Reducing emissions of PCDD/F in sintering plant: numerical and experimental analysis

Abstract

The sintering operation in integrated steelworks is one of the main sources for the production of polychlorinated dibenzo-p-dioxins, polychlorinated-dibenzo-furans, NO_x and SO_x. In the present study, the operating conditions, through which a reduction in emissions can be achieved, were defined through numerical analysis. The following process parameters were evaluated: gas temperature, quantities of chlorine and copper and additions of hydrated lime, sulphur and urea. Using the optimisation software modeFRONTIER (ESTECO), a virtual surface that can reproduce the actual process of sintering was created. Additionally, with the application of filtering to post-sintering gas, such as electrostatic precipitator and wetfine scrubber, it was possible to obtain a reduction in emission values within the limits of international protocol Aarhus.

List of symbols

chlorine rate

carbon monoxide

CO₂

carbon dioxide rate

copper rate

DoE

design of experiment

DOF

design objective function

ESP

electrostatic precipitator

Lim

maximum value of the output

min.

minimum value of the output

MOGA

multiobjective genetic algorithm

MOGT

multiobjective games theory

Moi

moisture

NO_x

nitrides

NSGA II

non-dominated sorting genetic algorithm

O₂

oxygen rate

PCDD

polychrorinated dibenzo-p-dioxins

PCDF

polychrorinated dibenzo-furans

rate

air flowrate

response surface

Sulphur rate

SO_x

sulphides

T _wbox

windbox temperature

T _wleg

windleg temperature

TCDD

2,3,7,8,-tetrachlorodibenzo-p-dioxin

TEF

toxic equivalency factor

TEQ

toxicity equivalent

W _b

windbox number

wetfine scrubber

Introduction

Process description

The process of sintering to improve the physical and chemical properties of iron ore for use in blast furnaces is well documented.1 ^– 5 The agglomeration process gives rise to many different physical and chemical phenomena. During heating, the following main steps can be distinguished:

around 100°C, drying of the mixture; at higher temperatures, the water of crystallisation is removed

ii.

between 600 and 800°C, the first agglomeration of fine particles into a porous material takes place, and the swelling grains adhere weakly to each other

iii.

above 1000°C, the grains soften, and the physical and chemical conditions lead to the completion of the agglomeration process.

At the end of the grate, a sinter breaker reduces the sintered material to the desired size.6 Here, PCDD/Fs form in the presence of carbon containing materials;7,8 the process is favoured by the presence of specific organic compounds or a carbonaceous matrix sand source of chlorine and oxygen, plus increased temperatures (200–800°C; at higher temperatures, PCDD/Fs will rapidly decompose). It was observed that the presence of catalytic metals (Cu) can be essential at modest temperatures.9 10

In the sinter bed, basically three layers can be recognised: raw material (wet and cold), the burning front and the cool down zone, consisting of sintered material. In this region, the products of incomplete combustion surviving the heat of the burning front may condense, while the temperature is high enough to enable reactions with species in the raw materials acting as catalysts. Furthermore, the native carbon containing materials may react via the so called ‘de novo route’. During sintering, conditions are encountered wherein dioxins can be formed and, for some parts, survive.10

Emission formation

The gas temperature inside the windbox and windlegs is lower (100–500°C) compared to the sintering grate; such conditions lead to the optimal physical and chemical conditions for the formation of pollutants, such as PCDD/F, NO_x and SO_x.11 Both PCDDs and PCDFs are persistent stable organic pollutants formed in all those high temperature processes with an abundance of organic material in the presence of chlorine and copper. Dioxins and furans are chlorinated tricyclic organic compounds resulting from the combination of organic compounds impregnated with halogens (i.e. fluorine, chlorine, bromine or iodine) with a specific molecular heterocyclic structure.12 A deep and complete thermodynamic description of the PCDD/F formation has been presented by Tan et al. 13 These compounds are commonly grouped under the name ‘dioxins’, but their chemical structures and their properties can be very different.

Dioxins are a class of heterocyclic organic compounds whose basic structure consists of rings with four carbon and two oxygen atoms. On the other hand, furans have only one oxygen atom (Fig. 1), and the two outer benzene rings are linked by a pentagonal structure. Among the 200 types of known dioxins, the most famous are certainly the PCDD, characterised by the presence of chlorine atoms that will complement the aromatic rings. The chemical stability of such compounds derives from the presence of these rings. The most dangerous of dioxins, for serious problems of bioaccumulation and environmental contamination, is certainly TCDD (Fig. 2).

Figure 1.

Polychrorinated dibenzo-p-dioxins/dibenzo-furans structure

Figure 2.

2,3,7,8,-tetrachlorodibenzo-p-dioxin structure

A detailed description of their formation is presented in the literature.14 ^– 16 The PCDDs are generally measured in terms of TEQ relative to TCDD as a reference, being the most polluting and dangerous. The poly dibenzo-dioxins have different toxicities in relation to their structure. The TEQ expresses the quantity of a ‘toxic’ substance as the concentration of the reference substance that can generate the same toxic effects of TCDD. It is also possible to obtain the concentration of a PCDD with its toxic equivalency through the use of the TEF. The TEF for TCDD is assigned equal to 1, while the other dioxins have a factor of <1. This dimensionless parameter, multiplied by the actual concentration, results in the TEQ.

The World Health Organization has identified the seven most toxic PCDDs and the 10 most toxic PCDFs, giving them an international toxic equivalency factor (equation (1)) (1)

Multiobjective analysis

In the present study, a broad range of processing parameters affecting the development of PCDD/Fs in the sintering process has been evaluated. The main aim was the possible reduction of dangerous emissions through numerical and experimental analysis, allowing the definition of the optimal conditions for the minimisation of pollutants. The employed multiobjective optimisation software is modeFRONTIER (ESTECO), through which a set of input parameters, governing the plant and the production process, were defined. They were evaluated on the basis of an optimisation algorithm chosen for the multiobjective analysis (Fig. 3).

Figure 3.

modeFRONTIER (mF) operative optimisation flow

Starting from a database, built by employing experimental and literature data, a computational model (n-dimensional virtual surfaces) capable of reproducing at best the actual process was developed. The analysis performed led to the minimisation of the output variables (PCDD/F, NO_x and SO_x). For PCDD/F, it was necessary to apply a filtering system in order to obtain quantities of emissions below the legal limit of 0·4 ng I-TEQ/N m³ as required by the Aarhus protocol.17 ^– 21

Experimental and numerical procedure

Work definition

The sintering process is outlined in the workflow through the analysis carried out by modeFRONTIER, as shown in Fig. 4.

Figure 4.

Workflow of analysis

The workflow is divided into data flow (solid line) and logic flow (dotted line), which have a common node, i.e. the calculator node, in which mathematical functions and chemical reactions representative of the process are introduced. In the data flow, all the input parameters are grouped; such input parameters should be optimised during numerical simulations as a function of the multiobjectives (in the present case, the reduction of emissions). In the present case, the following input parameters are considered and then introduced:

number of the windbox: progressive value that indicates in which windbox there was a known level of emissions

ii.

gas temperatures in the windbox and windleg

iii.

percentage of O₂, CO₂, CO and Moi inside the windbox that affects the development of PCDD/F22

iv.

exit gas rate (in m s⁻¹): it appears to be an important parameter because it defines how long the gas remains within the windbox23

Cl and Cu: both elements improve the production of PCDD/F, although in different ways; chlorine is a key component of the structure of PCDD/F, and depending on the number of atoms on the rings, it defines the hazards and toxicity; copper is a strong catalyst and thus fosters a series of chemical reactions, leading to the development of PCDD/F19,20

vi.

addition of S, according to the following three ways: through gas SO₂ added to the combustion gases, by the addition of coal containing sulphur with more impact than the previous case of pollutants SO₂, in the form of sulphur based reagents added to crude oil24

vii.

addition of urea, which has a dual effect of inhibition: it can act on the urea functional groups by blocking some surface complexes and thereby reducing the availability of catalytic metal sites and can coat the surface of the particulates and prevent chemical reactions25 ^– 28

viii.

addition of hydrated lime: capable of increasing the economic productivity of sintering; it is demonstrated to be a good suppressor of PCDD/F;29 ^– 31 normally, HCl reacts with oxygen to form water and Cl₂; the lime reduces the atmosphere of chlorination by setting HCl in CaCl₂, which has the lowest vapour pressure between the various metal chlorides.32

The analysis of the sintering process was performed on a sintering plant (Dwight-Lloyd) belonging to an Italian steel company The emissions levels in the past years before the study are shown in Fig. 5 and compared with the Aarhus protocol and the European legislation.

Figure 5.

Dioxin emission in sintering plan monitored in present study: data are compared with levels indicated by Aarhus protocol17 and European legislation

Each windbox was equipped with thermocouples (k type) in order to monitor the off-gas temperature during sintering. The flue gas composition was monitored according to EN1948 parts 2 and 3, EN1948SS (sampling standards, Wellington Laboratories), EN1948ES (extraction standards, Wellington Laboratories) and EN1948IS (injection standards, Wellington Laboratories) by employing a high resolution gas chromatograph and a high resolution selective mass detector.

The output variables (PCDD/F, NO_x and SO_x) define a multigoal analysis and have been minimised, taking into account some constraints or limitations typical of the actual process of sintering. At this stage, the nodes that make up the logic flow of numerical analysis are defined. The first node is the DoE, which is a set of different designs reproducing different possible working conditions, among which the most effective ones are highlighted. Therefore, it means creating a set number of designs that will be used by the scheduler (the node where the best algorithm is introduced) for the optimisation. Depending on how this space is filled, the designs, defined by the scheduler, are more or less truthful. Therefore, the choice of the DoE is to be assessed correctly. In the present case, an appropriate method of assessment proposed by the modeFRONTIER was used, i.e. ‘reduced factorial’. This method is characterised by the independence between all the considered variables, and it allows the creation of a space design that can start covering all the different possible configurations and more easily achieve the optimum.

The second node filters the input experimental data; the filtering is possible by employing three types of different algorithms. Such algorithms are MOGA II, MOGT and NSGA II.

The MOGA is set to obtain a fast convergence to the Pareto curve, supports the geographic selection and directional crossover and allows the simultaneous assessment of independent design. The MOGT is based on the competitive game theory by Nash linked to the simplex algorithm. It is particularly suitable for studies with many constraints, highly non-linear objectives. It finds a compromise solution (Nash equilibrium) from a small number of rating points.

The NSGA II is based on the crossing over method. The different performances of all the available algorithms were analysed; NSGA II was found to be the most suitable for this kind of study. The main reasons are the possibility to analyse a large number of input parameters and to produce a series of designs able to investigate all the possible combinations of input parameters in a broad range of conditions. A number of generations equal to 10 or 100 (depending on the test) and a probability of crossover equal to 0·9 were set. The main features of the NSGA II are the following:

the allowance of continuous (real code) and discrete variables (binary code)

ii.

allowing user defined discretisation

iii.

the method of handling constraints does not use the parameter penalty

iv.

the implementation of elitism for multiobjective research

the diversity and distribution of the solutions are guaranteed without the use of sharing parameters

vi.

the allowance of the competitive assessment of the n independent variables.

Multiobjective analysis

By continuing the analysis, the core work flow is defined, which, in the present case, is a specific RS, which proves to be the only node common between logical and data flow. Generally, in this kind of analysis, the heart of the optimisation is represented by a series of equations of chemical and physical nature of a given resolution to get the desired output. In the present case, all this information is not clear due to the complexity of the process, and so it was decided to employ the methodology of response surfaces. Optimisation software allows the following different kinds of RS. For each output variable to be minimised, it is necessary to create a response surface. The analysis starts from a database built with data of operating conditions of the sintering plants obtained from experimental measurements and other related values found in the literature.

Database construction

The database was built by introducing the input parameters, the corresponding output for each working condition experimentally analysed and the physical correlations between the different conditions. The global employed database consists of 578 different designs; an example of input and output parameters is shown in Table 1.

Table 1.

Example of database

Windbox no.	O₂/%	CO₂/%	CO/%	Moi/%	T _wb/°C	T _wleg/°C	Rate/m s⁻¹	Cu/mg kg⁻¹	Urea/mg kg⁻¹	Cl/mg kg⁻¹	S/%	Lime/%	SO_x/mg kg⁻¹	NO_x/mg kg⁻¹	Dioxin/ng I-TEQ/N m³
7	9	13	2	15	90	90	16	100	10	150	0·1	0·01	94	660	2·4
15	10	12	1·4	13	100	80	19	100	10	100	0·1	0·01	660	297	2·4
17	14	6	0·9	8	250	225	19	100	10	100	0·1	0·01	770	176	4·0
18	19	3	0·6	5	350	310	20	100	10	100	0·1	0·01	407	154	6·4
19	20	3	0·3	5	400	375	21	100	10	100	0·1	0·01	286	110	10·4
15	10	12	1·4	13	100	80	19	100	10	100	0·1	0·25	396	208	2·1
18	19	3	0·6	5	350	310	20	100	10	100	0·4	0·01	618	154	3·6
20	20	1	0·1	3	480	440	21	100	10	80	0·1	0·01	220	110	3·6
17	14	6	0·9	8	250	225	19	70	10	100	0·1	0·01	770	176	3·0
18	19	3	0·6	5	350	310	20	70	10	100	0·1	0·01	407	154	4·8
12	9	13	1·8	15	90	90	17	100	10	100	0·1	0·18	144	402	1·5

Of the 578 starting designs, 572 were used to generate metamodels, while six designs were employed as designs of control to verify the affordability of the response surfaces. The choice of these six was taken in order to get the right information on the entire range of existence of the output variables. The designs of control are the following

ID = 126; low value of PCDD/F, low NO_x, SO_x low

ID = 184, average value of PCDD/F, low NO_x, SO_x high

ID = 269, average value of PCDD/F, low NO_x, SO_x average

ID = 346; low value of PCDD/F, high NO_x, SOx _x low

ID = 501; high value of PCDD/F, low NO_x, SO_x low

ID = 534, mean PCDD/F, high NO_x, SO_x low.

In the present study, six response surfaces that are best suited to deal with multiobjective optimisation were obtained. The six response surfaces are a function of the chosen response surface.

The characteristics of each family of RS are as follows.

Single value decomposition (SVD) is the simplest method for generating a surface, and with this method, it is possible to choose the degree of the polynomial interpolation with which the different information can build a virtual model.

Radial basis function (RBF) is a powerful tool for the multivariate interpolation of scattered data. The term ‘scattered data’ means that the points of training should not be sampled on a regular grid because RBF is a correct method without the use of mesh. Since the RBF interpolant is a response surface, it passes through the points of training. With this method, a policy of fully automatic scaling based on the minimisation of the mean ‘leave one out’ is implemented. Through a scale parameter, the shape of the radial function can be determined. The leave one out method is an effective way to control the efficiency of interpolating a response.

Neural network (NN) is one of the most powerful and efficient methods of interpolation. Inspired by the structure and functions of the human brain, NNs can learn from a training set proposed by the user. The interpolating function is usually a sigmoid function. An NN may generate non-linear relationships between input and output variables. The network that is generated consists of a sequence of hidden layers of neurons allowing the creation of relationships between input and output variables. One of the problems occurring with the use of NNs is overfitting.

The next step is to evaluate the performance surface and use them as a node operator in our workflow. The available tools are the ones offered by modeFRONTIER, such as the response surface methodology (RSM) distance, the RSM residual and the RSM function plot. Initially, the tool RSM distance, allowing to assess graphically the distance between the real values provided by the database and those generated by the virtual meta-model, was employed. The virtual profile is very close to that of the actual design but, in some cases, cannot reflect it perfectly. The difference is greater with respect to the SVD surfaces. The situation improves with the NN. At first glance, using such a tool, the best Dioxin_RBF_0 and Dioxin_NN_1 were found, but in this case, a more detailed analysis is necessary.

With regard to the NO_x variable, the same kind of analysis was performed. The two SVD surfaces cannot play the best sequence of real design. It can be concluded, then, that the choice falls on the type RBF or NN, which are more efficient. In addition, for the SO_x, the same kind of analysis was performed. The surface SO_x_SVD_0 cannot cover the design that has a high value of SO_x, and it behaves better in the case of a design with smaller values. This leads to the formation of a poor surface for higher values of this parameter. This first analysis led to the realisation of how poorly performing the SVD_0 surface for all three output variables was, but not to narrow the field to the point of making a safe choice. Therefore, the second tool provided by mF or residual RSM was employed. This provides graphical and numerical information on the error between the created surface and the real distribution of the starting design. In this way, it was possible to find the average and maximum relative and absolute error, regression and principal value of the error.

Optimisation procedure

For the areas related to PCDD/F, the numerical value of the regression is about the same (close to 0·99). For the mean error, the order of magnitude is 10⁻³, except for areas where NN_1 e RBF_0 decreases to 10⁻⁴. At this point, they must be considered the maximum and average error, both absolute and relative. The maximum error is the same for all RS, improving slightly as it rises from the SVD to the NN. Instead of evaluating the average error, the known lowest values are those of the RBF surfaces that decrease to orders of magnitude of 10⁻²/10⁻³. Observing the error, both absolute and relative, the surface method appears to be the most powerful. By performing the same analysis on the NO_x variable, it was observed that the poorer areas are the SVD with residual high values. Furthermore, it shows how the lowest levels of residues are those of the two surfaces of RBF type. The most powerful, at least limited to this tool, seems to be the surface NO_x_RBF_0.

Finally, the same analysis was made to the output SO_x variable. In the same way, it was found that the best is the SO_x_RBF_0.

The last tool to be used is the RSM plot function, which allows us to understand how the surface reconstructs a pattern of the three outputs as a function of the input variable.

After the analysis of all the areas carried out through three different tools proposed in the design space of the mF panel, the optimal condition of the analysis can be chosen for each of the three output variables. The choices are the following:

PCDD/F = Dioxin_RBF_0

ii.

NO_x = NO_x_RBF_0

iii.

SO_x = SO_x_RBF_O

The choices lead to the use of RBF type surfaces with the MultiQuadrics Hardy’s radial function. In fact, by looking at a distance, only the RSM distance and the RSM plot function, the RBF surfaces are very good. The contribution of residual RSM leads to the choice of RBF_0 permanently.

At the beginning of the analysis, modeFRONTIER generates the space of DoE following the reduced factorial method. Then, these designs are transformed by the NSGA II algorithm. The new designs created by mF fill all the ranges of analysis. These designs are introduced in the response surface that has been set in the first step of the study. In this way, the mF generates a determined number of working parameters, which lead to a particular emission value. At this point, the user has to choose the set of input that produces the lower emission value for each output, considering the physical constraints and the legal limit.

Results and discussion

Before starting to analyse the results of numerical simulation, the influence of input parameters should be evaluated. Concerning the gas temperature in the windbox and windleg, it must be noted that their trends are very similar and differ only from 30 to 50°C; the last box can reach even higher temperatures, up to 500–550°C.

As shown in Fig. 6, the PCDD/F distribution has the maximum value around windbox no. 19, while the amount of emissions remains low in the first part of the sinter bed and in the end. When the gas temperature is higher than 500°C, the amount of PCDD/F is reduced. For NO_x emissions, the maximum value is in windbox no. 7, while the maximum value of SO_x is in windbox no. 16.

Figure 6.

NO_x, SO_x, dioxin versus windbox and temperature

The role of sulphur in the reduction of emissions in the sintering can be noted. In all three ways previously observed, there has been a reduction of PCDD/F, especially in the second case, and it is probably due to the presence of SO_x in flue gas. It is believed that these sulphides can be converted to SO₂, reducing the chlorine in HCl.

The influence of urea is very important in the reduction of polluting emission. In particular, the emissions levels are reduced as the urea levels increase. For the PCDD/F, it occurs by means of physical deposition or by poisoning the catalytic sites (Fig. 7).

Figure 7.

NO_x, SO_x, dioxin versus urea and windbox

A positive finding of urea in the reduction of SO_x and NO_x emissions, by up to 32 and 15% respectively, was also noted.

The importance of lime in the reduction of PCDD/F should be outlined when it is introduced in the raw material. The emission levels are reduced as the lime quantity in the raw materials increases. The lime also brings a reduction of NO_x from 16 to 30% of initial value and up to over 70% for SO_x in particular working conditions (Fig. 8).

Figure 8.

NO_x, SO_x, dioxin versus hydrated lime and windbox

Table 2 summarises the range of existence of all the input parameters analysed in the present study.

Table 2.

Input parameters

Variable	Name	Lower bound	Upper bound	Unit	Step
Temperature of windbox	T _wbox	90	550	°C	1
Temperature of windleg	T _wleg	90	550	°C	1
Oxygen	O₂	8	21	%	0·5
carbon monoxide	CO	0·1	2	%	0·1
carbon dioxide	CO₂	1	13	%	0·5
Moisture	Moi	1	15	%	0·5
No. of windbox	wb	1	21		1
Copper	Cu	70	200	mg kg⁻¹	5
Chlorine	Cl	40	150	mg kg⁻¹	5
Urea	Urea	50	400	mg kg⁻¹	5
Sulphur	S	0·1	0·8	%	0·1
Hydrated lime	lime	0	0·25	%	0·05
Exit gas rate	rate	10	22	m s⁻¹	1

The range of existence of any input parameter is characterised by chemical and physical constraints that have to be respected to obtain realistic results from the analysis. For example, the gas temperature in the windbox has to be higher than 450–500°C because, at this point, PCDD/Fs begin to decompose, but at the same time, the temperature should not increase too much because the process would become too expensive. Cu and Cl have to be reduced, but there are physical and technological constraints that have to be respected by limiting the reduction of such elements in the raw material. Urea, sulphur and hydrated lime lead to a reduction of emissions, but too large an amount of these leads to the deterioration of the mechanical and technological properties of the sintered material. If the sulphur percentage rises, then the amount of SO_x increases. For this reason, some designs have been excluded from the analysis.

During the preliminary analysis of the training database, the windbox with the highest level of emissions was noted to be no. 19, so the first analysis was performed only on that windbox to look for a set of values to be assigned to the different parameters in order to reduce the production of PCDD/F, NO_x and SO_x. With the numerical simulation by mF software, a series of operating conditions for the sintering process has been defined. However, not all the numeric strings have produced low amounts of emissions. In some cases, the value is low for PCDD/F but very high for the other output, NO_x and SO_x. It is very important to consider all the aspects of the physical process. The user has to analyse the different sets of parameters and the output values reached, and he has to choose the best operating conditions. From the list offered by the first step of analysis, with numerical simulations, the three most suitable designs have been proposed because these lead to the minimum value of PCDD/F, NO_x and SO_x.

Such optimum designs are summarised in Table 3.

Table 3.

Windbox optimum design no. 19

	Windbox no.	O₂/%	CO₂/%	CO/%	Moi/%	T _wb/°C	T _wleg/°C	Rate/m s⁻¹	Cu/mg kg⁻¹	Urea/mg kg⁻¹	Cl/mg kg⁻¹	S/%	Lime/%	SO_x/mg kg⁻¹	NO_x/mg kg⁻¹	PCDD/F/ng-I TEQ/N m³
1	19	21	3	0·5	7	502	463	21	80	400	110	0·8	0·25	371	106	0·56
2	19	21	5	0·6	6	498	422	21	80	400	150	0·6	0·15	351	101	0·58
3	19	20	3	0·2	5	503	663	19	90	350	120	0·8	0·2	276	115	0·59

It should be noted that in all these cases, there are high values of oxygen, while those of monoxide and carbon dioxide and moisture are relatively low.

The gas temperatures in the windbox turn out high; the chlorine and copper values are low, while the levels of additives cover upper middle values. Maximum emissions of SO_x and NO_x are found in windbox nos. 17 and 7 respectively. When sulphur rises, the level of SO_x emissions increases, while the increase in the lime addition leads to a reduction in the levels of NO_x and PCDD/F emissions. At this point, the best operating conditions of all the 21 windboxes of the system were fixed, and the medium value, weighed in the three different cases, was estimated. Some parameters, such as lime, sulphur, urea, chlorine and copper, remain similar for all the 21 windboxes, while the remaining input (temperatures, moisture, oxygen, etc.) assumes different values according to the position in the sintering bed.33

In Table 4, the values of the emissions for the different designs are summarised.

Table 4.

Emissions of three best designs

	SO_x/mg kg⁻¹	NO_x/mg kg⁻¹	PCDD/F/ng I-TEQ/N m³
Design 1	291	177	0·43
Design 2	298	202	0·455
Design 3	304	207	0·47

In all three cases, the medium values of emissions of SO_x and NO_x are largely below the legal limit indicated by the international protocol Aarhus. Unfortunately, with such operating conditions, the PCDD/F levels still exceed the value limit of 0·4 ng I-TEQ/N m³. In addition to offering minimal value in the optimisation regarding windbox no. 19, design 1 proposes valid operating conditions for the whole system and the lowest values of pollutants. In order to define the optimisation strategy, reference to design 1 was chosen. The process parameters, which result independent from the position on the belt conveyor, were fixed. They are shown in Table 5.

Table 5.

Parameters fixed for all windboxes

Cu/mg kg⁻¹	Urea/mg kg⁻¹	Cl/mg kg⁻¹	S/%	Lime/%
80	400	110	0·8	0·25

For the remaining process parameters, the choice of the values to apply to the single windbox is necessary. The results offered from the first phase of optimisation were not followed because the proposed profiles are discontinuous and inhomogeneous and difficult to apply to a real system. Therefore, another set of more homogenous profiles was proposed taking into account the data of the first phase of analysis, which is defined as new design (Fig. 9).

Figure 9.

CO₂, O₂, CO and moisture versus windbox

At last, the temperatures of the windboxes and windlegs for the new design were chosen and are shown in Table 6.

Table 6.

Temperature fixed in windboxes and windlegs

Windbox/windleg	T _wbox/°C	T _wleg/°C
1–15	115	95
16	220	190
17–21	500	460

With these values attributed to the input variables, the minimum value of emissions obtained can be discovered. In Table 7, the results of the different designs are compared.

Table 7.

Emission values

	SO_x/mg kg⁻¹	NO_x/mg kg⁻¹	PCDD/F/ng I-TEQ/N m³
New design	297	201	0·45
Design 1	291	177	0·43
Legal limit	400	400	0·4

Some of the optimal operating conditions were removed in favour of profiles easier to apply to the system. Consequently, an increase in the medium levels of emissions can be expected. In fact, the level of PCDD/F was found to vary from 0·43 to 0·45 ng I-TEQ/N m³, with also a contemporary increase in NO_x and SO_x. With the exception of the latter, the PCDD/F still exceeded the legal limits. Despite an increase in emissions, the application of the set of parameters of the new design was chosen because it is technologically simpler to realise. In addition, a parallel analysis method was carried out. This method is based on the evolutionary design (ED). This allowed the extrapolation of a mathematical function from each of the three RS, with which the real system is reproduced. These functions are less realistic than RS. In Table 8, the values obtained with the two methods of calculation were compared.

Table 8.

Radial basis function and ED emission

	SO_x/mg kg⁻¹	NO_x/mg kg⁻¹	PCDD/F/ng I-TEQ/N m³
Design RBF	297	201	0·45
Design ED	277	210	0·5

This study made it possible to implement a setting of the system through which it is possible to obtain a clean reduction of the emissions of polluting substances. Thus, just acting on input parameters, all the values of PCDD/F below the legal limit were not possible to achieve. A further possible improvement was studied, which consists of the application of a determined filtered device that can carry a further reduction of pollutants.34 The employed devices were as follows:

ESP: Such a device is used mainly in order to collect and control particles produced in metallurgical systems.35,36 The operation of this device is based on the application of a strong electric field (10 000–20 000 V) through which particles contained in the exit gas are forced to pass. Successively, these run through a wide series of collection slabs, with opposite sign charges, which block such polluting particles

ii.

WS: this device contributes to reduce the emissions of PCDD/F in vapour form. The device makes possible the reduction of emissions by means of a two stage process: the first consists of the passage through a quenching unit, i.e. the scrubber, and the second is the passage through an electrostatic precipitator.

The objective of our work is to understand how much these devices can be influential in the reduction of polluting emissions. In Ref. 37, the ESP and a more complex system like WS in a typical sintering system were analysed. Since, in the study, there are some small differences regarding the system considered in this analysis, a safety value of reduction was assumed, limiting the efficiency of the devices analysed in Ref. 37.

We assume the following values of efficiency:

ESP

Reduction PCDD/F = 40%

Reduction PCDD/F = 65%

Reduction SO_x = 5%

Consequently, applying such reductions to the values obtained from our numerical simulations, we succeed in obtaining the values of emissions shown in Table 9.

Table 9.

Emission with filter devices

	SO_x/mg kg⁻¹	NO_x/mg kg⁻¹	PCDD/F/ng I-TEQ/N m³
New design	297	201	0·45
ESP	297	201	0·27
WS	282	201	0·16

Table 9 summarises the choices carried out and the final values obtained. It should be noted that the filtering device does not have an influence on the reduction of the NO_x, but this turns out negligible as we are already within legal limits thanks to the choices made in the input parameters. The main situation is identical for the values of SO_x; even with WS, they are reduced by 5%. The reduction obtained from the PCDD/F emissions is the key aspect of the present study. In the event of the application of the ESP, the emission value is 0·27 ng I-TEQ/N m³, while in the second case, the value is 0·16 ng I-TEQ/N m³, largely below the legal limit set in 31 December 2010.

Conclusions

Using the optimisation software modeFRONTIER (ESTECO), a virtual surface that can reproduce the actual process of sintering was created. Optimisation of the sinter raw mix and, in particular, the operation of windbox no. 19, the main source of emissions, resulted in a 10-fold reduction in dioxins, but they were still marginally above the legal limit. The use of post-sintering scrubbers or precipitators reduced emissions to below the legal maximum. The NO_x and SO_x levels were below the legal maxima even without scrubbing or precipitators.

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