Environmental challenges for the iron- and steelmaking process

7 - Iron, Steel, and Coke

In this paper, we investigate the charge batching planning problem (CBP) arising from practical steelmaking production. The CBP transforms the primary order requirements into various production batches (charges) subject to the steelmaking processing constraints and composite batch conditions according to the similarity in steelgrade, dimension, physical property, and due-date of orders. On the basis of a practical steelmaking process, a novel mixed-integer programming model for the CBP is presented by considering above constraints and features here, and two kinds of Lagrangian relaxation (LR) methods are proposed to solve the CBP by using different relaxation methods. In the first LR method, the relaxed problem is separated into subproblems by relaxing assignment constraints which are solved optimally by dynamic programming. In the second method, variable splitting is presented by introducing identical copies of some subsets of the original variables. To guarantee the equivalence to the primal problem, a number of equality coupling constraints are added into the model which are relaxed during the course of the second Lagangian relaxation. The multipliers in all above LR methods are then iteratively updated along subgradient directions. Computational experiments have been carried out and the results show that both LR methods can produce satisfactory average duality gaps and the second LR method is little better than the first method. The iron and steel industry has played an important role in the global market economy during the past decade. Along with rapid development, the iron and steel industry also faces fierce competition. To enhance their competitive power, many iron and steel corporations have changed production mode by transforming large lot production into small lot, with multiple varieties for satisfying their customer’s diverse requirements. Since most of production equipment in the iron and steel industry is very large, it often operates in batch mode to save resources and energy consumption, but there is significant conflict between large batch mode and the customer’s diverse requirements. Batching planning groups customer requirements into batches in order to resolve this contradiction and to improve the utilization of large production equipment. This paper investigates a charge batching planning problem arising from practical steelmaking production operation management. A simplified steelmaking production process is illustrated in Figure 1. The steel production flow begins with iron making in the blast furnace. Iron ore is converted to pig iron which is transported by torpedo car to the steelmaking mill. Then pig iron is transformed to liquid steel in the converter. At last the melted steel is solidified into slab in continuous casters. In the steelmaking stage, a charge that is a basic production unit for steelmaking production refers to concurrent smelting in the same converter (or electric arc furnace). In the continuous casting stage, the charges are grouped into different casts. Each cast consists of several charges with similar steelgrades that are processed consecutively on the same continuous caster using the same crystallizer. Reasonable design of charges and casts can improve productivity and cut down resource and energy consumption. The design of charges or casts is also regarded as charge batching planning or cast batching planning, respectively. The charge batching planning (CBP) is a key element of the production operation management in the process industry. It is converting the primary order requirements into various production batches (charges) subject to the steelmaking processing constraints. Figure 2 illustrates the process of making a charge batching plan. The rectangles in the left column in different colors denote steel orders with different steelgrade and specification. The orders with similar steelgrade and specification are denoted by close colors. As shown in Figure 2, orders in similar gray color are put into the first charge and orders in similar black color are put into the second charge. The batching decision problem has recently received more attention from researchers. Trautmann and Schwindt 2 proposed a resource-constrained project scheduling method to deal with

A review on reduction technology of air pollutant in current China's iron and steel industry

Progress of CCUS technology in the iron and steel industry and the suggestion of the integrated application schemes for China

Steel Research Institutions in China

The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature. (Received July 30, 2018; Accepted September 28, 2018)

The goal of this paper is to describe the life cycle inventory (LCI) approach to steel produced by ArcelorMittal’s Basic Oxygen Furnace (AMBOF) in Krakow, Poland. The present LCI is representative for the reference year 2005 by application of PN-EN ISO 14040:2009 (PN-EN ISO 2009). The system boundaries were labeled as gate-to-gate (covering a full chain process of steel production). The background input and output data from the basic oxygen furnace (BOF) steelmaking process has been inventoried as follows: pig iron, scrap, slag forming materials (CaO), ferroalloys, Al, carbon and graphite carburizer (material for carburization of steel), isolating powder, consumption of energy and fuels including natural gas, blast furnace gas and coke oven gas, electric energy, steam, air, oxygen, industrial water and heat, emission of air pollutants, waste, internal transport and land use. LCI steelmaking process was developed mainly on the basis of the following sources: site-specific measured or calculated data, study carried out by the AGH University of Science and Technology in Krakow, AMP Environmental Impact Report, study carried out by the Faculty of Mining Surveying and Environmental Engineering of the AGH University of Science and Technology in Krakow, literature information and expert consultations. The functional unit (FU) is represented by 1,677,987 Mg of steel, produced by BOF steelmaking process. Time coverage is 2005. Operating parameters as well as air emissions associated with the BOF steelmaking process were presented. The production data (steel) was given. The emissions of SO2, NO2, CO, CH4, CO2, dust, heavy metals (Cr, Cd, Cu, Pb, Ni and Mn) and waste (slag and gas cleaning sludge) are the most important outcomes of the steel process. With regard to 1,677,987 Mg of steel produced by AMBOF, the consumption of natural gas, blast furnace gas and coke oven gas amounted to 10,671,997, 755,094 and 13,222,537.6 m3/year, respectively. Electric energy, steam, air, oxygen and heat input amounts were in the order of 45,003,611.3 kWh, 21,646.03 Mg, 107,592,526 m3, 90,611,298 m3 and 16,779.87 GJ, respectively. Direct emissions in air of SO2, NO2, dust, Cr, Cd, Cu, Pb, Ni, Mn, CO and CH4 from three converters (Nos. 1–3) were on the order of 28.966, 71.331, 752.05, 0.025, 0.024, 0.0216, 0.0156, 0.0163, 1.5694, 540.449 and 0.364 Mg, respectively. Total CO2 emission was 138,374 Mg. The amounts of slag and gas cleaning sludge were 276,709.64 and 16,749 Mg, respectively. The LCI study resulted in the development of a database with a vast inventory of data regarding steelmaking process in AMBOF referring to the year 2005. The output of the AMBOF LCI study is a set of gate-to-gate LCI data for steel production in BOF technology. This is the first tentative study to express steel production in Poland in terms of LCA/LCI in the steelmaking industry. The FU chosen for the present study is 1,677,987 Mg of steel produced in a classical BOF. The quality of data input in this LCI study is very good. The rules were used in accordance with ISO Standard for LCA. The methodological approach and boundaries that were made are transparent and fully documented. The purpose of this study is to help AMP authorities solve environmental and technical aspects as well as to train steel industry people in the field of life cycle assessment. In addition, this study can be extended to other processes involved in steelmaking route (via sintering plant/hot rolling plant). Moreover, these results move the LCI study on the steelmaking process one step forward. The LCI offers environmental information consisting of the list of environmental loads. The impact assessment phase aims to present more understandable results from the inventory analysis, and life cycle impact assessment (LCIA) will be the direction for future research. Another issue to discuss is the integration of LCA with risk assessment for industrial processes.

The iron and steel industry is arguably the most significant component of a country's industrial economic infrastructure, and the amount of steel consumed per person serves as a proxy for both industrialization and advancement. There are two processes in the extraction of ferrous metals: (1) the Ironmaking process (reduction process); and (2) the Steelmaking process (oxidation process). This study aims to suggest uses for Libyan iron ores in the iron and steel industry. The study covered thirteen areas (Sabkhat Ghuzayil, Al Fuqaha, Sabha, Tanahmu, Idri, Qararat Al Marar, Hasi Anjiwal, Bir Anzawa, Tikiumit, Al Awaynat, Wadi Tanezzuft, Anay, and Ghat sheets). This work can be regarded as the first of its kind because no previous study has been done on the raw materials used in the iron and steel industry in Libya. Numerous formations have been found to contain iron ore (Hasawnah Formation, Melaz Suqran Formation, Tanezzuft Formation, Akakus Formation, Tadrart Formation, Wan Kasa Formation, Awainat Wanin Formation, Marar Formation, Zarzaitine Formation, Taouratine Formation, Mesak Formation, Al Hishah Formation, Old wadi deposits, and sabkha sediments). Given that the results showed that the Fe concentrations in the studied sediments ranged from 15.21 to 54.53%, it is feasible to use these sediments in the iron and steel industry.

Blending of coals for coke making is a very important process for the steel industry globally. The importance of coal blend for coke making in India lies in its impact on the quality and efficiency of coke production. The coal blend composition affects crucial factors such as coke strength, reactivity, ash content, and gas generation during the coking process. This ensures optimal performance, cost-effectiveness, and environmental compliance in the steelmaking process and is critical for the sustainability of the steel industry in India. Globally, attempts have been made to prepare suitable blends for coke making and several models have been developed. This also depends on requirement of desired types of cokes and availability of domestic and imported coals for different countries. Presently, in most cases, the Indian coke and steel industry uses 80–85% imported coals and 15–20% domestic coals for blending. Here, six coals from different domestic local mines and three imported coals have been tried in different blend proportions, to show the feasible options to use domestic coals in higher proportions and use lesser proportions of imported coals. In this study, two case studies are presented, demonstrating the blending of indigenous and imported coals in various proportions based on weighted average. Imported coals from Australia, New Zealand and domestic coals from domestic mines i.e. Dugda 1, Kathara, Kargali, Argada Sirka, Jamadoba and Bhujudih mines in Damodar Valley basin were used. The proximate analysis, petrographic studies and properties of these coal blends, including caking index, swelling index, fluidity, plasticity, CRI, CSR, M40 & M10 values have been discussed. The strategies of these blends depended on reactive macerals, inerts, coal rank, mineral matters and other critical parameters. The findings of the paper indicate that, given the constraints of the Indian coke industry, the cokes produced using the blending strategy outlined in the study have achieved optimal values. On meticulous blending, high proportion of domestic inferior coals can be blended up to 38% to prepare suitable coke, while about 50% of imported coal can be blended with domestic coals. This suggests, through a carefully designed blend strategy, the Indian steel industry may address the challenges of coking coal scarcity and reduce reliance on costly coal imports.

Utilization of carbon dioxide injection in BOF–RH steelmaking process

The energy-intensive enterprises (EIEs), such as iron and steel enterprises, account for a significant part of the total energy consumption in society. The Linz–Donawitz converter gas (LDG) is a kind of crucial byproduct energy resource recycled during the steelmaking process, and its reasonable scheduling can effectively reduce the LDG emission and increase its efficiency. In this study, a granular-causality-based scheduling approach for the LDG system in steel industry is proposed. A granular causality technique is modeled to confirm the casual relationship of the LDG system based on the discontinuous production characteristic, in which a causality diagram is established and the phase space of the training sample is reconstructed to improve the prediction accuracy. Then, a multioutput least-square support vector machine model is constructed for the prediction of the gas tank levels. In order to consider the impact of multiple solutions on the scheduling result in a period of time, a scheduling objective function that combines the economy criterion and the safety one is designed and optimized by a modified particle swarm optimization (PSO) algorithm. The validation experiments using real-world data coming from the energy data center of a steel plant are carried out, and the results indicate that the proposed method exhibits reliable performance. Moreover, an application software system based on the proposed method is developed and implemented, which demonstrates the applicability of the proposed approach. Note to Practitioners —Given that the steelmaking process is a discontinuous one and the LDG system can hardly be described by a physical or mechanism-based model, its energy scheduling works is usually operated by the manual method currently, which leads to low accuracy and a waste of energy. Since a large number of real-time data had been accumulated by the existing SCADA system implemented in most steel plants, a data-driven long-term scheduling approach is proposed in this study. The proposed method aims at the long-term scheduling of the LDG system in steel industry, and the acquisition frequency of the real-time data is 1 min. The application system designed on the basis of the proposed method can provide effective scheduling solutions. Furthermore, it is necessary to ensure the data integrity and reliability via data imputation and filtering methods since there may exist some missing data or outliners in the acquired data from the SCADA system onsite. This study avoids the redundant introduction of such preliminary preprocessing methods for the sample data.

Dry cooling as a way toward minimisation of water consumption in the steel industry: A case study for continuous steel casting

Potential of renewable agricultural wastes in the smart and sustainable steelmaking process

Throughout the 20th century, most industries have focused on a similar set of challenges-specifically, the design of competitive products, cost-effective production and expeditious marketing. From research 'and design to commercial distribution, companies have guided their products through a life cycle which typically ended with a sale to the enduse customer. Suddenly, however, industry ,is being called upon to redesign their participation in the product life cycle to include the additional stages of collection and reuse. These changes represent a more wholistic, pragmatic way of looking at product design, application and marketing. For the steel industry, this total lifecycle philosophy has been standard business practice for decades. It represents an attentiveness that has grown from a combination of factors, including the consumption of ferrous scrap in the steelmaking process and the ability to employ scrap economically. The mission of the Steel Can Recycling Institute (SCRI), located in Pittsburgh, Pennsylvania, is to promote and sustain one aspect of steel recycling-steel cans. The SCRI serves,as a steel can information and technical resource and encourages cooperation among communities, recyclers and intermediate processors who are implementing or are involved in steel can recycling. After all, an industry cannot undertake a significant recycling initiative without the cooperation of all participants. The steel industry learned early about cooperation. For instance, ferrous scrap dealers have been a major player in returning waste scrap and spent steel products to the mills. And for years, ddinning companies have helped the industry maintain the critical balance of tinned and detinned scrap usage. These systems of collection, processing and reuse have evolved through time and joint efforts. But for the steel industry, and other industries as well, the luxury of time in retrieving post-consumer recyclables is not an option. Fortunately for the steel industry, the evolution of the steelmaking process has met with environmental awareness in a hand-in-glovefashion. Theindustryrecognizes its responsibility for retrieving steel cans from the municipal solid-waste stream and possesses the means to recycle them effectively. The combination of a growing supply of steel cans and the steady technological achievements of the industry have made the steel can not only an efficient package, but a bountiful supply of high-quality scrap for the steel industry. Communities benefit from steel can recycling in a very direct and immediate manner because the recycling of steel cans extends landfill life. Further, the environmental benefits of recycling all forms of steel have been demonstrated in terms of reducing air emissions, water pollution and water usage. Further, significant energy savings are realized by recycling steel. At today's level of scrap usage, it is estimated that steel recycling annually saves enough energy to meet the power needs of the entire Los Angeles metropolitan area for over eight years. Additionally, domestic natural reserves of iron ore, coal and limestone are conserved for future generations. The steel industry could today effectively recycle all of the 100 million steel cans used daily in the U.S. if all were collected. As the industry's internal scrap resources continue to decline, its demand for post-consumer steel cans will rise steadily. The SCRI works with municipalities, recyclers, public works officials, legislative representatives and consumers to expand and strengthen the steel can recycling infrastructure. In most phases, this is the same infrastructure that supports all recycling. Recently, the SCRI opened regional offices from which recycling representatives serve the communities and states in their regional area. The SCRI anticipates that it will have opened offices in the majority of the U.s. regions by the end of 1990. As recycling continues to grow and dominate as the primary solid waste solution in the U.S., SCRI anticipates that, by 1995, steel can recycling will reach the rate that matches today's overall steel industry recycling rate-over 66 percent. In the long term, the goal, of course, is to go well beyond 66 percent to ensure that steel can recycling remains one of the primary elements of the municipal solid waste solution for the environment. Today, consumer packaging is the recycling target; tomorrow, who knows? The steel industry has an advantage-an impressive and unmatched recycling history. We have not been waiting for someone to tell us the recyclable target of tomorrow. We are working on it today-through product design, marketing and use, to ensure that the maximum number and variety of steel products are recycled.

This paper discusses the development of design methods for the steel industry that consider the geographical and social characteristics of industrial host communities for realizing a carbon–neutral society by 2050. The steel industry is an energy-intensive and highly CO 2 -emitting industry because it uses large amounts of coal and coke as blast furnace reducing agents. Using a case study in Japan as an example, we summarized the conceptual design and a base model of the iron and steelmaking process in 2050, considering collaboration with local communities to expand local renewable energy and iron scrap collection and reduce CO 2 emissions and associated abatement costs. For the effective design of a lower-emissions steel industry, local characteristics need to be considered. We considered (1) the collaboration of the steel industry with local communities for long-term symbiosis and (2) steel industry and local community modeling based on the conceptual design.