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Process integrated modelling for steelmaking Life Cycle Inventory analysis
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Environmental Impact Assessment Review 28 (2008) 429 – 438 www.elsevier.com/locate/eiar
Process integrated modelling for steelmaking Life Cycle Inventory analysis Ana-Maria Iosif a,⁎, Francois Hanrot a,1 , Denis Ablitzer b,2 b
a Arcelor Research, Voie Romaine, BP30320, Maizieres-les-Metz, 57283, France LSG2M, Ecole des Mines de Nancy, Parc de Saurupt, F-54042 Nancy cedex, France
Received 17 August 2007; received in revised form 16 October 2007; accepted 17 October 2007 Available online 15 January 2008
Abstract During recent years, strict environmental regulations have been implemented by governments for the steelmaking industry in order to reduce their environmental impact. In the frame of the ULCOS project, we have developed a new methodological framework which combines the process integrated modelling approach with Life Cycle Assessment (LCA) method in order to carry out the Life Cycle Inventory of steelmaking. In the current paper, this new concept has been applied to the sinter plant which is the most polluting steelmaking process. It has been shown that this approach is a powerful tool to make the collection of data easier, to save time and to provide reliable information concerning the environmental diagnostic of the steelmaking processes. © 2007 Elsevier Inc. All rights reserved. Keywords: Steelmaking; Sintering process; Life Cycle Inventory; Emissions
1. Introduction Consideration of environmental aspects in traditional product development has become one of the greatest challenges for the steelmaking industry. The European Steel Industry has measured up to this challenge by creating a consortium of industries and research organisations that has taken up the mission of developing such breakthrough processes, namely the
⁎ Corresponding author. Tel.: +33 3 87 70 43 78; fax: +33 3 87 70 41 03. E-mail addresses: [email protected] (A.-M. Iosif), [email protected] (F. Hanrot), [email protected] (D. Ablitzer). 1 Tel.: +33 3 87 70 43 56; fax: +33 3 87 70 41 03. 2 Tel.: +33 3 83 58 42 37; fax: +33 3 83 58 40 56. 0195-9255/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.eiar.2007.10.003
ULCOS (Ultra Low CO2 Steelmaking) consortium. This consortium plans to develop a breakthrough steelmaking process that has the potential of meeting the target of markedly reducing green houses gases emissions. The ULCOS project will last for 5 years (2004–2009) and involves 48 partners from 14 European countries and aims to developing new technologies for reducing steelmaking emissions, compared to the iron ore based benchmark (Eurofer, 2005). It will test coal, natural-gas, electricity and biomassbased steelmaking routes, which all have the potential for meeting the reduction target for complementary reasons. However, in order to develop technologies to reduce emissions, it is necessary to assess the environmental impact of the classical steelmaking route (coke plant, sinter plant, blast furnace, basic oxygen furnace,
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Fig. 1. New methodological framework for steelmaking LCI analysis.
continuous casting and hot rolling). Life Cycle Assessment (LCA) method has been undertaken in ULCOS as the most holistic approach of assessing environmental impact and selecting new technologies. Previous to the impact assessment, it is essential to carry out the Life Cycle Inventory (LCI) of the process which is the core part of LCA methodology. According to the current LCA standards (ISO 14040, 1997), the quality of the data used for carrying out this inventory is one of the most important limiting factors. The LCI analysis involves data collection, calculation and procedures to quantify the relevant inputs and outputs of the product system. The data collection should correspond to some conditions: precision, completeness, representativeness, consistency and reproducibility (ISO 14040, 1997). It is obvious that such conditions are not easy to respect when data are supplied only by the industrial practice and/or by the literature issues. Indeed, the environmental performance of complex steelmaking processes such as iron ore sintering are strongly dependent on operational conditions. Consequently, the environmental burden of the system can change significantly when different types of fuels and recycled wastes are used in the process. That's why it is important to improve the way of assessing the LCI of the steelmaking industry in order to guarantee the quality of the data and to predict the change of the environmental performances with respect to the operational conditions.
2. Description of the steelmaking integrated modelling In the frame of the ULCOS project, the objective of our work was to develop a new concept which combines the process integrated model approach with LCA in order to innovate the way of assessing the LCI for steel production, see Fig. 1. Based on physicochemical considerations, thermodynamics laws and mathematical equations, Aspen software have been used in order to develop modules for each steelmaking processes as presented in Fig. 2. Aspen (Advanced System for Process Engineering) is a process engineering software package that is used to simulate processes based on the thermodynamic models, properties of materials and several ready-made unit operation models. The developed modules calculate the mass and heat balances, emissions, and the chemical compositions of products and by-products simultaneously. Each module has been validated with industrial data and finally connected together by each primary product and any possible by-product interactions. Thanks to the developed integrated model, the inventory of the steelmaking process has been easily carried out. Even though the process modeling approach is commonly used in LCA in chemical processes, it becomes quite difficult when we try to apply it in the steelmaking field. Indeed, the complexity of the processes and the large number of connections between them make the application of this concept complicated.
Fig. 2. Process description for the integrated steelmaking plant.
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However, this concept has already been applied by Larsson and Dahl (2003) in steelmaking, aiming to analyze the different possibilities for energy savings and consequently to minimize CO2 emissions. This model remains quite simplified and focuses only on the calculation of CO2 emissions based on carbon and energy balances. The challenge in our work was to develop a model which is a trade-off between complexity and accuracy. The current model proposed in this paper is quite simplified compared to sophisticated models already existing for each steelmaking processes, but still complex enough to allow the calculation of the mass of various pollutants. Furthermore, the masses and the composition of products and by-products such as the steelwork gases are also calculated. This last information is very important because the steelwork gases (coke oven, blast furnace and converter gases) are used as fuels by all of the steelmaking processes described in Fig. 2. Consequently, the contribution of these gases to the total environmental burden of the system can be easily estimated. Under the ULCOS umbrella, our work has two main applications. First, the steelmaking model has been used by different companies to rapidly assess their environmental impacts with respect to their own industrial configuration. Secondly, based on the proposed approach, a benchmark LCI has been carried out for the classical route of steel production. This inventory has been considered as the “reference LCI” for the selection of new steelmaking
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technologies. Indeed, before the selection of steelmaking alternatives and before testing any technological proposal at the industrial scale, it is crucial to compare their LCI to a reference case which is the classical route of steel production. It is important to mention that the new approach offers some important benefits that cannot be obtained when the LCI is carried out using data only from industry and literature. First of all, the model allows us to control the mass and energy balances of the calculated inventory, something that is nearly impossible to assure when the LCI is carried out using only average data from literature or experimental works. Secondly, calculating emissions based on physicochemical and mathematical considerations gives a strong credibility to the inventory. Finally, model simulations for special operating conditions, such as recycling of different wastes, the use of new fuels, or the mixing of fuels, give access to certain environmental information which is not available among the industrial or literature outputs. Based on the proposed approach, the LCI of the integrated steel plant can be carried out for different operational practices and the best scenario can be identified in minimal time. For a better understanding of this approach, a brief description of one module developed for sintering plant is given in the following section of the paper. The presentation of the sinter plant module was determined by the complexity of the process and by the fact that the
Fig. 3. Schematic view of the sinter plant.
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sinter plant is considered the most polluting unit among all the steel works. 3. Presentation of the iron ore sintering model Currently, there are several sintering process models with different structure ranging from simple 1-D simulation to more complexes (Patisson, 1987; Vanderheyden and Mathy, 2001). These models are reaching a higher level of sophistication, which is usually achieved at the expense of model transparency. This can lead to a model almost as complicated as the real process. However, a simplified model for iron ore sintering process using Aspen software has been developed in the past by Schultmann et al. (2003) in the frame of KOSIMEUS concept applied to the steel industry. This simplified model is developed only for the sintering machine and calculates mainly the system mass balances. In terms of pollutants, only CO2 emissions are estimated based on carbon balance calculation. The originality of our work consists in the model boundary, i.e. the elementary processes which are taken into consideration. Hence, the current model was developed for the entire sinter plant system: the preparation of the sinter raw mix, the ignition process, the sinter strand, the exhaust gas cleaning and the sinter cooler (see Fig. 3). The model has been built as a mathematical matrix, based on chemical reactions and correlations between emissions and different parameters of the process in order to allow an accurate simulation of various mechanisms. The main pollutants evolved by the system, namely CO, CO2, NOx, SOx, VOC (CH4 equ.), HCl, heavy metals and dust are the most important outcomes of the model. Also, the mass and the chemical composition of the sinter have been calculated and the energy balance has been attentively checked. For simplicity sake, only the description of the sinter strand modelling is given in the following section of the paper. 3.1. Modelling of the sinter strand The main physicochemical and thermal mechanisms which take place on the sinter strand deliver the agglomeration of iron ore fine particles into porous clinker referred to as sinter. In the model, the sinter strand was virtually divided into two parts: sinter formation and gas phase generation as a consequence of the solid fuel combustion. 3.1.1. Modelling of the sinter formation The chemical reactions involved in sinter formation are complex. Therefore, we have proposed a simplified
system of reactions (shown below) representing evaporation of moisture, limestone decarbonatation, calcium silicate and calcium ferrite formation and iron oxide reduction. H2 OðlÞYH2 OðgÞ;
ð1Þ
CaCO3 YCO2 þ CaO;
ð2Þ
CaO þ SiO2 YCaSiO3 ;
ð3Þ
Fe2 O3 þ CaOYCaFe2 O4 ;
ð4Þ
3Fe2 O3 Y2Fe3 O4 þ 0:5O2 :
ð5Þ
During the sintering process, hematite is partially reduced to magnetite. In order to calculate the mass of magnetite in the sinter, in the model the ratio of Fe++ in the sinter composition was imposed at an average value. Thus, the ratio of Fe++ in the sinter composition is one parameter of the model. 3.1.2. Modelling of the sinter waste gas formation Based on simplified chemical reactions and experimental observations we have calculated the flow rate and the chemical composition of the sinter waste gas. The pollutants within the gas phase are a consequence of complex phenomena which take place during the sintering process, namely combustion of solid fuel, vaporisation and particles passed over. All the pollutants which were calculated by the model characterise the sinter waste gas composition before the gas cleaning facilities. In order to avoid explosion in the electrostatic precipitator which is part of the waste gas cleaning facilities, the fuels used are generally anthracites with a low fraction of volatiles matters (FVM), coke breeze and mixtures of both. 3.1.2.1. Calculation of CO and CO2. The combustion of the solid fuels mixture produces the major pollutants which are CO and CO2. In the model, the ratio between these two compounds (CO/(CO+CO2)) has been taken equal to 0.15 (Arion et al., 1999; Iosif, 2006). 3.1.2.2. Calculation of SOx. During the combustion process, sulphur within the raw mixture (mainly from fossil fuel) is released essentially as SO2. The contribution from iron ore is normally about ten times smaller than from fuel. Besides the sulphur from raw materials, the quantity of emitted SO2 is influenced by the basicity of the sinter feed and by the solid fuel grain size (IPPC,
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zone is lower than 1350 °C, only the fuel NO was considered in the model. Based on a relatively large number of experiments, a linear dependency between NOx emissions and the nitrogen within the fuel has been established (see Fig. 4)(Iosif, 2006). It should be pointed out that this correlation has been confirmed by industrial experiments and the results are plotted on the same figure.
Fig. 4. Correlation between nitrogen within the fuel and NOx emissions.
2003). Sinter pot trials carried out by Arion et al. (1999) have shown that the sulphur within the waste gas is about 64% (wt.) of the sulphur brought by the fuel. This result has been further confirmed by industrial trials. However, more investigations on sulphur behaviour during the sintering process are recommended. For the sake of simplicity, in the present model, it has been assumed that SO2 formation depends only on the sulphur content in the fuel, whereas the sulphur contained in iron ore is considered to be inert. Thus, 64% of the sulphur from the fuel oxidised as sulphur dioxides and the rest will be located into the solid phase (sinter, return fines and dust). 3.1.2.3. Calculation of NOx. As consequence of the fuel combustion, NOx emissions are generally formed due to the presence of nitrogen in the combustion air and the fuel itself. It is mainly nitric oxide (NO) which arises during the combustion on the sinter strand by three different formation mechanisms: thermal NO, prompt NO and fuel NO. During the sintering process, it was demonstrated by industrial and pilot trials that the NO2 emissions represent less than 10% of total NOx emissions. Because the temperature of the combustion
3.1.2.4. Calculation of Volatile Organic Compounds (VOC). The main sources of VOC emissions are the solid fuels and the recycled materials, i.e. blast furnace dust, coking sludge, mill scales, etc. During the sintering process, VOC are formed by volatilisation, at temperatures exceeding 200 °C, by pyrolysis and partial oxidation of the organic compounds at temperatures higher than 400 °C. Investigations on VOC formation during the sintering process using different kinds of fuels have shown a linear dependency between the concentration of these compounds within the waste gas and the FVM in the fuel. Fig. 5 shows the results of the experimental measurements carried out into the sinter pot by Arion et al. (1999). These correlations have been confirmed by Petitnicolas and Le Louer, (1999) from multiple experiments in two different sinter plants. These results are also shown in Fig. 5. It has been observed that for the low values of FVM, the evolution of VOC in the sinter gas is different according to the nature of fuel used in the process, either coke breeze or anthracite. In the model, the volatile compounds are calculated as CH4 equivalent based on correlations obtained from Fig. 5. In the case of fuel mixture (coke breeze and anthracite), it has been considered that VOC compounds are additive and the total amount of VOC (mVOC total) is calculated through correlation (6). Hence, the content of
Fig. 5. Correlation between the fraction of volatiles matters (FVM) in fuels and VOC emissions (in methane equivalent).
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Fig. 6. Correlation between dust within the exhaust gas (downstream from ESP) and the average temperature of the waste gas.
volatiles matters in each type of fuel and there proportion in the fuel mix, are considered as inputs of the model. VOC mVOC total ¼ ðmcoke breeze =mmix Þ⋅mcoke breeze þ ðmanthracite =mmix ÞÞ⋅mVOC anthracite
ð6Þ
The mmix is the total mass of mix fuel, mcoke breeze and manthracite are the masses of coke breeze and anthracite in the mix fuel. However, it is important to mention that the correlations used for NO and VOC calculation are given only by the sinter pot tests, the results from industrial experiments are plotted only with the aim to confirm the sinter pot results. 3.1.2.5. Calculation of sinter dust. According to Ferreira et al. (2005), the dust formation is determined by factors related to the process parameters (mainly the temperature of the exhaust gases), raw materials (alkali content) and mechanical equipments. In the model, the total amount of dust has been calculated in relation with the final temperature of the exhaust gas downstream from the first dedusting stage. Most sinter strands are equipped with conventional dry electrostatic precipitators (ESP) for the first stage of exhaust gas dedusting. Industrial measurements plotted on Fig. 6 have shown a clear dependency between the dust retained by the ESP and the average temperature of exhaust gas in the common duct. Indeed, the higher the fume temperature, the higher is the dust electrical resistivity of ESP. The real mass of dust within the exhaust gas before the dedusting stage (mdust) has been calculated with the aid of correlation (7), obtained from Fig. 6. dust
m
V waste gas ⋅ð432−2:5T -CÞ ¼ 1−η
ð7Þ
The ESP efficiency (η) is considered as parameter of the model and it is fixed to 90% (real efficiency in industrial conditions) (FFA, 2004). Although this
correlation is based on a large number of experimental data, it remains very approximate. Dust results should therefore be considered cautiously. The elementary dust composition has been considered equal to the elementary composition of sinter. For simplicity sake, the calculation of HCl emissions and the simulation of heavy metals volatilisation (Cd, Pb and Hg) are intentionally skipped in the present paper. 3.2. Modelling of the sinter waste gas cleaning system The sinter waste gas treatment consists in two stages: main dedusting and a semi dry gas cleaning. As already mentioned, the main part of dust is collected through the dry electrostatic precipitator (ESP). Considering the real efficiency of the ESP equal to 90%, the total amount of dust is separated into two streams: dust for recycling or for disposal (the model offer the two possibilities) and dust within exhaust gas towards the final gas cleaning equipment. Downstream from ESP system, the fumes are sent to a semi dry gas cleaning equipment (see Fig. 3) which removes mainly fine dust, acid gases and heavy metals. The configuration chosen to be modelled is one possibility; other options are available for the fumes cleaning. The simulated semi dry cleaning system consists of a baghouse filter and a mixing drum. Calcium hydroxide is fed directly into the mixer and the necessary water to condition the gas is added. Recycled dust from the baghouse filter (1.14 kg/Nm3) is injected in the inlet Table 1 Operating data for the semi dry gas cleaning equipment Element
Efficiency
Dust SO2 HCl Hg Pb Cd
90% 50% 96% 97% 99% 99%
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Fig. 7. Composition of the sinter waste gas (main components).
duct of the line and mixed with the raw gas thanks to the mixing drum. The reagent (activated carbon) is added to secure the efficient removal of heavy metals and is injected into the reactor prior to entering the baghouse filter. The activated carbon ensures also the retention of dioxins but these pollutants are not calculated by the model. The key point for simulating the fumes cleaning system described above are the separation of pollutants, namely SO2, HCl, Hg, Pb, Cd and fine dust escaped from the ESP. The chemical reactions considered in the model are (Biococchi, 1998): CaðOHÞ2 þ2HClYCaCl2 þ 2H2 O;
ð8Þ
CaðOHÞ2 þSO2 YCaSO3 þ 2H2 O;
ð9Þ
CaSO3 þ 0; 5O2 YCaSO4 :
ð10Þ
The efficiency of the gas cleaning system depends on the gas temperature, moisture content and retention time inside the baghouse filter. Operating parameters typical of gas cleaning installation are given in Table 1 (Märker,
2005; Voelker, 2005). These average data are considered as representative for normal operating conditions and are taken as parameters of the model. Thereafter, the model calculates the final amounts of SO2, HCl, Hg, Pb, Cd and dust within the waste gas flow, the mass of additives and finally the mass of solid wastes. However, the most important part of the dust recovered from the baghouse filter is recycled in the mixing drum and the rest is put on landfill site. 4. Presentation of the results In order to see the behaviour of the model for industrial input data, an European sinter plant has been selected as a reference case. Hence, the most relevant mass streams which feed the model, there chemical compositions and the key parameters are supplied by this plant. Some of the results generated by the model for the simulation of this sinter plant are summarised in Figs. 7 and 8, and discussed in the current section. For 1 t of sinter, the flow rate of waste gas calculated by the model is equal 1505 Nm3/tsinter and consistent with industrial figure. For standard operational conditions, the
Fig. 8. Composition of the sinter waste gas (secondary components).
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Fig. 9. Sinter composition.
content of nitrogen within the fuel is lower than 1.5% and the NOx emission is limited to 449 mg/Nm3. Besides, the calculated SO2 and NOx emissions (shown in Fig. 10) for different amounts of nitrogen and sulphur within the solid fuel, have been confirmed by industrial experience. Finally, good consistency of the results obtained for simulation of different industrial cases the allowed the validation of the model. To this point, the sinter plant module was considered mature enough for being integrated in a global model developed for steelmaking plant. 4.2. Life Cycle Inventory of the sintering plant
industrial waste gas flow rate varies normally between 1200 and 1600 Nm3/tsinter (Iosif, 2006) depending of the false air inlet in the system. As it can be seen in Fig. 7, the sinter waste gas composition in terms of main components is perfectly confirmed by the industrial average composition. The other emissions calculated by the model are shown in Fig. 8 and characterize the sinter waste gas before the semi dry gas cleaning device. This choice was made because currently in Europe there are sinter plants operating without advanced technologies for waste gas treatment. Consequently, the only installation for the abatement of emissions is the ESP for dedusting. In Fig. 8, maximum and minimum values of emissions, originating from the industrial measurements, are also plotted. As demonstrated, the results of the model are perfectly framed by these industrial data. The elementary composition of the sinter is given in Fig. 9 and matches quite satisfactorily with the industrial one. However, a small gap between the calculated and industrial value for sinter silicate content has been observed. The origin of this gap is probably the approximation made for the elementary composition related to the recycled waste addition because of missing information. As a consequence, the basicity index of the sinter is lower than the industrial value. However, with respect to the accuracy of results, the sinter composition is well predicted by the model. Finally, the heat recovered from the cooling of sinter was determined to be 422 MJ/ tsinter. This result has been confirmed by Yamada et al. (1990) which gives an average value of 390 MJ/tsinter.
Using the Aspen model and industrial data, the LCI of an existing European sinter plant has been successfully calculated and a part of this inventory is summarized in Table 2. In the same table, the inventory calculated by the model for the specific European sintering plant has been compared to another developed by the International Iron and Steel Institute (IISI) (IISI, 1998). This inventory is considered today as the “reference” of iron and steelmaking LCI. This inventory quantifies the use of resources, energy and environmental emissions associated with the processing of fourteen worldwide steel plants. The objective of this comparison is to demonstrate the advantages of the model for given reliable inventories of real cases. In the industrial practice, for the production of 1 t of sinter, the input flow masses and the nature of raw materials requested vary from one plant to another. Moreover, the type and the mass of solid fuels and recycled wastes have a direct impact on the emissions to air and land. As can be seen also in Table 2, the use of recycled wastes leads to more important quantities of heavy metals in the fumes but less CO2 emissions. The recycled wastes contained important quantity of iron and in this case the consumption of solid fuels in the process decreases. Unfortunately this effect can not be seen when the LCI is calculated with average data. Furthermore, the consumption of important quantities of coke breeze increases the emission of acid compounds, especially
4.1. Sensitivity test of the model parameters In order to check the influence of the key parameters of the model on the emissions calculation, two sensitivity tests have been carried out and the results are shown in Fig. 10. For sulphur within the solid fuel ranging between 0.1 and 1%, the SO2 emissions reach up to 435 mg/Nm3 which translates to almost 585 g/tsinter. Normally, the
Fig. 10. The influence of nitrogen and sulphur within the fuel on the NOx and SO2 emissions.
A.-M. Iosif et al. / Environmental Impact Assessment Review 28 (2008) 429–438 Table 2 Summary of the sinter plant inventory calculated by the model Flux
Identification
Materials inputs
Iron ore Coke breeze Anthracite Recycled waste Additions Return fines Natural gas Activated carbon Calcium hydroxide Water Energy inputs Internal electricity Waste for recycling Sinter fines Dust Product Sinter Emissions to air CO2 CO SO2 NOx VOC HCl HM⁎ Dust Emissions to land Fabric filter dust
Unit
Model IISI
kg/tsinter kg/tsinter kg/tsinter kg/tsinter kg/tsinter kg/tsinter MJ/tsinter g/tsinter g/tsinter l/tsinter MJe/tsinter kg/tsinter kg/tsinter kg kg/tsinter kg/tsinter g/tsinter g/tsinter g/tsinter g/tsinter g/tsinter g/tsinter g/tsinter
826 28.5 17.8 57 164 445 51 40 530 137 126 405 1.6 1000 187 21.1 391 424 196 1 3.7 10.3 870
827 42.2 1.3 – 155 – 10 – – 152 122 – 1.4 1000 234 23.5 793 628 291 23.6 3 44.5 –
⁎only Cd, Pb and Hg. – missing data.
HCl, because this fuel has high content of chlorine (see also Table 2). It is also important to mention, that on a global scale, there are still sinter plants which are not equipped with “end of pipe” facilities for fume cleaning. In this case, the air emissions are higher but the solid wastes and water emissions can be produced in less quantity. The actual reference of LCI carried out by IISI does not take into consideration all the operational characteristics of the sinter plants. It seems obvious that the calculation of inventory based on average data collected from various plants is unsatisfactory and can not be acceptable further. In this way, the calculation of LCI based on process modelling concept is a promising perspective notably for iron and steelmaking. With the example of the sinter plant module we have proved that the LCI calculated with the model is flexible in relation with special operating conditions of the process. 5. Conclusions In the present paper we have proposed and developed a new methodological framework based on the connection of process modelling approach and LCA method, in order to carry out the LCI of iron and steelmaking integrated process. Using Aspen software, the integrated classical steelmaking route (via blast furnace/converter)
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has been modelled and the results were successfully compared with industrial data. The main use of the model is to perform the LCI of the process, to study the effect of the main input streams on the emissions, product and by-products quality. Thanks to this concept, the LCI analysis is preceded more quickly with respect to particular operation conditions. Moreover, the emissions are calculated for different flowsheets, the mass and the heat flows are completely balanced. These attributes improve the quality of data used for inventory calculation and give a strong credibility to the LCI results. For exemplification of model building, the sinter plant module has been presented in the current paper. It has been shown that the emissions calculated by the model are in good agreement with the industrial data and the composition of the sinter with respect to the quality request in terms of iron content and basicity index. In the future, the modelling of dioxins formation may present an interesting perspective of the current model. In the frame of ULCOS project, the developed model has been considered as a powerful tool for carrying out the LCI of steelmaking processes. Moreover, the model has been used by the different companies for the environmental burden diagnostic of integrated plants with respect to their own industrial configuration. Acknowledgements This work was supported by the Arcelor Research and the ULCOS European project. The authors would like to express their gratitude to Jean-Pierre Birat and Elisabeth Marlière for their helpful contribution. References Arion A, Florimond P, Marlière E. Influence du combustible sur les rejets dans les fumées à l'agglomération et sur les principaux résultats métallurgiques. IRSID; 1999. 49–59. Biococchi S. Les polluants et les techniques d'épuration des fumées. Cas des unités de destruction thermique des déchets; 1998. R.E.C.O.R.D. Eurofer. Steel strategic research agenda to 2030, EU steel technological platforms. Brussels: European Commission; 2005. Eurofer. Ferreira AM, Harano ELM, Oliveira HL, Medeiros MA, Abreu GC, Adrade MWM, et al. Emissions control evaluation at CST sintering machine stack. La Revue de Métallurgie; 2005. 8: 508–515. FFA. Guide méthodologique pour l'évaluation des émissions dans l'air des installations de production et de transformation de l'acier; 2004. IISI. Worldwide LCI database for steel industry products. International Iron and Steel Institute; 1998. Iosif AM. Modélisation physico-chimique de la filière classique de production d'acier pour l'analyse de l'Inventaire du Cycle de Vie. Nancy: INPL; 2006. 41–67.
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ISO 14040 Standards. Environmental management. Life Cycle Assessment. Principles and framework; 1997. Integrated Pollution Prevention and Control (IPPC). Best available techniques reference document on the production of iron and steel. European Commission; 2003. Larsson M, Dahl J. Reduction of the specific energy use in an integrated steel plant—the effect of an optimisation model. ISIJ International; 2003. 43:166461673. Märker, http://www.maerker-gruppe.de/seiten/umweltt.html, 2005. Patisson F. Modélisation physico-chimique et thermique de l'opération d'agglomération des minerais de fer. Nancy: INPL; 1987. 6–25. Petitnicolas L, Le Louer P. Synthèse de la campagne environnement. Arcelor Research; 1999. 31–33. Schultmann F, Rentz O, Engels B. Flowsheeting-based simulation of recycling concepts in the metal industry. J Clean Prod 2003;12(7): 737–51. Vanderheyden B, Mathy C. Mathematical model of the sintering process taking into account different input gas conditions. La Revue de Métallurgie; 2001. 3: 251–257. Voelker BM. Waste-to-energy: solutions for solid waste problems for the 21st century; 2005. www.p2pays.org/ref/09/08624.pdf. Yamada S, Kondo H, Shiraishi H. Anti-pollution and waste heat recovery for sintering plant. in The Sixth International Iron and Steel Congress. Japan: Nagoya Congress Centre; 1990. oct. 21–26.
Dr. Ana Maria Iosif earned her Ph.D. in Materials Science and Engineering. Her main research interests concern new proposals for innovating Life Cycle Assessment in order to improve the quality of data for inventories calculation in iron and steel industry. She has experience in physicochemical modelling and ecodesign for environmental friendly production of steel. Currently her professional interests focus on design and engineering of waste gases treatment and investigations on environmental issues related to waste storage and recycling.
Denis ABLITZER is Professor at School of Mines of Nancy since 1980. He is the Head of “Materials Processing” research group in the Laboratory of Science and Engineering of Materials and Metallurgy (LSG2M). He has particularly professional interests on gas–solid reactions, liquid metal treatment, remelting processes and clean processes. He is supervisor of 46 doctorate theses and author of ca. 213 publications in scientific journals and proceedings.
Dr. Francois Hanrot is senior researcher in ArcelorMittal R&D with 20 years of experience conducting and managing studies involving processes modelling, energy efficiency in process engineering, waste conversion and utilisation. He is an author of numerous scientific publications and conference proceedings.
Environmental Impact Assessment Review 28 (2008) 429 – 438 www.elsevier.com/locate/eiar
Process integrated modelling for steelmaking Life Cycle Inventory analysis Ana-Maria Iosif a,⁎, Francois Hanrot a,1 , Denis Ablitzer b,2 b
a Arcelor Research, Voie Romaine, BP30320, Maizieres-les-Metz, 57283, France LSG2M, Ecole des Mines de Nancy, Parc de Saurupt, F-54042 Nancy cedex, France
Received 17 August 2007; received in revised form 16 October 2007; accepted 17 October 2007 Available online 15 January 2008
Abstract During recent years, strict environmental regulations have been implemented by governments for the steelmaking industry in order to reduce their environmental impact. In the frame of the ULCOS project, we have developed a new methodological framework which combines the process integrated modelling approach with Life Cycle Assessment (LCA) method in order to carry out the Life Cycle Inventory of steelmaking. In the current paper, this new concept has been applied to the sinter plant which is the most polluting steelmaking process. It has been shown that this approach is a powerful tool to make the collection of data easier, to save time and to provide reliable information concerning the environmental diagnostic of the steelmaking processes. © 2007 Elsevier Inc. All rights reserved. Keywords: Steelmaking; Sintering process; Life Cycle Inventory; Emissions
1. Introduction Consideration of environmental aspects in traditional product development has become one of the greatest challenges for the steelmaking industry. The European Steel Industry has measured up to this challenge by creating a consortium of industries and research organisations that has taken up the mission of developing such breakthrough processes, namely the
⁎ Corresponding author. Tel.: +33 3 87 70 43 78; fax: +33 3 87 70 41 03. E-mail addresses: [email protected] (A.-M. Iosif), [email protected] (F. Hanrot), [email protected] (D. Ablitzer). 1 Tel.: +33 3 87 70 43 56; fax: +33 3 87 70 41 03. 2 Tel.: +33 3 83 58 42 37; fax: +33 3 83 58 40 56. 0195-9255/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.eiar.2007.10.003
ULCOS (Ultra Low CO2 Steelmaking) consortium. This consortium plans to develop a breakthrough steelmaking process that has the potential of meeting the target of markedly reducing green houses gases emissions. The ULCOS project will last for 5 years (2004–2009) and involves 48 partners from 14 European countries and aims to developing new technologies for reducing steelmaking emissions, compared to the iron ore based benchmark (Eurofer, 2005). It will test coal, natural-gas, electricity and biomassbased steelmaking routes, which all have the potential for meeting the reduction target for complementary reasons. However, in order to develop technologies to reduce emissions, it is necessary to assess the environmental impact of the classical steelmaking route (coke plant, sinter plant, blast furnace, basic oxygen furnace,
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Fig. 1. New methodological framework for steelmaking LCI analysis.
continuous casting and hot rolling). Life Cycle Assessment (LCA) method has been undertaken in ULCOS as the most holistic approach of assessing environmental impact and selecting new technologies. Previous to the impact assessment, it is essential to carry out the Life Cycle Inventory (LCI) of the process which is the core part of LCA methodology. According to the current LCA standards (ISO 14040, 1997), the quality of the data used for carrying out this inventory is one of the most important limiting factors. The LCI analysis involves data collection, calculation and procedures to quantify the relevant inputs and outputs of the product system. The data collection should correspond to some conditions: precision, completeness, representativeness, consistency and reproducibility (ISO 14040, 1997). It is obvious that such conditions are not easy to respect when data are supplied only by the industrial practice and/or by the literature issues. Indeed, the environmental performance of complex steelmaking processes such as iron ore sintering are strongly dependent on operational conditions. Consequently, the environmental burden of the system can change significantly when different types of fuels and recycled wastes are used in the process. That's why it is important to improve the way of assessing the LCI of the steelmaking industry in order to guarantee the quality of the data and to predict the change of the environmental performances with respect to the operational conditions.
2. Description of the steelmaking integrated modelling In the frame of the ULCOS project, the objective of our work was to develop a new concept which combines the process integrated model approach with LCA in order to innovate the way of assessing the LCI for steel production, see Fig. 1. Based on physicochemical considerations, thermodynamics laws and mathematical equations, Aspen software have been used in order to develop modules for each steelmaking processes as presented in Fig. 2. Aspen (Advanced System for Process Engineering) is a process engineering software package that is used to simulate processes based on the thermodynamic models, properties of materials and several ready-made unit operation models. The developed modules calculate the mass and heat balances, emissions, and the chemical compositions of products and by-products simultaneously. Each module has been validated with industrial data and finally connected together by each primary product and any possible by-product interactions. Thanks to the developed integrated model, the inventory of the steelmaking process has been easily carried out. Even though the process modeling approach is commonly used in LCA in chemical processes, it becomes quite difficult when we try to apply it in the steelmaking field. Indeed, the complexity of the processes and the large number of connections between them make the application of this concept complicated.
Fig. 2. Process description for the integrated steelmaking plant.
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However, this concept has already been applied by Larsson and Dahl (2003) in steelmaking, aiming to analyze the different possibilities for energy savings and consequently to minimize CO2 emissions. This model remains quite simplified and focuses only on the calculation of CO2 emissions based on carbon and energy balances. The challenge in our work was to develop a model which is a trade-off between complexity and accuracy. The current model proposed in this paper is quite simplified compared to sophisticated models already existing for each steelmaking processes, but still complex enough to allow the calculation of the mass of various pollutants. Furthermore, the masses and the composition of products and by-products such as the steelwork gases are also calculated. This last information is very important because the steelwork gases (coke oven, blast furnace and converter gases) are used as fuels by all of the steelmaking processes described in Fig. 2. Consequently, the contribution of these gases to the total environmental burden of the system can be easily estimated. Under the ULCOS umbrella, our work has two main applications. First, the steelmaking model has been used by different companies to rapidly assess their environmental impacts with respect to their own industrial configuration. Secondly, based on the proposed approach, a benchmark LCI has been carried out for the classical route of steel production. This inventory has been considered as the “reference LCI” for the selection of new steelmaking
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technologies. Indeed, before the selection of steelmaking alternatives and before testing any technological proposal at the industrial scale, it is crucial to compare their LCI to a reference case which is the classical route of steel production. It is important to mention that the new approach offers some important benefits that cannot be obtained when the LCI is carried out using data only from industry and literature. First of all, the model allows us to control the mass and energy balances of the calculated inventory, something that is nearly impossible to assure when the LCI is carried out using only average data from literature or experimental works. Secondly, calculating emissions based on physicochemical and mathematical considerations gives a strong credibility to the inventory. Finally, model simulations for special operating conditions, such as recycling of different wastes, the use of new fuels, or the mixing of fuels, give access to certain environmental information which is not available among the industrial or literature outputs. Based on the proposed approach, the LCI of the integrated steel plant can be carried out for different operational practices and the best scenario can be identified in minimal time. For a better understanding of this approach, a brief description of one module developed for sintering plant is given in the following section of the paper. The presentation of the sinter plant module was determined by the complexity of the process and by the fact that the
Fig. 3. Schematic view of the sinter plant.
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sinter plant is considered the most polluting unit among all the steel works. 3. Presentation of the iron ore sintering model Currently, there are several sintering process models with different structure ranging from simple 1-D simulation to more complexes (Patisson, 1987; Vanderheyden and Mathy, 2001). These models are reaching a higher level of sophistication, which is usually achieved at the expense of model transparency. This can lead to a model almost as complicated as the real process. However, a simplified model for iron ore sintering process using Aspen software has been developed in the past by Schultmann et al. (2003) in the frame of KOSIMEUS concept applied to the steel industry. This simplified model is developed only for the sintering machine and calculates mainly the system mass balances. In terms of pollutants, only CO2 emissions are estimated based on carbon balance calculation. The originality of our work consists in the model boundary, i.e. the elementary processes which are taken into consideration. Hence, the current model was developed for the entire sinter plant system: the preparation of the sinter raw mix, the ignition process, the sinter strand, the exhaust gas cleaning and the sinter cooler (see Fig. 3). The model has been built as a mathematical matrix, based on chemical reactions and correlations between emissions and different parameters of the process in order to allow an accurate simulation of various mechanisms. The main pollutants evolved by the system, namely CO, CO2, NOx, SOx, VOC (CH4 equ.), HCl, heavy metals and dust are the most important outcomes of the model. Also, the mass and the chemical composition of the sinter have been calculated and the energy balance has been attentively checked. For simplicity sake, only the description of the sinter strand modelling is given in the following section of the paper. 3.1. Modelling of the sinter strand The main physicochemical and thermal mechanisms which take place on the sinter strand deliver the agglomeration of iron ore fine particles into porous clinker referred to as sinter. In the model, the sinter strand was virtually divided into two parts: sinter formation and gas phase generation as a consequence of the solid fuel combustion. 3.1.1. Modelling of the sinter formation The chemical reactions involved in sinter formation are complex. Therefore, we have proposed a simplified
system of reactions (shown below) representing evaporation of moisture, limestone decarbonatation, calcium silicate and calcium ferrite formation and iron oxide reduction. H2 OðlÞYH2 OðgÞ;
ð1Þ
CaCO3 YCO2 þ CaO;
ð2Þ
CaO þ SiO2 YCaSiO3 ;
ð3Þ
Fe2 O3 þ CaOYCaFe2 O4 ;
ð4Þ
3Fe2 O3 Y2Fe3 O4 þ 0:5O2 :
ð5Þ
During the sintering process, hematite is partially reduced to magnetite. In order to calculate the mass of magnetite in the sinter, in the model the ratio of Fe++ in the sinter composition was imposed at an average value. Thus, the ratio of Fe++ in the sinter composition is one parameter of the model. 3.1.2. Modelling of the sinter waste gas formation Based on simplified chemical reactions and experimental observations we have calculated the flow rate and the chemical composition of the sinter waste gas. The pollutants within the gas phase are a consequence of complex phenomena which take place during the sintering process, namely combustion of solid fuel, vaporisation and particles passed over. All the pollutants which were calculated by the model characterise the sinter waste gas composition before the gas cleaning facilities. In order to avoid explosion in the electrostatic precipitator which is part of the waste gas cleaning facilities, the fuels used are generally anthracites with a low fraction of volatiles matters (FVM), coke breeze and mixtures of both. 3.1.2.1. Calculation of CO and CO2. The combustion of the solid fuels mixture produces the major pollutants which are CO and CO2. In the model, the ratio between these two compounds (CO/(CO+CO2)) has been taken equal to 0.15 (Arion et al., 1999; Iosif, 2006). 3.1.2.2. Calculation of SOx. During the combustion process, sulphur within the raw mixture (mainly from fossil fuel) is released essentially as SO2. The contribution from iron ore is normally about ten times smaller than from fuel. Besides the sulphur from raw materials, the quantity of emitted SO2 is influenced by the basicity of the sinter feed and by the solid fuel grain size (IPPC,
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zone is lower than 1350 °C, only the fuel NO was considered in the model. Based on a relatively large number of experiments, a linear dependency between NOx emissions and the nitrogen within the fuel has been established (see Fig. 4)(Iosif, 2006). It should be pointed out that this correlation has been confirmed by industrial experiments and the results are plotted on the same figure.
Fig. 4. Correlation between nitrogen within the fuel and NOx emissions.
2003). Sinter pot trials carried out by Arion et al. (1999) have shown that the sulphur within the waste gas is about 64% (wt.) of the sulphur brought by the fuel. This result has been further confirmed by industrial trials. However, more investigations on sulphur behaviour during the sintering process are recommended. For the sake of simplicity, in the present model, it has been assumed that SO2 formation depends only on the sulphur content in the fuel, whereas the sulphur contained in iron ore is considered to be inert. Thus, 64% of the sulphur from the fuel oxidised as sulphur dioxides and the rest will be located into the solid phase (sinter, return fines and dust). 3.1.2.3. Calculation of NOx. As consequence of the fuel combustion, NOx emissions are generally formed due to the presence of nitrogen in the combustion air and the fuel itself. It is mainly nitric oxide (NO) which arises during the combustion on the sinter strand by three different formation mechanisms: thermal NO, prompt NO and fuel NO. During the sintering process, it was demonstrated by industrial and pilot trials that the NO2 emissions represent less than 10% of total NOx emissions. Because the temperature of the combustion
3.1.2.4. Calculation of Volatile Organic Compounds (VOC). The main sources of VOC emissions are the solid fuels and the recycled materials, i.e. blast furnace dust, coking sludge, mill scales, etc. During the sintering process, VOC are formed by volatilisation, at temperatures exceeding 200 °C, by pyrolysis and partial oxidation of the organic compounds at temperatures higher than 400 °C. Investigations on VOC formation during the sintering process using different kinds of fuels have shown a linear dependency between the concentration of these compounds within the waste gas and the FVM in the fuel. Fig. 5 shows the results of the experimental measurements carried out into the sinter pot by Arion et al. (1999). These correlations have been confirmed by Petitnicolas and Le Louer, (1999) from multiple experiments in two different sinter plants. These results are also shown in Fig. 5. It has been observed that for the low values of FVM, the evolution of VOC in the sinter gas is different according to the nature of fuel used in the process, either coke breeze or anthracite. In the model, the volatile compounds are calculated as CH4 equivalent based on correlations obtained from Fig. 5. In the case of fuel mixture (coke breeze and anthracite), it has been considered that VOC compounds are additive and the total amount of VOC (mVOC total) is calculated through correlation (6). Hence, the content of
Fig. 5. Correlation between the fraction of volatiles matters (FVM) in fuels and VOC emissions (in methane equivalent).
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Fig. 6. Correlation between dust within the exhaust gas (downstream from ESP) and the average temperature of the waste gas.
volatiles matters in each type of fuel and there proportion in the fuel mix, are considered as inputs of the model. VOC mVOC total ¼ ðmcoke breeze =mmix Þ⋅mcoke breeze þ ðmanthracite =mmix ÞÞ⋅mVOC anthracite
ð6Þ
The mmix is the total mass of mix fuel, mcoke breeze and manthracite are the masses of coke breeze and anthracite in the mix fuel. However, it is important to mention that the correlations used for NO and VOC calculation are given only by the sinter pot tests, the results from industrial experiments are plotted only with the aim to confirm the sinter pot results. 3.1.2.5. Calculation of sinter dust. According to Ferreira et al. (2005), the dust formation is determined by factors related to the process parameters (mainly the temperature of the exhaust gases), raw materials (alkali content) and mechanical equipments. In the model, the total amount of dust has been calculated in relation with the final temperature of the exhaust gas downstream from the first dedusting stage. Most sinter strands are equipped with conventional dry electrostatic precipitators (ESP) for the first stage of exhaust gas dedusting. Industrial measurements plotted on Fig. 6 have shown a clear dependency between the dust retained by the ESP and the average temperature of exhaust gas in the common duct. Indeed, the higher the fume temperature, the higher is the dust electrical resistivity of ESP. The real mass of dust within the exhaust gas before the dedusting stage (mdust) has been calculated with the aid of correlation (7), obtained from Fig. 6. dust
m
V waste gas ⋅ð432−2:5T -CÞ ¼ 1−η
ð7Þ
The ESP efficiency (η) is considered as parameter of the model and it is fixed to 90% (real efficiency in industrial conditions) (FFA, 2004). Although this
correlation is based on a large number of experimental data, it remains very approximate. Dust results should therefore be considered cautiously. The elementary dust composition has been considered equal to the elementary composition of sinter. For simplicity sake, the calculation of HCl emissions and the simulation of heavy metals volatilisation (Cd, Pb and Hg) are intentionally skipped in the present paper. 3.2. Modelling of the sinter waste gas cleaning system The sinter waste gas treatment consists in two stages: main dedusting and a semi dry gas cleaning. As already mentioned, the main part of dust is collected through the dry electrostatic precipitator (ESP). Considering the real efficiency of the ESP equal to 90%, the total amount of dust is separated into two streams: dust for recycling or for disposal (the model offer the two possibilities) and dust within exhaust gas towards the final gas cleaning equipment. Downstream from ESP system, the fumes are sent to a semi dry gas cleaning equipment (see Fig. 3) which removes mainly fine dust, acid gases and heavy metals. The configuration chosen to be modelled is one possibility; other options are available for the fumes cleaning. The simulated semi dry cleaning system consists of a baghouse filter and a mixing drum. Calcium hydroxide is fed directly into the mixer and the necessary water to condition the gas is added. Recycled dust from the baghouse filter (1.14 kg/Nm3) is injected in the inlet Table 1 Operating data for the semi dry gas cleaning equipment Element
Efficiency
Dust SO2 HCl Hg Pb Cd
90% 50% 96% 97% 99% 99%
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Fig. 7. Composition of the sinter waste gas (main components).
duct of the line and mixed with the raw gas thanks to the mixing drum. The reagent (activated carbon) is added to secure the efficient removal of heavy metals and is injected into the reactor prior to entering the baghouse filter. The activated carbon ensures also the retention of dioxins but these pollutants are not calculated by the model. The key point for simulating the fumes cleaning system described above are the separation of pollutants, namely SO2, HCl, Hg, Pb, Cd and fine dust escaped from the ESP. The chemical reactions considered in the model are (Biococchi, 1998): CaðOHÞ2 þ2HClYCaCl2 þ 2H2 O;
ð8Þ
CaðOHÞ2 þSO2 YCaSO3 þ 2H2 O;
ð9Þ
CaSO3 þ 0; 5O2 YCaSO4 :
ð10Þ
The efficiency of the gas cleaning system depends on the gas temperature, moisture content and retention time inside the baghouse filter. Operating parameters typical of gas cleaning installation are given in Table 1 (Märker,
2005; Voelker, 2005). These average data are considered as representative for normal operating conditions and are taken as parameters of the model. Thereafter, the model calculates the final amounts of SO2, HCl, Hg, Pb, Cd and dust within the waste gas flow, the mass of additives and finally the mass of solid wastes. However, the most important part of the dust recovered from the baghouse filter is recycled in the mixing drum and the rest is put on landfill site. 4. Presentation of the results In order to see the behaviour of the model for industrial input data, an European sinter plant has been selected as a reference case. Hence, the most relevant mass streams which feed the model, there chemical compositions and the key parameters are supplied by this plant. Some of the results generated by the model for the simulation of this sinter plant are summarised in Figs. 7 and 8, and discussed in the current section. For 1 t of sinter, the flow rate of waste gas calculated by the model is equal 1505 Nm3/tsinter and consistent with industrial figure. For standard operational conditions, the
Fig. 8. Composition of the sinter waste gas (secondary components).
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Fig. 9. Sinter composition.
content of nitrogen within the fuel is lower than 1.5% and the NOx emission is limited to 449 mg/Nm3. Besides, the calculated SO2 and NOx emissions (shown in Fig. 10) for different amounts of nitrogen and sulphur within the solid fuel, have been confirmed by industrial experience. Finally, good consistency of the results obtained for simulation of different industrial cases the allowed the validation of the model. To this point, the sinter plant module was considered mature enough for being integrated in a global model developed for steelmaking plant. 4.2. Life Cycle Inventory of the sintering plant
industrial waste gas flow rate varies normally between 1200 and 1600 Nm3/tsinter (Iosif, 2006) depending of the false air inlet in the system. As it can be seen in Fig. 7, the sinter waste gas composition in terms of main components is perfectly confirmed by the industrial average composition. The other emissions calculated by the model are shown in Fig. 8 and characterize the sinter waste gas before the semi dry gas cleaning device. This choice was made because currently in Europe there are sinter plants operating without advanced technologies for waste gas treatment. Consequently, the only installation for the abatement of emissions is the ESP for dedusting. In Fig. 8, maximum and minimum values of emissions, originating from the industrial measurements, are also plotted. As demonstrated, the results of the model are perfectly framed by these industrial data. The elementary composition of the sinter is given in Fig. 9 and matches quite satisfactorily with the industrial one. However, a small gap between the calculated and industrial value for sinter silicate content has been observed. The origin of this gap is probably the approximation made for the elementary composition related to the recycled waste addition because of missing information. As a consequence, the basicity index of the sinter is lower than the industrial value. However, with respect to the accuracy of results, the sinter composition is well predicted by the model. Finally, the heat recovered from the cooling of sinter was determined to be 422 MJ/ tsinter. This result has been confirmed by Yamada et al. (1990) which gives an average value of 390 MJ/tsinter.
Using the Aspen model and industrial data, the LCI of an existing European sinter plant has been successfully calculated and a part of this inventory is summarized in Table 2. In the same table, the inventory calculated by the model for the specific European sintering plant has been compared to another developed by the International Iron and Steel Institute (IISI) (IISI, 1998). This inventory is considered today as the “reference” of iron and steelmaking LCI. This inventory quantifies the use of resources, energy and environmental emissions associated with the processing of fourteen worldwide steel plants. The objective of this comparison is to demonstrate the advantages of the model for given reliable inventories of real cases. In the industrial practice, for the production of 1 t of sinter, the input flow masses and the nature of raw materials requested vary from one plant to another. Moreover, the type and the mass of solid fuels and recycled wastes have a direct impact on the emissions to air and land. As can be seen also in Table 2, the use of recycled wastes leads to more important quantities of heavy metals in the fumes but less CO2 emissions. The recycled wastes contained important quantity of iron and in this case the consumption of solid fuels in the process decreases. Unfortunately this effect can not be seen when the LCI is calculated with average data. Furthermore, the consumption of important quantities of coke breeze increases the emission of acid compounds, especially
4.1. Sensitivity test of the model parameters In order to check the influence of the key parameters of the model on the emissions calculation, two sensitivity tests have been carried out and the results are shown in Fig. 10. For sulphur within the solid fuel ranging between 0.1 and 1%, the SO2 emissions reach up to 435 mg/Nm3 which translates to almost 585 g/tsinter. Normally, the
Fig. 10. The influence of nitrogen and sulphur within the fuel on the NOx and SO2 emissions.
A.-M. Iosif et al. / Environmental Impact Assessment Review 28 (2008) 429–438 Table 2 Summary of the sinter plant inventory calculated by the model Flux
Identification
Materials inputs
Iron ore Coke breeze Anthracite Recycled waste Additions Return fines Natural gas Activated carbon Calcium hydroxide Water Energy inputs Internal electricity Waste for recycling Sinter fines Dust Product Sinter Emissions to air CO2 CO SO2 NOx VOC HCl HM⁎ Dust Emissions to land Fabric filter dust
Unit
Model IISI
kg/tsinter kg/tsinter kg/tsinter kg/tsinter kg/tsinter kg/tsinter MJ/tsinter g/tsinter g/tsinter l/tsinter MJe/tsinter kg/tsinter kg/tsinter kg kg/tsinter kg/tsinter g/tsinter g/tsinter g/tsinter g/tsinter g/tsinter g/tsinter g/tsinter
826 28.5 17.8 57 164 445 51 40 530 137 126 405 1.6 1000 187 21.1 391 424 196 1 3.7 10.3 870
827 42.2 1.3 – 155 – 10 – – 152 122 – 1.4 1000 234 23.5 793 628 291 23.6 3 44.5 –
⁎only Cd, Pb and Hg. – missing data.
HCl, because this fuel has high content of chlorine (see also Table 2). It is also important to mention, that on a global scale, there are still sinter plants which are not equipped with “end of pipe” facilities for fume cleaning. In this case, the air emissions are higher but the solid wastes and water emissions can be produced in less quantity. The actual reference of LCI carried out by IISI does not take into consideration all the operational characteristics of the sinter plants. It seems obvious that the calculation of inventory based on average data collected from various plants is unsatisfactory and can not be acceptable further. In this way, the calculation of LCI based on process modelling concept is a promising perspective notably for iron and steelmaking. With the example of the sinter plant module we have proved that the LCI calculated with the model is flexible in relation with special operating conditions of the process. 5. Conclusions In the present paper we have proposed and developed a new methodological framework based on the connection of process modelling approach and LCA method, in order to carry out the LCI of iron and steelmaking integrated process. Using Aspen software, the integrated classical steelmaking route (via blast furnace/converter)
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has been modelled and the results were successfully compared with industrial data. The main use of the model is to perform the LCI of the process, to study the effect of the main input streams on the emissions, product and by-products quality. Thanks to this concept, the LCI analysis is preceded more quickly with respect to particular operation conditions. Moreover, the emissions are calculated for different flowsheets, the mass and the heat flows are completely balanced. These attributes improve the quality of data used for inventory calculation and give a strong credibility to the LCI results. For exemplification of model building, the sinter plant module has been presented in the current paper. It has been shown that the emissions calculated by the model are in good agreement with the industrial data and the composition of the sinter with respect to the quality request in terms of iron content and basicity index. In the future, the modelling of dioxins formation may present an interesting perspective of the current model. In the frame of ULCOS project, the developed model has been considered as a powerful tool for carrying out the LCI of steelmaking processes. Moreover, the model has been used by the different companies for the environmental burden diagnostic of integrated plants with respect to their own industrial configuration. Acknowledgements This work was supported by the Arcelor Research and the ULCOS European project. The authors would like to express their gratitude to Jean-Pierre Birat and Elisabeth Marlière for their helpful contribution. References Arion A, Florimond P, Marlière E. Influence du combustible sur les rejets dans les fumées à l'agglomération et sur les principaux résultats métallurgiques. IRSID; 1999. 49–59. Biococchi S. Les polluants et les techniques d'épuration des fumées. Cas des unités de destruction thermique des déchets; 1998. R.E.C.O.R.D. Eurofer. Steel strategic research agenda to 2030, EU steel technological platforms. Brussels: European Commission; 2005. Eurofer. Ferreira AM, Harano ELM, Oliveira HL, Medeiros MA, Abreu GC, Adrade MWM, et al. Emissions control evaluation at CST sintering machine stack. La Revue de Métallurgie; 2005. 8: 508–515. FFA. Guide méthodologique pour l'évaluation des émissions dans l'air des installations de production et de transformation de l'acier; 2004. IISI. Worldwide LCI database for steel industry products. International Iron and Steel Institute; 1998. Iosif AM. Modélisation physico-chimique de la filière classique de production d'acier pour l'analyse de l'Inventaire du Cycle de Vie. Nancy: INPL; 2006. 41–67.
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ISO 14040 Standards. Environmental management. Life Cycle Assessment. Principles and framework; 1997. Integrated Pollution Prevention and Control (IPPC). Best available techniques reference document on the production of iron and steel. European Commission; 2003. Larsson M, Dahl J. Reduction of the specific energy use in an integrated steel plant—the effect of an optimisation model. ISIJ International; 2003. 43:166461673. Märker, http://www.maerker-gruppe.de/seiten/umweltt.html, 2005. Patisson F. Modélisation physico-chimique et thermique de l'opération d'agglomération des minerais de fer. Nancy: INPL; 1987. 6–25. Petitnicolas L, Le Louer P. Synthèse de la campagne environnement. Arcelor Research; 1999. 31–33. Schultmann F, Rentz O, Engels B. Flowsheeting-based simulation of recycling concepts in the metal industry. J Clean Prod 2003;12(7): 737–51. Vanderheyden B, Mathy C. Mathematical model of the sintering process taking into account different input gas conditions. La Revue de Métallurgie; 2001. 3: 251–257. Voelker BM. Waste-to-energy: solutions for solid waste problems for the 21st century; 2005. www.p2pays.org/ref/09/08624.pdf. Yamada S, Kondo H, Shiraishi H. Anti-pollution and waste heat recovery for sintering plant. in The Sixth International Iron and Steel Congress. Japan: Nagoya Congress Centre; 1990. oct. 21–26.
Dr. Ana Maria Iosif earned her Ph.D. in Materials Science and Engineering. Her main research interests concern new proposals for innovating Life Cycle Assessment in order to improve the quality of data for inventories calculation in iron and steel industry. She has experience in physicochemical modelling and ecodesign for environmental friendly production of steel. Currently her professional interests focus on design and engineering of waste gases treatment and investigations on environmental issues related to waste storage and recycling.
Denis ABLITZER is Professor at School of Mines of Nancy since 1980. He is the Head of “Materials Processing” research group in the Laboratory of Science and Engineering of Materials and Metallurgy (LSG2M). He has particularly professional interests on gas–solid reactions, liquid metal treatment, remelting processes and clean processes. He is supervisor of 46 doctorate theses and author of ca. 213 publications in scientific journals and proceedings.
Dr. Francois Hanrot is senior researcher in ArcelorMittal R&D with 20 years of experience conducting and managing studies involving processes modelling, energy efficiency in process engineering, waste conversion and utilisation. He is an author of numerous scientific publications and conference proceedings.