• Energy Research

Industrial activities are generally energy and air emissions intensive, requiring bulky inputs of raw materials and fossil fuels and emitting huge waste gases including particulate matter (PM, or dust), sulphur dioxide (SO2), nitrogen oxides (NOx), carbon dioxide (CO2), and other substances, which are severely damaging the environment. Many studies have been carried out on the quantification of the concentrations of these air emissions. Although there are studies published on the co-effect of multi-air emissions, a more fair and comprehensive method for assessing the environmental impact of multi-air emissions is still lacking, which can simultaneously consider the flow rate of waste gases, the availability of emitting sources and the concentrations of all emission substances. In this work, a Total Environmental Impact Score (TEIS) approach is proposed to assess the environmental impact of the main industrial processes of an integrated iron and steel site located in the northeast of China. Besides the concentration of each air emission substance, this TEIS approach also combines the flow rate of waste gases and the availability of emitting sources. It is shown that the processes in descending order by the values of TEIS are sintering, ironmaking, steelmaking, thermal power, steel rolling, and coking, with the values of 17.57, 16.68, 10.86, 10.43, 9.60 and 9.27, respectively. In addition, a sensitivity analysis was conducted, indicating that the TEIS order is almost the same with the variation of 10% in the permissible CO2 concentration limit and the weight of each air emission substance. The effects of emitting source availability and waste gas flow rate on the TEIS cannot be neglected in the environmental impact assessment.

Integrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies.

AbstractSteam recovery and consumption contributes greatly to energy consumption per ton of steel. It is studied in this paper that the relationship between energy consumption per ton of steel and steam recovery & consumption, including main processes, auxiliary processes, diffusion and other users, respectively. And the factors attributing to energy consumption per ton of steel is analyzed. The expressions of steam consumption per ton of steel and energy consumption per steam supply are proposed in this paper.

The study is to provide a detailed physical and chemical characterization of particles collected in the ironmaking process, including a bunker system, a cast house and a pulverized coal feeding system. Using gravimetric, scanning electron microscope coupled with energy dispersive X-ray spectrometry (SEM-EDS), X-ray fluorescence spectrometry (XRF), inductively coupled plasma optical emission spectrometry (ICP-OES) analyses, the size distribution, morphology, elemental composition and emission factor of particles were investigated. The contribution rates of cast house for emission factors of total suspended particulates (TSP), PM10 and PM2.5 are the largest, 57.0%, 75.5% and 83.3%, respectively. SEM-EDS analysis indicated that cast house particle shapes are mainly formed by polymerization from spherical particles and ultrafine particles, whose main component is Fe. But, the particles of the bunker system or the pulverized coal feeding system are mainly the large ones of irregular block or powder particles and the main component is carbon. The highest content of the element in particles of the bunker system and cast house is Fe, followed by C, Si, Ca and Al. The main elements of particles in the pulverized coal feeding system are C, Si, Al and Ca, and their contents are 63.6%, 7.83%, 3.07% and 1.47%, respectively.

This work aims to identify the main factors influencing the energy-related carbon dioxide (CO2) emissions from the iron and steel industry in China during the period of 1995–2007. The logarithmic mean divisia index (LMDI) technique was applied with period-wise analysis and time-series analysis. Changes in energyrelated CO2 emissions were decomposed into four factors: emission factor effect, energy structure effect, energy consumption effect, and the steel production effect. The results show that steel production is the major factor responsible for the rise in CO2 emissions during the sampling period; on the other hand the energy consumption is the largest contributor to the decrease in CO2 emissions. To a lesser extent, the emission factor and energy structure effects have both negative and positive contributions to CO2 emissions, respectively. Policy implications are provided regarding the reduction of CO2 emissions from the iron and steel industry in China, such as controlling the overgrowth of steel production, improving energy-saving technologies, and introducing low-carbon energy sources into the iron and steel industry.

Abstract Blast furnace gas (BFG) is a byproduct gas and a significant energy source in integrated steelworks. Precise BFG generation prediction plays a pivotal role in site energy scheduling and management. However, it is difficult to accurately predict fluctuations in BFG generation due to the variable operational statuses and complex chemical reactions that occur inside the blast furnace, hindering efficient energy utilization and accordingly causing BFG to flare and contribute to environmental pollution. To tackle this problem, a hybrid event-, mechanism- and data-driven prediction method is proposed in this work. In this novel approach, blast furnace operational events are considered when predicting BFG generation, thus making predictions more accurate by integrating a priori mechanism knowledge associated with the blast furnace ironmaking process; additionally, this approach ensures high accuracy by selecting the best available data-driven prediction model for different event-associated periods. To demonstrate the predictive performance of the proposed hybrid method, comparative experiments are conducted using practical data from integrated steelworks. The results highlight the excellent performance and accuracy of the proposed method when compared with the results of widely used moving average and artificial neural network models.

Abstract There is large amount of waste heat resources in industrial processes. However, most low-temperature waste heat is directly discharged into the environment. With the advantages of being energy-efficient, enabling investment-savings and being environmentally friendly, the Organic Rankine Cycle (ORC) plays an important role in recycling energy from low-temperature waste heat. In this study, the ORC system driven by industrial low-temperature waste heat was analyzed and optimized. The impacts of the operational parameters, including evaporation temperature, condensation temperature, and degree of superheat, on the thermodynamic performances of ORC system were conducted, with R113 used as the working fluid. In addition, the ORC-based cycles, combined with the Absorption Refrigeration Cycle (ARC) and the Ejector Refrigeration Cycle (ERC), were investigated to recover waste heat from low-temperature flue gas. The uncoupled ORC-ARC and ORC-ERC systems can generate both power and cooling for external uses. The exergy efficiency of both systems decreases with the increase of the evaporation temperature of the ORC. The net power output, the refrigerating capacity and the resultant exergy efficiency of the uncoupled ORC-ARC are all higher than those of the ORC-ERC for the evaporation temperature of the basic ORC >153 °C, in the investigated application. Finally, suitable application conditions over other temperature ranges are also given.

This study comprehensively evaluates the potential of biochar as a substitute for high‐rank pulverized coal in various aspects including physicochemical properties, combustion performance, environmental emissions, and application costs. Biochar, characterized by its small particle size, reduced emissions, high volatility, elevated calorific value, and facile combustion, emerges as a promising alternative fuel to pulverized coal. Despite a lower ignition temperature, biochar demonstrates superior burnout efficiency and combustion kinetics, as indicated by its lower activation energy compared to pulverized coal. Moreover, considering China's substantial energy consumption, the substitution of coal with biochar could significantly reduce CO2 and SO2 emissions, making it a viable strategy for mitigating environmental pollution. In addition, the application cost of biochar is not higher than that of pulverized coal. This study underscores the feasibility and effectiveness of utilizing biochar as a sustainable alternative to high‐rank pulverized coal, offering valuable insights into cleaner and more efficient energy utilization.

With more variable and uncertain patterns of electricity production and consumption, the need for flexibility in the power grid is becoming increasingly crucial. Industrial energy systems have the potential to contribute to providing such flexibility. Yet, there is still a lack of effective methods to quantify the magnitude of available flexibility from industrial energy systems that can be optimally dispatched to support the operation of the power grid. This paper studies the flexibility provision from steam systems, which exist in many energy-intensive industries. A generic model of industrial steam systems with turbine-generators is presented to reflect its interactions with the power grid. Then, a hybrid physics-based and data-driven approach is developed to approximate the boundaries of the flexibility domain at different operating conditions of the steam systems. The proposed flexibility quantification method is applied to two real industrial steam systems in a paper mill and a steel mill. The results show that the proposed method can approximate the flexibility boundaries under uncertainty steam states and reflect key factors that affect the boundaries. Also, it is shown that neglecting the limits imposed by the steam network leads to an overestimation of flexibility boundaries at certain operating conditions.