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The aim of this study is to investigate the ongoing energy management and energy efficiency practices in the industries of Peshawar division of Khyber Pakhtunkhwa, Pakistan. The outcomes of this research shows that Khazana Sugar Mills, Caliph Pharmaceutical, Naguman Flour Mill have no or low awareness of energy management practices (awareness level mainly includes barriers to energy efficiency and drivers for energy efficiency) except FF steel mill. The major paces observed in lacking the awareness are (1) “understanding level of staff and workers about energy savings due to energy management practices” (2) “interest level of top managers for energy management implementation” (3) “awareness level regarding energy efficiency” (4) “lack of technical competence” (5) “use of standardized procedures”. When the industry managers were inquired about the relationship with energy service providers it is found that there is absolute absence of energy engineers, relationship with energy consultancies and long-term energy strategy in all the industries. It was also found that there is lack of using renewable energy technologies in all industries as they were relying upon using the generators during the blackout hours. In terms of percentage, the energy saving factor of 36 kWh/day has also observed with the replacement of fluorescent tube lights with light emitting diode (LED) tube lights. Proper maintenance and energy management practices in the heating, ventilation and air conditioning system (HVAC) would also save energy in a significant amount. The study shows that there is an overall improvement factor of 4% to 8% if the inclusion of energy management and energy efficiency practices would have been applied in all the stated industries.

Water from the River Niger in Ajaokuta, Kogi State, Nigeria, was tested for physico-chemical characteristics.Temperature, dissolved oxygen (DO), electrical conductivity (EC), total hardness, total alkalinity, magnesium(Mg), calcium (Ca) and chloride were the variables studied (Cl). During the rainy and dry seasons, watersamples were taken in triplicate from six separate locations along the river and were subjected to physicochemicalanalyses using APHA standardized procedures. The results were compared to World HealthOrganization (WHO) and Nigerian Standards for Drinking Quality Water (NSDQW). Consequently, themajority of the parameters examined were below the WHO's recommended safe drinking water threshold.However, the pH and surface water temperature were above WHO guidelines. Comparison of data obtainedbetween rainy and dry seasons was made using t-statistics at P≤0.05 and this revealed significant seasonalvariation in water temperature (28.80oC and 29.40oC), DO (6.43mg/l and 7.18mg/l), Ca (5.21mg/l and 4.16mg/l)and Cl (5.20mg/l and 3.50mg/l). Therefore, in order to protect the communities living along the water body, it isrecommended that regular water quality testing be made mandatory.

The heating furnace is a sizeable energy-consuming device in iron and steel industry. In compact strip production (CSP), the size of the heating furnace is large and the working conditions are complicated. Furthermore, the large fluctuation of furnace temperature leads to large loss of billet combustion and energy consumption. This paper deals with the design of hybrid intelligent control based on condition identification for the combustion process of the CSP heating furnace. By analyzing the process of the heating furnace and existing problems, the structure of a hybrid intelligent control system is proposed, and the working conditions are divided into two categories: stable and fluctuating. A fuzzy controller is designed to improve the control accuracy of furnace temperature under the stable working condition, and an expert controller is used to adjust the temperature rapidly under the fluctuating working condition. Besides, calorific value compensation is introduced to reduce the influence of calorific value of gas fluctuation on temperature. After verifying the effectiveness, the proposed method has been used in a steel plant and achieved a good control result. The intelligent control system improves the control precision of the furnace temperature and reduces energy consumption compared with the traditional control system.

Early guidance is required to match Industry 4.0 ambitions with an understanding of the evolving Industrial Internet of Things (IIoT) system and its capabilities. Considering IIoT systems and operational methodologies within steel manufacturing, a three-stage evolving IIoT system is proposed. IIoT requirements for selected Industry 4.0 applications are presented and generalized guidance matches the application with the relative support level of each evolutionary stage. The guidance specifically considers digital twins, cyber-physical systems, horizontal and vertical integration, integrated worker health and safety, increased workforce mobility, and advanced maintenance. To demonstrate how the evolving IIoT system aids or potentially prevents Industry 4.0 applications, the feasibility of advanced maintenance strategies within steel manufacturing are presented, including predictive and self-maintenance.

Based on the core developmental demands of smart steel manufacturing plants, this review analyzes the essential characteristics of the steel manufacturing process and its implications for production and operation decision optimization (PODO). A discussion on the potential application of technologies, including the Industrial Internet, big data, cloud computing, and 5G, in the PODO of smart steel plants based on the Industry 4.0 intelligent manufacturing and human-cyber-physical system (HCPS) is also presented. An adaptive update conception for a new dual-level cyber system (for management control and unit operation) of a flat-structured HCPS for steel plants is proposed to eliminate issues such as the hierarchical structure of the cyber system, the occurrence of data islands, and weak intelligence in PODO ability. Additionally, the review provided an in-depth analysis of the closed-loop decision logic of a dual-level HCPS. The critical technologies required for developing the HCPS model include data platforms and their associated application technologies, intelligent modeling, human-machine collaboration, and platform-based integration. Finally, suggestions are proposed to develop smart steel plants, including concept renewal, technological integration, and talent training.

Recycling plays a vital role in preserving resources like steel and consequently in a circular economy. Scrap dealers and steel mills, the main business units in this system, often encounter opposing financial and sustainability incentives in using scrap as feedstock because regular sorting and scrap-preparation infrastructure cannot deal with the increasing complexity of steel scrap. Mismatches between the inputs and the target composition of the recycled steel result in trade-offs that favor the economics at the expense of resource efficiency. By examining literature and interviewing several actors, different scrap characteristics were identified as dimensions of scrap quality. Quality is typically associated with chemical composition, which is important, but this study aims to elaborate the concept of quality further and to connect it to the realities of scrapyard operations. Industry actors have different definitions for desired content, physical condition, shape and size, and homogeneity, based on their needs. Very few studies examine these details. Additionally, the relationship of quality and the level of information about the characteristics of the material was established. Having more definitive information on scrap increases opportunities for resource-efficient actions. This work offers an alternative perspective on how to address issues pertinent to recycling. Graphical Abstract

Steel slag is an industrial by product of steel manufacturing processes and has been widely utilized within civil and construction materials for road materials and environmental remediation in countries like Japan, USA, and European Union nations. However, the current utilization of steel slag in Vietnam is very low mainly because of lack of quality control of slag treatment and chances for reuse of treated steel slag. This paper presents the up to date steel slag production status in Vietnam through the extensive survey and sampling at seven large steel factories. The paper also highlights the environmental and quality control issues of these steel slags to use as road construction aggregates by assessing the heavy metals concentration in the leachate. The basic oxygen furnace (BOF) and electric arc furnace (EAF) slag samples were collected to evaluate leaching properties of metals leached from the slags. The two standardized batch leaching tests of steel slag roadbed material in Japan (JIS K 0058-1) and toxicity characteristics leaching procedure (TCLP-EPA method 1311) were performed to the evaluated the hazardous metals. The results of the leaching test show that almost all of the concentration of the metals in the leached solution does not exceed the National Standard for Industrial Wastewater Discharge (QCVN 40-2011). The pH and parameters such as total chromium, nickel, copper, lead, arsenic, and manganese differ from the two test methods. The acidic conditions employed in the EPA 1311 were not representative of condition excepted during slag reuse in road constructions because in the operation condition of the road, acidic liquid is absent. The leaching test results confirmed that JIS test which uses deionized water with gentle mixing prevents the slag sample from size degradation is suitable for the environmental assessment of steel slag use for roadbed material. This research suggests that the adjustment of pH value prior to disposal or reuse as base materials and official guideline should be promulgate by the authorities to ensure the leachate meet the surface water quality standard.

To optimize ladle scheduling in the empty-ladle operation stage of steel plants, a mathematical scheduling model was established for the empty-ladle operation stage, taking the minimum total waiting time in the empty-ladle operation stage as the optimization goal and setting the equipment assignment uniqueness as the key constraint. An improved genetic algorithm was designed to calculate the mathematical scheduling model. In the operation of the genetic algorithm, the strategy of “ladle temperature drop control” was adopted to solve the problem of equipment conflicts and reduce unreasonable ladle temperature drops to enhance “red-ladle” utilization. Five main production modes operating at 95% capacity in a Chinese steel plant were simulated using the genetic optimization model. The results showed that the genetic optimization model could improve the efficiency of ladle operation and reduce the total waiting time in the empty-ladle operation stage by 868–1147 min.

Purpose The purpose of this paper is to establish the structural relationships between the environmental sustainability enablers (ESEs) for the steel supply chain in India and also to classify them on the basis of driving and dependence power with an objective of achieving enhanced environmental sustainability performance. Design/methodology/approach The ESEs were identified through extensive literature review and discussion held with experts from the Indian steel industry. The interpretative structural modeling (ISM) approach was applied to an Indian steel supply chain to arrange these 12 ESEs in different hierarchies of the structural framework on the basis of their driver dependence power and also diagraph was developed to classify them. Findings The water pollution control system, air pollution control system and soil pollution control system were found to be in the bottom hierarchy of the ISM framework and these enablers were also observed in the driving quadrant of the diagraph. These driving enablers should be given priority for its full-scale development, management and monitoring to sustain and enhance the environmental sustainability in the Indian steel supply chain, whereas top management commitment, environment compliance certification and government regulation and incentives are the antecedents for carrying out the sustainability program. Research limitations/implications The outcomes of the current study are limited to the Indian steel manufacturing company in specific but the results will not change significantly for the steel sectors in India. However, an empirical analysis can be used to explore and validate the structural framework for its acceptability. Practical implications The outcomes from the methodology provide the basis for an Indian steel manufacturing industry to develop the right strategy in their quest for environmental sustainability. Originality/value Although the study on ESEs of various industries was reported in the literature, the comprehensive study to identify the significant ESEs related to the Indian steel supply chain for environmental sustainability in specific have been hardly carried out. The current study will definitely be a valuable addition to environmental sustainability literature in general and steel supply chain environmental sustainability in specific.

In the BOF steelmaking process, lime quality and quantity directly affect slag quality, affecting metallurgical results, liquid metal yield, productivity, and therefore the total cost and environmental impact of the steel production. In this paper, a value in use model based on mass balance and heat balance was developed and used to evaluate the impact of lime quality on the BOF process. The model is capable of calculating the consumption of scrap, lime, oxygen, the volume and composition of the slag and the volume and composition of off-gas and facilities to calculate the cost-benefit contributions and potential cost saving for a configured choice of various types of lime and operating parameters. The model has been successfully validated using the data of the Steel Plant. Then three types of lime with different qualities were used to elaborate on the impact of lime characteristics in optimizing the steelmaking process, metallurgical benefits, overall cost impact, potential savings, and environmental benefits. The calculation results show that good-quality lime could increase the scrap ratio, reduce the lime consumption, and reduce iron loss, accordingly, improve the steel quality, increase steel yield, reduce the smelting costs, stabilize smelting operations, and reduce CO2 emissions.

In order to promote the intelligent transformation and upgrading of the steel industry, intelligent technology features based on the current situation and challenges of the steel industry are discussed in this paper. Based on both domestic and global research, functional analysis, reasonable positioning, and process optimization of each aspect of steel making are expounded. The current state of molten steel quality and implementation under narrow window control is analyzed. A method for maintaining stability in the narrow window control technology of steel quality is proposed, controlled by factors including composition, temperature, time, cleanliness, and consumption (raw material). Important guidance is provided for the future development of a green and intelligent steel manufacturing process.

After nearly one hundred years of research, metallurgy (metallurgical science and engineering) has gradually become a system with three levels of knowledge: (1) micro metallurgy at the atomic/molecular scale, (2) process metallurgy at the procedure/device, and (3) macro-dynamic metallurgy at the full process/process group. Macro-dynamic metallurgy development must eliminate the concept of an “isolated system” and establish concepts of “flow,” “process network,” and “operating program” to study the “structure-function-efficiency” in the macro-dynamic operation of metallurgical manufacturing processes. It means considering “flow” as the ontology and observing dynamic change by “flow” to solve the green and intelligent potential of metallurgical enterprises. Metallurgical process engineering is integrated metallurgy, toplevel designed metallurgy, macro-dynamic operated metallurgy, and engineering science level metallurgy. Metallurgical process engineering is a cross-level, comprehensive, and integrated study of the macro-dynamic operation of manufacturing processes. Metallurgical process engineering studies the physical nature and constitutive characteristics of the dynamic operation of steel manufacturing process, as well as the analysis-optimization of the set of procedure functions, coordination-optimization of the set of procedures’ relations, and reconstruction-optimization of the set of procedures in the manufacturing process. The study establishes rules for the macro operation of the manufacturing process, as well as dynamic and precise objectives of engineering design and production operation.

Green supply chain is the process used for diminishing the exploitation of resources and producing sustainable technological development. The aim of the research work is to build the proficient Industry 4.0 green architectures index for assessing the feasible candidate choice. In the research work, a crisp numbers set-based MOORA-FMF advanced technique is executed for examining the Industry 4.0 green architectures of vendors firms. The managers are assisted with an Industry 4.0 green architectures index with optimization technique to quantify the performances of alternatives. Therefore, a real case study of a steel manufacturing plant is carried to simulate the proposed Industry 4.0 GSC architectures. Eventually, the results are presented in the conclusion.

The steel industry is one of the most important industry sectors, but also one of the largest greenhouse gas emitters. The process gases produced in an integrated steel plant, blast furnace gas (BFG), basic oxygen furnace gas (BOFG) and coke oven gas (COG), are due to high shares of inert gas (nitrogen) in large part energy poor but also providing a potential carbon source (carbon monoxide and carbon dioxide) for the catalytic hydrogenation to methane by integration of a power-to-gas (P2G) plant. Furthermore, by interconnecting a biomass gasification, an additional biogenic hydrogen source is provided. Three possible implementation scenarios for a P2G and a biomass gasification plant, including mass and energy balances were analysed. The scenarios stipulate a direct conversion of BFG and BOFG resulting in high shares of nitrogen in the feed gas of the methanation. Laboratory experimental tests have shown that the methanation of BFG and BOFG is technically possible without prior separation of CO2. The methane-rich product gas can be utilised in the steel plant and substitutes for natural gas (NG). The implementation of these renewable energy sources results in a significant reduction of CO2emissions between 0.81 million tonnes CO2eqand 4.6 million tonnes CO2eqper year. However, the scenarios are significantly limited in terms of available electrolysis plant size, renewable electricity and biomass.

Asansol-Durgapur Industrial Region serves as nerve-knot of the economy of West Bengal as well as the entire economic region of Eastern India because of its enormous reservoir of excellent quality of coal of Raniganj Coalfield, outstanding network of transport by rail and road, skillful and cheap labour in close proximity of states. With the establishment of a number of large industrial units like Iron & Steel Plant, Durgapur Steel Plant, Chittaranjan Locomotive works, Durgapur Alloy Steel Plant, Durgapur Thermal Power Station, and hundreds of medium and small-scale industries have made the region great industrial belt after Hooghly industrial belt in West Bengal. Though there was rapid industrialization after independence period but the growth of industries has not taken place uniformly throughout the study area. This is because, high manufacturing intensity found in Durgapur, Asansol, Raniganj and Kulti areas whereas the Salanpur, Jamuria, Fraidpur, Barabani areas witnesses low manufacturing intensity. The decadal growth of industries also noteworthy, in 1951 there were 46 industrial units and it increased to 430 in 2019. The number of basic metal and engineering units was 2 and 18 respectively in the year 1951 whereas in 2019 the basic metal and engineering unit increased to 122 and 66 respectively. This paper primarily focuses on the estimation of spatial and temporal distribution of manufacturing industrial units, growth and structural changes, manufacturing intensity and concentration of the manufacturing sector during the period of 1951 to 2019 in Asansol-Durgapur Industrial Region.

Intelligent iron/steel manufacturing has been drawing increasing attention in recent years. Slagging is of importance for clean steel production in the ladle furnace (LF) refining process. In this ...

This paper aims to present a procedure that allows identifying savings opportunities in a steel manufacturing company. The procedure based on the ISO 50001, 50004, and 50006 standards comprise the use of tools such as energy baselines, the goal line, energy performance indicators, the Pareto chart, and an energy review. As a result of the implementation of the procedure, it was possible to obtain the baseline, the goal line, and energy performance indicators that allow the control of energy consumption and efficiency of the company in general and of the area with the highest electricity consumption. It was possible to identify that there is a potential savings of up to 6% throughout the company and up to 13% in the area with the highest electrical energy consumption. From an energy review carried out in the area with the highest consumption, motors operating with low load and idle for long periods were identified, as well as a lack of maintenance. Besides, the replacement of traditional technology lamps by LED technology lamps was proposed. The procedure can be generalized in steel industries with similar characteristics, which is one of the sectors that consume the most energy worldwide.Keywords: Electricity, Energy, Energy Efficiency, Energy Saving, Energy Performance Indicator, Steel IndustryJEL Classifications: Q4, L610DOI: https://doi.org/10.32479/ijeep.11142

One of the four conditions gives rise to the need for OD interventions. First, the organization has a problem; something is “broken” Corrective actions - interventions - are implemented to “fix” the problem. Second, the organization sees an unrealized opportunity; something it wants is beyond its reach. Enabling actions - interventions - are developed to seize the opportunity. Third, features of the organization are out of alignments; parts of the organization are working at crosspurposes. Alignment activities - interventions - are developed to get things back “in sync”. Fourth, the vision guiding the organizations changes; yesterday’s vision is no longer good enough. Actions to build the necessary structures, processes, and culture to support the new vision. Interventions are developed to make the new vision a reality.

The paper presents a mathematical model for predicting the mechanical properties of hot rolled strips. The realization of this mathematical model relied on statistical measurements of the mechanical properties (Rm, Rp0,2 and A5) for the laminated strip in the LBC rolling mill from the Liberty Steel Galati steel plant. To achieve this mathematical model, there has been used the active experiment method.By means of this mathematical model, significant time and material savings can be made in the process of testing the mechanical properties for hot rolled tape.

Street Dust (SD) acts as a sink and source of atmospheric particles containing Potentially Toxic Elements (PTEs) and can pose a possible pathway of PTEs to human bodies. Comprehensive SD study, where 249 samples were collected from rural, urban and industrialized areas aimed to increase the understanding between sedimentation of atmospheric dust derived from anthropogenic activities and elemental composition of SD. Elemental composition for 53 elements (ICP-MS, aqua regia digestion) was determined on fraction

The goal of this research is to study the reasons for the formation of rigid non-metallic inclusions in rail steel and ways to reduce the rejection of finished rails for defects in ultrasonic testing. The research was carried out at the arc-furnace plant of JSC Ural Steel. The central plant laboratory carried out a chemical analysis of non-metallic inclusions in the specimens of finished rails, rejected by ultrasonic inspection during the rail production by Aktobe Rail and Structural Steel Plant, LLC from a billet produced by Ural Steel. In terms of composition, non-metallic inclusions are represented by aluminum oxides. The most probable reasons for the presence of non-metallic inclusions are determined as a result of ferroalloys containing aluminum in their composition, or the interaction of melt components with refractories and a mold powder. The analysis of ferroalloys used in the production of rail steel is carried out. Industrial testing of the continuous rail steel casting technology is carried out with the replacement of FS65 ferrosilicon, which contains aluminum with silicon carbide. An increase in the degree of silicon and carbon recovery is noted in experimental smeltings. The assessment of contamination with non-metallic inclusions and mechanical properties of the rail steel obtained by the experimental technology shows that the service characteristics of the rail steel metal comply with the requirements of GOST R 51685–2013. The full-scale experiment has confirmed that the technology of alloying E76F rail steel with silicon carbide under conditions of Ural Steel is technically feasible. A 17% increase in the yield of useful 100-meter rails was obtained for a pilot batch of metal, produced from Ural Steel’s contcast steel billets.

The operation of the furnace depends largely on the handling of gas distribution which in-turn is reliant on the burden surface profile. Measurement of burden profile has been a challenge because of the harsh conditions inside the furnace. Phased array radar will suffice this purpose with no moving parts; however, it imposes its own challenges. One of them is measurement of extremely short distances as the stockline in the blast furnace is just few meters away from the topmost part. Unlike conventional radar where the target is kilometre away the short distance measurement involves with time measure din nano seconds with pico-seconds resolution to achieve the desired accuracy. This paper implements a method for ultra-short distance measurement using radars for blast furnace application in steel plant.

Continuous casting is the most important route for the production of steel today. Due to the physical, mechanical, and chemical components involved in the production, continuous casting is a very complex process, pushing conventional methods of monitoring and control to their limits. In recent years, this complexity and the increasing global competition created a demand for new methods to monitor and control the continuous casting process. Due to the success and associated rise of machine learning techniques in recent years, machine learning nowadays plays an essential role in monitoring and controlling complex processes. This publication presents a scientific survey of machine learning techniques for the analysis of the continuous casting process. We provide an introduction to both the involved fields: an overview of machine learning, and an overview of the continuous casting process. Therefore, we first analyze the existing work concerning machine learning in continuous casting of steel and then synthesize the common concepts into categories, supporting the identification of common use cases and approaches. This analysis is concluded with the elaboration of challenges, potential solutions, and a future outlook of further research directions.

After finalization of process flow for the capacity expansion of an Integrated Steel Plant, considering the overall design requirements and inter-relationship amongst new and existing facilities, overall layout is developed on available space. Optimization of layout is proposed considering sizes, locational relationship of new units with existing operating units, required limiting conditions, transportation logistics and other auxiliary facilities. Prime objective of layout optimization should be proper utilization of available space which has been identified for expansion. Optimized layout can be developed through 3D integrated approach, which considers a common digital model of the entire plant. It can be achieved by merging the engineering output from various vendors, merging the model of existing plants obtained from works, surveys, and vendors so as to create a seamless layout. The final layout shall be best possible and can be executed in time bound manner.

This article presents a methodology to evaluate protection schemes in grids with nonlinear loads. For this purpose, a protection library developed in MATLAB/Simulink is presented, which is complemented with a communications system and an electric arc furnace (EAF). Subsequently, an application example is shown, in which the protection system of a steel manufacturing industry is developed, considering the linear and nonlinear loads, and tested using the methodology compound of four stages: the definition of the protections, the settings, the fault analysis, and the hardware implementation.

A large steel company needs to improve its efficiency by integrating the decision making from suppliers to buyers. Therefore, an optimization model is required to determine simultaneously the optim...

Green factors toward an environmental performance of suppliers have impeccable importance and contributions. The research study is carried out by employing fuzzy-based multi-criteria decision-making processes in order to evaluate the environmental performance of suppliers. Firstly, the criteria are identified for assessing the environmental performance of suppliers and secondly, the rating of selected criteria and its alternatives (suppliers) are framed as per the expert’s opinion, by combining the qualitative criteria through fuzzy technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to generate an overall score for each alternative. The supplier obtained the highest score which is ranked one. It means that the supplier has the highest environmental performance. Fuzzy TOPSIS differentiates between benefit (higher the better) and the cost (lower the better) criteria. VIKOR method selects and ranks the best supplier among the alternatives in supply chain, whereas TOPSIS method chooses the best alternatives which are closest to the positive ideal solution and the worst alternative is farthest to the negative ideal solution. Sensitivity analysis is performed to analyze the effect of criteria weights on environmental performance. Radar diagrams of fuzzy TOPSIS and fuzzy VIKOR are developed from sensitivity analysis for varied criteria weights. A case study has been adopted on a small-scale sponge iron and steel industry, located in the eastern part of India.

Hot strip mill, one of the most important units of an integrated steel plant, is operated by mill setup model. The conventional mill setup models calculate thermal, reduction and speed schedules of...

Linz–Donawitz converter gas (LDG) is a class of significant secondary energy in steel enterprises, and its adequate recycling can help save energy, reduce emission and improve profit. Aiming at the scheduling problem with uncertainties in both gas generation and consumption, a three-layer-causal-network-based approach for LDG system is proposed in this study. Two causality-based methods by employing causal exception estimation and working condition clustering are designed to construct the prediction intervals (PIs) of the gas generation and consumption flows respectively. Given that the surplus or shortage range of the LDG that can keep the tank levels balance can be calculated, a three-layer causal network of “generation-storage-consumption” is established to describe and evaluate the scheduling rules. Then, the solution is finally optimized based on the objective function and its corresponding constraints constructed by the safety, environmental and economic indicators. To verify the performance of the proposed method, the experiments by using real-world data coming from a steel plant are carried out, where the manual approach and the existing mixed-integer-linear-programming-based one in literature are also employed as comparative ones. The results indicate that the proposed method is capable of providing superior performance for such an industrial application.

The automotive customers have been down-gauging steel sheets for making door panels to reduce weight of the passenger cars and improve fuel efficiency under “Go Green” initiative. Tata Steel has been extending support in weight reduction initiative of the automakers through supplying thinner gauge interstitial free (IF) steel sheets. It was reported that in some cases, the thinner sheets could not withstand the intricate forming and stretching operation and resulted in pin-dent or press crack defect generation in deep drawn areas of the components. The defect was reported for the first time to TSL by auto customer, and in the sense, it was a unique defect. At times, the defect impacted 20–40% of deep drawn door components and led to a near line stoppage situation in the press shop. Representative samples containing the defects were collected from the site, and detailed investigation was carried out employing metallographic techniques such as scanning electron microscope (SEM) attached with energy-dispersive x-ray analysis (EDX). The investigation revealed non-metallic inclusions (NMI) to be the cause of such defects. A thorough process and data analysis was carried out to identify the sources of such inclusions and arrive at appropriate countermeasures to fix this problem. Subsequently, countermeasures were implemented in the steel manufacturing process, and such pin-dent defects were eliminated bringing much respite to the customer. The paper will highlight the systematic approach in dealing with the problem and successful resolution of the intriguing quality issue.

Fine aggregate is one of the large scale consuming materials in the construction industry. In general, river sand has been used as a fine aggregate for making concrete, mortar, etc. This river sand becoming one of the expensive materials due to its transportation from the source point and rapid consumption in the construction sector. Owing to the above circumstances, the concrete and construction industry needs a substitute for fine aggregate to be found. In this investigation, M20 grade concrete with the replacement of sand by steel mill scale waste and granite powder waste from 10% to 100% with the increments of 10%. This paper presents a detailed experimental study on compressive strength, bulk density, and water absorption of the M20 grade concrete containing steel mill scale and granite powder. The experimental outcomes indicate that the use of steel mill scale and granite powder in concrete showed improvement in the performance of concrete in strength and as well as in durability aspects.

Carbon capture and storage is considered of fundamental importance to achieve a remarkable decarbonisation of steel, cement and refining sectors. To operate carbon capture and storage at scale and address its inherent complexity, mathematical programming techniques can be exploited to optimise such systems. This contribution proposes a Europe-wide, spatially-explicit, time-dependent, carbon capture and storage chains optimisation, based on mixed integer linear programming architecture. Capture plants can be installed in all significant industrial CO2 emitters, which comprise 25 steel mills, 111 cement plants and 59 refineries. A techno-economic description of capture plants is provided, based on scale effects and different options. Transport can be operated through pipelines and offshore storage is taken into account in the North Sea and Adriatic area. The analysis allows identifying the most promising sectors and optimal specific plants where capture should be operated, and the evolution of the system throughout the time horizon. Considering a time-varying carbon reduction target, the avoidance cost is 75.6 €/t of CO2 for a North Sea targeted network, and decreases by 1.9% when sequestration in the Adriatic Sea is also taken into account.

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation.

The new generation of steel manufacturing processes shaped by Industry 4.0 are more digitalized, networked, flexible and adaptable. Production processes use distributed information and communication structures, are more autonomous and capable to react to dynamic changings of the environment. Agent-based systems represent a paradigm, which is well suited to address these new generation of smart processes. The paper presents a hybrid peer-to-peer architecture for agent-based steel production processes. The architecture exploits a central database server for storing and retrieving updated information from peers about a cold rolling manufacturing process. The cold rolling process is modeled as a multi-agent system composed of four types of autonomous agents, each playing a different role in the steel production chain. Agents are designed to take autonomous decisions, and to coordinate and collaborate with each other, by ensuring the dynamic plant resources allocation even if unforeseen interruptions of the production flow may happen. The proposed approach is designed for the steel strip manufacturing process but can be easily readapted to any flat production process. The test of the design multi-agent system with the proposed architecture is supported though the simulation of the dynamic plant resources allocation under changing dynamic conditions.

The goal of the study was to determine the relationship between the annual performance of trucks and their age in connection with vehicle fleet operation at a large iron and steel plant. After identifying the patterns of changes in these performance indicators, we aimed to study the main factors affecting the intensity of these changes. Based on the performed analysis, we determined the factors affecting the operation of the vehicle life cycle management system and developed recommendations to reduce the intensity of changes in the annual performance. By using the data obtained, we propose mathematical models for changes in the annual performance of vehicles of various ages and brands, belonging to various functional groups. The obtained results allowed us to study the influence of the vehicle age on the annual decrease in performance depending on the existing factors and use the corresponding relationships to model the optimization processes in the existing vehicle fleets at enterprises.

A crucial task of steel plant logistics is the transportation and intermediate storage of semi-finished casting products, so-called steel slabs. We consider a fleet that consists of high-capacity vehicles. The vehicles are used to transport steel slabs within the steel plant, i.e., between facilities and distinct areas of the slab yard, and they can carry out stacking operations autonomously. The slab yard stores a majority of all steel slabs. It is organized in rows that function as a two-dimensional stacking area. Each row consists of multiple stacks built up by steel slabs that are located on top of each other. Retrieving steel slabs from stacks amounts to solving an extended block relocation problem (BRP). We study a combined approach for solving both the routing of steels slabs within the plant and the stacking of steel slabs in storage as part of their retrieval during a vehicle’s pickup operation.

The overall objective of this study was to establish the effects of steel industrial effluent on Nairobi metropolitan water system and its impact to the society. The study sought to identify various types of wastes produced by factories, assess how the waste generated is managed and disposed, examine the effects of effluent discharge on Nairobi River and finally propose mitigation measures. The research adopted a qualitative design and employed a number of methods: direct observations, document reviews to content analysis of the past studies, which in the end generated invaluable data. The study revealed that there are four categories of waste generated: solid waste, liquid waste, footbath chemicals as well as thermal wastes. It was also found that waste management in place was not effective enough leading to water and soil pollution. Other causes of pollution were found out to emanate from gasses contamination to the air and chemicals used during steel processing. The findings will inform the community of the harmful effects of untreated water and how it impacts on their health and productivity. It will also help the stakeholders in the environmental conservation to articulate issues of policy and influence agenda setting in the national and sub-national levels.

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.

A ship-unloading scheduling problem (SUSP), also called “sequencing ship” problem arising from a large steel plant with bulk material port is studied in this paper. Raw materials such as iron ore, coal and stone are transported to the steel plant by ships. Each ship has an expected departure time based on a contract between the steel plant and ship-owner. If the steel plant cannot effectively schedule the unloading operation of the ship to make it leave the port before its expected departure time, a demurrage penalty cost which increases with the delay will be charged to the steel plant by the ship-owner. SUSP is to find an optimal schedule for these raw material ships so as to minimize the total demurrage costs within a given planning time horizon. The problem is formulated as a mixed integer programming, and can be regarded as a flexible job shop scheduling problem, where the ships are regarded as the jobs and the belt conveyors as the machines. A column generation approach is proposed for solving the optimization model. The computational results on the real-world data obtained from the steel plant are compared with CPLEX to verify the effectiveness and merits of the proposed model and solution. The proposed optimization technique has great potential to be applied in the steel plant, and it may save 20 million of CNY caused by the delay cost per year.

The process of unloading iron ore raw materials of the metallurgical plant to the sinter plant has not been considered for a long time as a particular topic. Over the last years the conditions of metallurgical production have undergone certain changes: its intensification has occurred, which entails the need to re-evaluate parameters of stable operation of sinter production. The objective of the suggested study is to consider the issues of possible technical and technological improvement harmonizing transport flows with the requirements of the main production process. To achieve this goal, a technique of mathematical modelling is used, based on the mathematical apparatus of linear programming; graphic-analytical method; method of probability theory.The issues of the process of unloading, storage of iron ore raw materials, of their preparation for sinter production are primarily considered.The article presents the main positions of the process of optimizing unloading of sinter batch of a metallurgical plant.The general characteristics of the technology of metallurgical production and of each particular process are described. The agglomeration process is considered in more detail. The tables of initial iron ore raw materials for sintering production for certain reporting periods are given, for the sake of clarity, the data of one of the tables are summarized in a diagram. A diagram of a standard trestle-type ore warehouse is considered, which sequentially shows the main processes of sintering production from supply of raw materials to the car dumper to getting through a belt conveyor into a stack, and then into receiving bins. The article also provides an example of formation of piles of iron ore raw materials using manganese limestone.The study is based on a dynamic transport problem with delays (DTPD), which considers time of transportation of goods, the dynamics of production volumes over time, the dynamics of consumption, the dynamics of stocks of suppliers and consumers, dynamics of cost of transportation and storage, dynamics of cost of consumption losses. The study contains several mono-problems with a mono load, which overlap each other, forming a multiplicative overlay of single-product problems.

Yeah, it’s not like I liked working there,The fire and the shovel,And the smoke in my hair.Oh but lord, it’s all I know.I miss being needed,Having somewhere to go. –David Berkeley, “Steel Mill” Aut...