Steelmaking Plant Research Articles

Optimization and exergoeconomic analyses of water-energy-carbon nexus in steel production: Integrating solar-biogas energy, wastewater treatment, and carbon capture

The steel industry is one of the hard-to-abate sectors for decarbonization, and direct electrification is not possible or economically infeasible. This study investigates the application of the Water-Energy-Carbon nexus in the steel industry through a multi-generation plant, addressing the industry's demands for water, energy, and alternative fuel. The proposed multi-generation plant consists of parabolic trough solar collectors, an organic Rankine cycle, urban wastewater treatment, carbon capture, anaerobic digestion combined with heat and power, a proton exchange membrane electrolyzer, and a steelmaking plant. Thermodynamic and exergoeconomic analyses are conducted to investigate the system's efficiency and economic performance. A sensitivity analysis is conducted to identify the optimal links between nexus resources, followed by a multi-dimensional evaluation and multi-objective optimization. The results showed that under base conditions, the plant can annually produce 900 ktons of steel, 105 GWh of net power, 430.1 tons of water, and 517 tons of hydrogen, while preventing 34.7 ktons of CO2 emissions. The exergy efficiency and unit exergy cost of the products are 46.1% and 57.8 $/GJ, respectively. Under optimized conditions, the plant achieves a maximum net annual power output of 160 GWh, an exergy efficiency of 46.9%, and a minimum unit exergy cost of 51.4 $/GJ.

Optimization of BOFS high-gravity carbonation technology: Consideration of theoretical guidance for up-scaling applications

This study investigated the high-gravity (higee) carbonation reaction of a basic oxygen furnace slag (BOFS)-water-CO2 three-phase system via a rotating packed bed (RPB). The effect of different carbonation conditions on the higee carbonation process was explored. To achieve enhanced mass transfer, energy savings, and cost-effectiveness, this study aimed to optimize parameters such as BOFS component, acceleration, gas-liquid velocity ratio, and gas flow rate. Comparative analysis revealed that portlandite-rich BOFS exhibited a significantly higher Ca conversion ratio (2.1 times) and mass transfer coefficient values (KGαe) (5.5 times) than calcium silicate-rich BOFS. Systematic graphical presentation identified an acceleration of 430 m/s2, a gas-liquid velocity ratio of 25–30, and a gas velocity to RPB volume range of 4.7–6.6 min−1 as the optimal operating conditions for high KGαe (>0.2 s−1) and low energy consumption (144–151 kWh/t-CO2). Furthermore, prolonged RPB operation led to a 1.5-fold reduction in mass transfer performance, but reducing the liquid-to-solid ratio was a cost-effective measure to compensate for efficiency losses. The estimated cost for the higee carbonation process ranged from 106 to 111 CNY/t-CO2. These findings can provide valuable insights and practical recommendations for the industrial application of the higee carbonation technology in steel-making plants.

Stochastic Capacity Optimization of an Integrated BFGCC–MSHS–Wind–Solar Energy System for the Decarbonization of a Steelmaking Plant

Deploying renewable generation to replace conventional fossil-fuel-based energy supplies provides an important pathway for the decarbonization of steelmaking plants. Meanwhile, it is also crucial to improve the flexibility of blast-furnace-gas-fired combined-cycle power plants (BFGCCs) to ease the accommodation of uncertain renewable generation. To this end, this paper proposes the deployment of a molten salt heat storage (MSHS) system in BFGCCs to store the heat of gas turbine flue gas so that the power–heat coupling of these BFGCCs can be unlocked to enhance the flexibility of the energy supply. A stochastic capacity optimization of an integrated BFGCC–MSHS–wind–solar (BMWS) energy system is presented to determine the optimal installed capacities of a BFG holder, MSHS, wind turbine, and PV panel, aiming to achieve an economic and safe energy supply for the entire system. Multiple scenarios considering uncertain fluctuations in load demands and renewable generation are generated with the Monte Carlo method based on a typical scenario. These scenarios are then reduced to representative scenarios using the synchronous substitution and reduction method for stochastic capacity optimization to enhance the reliability of the results. The case study results demonstrate that configuring MSHS reduces the total annualized cost of the BMWS system by 2.28%. Furthermore, considering the uncertainties of the power/heating load and wind/PV generation can reduce the expected annualized total cost of the BMWS system and the corresponding standard deviation by 5.66% and 81.45%, respectively. The BMWS system can achieve 730.68 tons of equivalent CO2 reduction in 24 h due to the successful utilization of renewable energy. This paper provides an effective approach for the decarbonization of energy generation systems in steelmaking plants.

Using dynamic life cycle assessment to evaluate the effects of industry digitalization: A steel case study

AbstractDecarbonization of steelmaking has stagnated while it has a considerable share of global greenhouse gas emissions and a growing demand. Digitalization is seen as a viable option to reduce emissions and costs of the sector in the near term and life cycle assessment (LCA) as a comprehensive framework to evaluate changes in production practices. In this study, we analyze the potential impact of using optimization algorithms to improve the operation of a steelmaking plant in Spain. Specifically, we study the potential effects of optimizing the sequence in which steel is produced to minimize losses during casting. The global warming (GW) impacts and economic costs are quantified using a dynamic LCA model, considering uncertainty and temporal variability using an open‐source LCA framework. The results indicate, on average, modest savings in costs and are inconclusive regarding GW emissions. Most of the cost savings come from a reduction in the use of additives and electricity, which are wasted when the steel is scrapped during casting. The methodological framework has proven useful in quantifying and interpreting the potential effects of digitalization. The implemented solution, tested in an industrial setting, allows an automated evaluation of production at the plant using the LCA model, facilitating the use of sustainability criteria in decision‐making.

Influence of the Basicity on Phosphorus Enrichment Properties of Decarbonization Slag in Low‐Carbon Double Slag Converter Steelmaking Process with Industrial Experiments, Mineralogy, and Ion–molecule Coexistence Theory

To achieve low‐cost clean production in steelmaking plants, a low‐carbon double slag converter steelmaking process (LCDSP) with high dephosphorization efficiency, low lime consumption, and low CO2 emissions is focused on. The average lime consumption in LCDSP has decreased about 25.8%, which can reduce at least about 10.3 million m3 CO2 emissions per converter per year in the current steelmaking plant. The phosphorus enrichment properties of decarbonization slag with different basicities are comprehensively evaluated through LCDSP industrial experiments, mineralogy, and ion–molecule coexistence theory (IMCT). The increase of decarburization slag basicity (DSB) significantly improves the dephosphorization efficiency of molten steel. With increasing the DSB from 2.93 to 3.52, the typical morphology of the P‐rich phase extends from small pieces to long strips and then changes to block shapes with smooth edges. Finally, it forms a massive P‐rich phase with an area of more than 10 000 μm2 with a DSB of 3.80. The phosphorus enrichment properties of decarbonization slag with different DSBs evaluated using multiple perspectives show a consistent change trend. IMCT provides an accurate thermodynamic method for quantitatively predicting the phosphorus enrichment capacity in slag in two stages of LCDSP.

Research on the Properties of Steel Slag with Different Preparation Processes.

To promote the resource utilization of steel slag and improve the production process of steel slag in steelmaking plants, this research studied the characteristics of three different processed steel slags from four steelmaking plants. The physical and mechanical characteristics and volume stability of steel slags were analyzed through density, water absorption, and expansion tests. The main mineral phases, morphological characteristics, and thermal stability of the original steel slag and the steel slag after the expansion test are analyzed with X-ray diffractometer (XRD), scanning electron microscope (SEM), and thermogravimetric analysis (TG) tests. The results show that the composition of steel slag produced by different processes is similar. The main active substances of other processed steel slags are dicalcium silicate (C2S), tricalcium silicate (C3S), CaO, and MgO. After the expansion test, the main chemical products of steel slag are CaCO3, MgCO3, and calcium silicate hydrate (C-S-H). Noticeable mineral crystals appeared on the surface of the steel slag after the expansion test, presenting tetrahedral or cigar-like protrusions. The drum slag had the highest density and water stability. The drum slag had the lowest porosity and the densest microstructure surface, compared with steel slags that other methods produce. The thermal stability of steel slag treated by the hot splashing method was relatively higher than that of steel slag treated by the other two methods.

Нейросетевое прогнозирование дефектов производства металлургической продукции

The paper is devoted to the current problem of reducing the percentage of defective products produced at serial steelmaking plants. To study the patterns of defect formation, a neural network was created that predicts the formation of defects such as “crack” in castings produced by the open-hearth method. To train the neural network, statistical data on the chemical composition of ore raw materials and the corresponding values of the percentage of defects were used. The data was taken under the conditions of an ongoing serial production process, which led to a high degree of noise in the information both on the chemical composition of the ore raw material and on the mechanical properties based on the results of its heat treatment. Outliers of statistical information were detected and removed using the original author’s neural network technique. A neural network model for controlling thermophysical and chemical-energy-technological processes of thermal processing of ore raw materials was created on the basis of a perceptron-type neural network with sigmoid activation functions. By conducting virtual computer experiments on a neural network model, some important dependences of the probability of formation of the defects under study on the content of manganese, phosphorus, silicon, chromium and sulfur were identified. Based on the identified dependencies, practical recommendations have been developed to reduce the percentage of defects by adjusting the chemical composition of ore raw materials. Despite the relatively low accuracy of the developed neural network model, the application of the practical recommendations obtained made it possible to reduce the percentage of defective products manufactured in a large-scale production process by 2.51 times.

Coupling of Solidification and Heat Transfer Simulations with Interpretable Machine Learning Algorithms to Predict Transverse Cracks in Continuous Casting of Steel

The formation of defects such as cracks in continuous casting deteriorates the quality of cast products and efficiency of steelmaking. To evaluate the risks and identify the root causes of defect formation, phenomenological quality criteria computed with a solidification and microstructure model known as InterDendritic Solidification (IDS) have previously been applied. This approach is computationally efficient and provides a fundamental perspective to defect formation in continuous casting. The aim of this work is to study the capabilities of these criteria as features in predicting transverse cracking with interpretable machine learning models. IDS is coupled with a heat transfer model known as Tempsimu to simulate the continuous casting process. Measured compositions are utilized in the simulations and defects reported at a steelmaking plant are used as labels in classification. Logistic regression, decision tree, and Gaussian Naïve Bayes classifiers are developed to predict transverse cracking in peritectic C–Mn, low‐carbon B–Ti microalloyed, and peritectic Nb‐microalloyed steels. The corresponding balanced accuracies of the best classifiers from cross‐validation are 92%, 94.6%, and 82.8%. Due to the good performance and the interpretability of the developed classifiers, the fundamental causes of transverse cracking and possibilities of avoiding it by changes in the compositions are identified.

Application of Automation and Intellectualization Technology in Steelmaking Plant

Steelmaking plant of Fujian San Gang min Guang Co., Ltd. is keeping pace with the trend of the times. Under the new situation that the labor cost is rising day by day . Constantly introducing intelligent technology to improve the level of automation operation and reducing labor intensity of staff and workers. Especially under the support of MES information system. Advanced and practical technology is adopted in converter, refining and continuous casting process. In recent years, production management and control, energy management and control , material tracking, cost control and equipment operation and maintenance and reducing personnel to new achievements in reducing personnel.

Production of refining alumina-containing fluxes by sintering from technogenic raw materials

Modern Russian steelmaking plants use predominantly alumina-containing materials for liquefying lime in a ladle-furnace unit, which replaced fluorspar. Alumina-containing materials currently available on the market cannot be used directly in steelmaking without preliminary preparation (refining, heat treatment or briquetting), or are simply unsuitable for ladle processing of steel. This work describes laboratory studies on the production of refining alumina-containing fluxes by sintering in units such as machines for pellets firing or producing agglomerate (in the temperature range of 1200 – 1500 °C) from clean metallurgical waste (fine dust from the production of alumina and burnt lime), meeting the requirements of steelmaking plants by chemical composition and mechanical properties. A comparison was made of sintering technological schemes with the introduction of hydra­ted lime and a mixture of hydrated lime and calcium carbonate in a 1:1 ratio as a source of CaO. We determined that the maximum permissible CaO content in sintered briquettes when using a mixture of hydrated lime and calcium carbonate in the charge, which does not lead to hydration destruction in air, is in the range of 2.3 - 3.6 %, depending on the holding temperature. The maximum permissible content of Al2O3 in sintered briquettes when using hydrated lime in the charge, which does not lead to hydration destruction in air, is in the range of 9.5 – 31.7 %, depending on the holding temperature. In existing fuel units it is possible to obtain fluxes by sintering only when using hydrated lime as a source of CaO, because adding calcium carbonate to the charge (9 – 22 %) requires an increase in holding temperature (above 1500 °C) or holding time (more than 25 min).

Alkali-based components as slag fluidizers in steelmaking processes: A thermodynamic assessment

Defining the optimized volume of dual saturated (CaO and MgO) slags is of utmost importance to secondary metallurgy, especially for the ladle furnace. To be effective, these slags may present high desulphurization capability, combining the ability to shield the electric arc flare, to avoid the skull formation in the ladle borders and bottom, and suitable viscosity to provide fast incorporation of additives and tapping. Some fluidizers are applied for attaining the purpose of taking CaO/MgO into solution, being fluorspar (calcium fluoride, CaF2) and silica sand (silicon oxide, SiO2) broadly used in Si-killed steelmaking plants. At the Al-killed plants, either bauxite (mainly aluminum hydroxide) or calcium-aluminates are recommended choices. Nevertheless, for various reasons, some steelmakers use alternative fluidizers containing a significant amount of Na2O and K2O, such as sodalite and nepheline. Based on thermodynamic calculations (CALPHAD method), the effects of such alkalis on the CaO/MgO solubility and the optical basicity of slags on the CaO–SiO2–MgO–Al2O3 system were explored in this paper. Moreover, the potential impact on the performance of MgO-based refractories in contact with these alkali-containing slags was also assessed by a thermodynamic approach. Although kinetic aspects must also be considered, it was seen that (Na2O)2·(K2O) additions into these systems must be carefully managed to make the most of their ability to improve desulphurization, while avoiding the earlier corrosion of the refractory lining.

Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span

As an energy-intensive industry, the steel industry grapples with increasing energy costs and decarbonisation pressures. Therefore, multi-objective optimisation is widely applied in the production scheduling of the steelmaking plant. However, the optimal solution prioritising energy savings and emission reductions may lead to impractical or less economically efficient solutions, since the processing time requirement (PTR) of steel production orders in real-world production is neglected. This study fills the research gap by discussing the impact of PTR on the make-span of the steelmaking process and incorporating it into the optimisation model. Considering the variability of PTR, the solving of the multi-objective scheduling problem is transformed into the selection from Pareto solutions with different make-spans. To better leverage the temporal flexibility of the steelmaking process, a what-if-analysis-based strategy coupled with the Normal Boundary Intersection method is proposed to generate a series of evenly distributed Pareto solutions. The energy storage system is integrated to improve the time granularity of the steelmaking plant's flexibility. Our case studies demonstrate that the electricity and emission costs are reduced by 68.5%, indirect emissions are reduced by 83.5%, and the on-site renewable energy self-consumption rate increases by 12.1%. The effectiveness of the proposed method implies that it is of great relevance to the development of a cleaner steel industry in the future.

A hybrid approach using multiple linear regression and random forest regression to predict molten steel temperature in a continuous casting tundish

ABSTRACT The temperature control of molten steel is essential to ensure operational stability in a steelmaking plant. The calculation of thermal losses in the steelmaking plant’s operations depends on highly dynamic variables, which motivates the construction of predictive models for the steel temperature. This paper proposed a hybrid ensemble method using multiple linear and random forest regression to predict the end molten steel temperature at the secondary refining required to achieve a target tundish temperature. Combining these two methods makes it possible to account for the linear and non-linear relationships in the data. The implemented models were trained on industrial data, and their performance was assessed using root mean squared error (RMSE) and a custom accuracy metric. The results showed that the proposed hybrid method achieves up to 5% better accuracy compared to linear regression or random forest regression methods alone, thus can enhance molten steel prediction in steelmaking plants.

Low carbon optimal planning of the steel mill gas utilization system

Decarbonization of the steelmaking industry is imperative in achieving the carbon neutrality target. This paper proposes a two-stage low carbon robust planning method for the steel mill gas utilization system (SMGUS), which is the major CO2 emission source of the conventional blast furnace route based-steelmaking plant. The planning aims to find the optimal installed capacities and relevant deployment time of multiple low-carbon technologies, such as renewable energy, energy storage, hydrogen energy and carbon capture & utilization, so that the demand of steel production can be met in an economically competitive and environmentally friendly way. Case studies are carried out on the low-carbon planning of a steelmaking plant with annual production of 1.5 million tons of crude steel, in which different carbon market conditions and CO2 reduction routes are considered. The results show that the low carbon planning can reduce the CO2 emissions to 1.22 tons per ton of steel by 2045–2050 through the proper deployment of the low carbon technologies. The CO2 emissions can be further reduced to 0.31 tons per ton of steel if the BF-BOF process is replaced by H2-DRI and EAF technologies. This paper presents an effective planning method for the decarbonization of steelmaking plant.

Mineral Phase Reconstruction and Separation Behavior of Zinc and Iron from Zinc-Containing Dust.

Zinc-containing dust can be found in ironmaking and steelmaking, and it is an important secondary resource of zinc. Zinc-containing dust from an electric furnace was used as a raw material to study the phase transformation behavior of the dust using a calcification roasting process and the zinc-iron separation behavior by using ammonia leaching. The zinc-bearing dust was mixed with CaO and roasted to transform the zinc ferrite into zinc oxide. The results showed that increasing the calcium oxide to dust ratio could promote the conversion of zinc ferrite to zinc oxide. When the calcium oxide ratio reached 60%, the peak of zinc ferrite in the calcined-roasted product in the zinc-containing dust basically disappeared. As the temperature increased, the zinc oxide grains increased but were still smaller than 10 µm. The calcined-roasted product was crushed and ground, and the zinc was leached by ammonia. A zinc-iron recovery rate of 86.12% was achieved by the ammonia leaching. The leachate could be used for zinc extraction by electrolysis. The leaching residue was mainly calcium ferrate, which could be used in sintering production. The proposed process may achieve on-site recovery of zinc-containing dust in steel-making plants.

High oxygen and SNG injection in blast furnace ironmaking with Power to Gas integration and CO2 recycling

In the last years, reduction of CO2 emissions from the steel industry has been of great importance. Carbon capture, oxygen blast furnaces and top gas recycling technologies, among others, have been deeply studied as low carbon solutions. In this paper, a novel integration of carbon capture and power to gas technologies in the steelmaking industry is presented. Green hydrogen via proton exchange membrane (PEM) electrolysis and CO2 via methyldiethanolamine (MDEA) scrubbing from the blast furnace gas (BFG) are used to produce synthetic natural gas in an isothermal fixed bed methanation plant. The latter gas is injected into the blast furnace, closing a carbon loop and reducing coal consumption. The oxygen by-produced in the electrolyser covers the entire oxygen demand of the steelmaking plant and avoids the need for an air separation unit (ASU). The novelty of this work relies on the variation of the oxygen enrichment and its temperature in the hot blast, and how it influences the power to gas integration concept. This power to gas integration is compared with a conventional BF-BOF plant from a technical, economic, energy and environmental point of view. Both plant process configurations were implemented in Aspen Plus simulations, assessing the fossil fuel demand, energy penalty, cost and CO2 emissions. Emission reduction up to 34% can be achieved with power to gas integration, with an energy penalty of 17 MJ/tHM and a cost of 352 €/tCO2.

Determination of the Influence of Steelmaking Parameters on Surface Defects in Quarto Plates

This work aimed to establish a relationship between the parameters affecting the steelmaking process and the quality of the quarto plates. We knew that the main causes of product defects in the plates were in the steelmaking process, so we sought to make changes to the process. All units in the steelmaking plant were equipped with sensors to control the working parameters, which were regularly stored in databases. These data are supplemented by the chemical composition of the molten steel at various stages of the process. To organise and analyse the huge amounts of data, data mining tools included in the Orange Software were used. For industrial use, the tree algorithm seems to be the most suitable, but we also used other models based on artificial intelligence. Unexpectedly, we obtained evidence of self-regulation and robustness in the steelmaking process. Another important result was that some additional parameters should be measured and analysed regularly, at least the amount of oligo-elements in the molten steel and the basicity of the final refining slag.

Exploring the emergence of waste recovery and exchange in industrial clusters

AbstractSelf‐organized industrial symbiosis (IS) starts with one actor's decision to invest in a waste recovery plant and the other actors' decision to buy the recovered flow. Technical and institutional conditions of the cluster influence actors' decisions. This paper explores the emergence of IS collaborations in industrial clusters under different techno‐economic conditions in the long term. We propose a mixed‐integer linear programming model that incorporates costs and constraints associated with waste recovery and exchange to study actors' investment decisions and investigate shaped symbiotic exchanges under rising energy prices and limited electricity supply. The approach is implemented in Iran's Persian Gulf Mining and Metals Special Economic Zone as a case study. The results revealed that changes in internal or external condition simultaneously influence the industrial and waste recovery plants. For instance, increasing energy prices without raising product prices significantly decreased the production level of industrial plants and, consequently, heat recovery potential. Furthermore, the waste heat recovery plants' contribution to improving the cluster's economic and environmental performance was not the same. Electricity recovery from a power plant's waste heat can result in 55 PJ grid electricity intake reduction and 720 M€ cluster cash flow increase. Recovered cooling or electricity from the steelmaking plant waste heat was consumed internally rather than shaping IS. These model outcomes show its capability to study IS within the socio‐technical structure of the cluster, not a standalone phenomenon. Implemented conceptualization offers a novel system‐level approach, which could be adjusted to assess other industrial development strategies.

Fault Diagnosis of Laminar Cooling Roller Motor Based on Morphological Recognition and Combination Patterns Mining of Multicurrent Signatures

Laminar cooling roller motor (LCRM) is the main driving equipment of laminar cooling section (LCS) in a steelmaking plant. To promote an intelligent upgrade of the safe management and operation of LCRMs, a novel fault diagnosis method based on the morphological recognition and combination pattern mining of LCRM’s multi-current signatures is proposed. Six key current signatures closely related to the faults are screened by the correlation analysis empirically in advance. A Bayesian L-BFGS algorithm is proposed to obtain the smooth boundary morphology by the curve fitting of the boundary sequences of the upper and lower quantiles of every windowed current signature. Then, a Triplet-CNN-fused KNN with GRU and Transformer, termed T-CKGT is proposed, to identify the fault-related abnormal current signature morphology under a constraint of limited samples. Specifically, the CNN and GRU are respectively used to mine the spatial and temporal features of the boundary morphology, which are fused to feed into a Transformer to learn recognizable features, and then a KNN classifier is employed to identify the abnormal morphological type of each current signature based on the recognizable features. To mine the abnormal morphological combination patterns (AMCPs) of different faults, the AMCPs of six key current signatures are taken as gene fragments of genetic algorithm to achieve the AMCP library for each fault type, so as to realize the online fault diagnosis efficiently by pattern matching. Extensive confirmatory and comparative experiments were performed for the online fault identification of 39 LCRMs in a real steelmaking plant. Experimental results indicate that the proposed method can achieve a high fault recognition accuracy, generally higher over 96%, which outperforms the state-of-the-art methods apparently. In addition, during the actual application, no critical false negatives and false positives were observed.

Forensic Analytical Approach for Hydrocarbon Fingerprinting in Soil Vapor Samplings: Example of a Residential Neighborhood in Brazil

Conventional analytical methods determine the concentration of compounds used to estimate the presence of environmental contamination capable of posing risk to human and ecological receptors (Murphy and Morrison, 2007). While these target analytes help regulators and environmental professional identify and manage potentially harmful conditions, these compounds can be generated by many natural and anthropogenic (man-made) sources and conventional methods alone fail to compositionally differentiate subsurface sources of contamination in complex environments (Morrison, 2000; Wang and Fingas, 2003; Gouvêa et al, 2019; Douglas et al, 2007). This case study demonstrates the advantage of using both conventional and forensic testing methods to accurately identify the source(s) of volatile hydrocarbon contamination in soil vapor samples for the development of an accurate conceptual site model in a neighbourhood near industrial facilities in Rio de Janeiro State, Brazil. The goal of this study was to differentiate potential impacts from a local steelmaking plant from fugitive hydrocarbons associated with more generic human activity in urban settings. This case study demonstrated the advantage of constructing chemical fingerprints from conventional volatile organic compound (VOC) method TO 15 and a forensic volatile hydrocarbon method using an enhanced version of method TO 15 (PIANO). The chemical fingerprints of vapor samples collected from subsurface soil gas and sewers were analysed and compared to laboratory reference samples. These data determined that the VOCs detected in neighbourhood soil vapor samples were associated with fugitive petroleum products migrating in the sewer pipelines and not with the steelmaking wastes emplaced near the residential area. This article discusses the forensic data and chemical signatures that support these findings, and the use of environmental forensic techniques to evaluate environmental data associated with complex scenarios, involving multiple contamination sources.