristics. For example, the energy types can be categorized into fossil fuels, nuclear energy, and renewable energy. For each technological application, geographic location, and temporal duration, the model necessitates the postulation and calculation of the corresponding technical and economic parameters to generate the required quantitative data [69].

The decision variables represent the choices made by the TIMES model and encompass elements such as technological capability, the adoption of novel technologies, and operational levels [70]. The primary decision variables and parameters incorporated in the objective function are systematically presented in

Table 1. The primary decision variables and parameters incorporated in the objective function.

Variables	Variables Description
OBJ(z)	the comprehensive cost of the system
R	the set of regions
years	the set of years
refy	the base year
ACT(r, t, p, s)	Operational Intensity of Technology p in Sub-Period s of Period t within Region r
NCAP(r, t, p)	Newly Commissioned Capacity of Technology p in Period t within Region r
CAP(r, t, p)	Total Installed Capacity of Technology p in Period t within Region r
CAPT(r, t, p, s)	Aggregate Installed Capacity of Technology p in Sub-Period s of Period t within Region r
ANNcost(r, y)	Annual Total Expenditure of Technology Pathway in Region r during Year y, including technology investment cost, annual operation and maintenance cost, variable cost, and associated tax subsidies
ACT_COST(r, t, p)	Variable Operational Cost Associated with Technology p in Period t within Region r
FLO_EMIS	Emission Factor per Unit Process or Activity
FLOW(r, t, p, c, s)	Production or Consumption Volume of Product c by Technology p in Sub-Period s of Period t within Region r
UC_CAP(uc_n, side, r, y, p)	Coefficient of Activity Variables in User-Defined Constraints
DEM(r, t, p)	Vector of Final Crude Steel Demand
dr,y	the discount rate
Salv(z)	the residual value of assets when the technical device is phased out

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In accordance with the scholarly inquiries conducted by Wan, et al. [69] and Andrea, et al. [71], the primary objective of this function is to minimize the overall expenditure of the energy system, as follows:

$$OBJ(z)={\displaystyle \sum _{r=1}^R{\displaystyle \sum _{years}(1+}}{d}_{r,y}{)}^{refy-y}\times ANN\cos t(r,y)-Salv(z)$$

(1)

In the present study, the TIMES model for China’s iron and steel sector was developed with reference to the project report on the strategic path of green, low-carbon innovative development for China’s coal–coke hydrogen iron industry chain, as delineated by the Chinese Academy of Engineering under the “double carbon” objective [72]. This model employed 2020 as the base year and used a five-year cycle to outline the energy system structure, which included energy, economy, environment, and steel and power demand for the period 2020 to 2060. By simulating the hydrogen metallurgy and EAF scrap smelting scenarios, the impact of CDQ processes on China’s crude steel production structure, CDQ installed capacity, and carbon dioxide and pollutant emissions was analyzed. Figure 1 displays the reference energy system diagram of the TIMES model utilized in this study.

As illustrated in Figure 1, the TIMES model of China’s ISI developed in this study is significantly different from the models employed in previous studies. Compared to the studies of Christos, et al. [70] and Andrea, et al. [71], this study presented a novel perspective by examining a regressive technological process rather than a cutting-edge technology within the steel manufacturing sector as the focal point of investigation. Furthermore, this study engaged in an exhaustive discourse on the sector’s production scale. The corpus of research in this particular domain is relatively scant, which means that the findings of this study have a distinct novelty. Compared to the research of Wan, et al. [69], this study meticulously elucidated the material conversion process in relation to CDQ waste heat power generation within the technical module of the TIMES model for the steel system. Furthermore, the impacts of diverse steelmaking techniques on energy and material utilization during the generation of CDQ waste heat power were meticulously evaluated, thereby extending the steel system’s TIMES model. Compared to the research of Rehman, et al. [68], the present study incorporated an additional layer of analysis concerning the repercussions of new steelmaking technology on traditional technology. This further enhanced the depth of the discourse surrounding the advancement of steel production technologies. In summary, compared to previously published literature, this study offered a distinct and definitive perspective, provided an all-encompassing portrayal of the steel system, and constructed a macro-level model. Thus, the results have a considerable reference value for the regulation of the future technological development scale within the steel system’s domain. The model is primarily composed of an economic module, an energy system module, an environmental module, and a demand module.

The economic module is primarily manifested in the construction model, which serves to outline the economic parameters of diverse technologies. This model serves as the foundation to determine the most cost-effective combination of steelmaking technologies through linear programming. This study primarily delved into the economic parameters associated with BF-BOF-CDQ steelmaking, as well as the development of hydrogen metallurgy and EAF scrap smelting. The discussed aspects encompass the resource supply cost and energy processing conversion cost in steelmaking.

The energy technology module primarily encompasses three components in the development of the technical pathway. The first component involves the establishment of the technical parameters for steelmaking in the base year. This refers to the efficiency, installed scale, and annual availability factor of steelmaking and front-end energy supply technology. The second component pertains to the growth rate constraint of installed capacity under existing power generation technology. The emissions from hydrogen metallurgy and EAF scrap smelting are lower than those from BF-BOF steelmaking. TIMES models aim to minimize system costs, tending to predominantly select new technologies. To address this issue, this study referred to the “14th Five-Year Plan for the Development of Renewable Energy” [73] and set a minimal growth rate for BF-BOF steelmaking. This approach was taken to ensure that this form of steelmaking is not rapidly phased out. The third component involves determining the installed capacity of various steelmaking technologies. This includes the minimum installed capacity for hydrogen metallurgy and EAF scrap smelting and the maximum installed capacity for BF-BOF steelmaking.

The primary function of the environmental module is to limit the release of pollutants, such as CO₂, particulate matter, SO₂, NOx, solid waste, and hazardous waste. The environmental module also establishes the emission coefficient of pollutants and the CO₂ emission coefficient for various steelmaking processes. However, given that one of the research objectives of this study was to investigate the impact of CDQ on pollutant emission reduction, the module presented in this study does not restrict the emission of pollutants and CO₂.

In the demand module, the foundational objective of the establishment of the steel system simulation model is to satisfy the demand for crude steel and electricity. Data pertaining to demand were acquired via exogenous variables. Specifically, to project China’s crude steel demand from 2020 to 2060, this study referred to the forecast data from the “Research on the Development Strategy of Hydrogen Metallurgy towards Carbon Neutrality”, which was published by the Natural Resource Defense Council [74], as well as to the findings of Chang, et al. [75]. A comprehensive evaluation of the calculations and analyses presented in this study has been made, and the details are illustrated in Figure 2.

2.2. Scenario Analysis

2.2.1. Scenario Analysis Method

Scenario analysis serves as a strategic methodology for evaluating future development trajectories and potential risks [76]. This approach constructs multiple hypothetical scenarios to simulate diverse future conditions and assess their organizational or decision-making implications, thereby offering valuable insights for strategic planning. Particularly effective in situations characterized by information scarcity or data deficiency, where precise future projections are unattainable, scenario analysis integrates both qualitative and quantitative methodologies to comprehensively capture evolving trends [77]. While extensively applied in power system research for development status assessment, cost analysis, and scale determination, its utilization in CDQ waste heat power generation systems remains limited. Notably, both systems exhibit methodological diversity: the power system encompasses various generation technologies, including wind, nuclear, photovoltaic, and thermal power, whereas the CDQ waste heat power generation system incorporates multiple steelmaking processes such as traditional long-process steelmaking, hydrogen metallurgy, direct reduced iron, and electric arc furnace scrap steel smelting. Given these parallel characteristics, this study applies scenario analysis to examine the steelmaking structure, CO₂ emissions, pollutant emissions, and quenching scale within the CDQ waste heat power generation system, demonstrating both methodological appropriateness and academic novelty.

2.2.2. Reference (Baseline) Scenario Setting

The established scenario setting is roughly identical to the current status in China. Specifically, crude steel production is reliant on the BF-BOF steelmaking process, where coke serves both as a reducing agent and a source of heat. The employed coke quenching method is full CDQ, with 2020 as the foundation year. The objective of this scenario is to examine the implications of BF-BOF advancement on the structure of crude steel production and CO₂ and pollutant emissions. The future evolution of the total quantity and installed capacity of CDQ is also analyzed, considering the current waste heat power generation technology.

2.2.3. Development of Hydrogen Metallurgy Scenario Setting

Due to its significant emission reduction potential, hydrogen metallurgy has emerged as a strategic focus for leading ISI enterprises. According to the “14th Five-Year Plan for the Development of Renewable Energy” [73], the setting of target years for hydrogen metallurgy’s carbon emission intensity reduction (compared to 2020) allows for an analysis of the effects of CDQ on the structure of crude steel production, the installed capacity of CDQ, and CO₂ and pollutant emissions within China’s ISI. This analysis can be conducted under various hydrogen metallurgy installed capacity targets. This study also referred to the “Hydrogen Metallurgy Development Strategy Study for Carbon Neutrality” [74] and China’s hydrogen metallurgy development roadmap and forecast data, sourced from the Rocky Mountain Institute, the Energy Transition Council, and the China Metallurgical Industry Planning and Research Institute [78]. On this basis, this study posited that the hydrogen metallurgy process substitution rate within China’s ISI will progressively increase to 15%, 30%, 70%, and 95% by 2030, 2040, 2050, and 2060, respectively. The high and low scenarios incorporate a five-percentage-point increase or decrease, with a constant growth rate assumed during the interpolation period. The specific scenario setting for the hydrogen metallurgy process substitution rate is presented in

Table 2. Target scenario setting for hydrogen metallurgy process substitution rate.

Scenario Setting	2030	2035	2040	2045	2050	2055	2060
High scenarios	20%	25%	65%	55%	75%	95%	100%
Medium scenarios	15%	20%	30%	50%	70%	90%	95%
Low scenarios	10%	15%	55%	45%	65%	85%	90%

Data sources: The “14th Five-Year Plan for the Development of Renewable Energy” [73]; the “Hydrogen Metallurgy Development Strategy Study for Carbon Neutrality” [74]; the Rocky Mountain Institute, the Energy Transition Council, and the China Metallurgical Industry Planning and Research Institute [78].

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2.2.4. Development of the EAF Scrap Smelting Scenario Setting

Compared to the BF-BOF steelmaking process, EAF scrap smelting exhibits significant advantages in terms of energy conservation and environmental protection. By establishing the ratio of EAF scrap smelting output to the total steel output in the target year, the effects of incorporating CDQ in various EAF scrap smelting scenarios on the crude steel production structure, the installed capacity scale of CDQ, and the ISI’s CO₂ and pollutant emissions can be analyzed. According to “The 14th Five-Year Plan Comprehensive Work Plan for Energy Conservation and Emission Reduction” [79] and the “China Electric Arc Furnace Short Process Steelmaking Development Research Report: Current Tendency, Potential for Carbon Reduction and Development Prospects” [80], in the first stage (2023–2035), EAF scrap smelting is projected to reach 20% of the total production in 2030 and over 35% in 2035. In the second stage, the proportion of EAF steel production in 2050 is expected to exceed 40%. By the end of the third stage (2060), the ratio of EAF steel production will persistently remain above 40%. Consequently, this study assumed that EAF steel production in China’s ISI will account for 20%, 30%, 40%, and 50% of the total steel production in 2030, 2040, 2050, and 2060, respectively, with an increment or decrement of five percentage points in the high and low scenarios, respectively. In addition, a uniform growth rate was assumed during the interpolation period. The target scenario setting of the proportion of EAF steel to the total steel output is presented in

Table 3. Scenario setting of EAF scrap smelting proportion target.

Scenario Setting	2030	2035	2040	2045	2050	2055	2060
High scenarios	25%	30%	35%	40%	45%	50%	55%
Medium scenarios	20%	25%	30%	35%	40%	45%	50%
Low scenarios	15%	20%	25%	30%	35%	40%	45%

Data sources: “The 14th Five-Year Plan Comprehensive Work Plan for Energy Conservation and Emission Reduction” [79] and the “China Electric Arc Furnace Short Process Steelmaking Development Research Report: Current Tendency, Potential for Carbon Reduction and Development Prospects” [80].

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2.3. Sources of Data

The required data for the model were categorized into four sections: demand; ISI production parameters; investment cost parameters; and main fuel and raw material parameters. Taking China as an example, the data sources are primarily government documents and research reports from Chinese government departments and authoritative organizations, as follows:

(1)

China’s crude steel demand and electricity demand data, as detailed in Section 2.1.2.

(2)

ISI production parameters, including the input and output data of the main technologies in each process and CO₂ emission intensity, which were mainly derived from Li, et al. [81]; Li, et al. [82]; Ren, et al. [83]; and Yilmaz, et al. [5].

(3)

Investment cost parameters, including the investment cost of each link in steelmaking, which were mainly derived from the research of Lu, et al. [16]; Lopez, et al. [84]; and Zang, et al. [41].

(4)

Main fuel and raw material parameters, including the reserves and costs of fuel and raw materials in 2020, were mainly taken from the China ISI Yearbook 2021 [85], the China Energy Big Data Report 2022 [86], and other sources.

3. Results and Discussion

Through the development of the TIMES model for steel systems, this study generated simulation outcomes across various scenarios. In the context of uncertainty analysis, some scholars implemented Monte Carlo Analysis to quantify the deviation of results from central data within a ±10% error margin of input parameters, thereby validating the reliability of the findings [87,88]. This methodological approach indicated that the research has systematically accounted for the impact of input data variations within a 10% error range on the outcomes, incorporating potential modifications to scenario analysis results. Consequently, based on simulation outputs from the TIMES model and supplemented by field investigations and expert consultations, this study has implemented necessary revisions and adjustments to specific scenario analysis outcomes. The following presentation of results and analysis was prepared.

3.1. Analysis of the Outcomes Under the Reference Scenario

3.1.1. Analysis of the Steelmaking Structure Under the Reference Scenario

Under the reference scenario, the model simulation revealed that the ISI is primarily dominated by the BF-BOF steelmaking process, exhibiting a pattern of initial growth, followed by a gradual decline. Specifically, the proportion of this method to total steel production is projected to marginally increase from 2020 to 2030, with the increase potentially being triggered by the rising dominance of cost-effective steelmaking scales. However, this proportion is expected to gradually decrease from 2030 to 2050 owing to the reduction in steel demand, which is declining at a rate of approximately 10%. In terms of crude steel production, the annual decline in steel demand will lead to a consistent drop in the output of BF-BOF steelmaking. Over the period 2020–2060, under the reference scenario, the proportion of hydrogen metallurgy, DRI, and EAF scrap smelting and steelmaking will remain relatively stable. Owing to infrastructure limitations and cost constraints, the share of hydrogen metallurgy will show a slight increase of 3.2%, with the steelmaking quantity oscillating around 96 million tons. The proportion of DRI is anticipated to fluctuate slightly in 2045, with production volumes projected to remain within the range of 0.8 to 100 million metric tons. In the long term, its technological share is expected to persist below 12%, signifying its constrained substitution potential. The share of EAF scrap smelting hovers around 15%, corresponding to a steelmaking quantity that is slightly above 123 million tons.

Under the reference scenario, the sluggish advancement of hydrogen metallurgy and EAF scrap smelting stems from multifaceted barriers, which this study systematically examines through technological, economic, and policy perspectives. Technologically, inadequate infrastructure for hydrogen production, storage, and transportation elevates the costs of high-purity hydrogen generation, while EAF operations face scalability constraints due to rapid electrode consumption, short furnace lining lifespans, and exorbitant maintenance expenditures. Persistent technical limitations—including immature hydrogen metallurgy processes and challenges in removing impurities from scrap steel—compromise steel product performance. Energy reliability further undermines decarbonization efforts: hydrogen production depends on intermittent renewable energy sources (e.g., wind and solar), whereas fossil fuel-derived electricity for EAFs negates their carbon emission reduction advantages. Economically, the capital-intensive requirements for retrofitting hydrogen metallurgy facilities or establishing new steel plants—particularly the investments in large-scale electrolyzers and renewable energy systems for green hydrogen production—drastically exceed the costs of conventional coke-based ironmaking. Market mechanisms also fail to incentivize low-carbon steel adoption, as stable price premiums remain unrealized and downstream industries exhibit limited tolerance for cost premiums. China’s EAF scrap supply chain, vulnerable to economic cyclicality and plagued by an underdeveloped recycling infrastructure, suffers severe price volatility. Policy-wise, insufficient government subsidies for hydrogen metallurgy inadequately cover R&D and infrastructure costs. Restrictive scrap trade policies exacerbate supply chain instability and expose industries to carbon border adjustment mechanisms (CBAMs), while stringent regulations on dioxin emissions during scrap melting impose substantial end-of-pipe treatment costs, further eroding economic feasibility.

Given that the ratio of raw material input to crude steel output is greater than 1 when the model is optimized, the ratio of total steel production in each year to the forecast demand is also greater than 1. The steelmaking structure from 2020 to 2060 under the baseline scenario is presented in

Table 4. Steelmaking structure from 2020–2060 under the base scenario. Unit: %/million tons.

		2020	2025	2030	2035	2040	2045	2050	2055	2060
BF-BOF	proportion	64.20%	67.70%	69.60%	62.80%	61.90%	61.90%	60.50%	59.10%	59.00%
	output	733	722	676	640	531	486	455	420	387
Hydrogen Metallurgy	proportion	11.10%	11.10%	11.10%	11.20%	11.50%	12.00%	13.40%	14.70%	14.3%
	output	127	124	111	102	95	90	88	93	96
DRI	proportion	8.80%	8.90%	8.90%	9.60%	10.20%	11.30%	10.80%	11.60%	12%
	output	100	98	89	82	81	80	83	75	76
EAF	proportion	15.90%	12.30%	10.40%	16.40%	16.40%	14.80%	15.30%	14.60%	14.70%
	output	182	169	123	96	139	129	109	106	96

Data source: calculated by this study.

.

3.1.2. Analysis of the Pollution Emissions Under the Reference Scenario

Under the reference scenario, both CO₂ emissions from China’s ISI and the discharge of CDQ pollutants will have a downward trend from 2020 to 2060, albeit with periodic fluctuations. Specifically, CO₂ emissions are projected to decline from 1408.34 million tons in 2020 to 727.15 million tons in 2060, while the discharge of CDQ pollutants will also decrease from 858,800 tons in 2020 to 445,600 tons in 2060, with the most significant reduction occurring between 2030 and 2035. This reduction is primarily attributable to the contraction of the BF-BOF process scale driven by the decline in steel demand. Given the limited adoption rate of clean technologies, the emission reduction predominantly relies on the passive scaling down of conventional processes rather than active technological substitution. Furthermore, the enhancement in energy synergy efficiency remains constrained, failing to establish a robust mechanism for emission reduction. Consequently, the emission reduction pathway under the baseline scenario manifests a gradual linear decline, devoid of the nonlinear acceleration effect typically driven by technological breakthroughs. The CO₂ emissions and pollutant emissions under the reference scenario are depicted in Figure 3 and Figure 4, respectively.

3.1.3. Analysis of the CDQ Quantity Under the Reference Scenario

In the reference scenario, the total quantity of CDQ will mirror the downward trend of the BF-BOF steelmaking development, which exhibits a linear contraction characteristic. During the initial phase (2020–2035), the average annual decline rate is approximately 1.8%. In the intermediate phase (2035–2050), this rate slightly increases to 2.3%, primarily due to the concentrated elimination of production capacity. In the terminal phase (post-2050), the decline rate decreases to below 1.5%. The reduction in CDQ demand is predominantly driven by the natural attenuation of crude steel demand and the passive reduction in the scale of the BF-BOF process owing to the absence of disruptive technological alternatives. The overall volume decline is gradual and lacks a distinct accelerated reduction phase, reflecting the loose coupling relationship between the energy recovery system and the main process flow under the low-technology pathway.

The evolution of CDQ quantity across different installed capacities becomes increasingly conservative, manifesting a pattern characterized by “existing capacity dominance and partial optimization”. Although large-scale units (190 t/h and above) are gradually phased out, the rate of elimination is significantly constrained. By 2060, 190 t/h units still retain 38% of their baseline scale, while 250 t/h units are reduced to merely 12% of their baseline scale. Medium- and small-sized units undergo no disruptive reconfiguration. Medium-sized 150 t/h units continue to dominate for an extended period, and the proportion of small-sized 70–90 t/h units increases by less than 15 percentage points. This relatively rigid installed capacity structure indicates that the steel system’s adjustment under the low-technology pathway focuses on optimizing the utilization efficiency of existing equipment, lacking the impetus for proactive transformation to enhance energy efficiency (

Table 5. The total CDQ quantity in China from 2020 to 2060 under the baseline scenario. Unit: ten thousand tons.

	2020	2025	2030	2035	2040	2045	2050	2055	2060
70 t/h	943	848	811	803	1024	1214	1334	1551	1752
90 t/h	2560	2304	2353	2562	2783	3061	3460	3983	6638
150 t/h	7770	7397	7271	6318	6209	6171	6384	6565	6866
190 t/h	15,372	14,028	13,743	10,996	8347	7711	7017	6493	3337
250 t/h	10,321	9289	7762	6218	6406	5178	3769	2095	523
CDQ quantity	36,966	33,867	31,940	26,897	24,769	23,334	21,964	20,687	19,116

Data source: calculated by this study.

).

The CDQ quantity evolution logic under the reference scenario reflects the passive adaptation mode of the traditional ISI under limited technological iterations. The total quantity contraction originates from the natural shrinkage of crude steel production scale rather than active reduction driven by process innovation. The adjustment of the installed capacity structure is constrained by the path dependence of existing capacity, serving only the local cost optimization of the existing process rather than aligning with the energy coordination needs of new production paradigms. This low-technology lock-in effect impedes the system’s ability to break through the “scale–efficiency” equilibrium state, underscoring the adaptability limitations of the traditional steel system in the absence of technological leaps.

3.2. Analysis of the Outcomes Under the Hydrogen Metallurgy Scenario

3.2.1. Analysis of the Steelmaking Structure Under the Hydrogen Metallurgy Scenario

The extensive implementation of hydrogen metallurgy technology has catalyzed a “three-stage leap” characteristic in China’s steel production structure. Under the baseline scenario, the predominance of the BF-BOF process remains consistent, with its market share sustained between 64.2% and 69.6% from 2020 to 2030. However, in the high, medium, and low hydrogen metallurgy scenarios, the output of this process declines from 733 million tons to 0, 3.05, and 31.10 million tons, respectively, by 2060 (Figure 5). The transformation of the steelmaking structure exhibits notable asymmetry: the growth rate of hydrogen metallurgy output experiences a step change around 2035. In the high scenario, the growth rate escalates to 23.20 million tons annually from 2040 to 2055, marking a 91% increase compared to the initial period (2030–2040). Figure 6 presents a four-year dataset, wherein each year’s data are categorized into three distinct columns representing high, medium, and low cases of hydrogen metallurgy scenarios, respectively. Within each case, the proportions of steelmaking quantity are meticulously categorized according to various methodologies, including EAF scrap smelting, DRI processes, hydrogen metallurgy, and BF-BOF, all relative to the total steelmaking quantity. This phenomenon underscores the synergistic effect of the maturity breakthrough in hydrogen metallurgy technology and the enhancement of supporting infrastructure (such as distributed hydrogen production stations), accelerating the transition of the primary steelmaking process from “carbon-based reduction” to “hydrogen-based reduction”.

The DRI process has played a pivotal transitional role in the evolution of the steelmaking structure. During the initial phase of hydrogen metallurgy promotion (2030–2040), the output of the DRI process exhibited a transient increase, reaching 72 million tons in 2040 from 45 million tons in 2020 under the high scenario. However, as the direct reduction technology of hydrogen metallurgy matures, the output of DRI contracts rapidly after 2050, with only 9 million tons remaining in 2060 under the high scenario. This shift reflects the gradual reorientation of the DRI process within the steelmaking system from an “alternative process” to a “supplementary process”.

The evolution of the EAF scrap smelting process illustrates the profound impact of the steelmaking structure transformation. In the base scenario, EAF output steadily rises from 210 million tons in 2020 to 380 million tons in 2060. However, in the high hydrogen metallurgy scenario, its output is compressed to less than 120 million tons after 2045. This structural transformation is attributed to the fact that, following the large-scale production of hydrogen metallurgy, the hydrogen-rich gas-based shaft furnace directly produces high-purity molten iron, thereby diminishing the raw material cost advantage of traditional scrap steelmaking. Concurrently, the low-carbon attributes of the hydrogen metallurgy process further erode the competitiveness of EAF in terms of emission reduction, leading to a substantial decline in the significance of the short process within the steelmaking system.

The driving forces behind the transformation of the steelmaking structure encompass not only technological advancements but also policy guidance and market mechanisms. The promotion of hydrogen metallurgy technology necessitates policy support, such as carbon pricing mechanisms, green financial instruments, and incentives for hydrogen infrastructure, to mitigate transformation costs for enterprises. Additionally, the escalating market demand for low-carbon steel provides impetus for the development of hydrogen metallurgy technology. In the long term, the deep decarbonization of the steelmaking structure will hinge on the continuous innovation of hydrogen metallurgy technology and the collaborative optimization of multiple processes to achieve a synergistic balance of economic and environmental benefits. This transformation process will reshape the global ISI landscape and propel China’s ISI towards high-quality development in a green and low-carbon trajectory.

3.2.2. Analysis of the Pollution Emissions Under the Hydrogen Metallurgy Scenario

Under the scenario of hydrogen metallurgy, from 2020 to 2060, the carbon emissions of China’s ISI and the emissions of CDQ pollutants will exhibit a year-on-year decline in all three scenarios. Specifically, the reduction rate of carbon emissions will be more gradual from 2020 to 2040, followed by a more rapid decline from 2040 to 2050, as illustrated in Figure 7. This declining trajectory is predominantly ascribed to the progressive advancement and implementation of hydrogen-based metallurgical technologies, which have effectively supplanted conventional metallurgical processes characterized by substantial carbon emissions. Simultaneously, pollutant emissions (including SO₂, NO_X, etc.) demonstrate a pronounced downward trajectory, particularly under the high scenario, wherein emissions are projected to reach zero by 2060, as illustrated in Figure 8. This underscores the substantial potential of hydrogen metallurgy technology in mitigating pollutant emissions.

3.2.3. Analysis of the CDQ Quantity Under the Hydrogen Metallurgy Scenario

Based on the simulation outcomes derived from the TIMES model, the total CDQ quantity demonstrates a consistent downward trajectory. Under the high case, the total CDQ quantity exhibits a progressive decline from 85.80 million tons in 2020 to complete depletion by 2060. In contrast, the medium and low cases project reductions to 10.89 million tons and 16.34 million tons, respectively. This substantial decrease is predominantly driven by the extensive implementation of hydrogen metallurgical technology: hydrogen, serving as an environmentally benign reducing agent, progressively supplants coke in BF-BOF operations, thereby substantially diminishing the requirement for CDQ. This comprehensive reduction not only underscores the carbon mitigation benefits of hydrogen metallurgical technology but also signifies the inexorable transition of the ISI towards cleaner production methodologies.

While large-scale centralized production facilitates the realization of economies of scale, the diminishing total CDQ quantity precipitates a distinct “de-scaling” phenomenon in its capacity structure: large-scale installed capacities exceeding 190 t/h are phased out post-2040 under the high case, with their proportions diminishing to 5% and 8% in the medium and low cases, respectively. Concurrently, the proportion of small-scale installed capacities (≤100 t/h) escalates from 22% in 2020 to 61% by 2060. This structural transformation primarily originates from the flexibility imperatives inherent in steel production process modifications: the advancement of hydrogen metallurgical technology reduces the scale of traditional BF-BOF operations, consequently decreasing CDQ demand while maintaining traditional steelmaking processes as supplementary components. Small-scale installed capacities demonstrate superior efficiency in resource utilization and system adaptability. This development provides substantive technical underpinnings for the ISI’s low-carbon transition while offering empirical validation for the dynamic optimization of clean technology configurations. Surprisingly, it was observed that, by the year 2060, the installed capacity of 150 t/h would reach 100% in the high scenario, which contrasts with this study’s initial expectations. However, this also indicates that a moderate and balanced development of the installed capacity for CDQ is crucial. Excessively large or small capacities cannot achieve the optimal goal of cost minimization. The comprehensive CDQ output in China from 2030 to 2060, under various hydrogen metallurgy scenarios, is presented in Figure 9. The figure presents a four-year dataset, wherein each year’s data are categorized into three distinct columns representing high, medium, and low cases of hydrogen metallurgy scenarios, respectively. Within each case, the coke quenching quantity is systematically categorized according to different installed capacities of CDQ.

The evaluation of the total CDQ output under the hydrogen metallurgy scenario indicates that, compared to the benchmark scenario, a more cautious development of CDQ will be required when vigorously promoting hydrogen metallurgy. A timely reduction in the scale of CDQ installation is among the strategies that could be employed to achieve minimal cost.

3.3. Analysis of the Outcomes Under the EAF Scrap Smelting Scenario

3.3.1. Analysis of the Steelmaking Structure Under the EAF Scrap Smelting Scenario

The structural evolution of steel production under the EAF scrap smelting scenario exhibits a triphasic transition characterized by nonlinear growth and systemic decarbonization. During the initial phase (2030–2040), the predominance of BF-BOF processes persists, with EAF scrap smelting stagnating at 15.9% due to constrained scrap availability and underdeveloped recycling infrastructure. However, the accelerated accumulation of scrap steel—projected to surpass 50% of crude steel production by 2040—triggers a fundamental shift in steelmaking dynamics, propelling EAF adoption to 29.3% of total production by 2050 and 40.7% by 2060. Concurrently, BF-BOF production diminishes from 733 million tons in 2020 to a projected 51–64 million ton range by 2060, achieving 92.3–95.4% decarbonization of conventional steelmaking processes through the EAF-driven displacement of coking coal-dependent metallurgical reactions. Figure 10 and Figure 11 present a four-year dataset, wherein each year’s data are categorized into three distinct columns representing high, medium, and low cases of EAF scrap smelting scenarios, respectively. Within each case, the steelmaking quantity and the proportions of steelmaking quantity are systematically delineated across various methodologies, including EAF scrap smelting, DRI processes, hydrogen metallurgy, and BF-BOF.

Hydrogen metallurgy demonstrates delayed yet complementary growth, contingent upon infrastructure scalability rather than explicit cost thresholds. Prior to 2040, its adoption persists at sub-5% penetration levels due to the dual constraints of underdeveloped hydrogen-based direct reduction systems and immature green hydrogen infrastructure. The post-2040 surge in hydrogen-reduced iron production achieves 17.2–32.4% market penetration by 2060 across decarbonization cases. This technological displacement specifically targets remaining conventional BF operations rather than challenging established EAF infrastructure, highlighting hydrogen metallurgy’s unique capacity to decarbonize primary iron production. This strategic deployment aligns with parallel infrastructure transitions, particularly CDQ’s capacity reduction to 31% of 2030 levels, demonstrating coordinated phaseout mechanisms across coal-dependent metallurgical systems.

DRI production demonstrates a parabolic growth trajectory under decarbonization constraints, peaking at 72 million metric tons by 2040 before declining to 9 million tons by 2060 in accelerated emission reduction scenarios. This structural contraction is driven by two key factors: First, material system evolution post-2050, where scrap-to-steel ratios exceed 60%, significantly reduces the demand for DRI as a secondary feedstock in advanced EAF economies. Second, the commercial viability of hydrogen–DRI hybrid systems by 2045 enables the retrofitting of conventional plants into hydrogen-ready operations, diminishing the role of standalone DRI units. Consequently, standalone DRI units face economic obsolescence, as hybrid configurations capture a large proportion of iron ore reduction capacity, restricting residual output to 9–38 million tons by 2060.

This structural transition emerges from the convergence of three mutually reinforcing drivers across material cycles, technological innovation, and policy frameworks. First, expanding scrap availability through sustained 3.2% annual stockpile growth progressively displaces virgin material consumption in EAF operations. Second, systematic CDQ capacity reductions averaging 4.8% annually from 2030 to 2050 dismantle conventional coke-based metallurgical systems. Third, tightening emission regulations and CBAM have accelerated BF obsolescence, with advanced economies leveraging established scrap networks to achieve 80% CDQ decommissioning by 2050. Crucially, the inherent energy efficiency advantages of EAFs (0.48–0.65 GJ/t compared to 1.8–2.4 GJ/t for BF-BOF) and their significant emission reduction potential (0.12 tCO₂/MWh grid carbon intensity by 2060) establish self-reinforcing substitution dynamics. These compounding advantages trigger irreversible sectoral realignment, positioning circular production models and hydrogen-based reduction technologies as dual pillars of post-2050 ferrous metallurgy.

3.3.2. Analysis of the Pollution Emissions Under the EAF Scrap Smelting Scenario

Under the scenario of EAF scrap smelting development, the emission characteristics of CO₂ and pollutants demonstrate a pronounced “scale-structure” dual-driven reduction trend, albeit through distinct pathways. The CO₂ emissions manifest a two-stage decarbonization process characterized by “ focus on process substitution—emphasis on energy optimization”: during the initial phase (2020–2040), the rapid expansion of EAF utilization directly diminishes the scale of BF processes, resulting in an average annual CO₂ emission reduction of 8%; and in the subsequent phase (2040–2060), the decarbonization of electricity emerges as the primary driver, with the emission reduction rate decelerating but the emission range differential reaching 12.3 million tons (21.4% of the average), ultimately reducing emissions to 51.32–63.62 million tons by 2060, representing a 92.3–95.4% decrease compared to the 2020 baseline. Pollutant emissions are more substantially constrained by process parameters, exhibiting a strong yet nonlinear correlation with CO₂ emissions: the emissions of CDQ pollutants decline as the scale of electric arc furnaces expands, but when the EAF proportion surpasses 30%, the increased complexity of scrap steel impurity treatment reduces the average annual reduction rate from 6% to 3–4%, with the emission range differential (12%) being lower than that of CO₂, indicating greater dependence on impurity control technology rather than energy structure optimization. This decline in emissions is illustrated in Figure 12 and Figure 13. In conclusion, achieving emission reductions in EAF operations necessitates a coordinated strategy encompassing process substitution and energy transition (for CO₂ reduction) alongside enhanced scrap steel pre-treatment (for pollutant control), thereby optimizing the efficiency of the two-stage synergy through low-carbon electricity and hierarchical resource recycling.

3.3.3. Analysis of the CDQ Quantity Under the EAF Scrap Smelting Scenario

Across various EAF scrap smelting scenarios, the total quantity of CDQ exhibits a nonlinear contraction pattern characterized by an initial acceleration followed by deceleration. The aggregate demand demonstrates a continuous decline from the 2020 baseline, contracting to 31% of the baseline by 2060, with an average annual reduction rate of 3.6%. This trajectory is intrinsically linked to the recycling cycle of scrap steel resources and the adoption rate of the EAF process: prior to 2030, the constrained growth in scrap steel supply results in a modest average annual CDQ reduction rate of 2.3%; from 2030 to 2050, the surge in scrap steel resources drives the average annual reduction rate to 4.8%; and post-2050, the deceleration in scrap steel resource growth, coupled with the diversion effect of hydrogen metallurgy technology, reduces the decline rate to 1.5%, forming a three-phase attenuation curve of “gradual–steep–gradual”. Notably, the synergistic benefits derived from structural transformation are substantial. The proportion of small-scale units increases by 46.2 percentage points, facilitating an average annual cost savings of 12.08 to 10.55 million tons of coke, thereby effectively mitigating the marginal cost pressure associated with clean technology implementation.

The evolution of CDQ capacity across different installed scales demonstrates a differentiated trajectory of “large-scale rapid exit and small-scale progressive penetration”. Large-scale units of 190–250 tons/hour are the first to be decommissioned, with 250 tons/hour units completely exiting the market post-2050 and 190 tons/hour units contracting to 22% of the baseline level by 2060. Medium-sized 150 t/h units exhibit a “delayed attenuation” characteristic, maintaining 78% of the baseline scale pre-2030 to ensure production stability during the transition period but accelerating their exit post-2040 as the electric arc furnace adoption rate exceeds 40%, with their proportion decreasing to 45% by 2060. Conversely, the proportion of small-scale units of 70–90 tons/hour demonstrates continuous growth, increasing from 22% in 2020 to 61% in 2060, with the contribution rate of 90 t/h units expanding by 160%, establishing them as the dominant model in the later phase. This “de-scaling” transformation aligns with the distributed production characteristics inherent in scrap steel smelting. The total amount of CDQ in China from 2030 to 2060, under various EAF scrap smelting scenarios, is presented in Figure 14. The figure presents a four-year dataset, wherein each year’s data are categorized into three distinct columns representing high, medium, and low cases of EAF scrap smelting scenarios, respectively. Within each case, the coke quenching quantity is systematically categorized according to different installed capacities of CDQ.

An analysis of the total quantity of CDQ under various EAF scrap smelting scenarios indicates that the structural transformation of the CDQ system represents not merely a reactive response to process substitution but also a proactive adaptation mechanism for the ISI’s low-carbon transition. Early-stage large-scale units provided a cost buffer for hydrogen metallurgy R&D; mid-term medium-sized units ensured production stability during the transition period; and late-stage small-scale units exhibit high compatibility with the decentralized demand for scrap steel smelting. This “scale–efficiency–timing” three-dimensional synergy mechanism validates the profound coupling logic between equipment renewal and process iteration in the steel system’s low-carbon transformation.

4. Conclusions

This study employs a “bottom-up” approach to construct the TIMES model for the ISI, incorporating CDQ waste heat power generation across diverse steelmaking processes. It distinctly elucidates the configuration of steel production, emissions of pollutants, and CO₂, as well as the requisite capacity for CDQ waste heat power generation under various scenarios of technological advancement in steelmaking. Furthermore, the study forecasts the temporal milestones in the emergence of hydrogen metallurgy and scrap smelting, together with the trajectory of installed capacity for CDQ waste heat power generation. Based on the findings, the following conclusions have been formulated:

(1) The economic downturn in the CDQ waste heat power generation is primarily driven by the predominant substitution of hydrogen metallurgy and the lack of synergy with EAF scrap smelting. Across the reference, hydrogen metallurgy, and EAF scrap smelting scenarios, the rationale behind the economic decline of CDQ waste heat power generation exhibits notable variations, yet all underscore the pivotal role of hydrogen metallurgy substitution. In the reference scenario, the gradual decline of the BF-BOF process results in a linear decrease in the total output of CDQ waste heat power generation, while its cost optimization is impeded by the inadequate substitution capacity of EAF scrap smelting, primarily due to the high costs associated with scrap steel impurity treatment and the absence of green power infrastructure. Under the hydrogen metallurgy scenario, the swift phase-out of the BF-BOF process precipitates a sharp decline in CDQ demand, and EAF scrap smelting further loses its synergy potential due to the high-purity pig iron displacement by hydrogen metallurgy. Although the scrap steel smelting scenario theoretically diminishes CDQ demand, the model indicates that it struggles to independently sustain the economic feasibility of the waste heat power generation system owing to the high costs of dioxin control and the volatility in the scrap steel supply chain. The comparison of these three scenarios reveals that the core of the cost reduction in CDQ lies in the irreversible substitution of hydrogen metallurgy for coke demand, and the lack of synergy with scrap steel smelting hastens this process.

(2) The technological substitution of hydrogen metallurgy and EAF scrap smelting has prompted the adaptive reconfiguration and reversal of scale effects in the CDQ waste heat power generation system. Under the scenarios dominated by hydrogen metallurgy and EAF scrap smelting, the total amount of CDQ exhibits a nonlinear decline, while the installed capacity structure demonstrates a “de-scaling” trend: large-scale units (≥190 t/h) are progressively phased out, and the proportion of small-scale units (≤100 t/h) increases to 61%. This structural transformation originates from the replacement of traditional processes by hydrogen metallurgy technology and short-process steel production. The coupling relationship between the diminishing demand for CDQ and the adjustment of production capacity structure reveals that the transition toward clean production not only alters the primary process pathways but also compels the energy synergy system to shift from being driven by economies of scale to prioritizing operational flexibility. Furthermore, the localized optimization of residual production capacity within traditional processes underscores the profound impact of technological iteration on the entire industrial chain.

(3) The dual constraints imposed by policy and market mechanisms, coupled with cross-system collaborative reconfiguration, have fundamentally reshaped the marginal cost advantage of CDQ waste heat power generation. The CBAM and hydrogen metallurgy subsidy policies constitute a dual constraint: the escalating carbon cost of the BF-BOF process necessitates the adoption of hydrogen metallurgy as a viable alternative, while the exorbitant cost of dioxin treatment in EAF scrap smelting impedes the ability to offset the CDQ demand gap, thereby entrapping CDQ waste heat power generation in a “policy squeeze–market contraction” cycle. The model demonstrates that the marginal value of the CDQ system must be realized through cross-system collaborative reconfiguration—specifically, through synergistic development with green hydrogen production and EAF scrap smelting. However, the incentive measures for hydrogen infrastructure and the regulatory framework for phasing out large-scale CDQ equipment are accelerating the divergence of technical pathways: the policy resource allocation favoring hydrogen metallurgy diminishes the viability of the scrap steel collaborative approach, while CBAM amplifies the full life-cycle carbon cost of BF-BOF, compelling enterprises to reposition CDQ waste heat power generation as a transitional supplement for green hydrogen production. This reconfiguration paradigm underscores that the marginal cost advantage of CDQ no longer hinges on traditional economies of scale but is contingent upon the targeted policy support for multi-energy co-generation systems (such as the prioritized grid integration of waste heat hydrogen production projects) and their deep integration with hydrogen infrastructure (such as incorporating waste heat recovery modules into distributed hydrogen production stations).

The present research has practical importance in managing the expansion of China’s CDQ waste heat power generation as a way to help achieve the country’s dual carbon emission reduction target. However, due to data collection constraints and the inherent uncertainty of the actual situation, several related issues require further investigation. While the TIMES model demonstrates reliability, it exhibits inherent limitations in long-term projections extending beyond 2060. Future research directions may incorporate supplementary analyses through advanced methodologies, including multi-task learning and deep belief network-based time series forecasting, to facilitate comprehensive comparative studies. With the advancement of data analytics, the technical characteristics, resource endowment, and regional distribution of the CDQ waste heat power generation system can be precisely modeled to improve the accuracy of the model, thereby providing valuable insights for industry planning and policy formulation. By employing methodologies such as nonlinear programming and input–output analysis, the key drivers for reducing the comprehensive economic and environmental costs of the system can be investigated, development strategies can be devised, and the primary driving factors at each stage can be identified.