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Spent refractories in steel mills cause landfills

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Spent refractories in steel mills cause landfills

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Laboratory of Business Informatics and Quantitative Methods, Department of Business Administration, School of Administrative, Economics and Social Sciences, University of West Attica, 250 Thivon & P.Ralli Str., 12241 Athens, Greece

Laboratory of Geoenvironmental Science and Environmental Quality Assurance, Department of Civil Engineering, University of West Attica, 250 Thivon & P.Ralli Str., 12241 Athens, Greece

Laboratory of Urban Planning and Architecture, Department of Civil Engineering, University of West Attica, 250 Thivon & P.Ralli Str., 12241 Athens, Greece

R&D Department, Mathios Refractories S.A., 5 Epidavrou Str., 18233 Athens, Greece

Author to whom correspondence should be addressed.

1. Introduction

Through applying alternative use cycles, the circular economy (CE) aims at reducing dependency on (new) natural resource extraction, while expanding the time that resources spent within the technology sphere [ 1]. In essence, this approach of CE imitates Earth's naturally circular systems [ 1]. CE envisions a balance between industry and the environment, ensuring that human civilization will continue to thrive in the long run. All waste is designed to become reusable material through different processes. Following the example of the ecosystems, material cycles in CE are closed [ 1]. Many researchers have studied the different aspects raised by the two main pillars of CE, namely, the technical model [ 2, 3, 4, 5, 6, 7] and the biological model [ 8, 9, 10]. Fereira et al. [ 2], Horckmans et al. [ 3], Fang et al. [ 4], Hanagiri et al. [ 5], Poirer et al. [ 6], and Ariannpour et al. [ 7] dealt with refractory characterization and recycling. Pantazopoulou et al. [ 8] and Kasina et al. [ 9] concluded on issues related to the stabilization and potential reuse of sewage sludge and municipal waste incineration ashes. Moreover, Vlachokostas et al. [ 10] suggested a framework supporting the decision making for biodegrabable waste management. On the other hand, Kyriakopoulos et al. [ 11] concluded that there are still many issues leading to the negative environmental impacts that arise from the fundamental assumptions of CE.

Environmental characterization is crucial throughout all stages of refractory material life as it assists in evaluating, mitigating, and controlling risks. Refractory solid materials are used in industries (steel, cement, aluminum, glass, ceramics, etc.) where there are requirements to withstand very high temperatures. Refractory materials are produced from abundant industrial minerals, including chromite, dolomite, SiO ₂, MgO, and Al ₂O ₃ [ 2]. Refractories are the primary materials applied as the internal lining of kilns, incinerators, and furnaces. Because these materials are exposed to elevated temperatures and corrosive liquids or gases, they have to meet different challenging specifications, such as thermal expansion, mechanical strength, and corrosion resistance [ 2]. Refractories are produced in shape form (bricks) for lining subsystems of the industry or in powder form, which is shaped on the installation site.

The industries (metal, cement, glass, etc.) from time to time (depending on the type of production) replace the refractories into the frame of maintenance of the industrial units. It is estimated that, worldwide, 35-40 million tons of refractories are produced and used yearly, and an amount of about 28 million tons of spent refractories are produced [ 3]. Even in this vast amount of spent refractory material, it is worth mentioning that, in the last 20 years, the recycling processes have not seen a massive utilization since (a) the production cost of the raw material was low, and (b) the cost of disposal of the used refractories was not high.

Many of the abovementioned elements were changed recently, while many environmental issues were raised concerning the handling of the refractory wastes and the increased cost of landfilling. Furthermore, in the EU, new regulations and directives were posed concerning the environment, such as the waste frame Directive 2008/98/EC, which poses the priorities for handling the wastes, ordered as follows [ 12]: (a) prevention, (b) preparation for reuse, (c) recycling, (d) recovery, and (e) disposal. Even the increasing interests in the waste disposal of recycling spent refractories are a common approach worldwide for the management of spent refractories. Therefore, reuse and recycling become of higher importance and influence handling of the spent refractories.

The decision to reuse or recycle the refractory waste depends on a set of parameters, the most important of which are (a) the quality of the spent refractories, (b) the previous use of the refractories and the substances deposited on their surface, (c) the type of refractory, (d) the composition of refractory materials which could contain potentially toxic elements posing a threat to human and ecosystems, and (e) the leachability of potentially toxic elements from refractories.

Another significant parameter to be considered is the type of recycling process. The closed-loop process concerns the use of the spent refractories producing new refractories. This is the most attractive since the recycling of the spent refractories is focused on added-value products with high production cost as far as the required energy is concerned.

The interest in reusing refractory spent materials with better environmental characteristics is expected to grow in the upcoming years. Therefore, it is crucial to evaluate the ecotoxicological potential of the refractory spent materials. The evaluation of potential ecotoxicity of products/materials has been applied in several works [ 13, 14]. The approach for evaluating the potential environmental risk of granular solid materials for ecotoxicological potential and landfilling was based on the Council Decision 2003/33/EC [ 15]. The appropriateness of the spent refractories for recycling is strongly dependent on the quality of the spent refractory and the clearness as far as the environmental impacts and health issues are concerned. The Council Decision 2003/33/EC discretized 15 components and their limits with respect to the content of the material used for which these can be characterized as inert, nonhazardous, and hazardous. In general, the proposed thresholds of concentration for the 15 components provide a guide for the acceptance for recycling of the wastes. A question is raised concerning the appropriateness of the spent refractories and the types of products of the recycling, and this decision is complicated related to (a) the kind of mineral of the refractory (chromite, dolomite, SiO ₂, MgO, Al ₂O ₃, etc.), (b) the use of the recycling products in the industries whereby different qualities of refractories are used for different industries, and (c) the chemical and physical characteristics of the spent materials and, most importantly, their environmental appropriateness.

However, there is no commonly accepted approach in the literature that evaluates and quantifies the environmental appropriateness of waste materials by considering the concentration of the 15 components together with expert judgment. This study aims to fill this gap and is focused on the development of a methodological approach for (a) the discretization of the spent refractories to those that can be recycled, and (b) the development of a measure for the identification of the environmental appropriateness of the spent refractories which can be used for recycling. The measure is based on a structured multicriteria decision aid (MCDA) approach described later on, which will support decisions concerning the kind of recycling that can be used for any type of waste.

The paper is organized as follows: Section 1 includes the introduction which provides an overview of the current literature related to the specific topic. Section 2 provides a review of the management options for refractory spent materials, the main types of recycling, and the guidelines for the characterization of raw wastes based on the assessment of chemical toxicity. This section also includes an overview of MCDA methods and their applications in CE. Section 3 presents the proposed methodology for evaluating the appropriateness of recycling for refractory spent materials, while Section 4 describes the application of the proposed MCDA approach to spent refractories. Section 5 concludes the main findings of the study and proposes new avenues for future research.

2. Background

2.1. Refractory Spent Materials and Recycling

Landfilling is a standard management option for refractories, while the recycling process is an alternative one. The recycling of refractory spent material has emerged in the last few years. The main types of recycling are (a) open-loop and (b) closed-loop.

The main advantages of closed-loop recycling are the evolution toward CE and the potential to generate higher economic benefits. In closed-loop recycling, the intrinsic properties of the virgin material are not significantly different from those of the recycled material. Therefore, the recycled material can replace the virgin material and be applied in products as before. In open-loop recycling, the intrinsic properties of the virgin material differ from those of the recycled material. Therefore, the recycled material can replace other materials for other product applications [ 16].

The classical recycling process for refractory materials involves the following stages [ 5]: sorting and classification, crushing, sieving, and drying.

Currently, the majority of spent refractories are employed in low-grade applications such as roadbed construction or disposed of in landfills. Using the spent refractories for producing other types (open loop) of refractories is considered good practice but cannot be considered the best. The parameters controlling the recycling of refractories are, among others, the (a) economic cost of the virgin raw material, (b) type of usage, (c) composition of substances deposited on its surface, and (d) leachability of potentially toxic substances.

The studied refractory materials were used at high temperatures, particularly in metallurgical processes. According to Poirer et al. [ 6], refractories are not inert materials. In the aluminum and iron making processes at steelworks, various refractories are applied for the furnace linings for refining, smelting, and conveying operations. The degraded elements and impurities of refractories mainly include slag, base metal, and iron, while, technically speaking, eradicating such impurities is impossible [ 5]. Arianpour et al. [ 7] reported that about 70% of spent refractory bricks could be recycled successfully; while some refractories are reused as decoration in landscaping.

2.2. Characterization of the Spent Refractory Materials

The assessment of chemical toxicity of raw wastes, as well as of spent refractory materials, was conducted according to the standard leaching protocol EN 12457.04, which provides information on the mobility behavior of refractory materials of a particle size below 4 mm and applying a liquid to solid ratio of 10 L/Kg dry matter [ 17]. The characterization of the spent refractory materials is based on the Council Decision 2003/33/EC, regarding the acceptance of spent refractories at landfills [ 15]. The compliance of the testing results of the examined spent refractories with the Council Decision 2003/33/EC is a prerequisite in order for a spent refractory material to be accepted for dumping in a specific landfill [ 8]. Values of pH and concentration of arsenic (As), barium (Ba), cadmium (Cd), chromium total (Cr total), copper (Cu), mercury (Hg), molybdenum (Mo), nickel (Ni), lead (Pb), antimony (Sb), selenium (Se), zinc (Zn), fluoride (F), chloride (Cl), and sulfate (SO ₄ 2−) determined in the leachates of the spent refractory materials are compared against values given by Council Decision 2003/33/EC. According to this comparison, the spent refractory materials are classified as inert, non-hazardous, and hazardous ( Table 1).

For the implementation of the proposed methodological approach, specific laboratory work and sample treatments have to be implemented.

The leachability of potentially hazardous substances from waste and spent refractory materials depends on the grain size distribution, solid-to-liquid ratio, mineral composition of the virgin material, content of amorphous components, and structure [ 9]. The As, Ba, Cd, Cr, Sb, Se, Hg, Mo, Ni, and Pb content of the leachates was obtained applying inductively plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES). The Cu and Zn concentration of the leachate was determined using atomic absorption spectroscopy (AAS). Fluoride, Cl −, and SO ₄ 2− concentrations in leachates were measured applying titrating and gravimetric methods, respectively. X-ray diffraction (XRD) was applied for mineralogical characterization of spent refractory materials. As a complementary technique with XRD analysis, scanning electron microscopic (SEM) analysis was applied. In general, the mineralogical and chemical composition results are discussed to explain further the findings of the leachability tests and mobility of inorganic substances.

2.3. Multicriteria Decision Aid Methodologies and Circular Economy

According to Siskos and Spyridakos [ 18] and Pardalos et al. [ 19], MCDA procedures can be organized into four main theoretical trends: (a) value system approaches (analytic hierarchy/network processes-AHP/ANP [ 20, 21, 22], multi-attribute value/utility theory-MAVT/MAUT [ 23, 24, 25, 26]), (b) outranking relations approaches [ 27, 28, 29, 30, 31], (c) disaggregation-aggregation approach [ 32, 33, 34], and (d) the multi-objective optimization approach [ 35, 36, 37, 38].

Over the last decades, many studies [ 39, 40, 41] have been conducted to determine the most appropriate MCDA method for various research topics. These topics consider a wide variety of features, e.g., the type of the decision problem, the information required by the decision-maker, the dimensions of the performance table (i.e., number of criteria), the treatment of uncertainty, and the available robustness analysis techniques. Moreover, a decision support system was recently developed by Cinelli et al. [ 42] for recommending multicriteria decision analysis methods.

Many researchers have applied MCDA methods on issues related to CE [ 43, 44, 45, 46, 47, 48, 49, 50]. According to Vlachokostas et al. [ 51], the AHP is mainly applied to real-life cases, while the MCDA approach is adopted to study issues related to incineration process, which present many similarities to the refractory material studied in the current research. More specifically, this study deals with the environmental appropriateness of the refractory materials which are involved, among others, in the incineration process.

3. Proposed Methodology for Appropriateness of Recycling

3.1. The On/Off Criteria Process

The on/off criteria process is triggered by the chemical analysis implemented to representative samples of the spent refractories. The acceptance or not for recycling of the spent refractories is based on a set of rules related to the limits suggested by the Council Decision 2003/33/EC [ 15] concerning the metals and the undesirable substance ( Table 1).

Figure 1 includes the steps for the proposed MCDA procedure according to the Council Decision 2003/33/EC.

The on/off criteria process examines the content of the refractory samples and the existence of a substance that exceeds the hazardous limits of Table 1, which can characterize the spent refractories as hazardous:

where d i is component i, T di represents the content of component d i, and Li is the hazardous limit.

If all the component contents fall within the permissible limits for inert and nonhazardous substances, we can proceed to the next step to identify the degree of the appropriateness of the refractories for recycling ( Figure 1). For example, a refractory sample with more than 70 mg·Kg −1 Cr content but all the other components lower than the hazardous limits is characterized as hazardous and has to be secondarily examined as far as the granularity and the mineral content are concerned.

In cases where the component content falls within the permissible limits for the examined components, the MCDA approach is applied to identify the environmental appropriateness index (EAI).

3.2. Assessment of the Nonhazardous Spent Refractory Recycling Compatibility

The proposed multicriteria decision aid approach aims to assess the additive value model described below. Given a sample of refractory material d evaluated on n preference monotone or nonmonotone attributes G = {g ₁,g ₂, ..., g n}, a global value [ 32, 52] is assigned through the utilization of the following formulae:

where n is the number of attributes, d(g) = (d(g ₁), ..., d(g n)) is the evaluation vector of the refractory material attributes, g i* and g i* are the least and most preferable levels of the attribute g i, respectively, and u i(g i) and p i are the value function and the relative weight of the i-th attribute; u(g i*) = 0, u(g i*) = 1.

For every pair of refractory material samples d k and d r, the additive value function satisfies the following conditions: (a) if U(d k(g)) > U(d r(g)), then d k is more environmentally compatible than d r; (b) if U(d k(g)) = U(d r(g)), then a k has the same environmental compatibility as a r.

The utilization of the marginal value functions supports the measurement of the relative impacts of every attribute, the importance of which concerns the environmental impacts.

3.2.1. Development of the Marginal Value Functions

The marginal value functions [ 52, 55] denote the degree of environmental impacts of a factor d i in the examined sample on a scale of 0-1, where 1 corresponds to totally inert materials and 0 corresponds to the level of concentration of hazardous materials (L i). It is worth mentioning that the marginal value functions are usually not linear and can be upward, downward, concave, or s-shaped ( Figure 2).

The curvature of the value function corresponds to the behavior of the factor as far as the environmental issues are concerned. A concave upward function means that significant environmental impacts are raised for low values of the attribute, while, in concave downward functions, only higher values of the attributes are significant for the environmental impacts. Siskos et al. [ 55] used a family of 12 general parametric forms f (a, b, c; g) to cover all types of monotone marginal value functions (concave, cave, and s-shaped general), as well as non-monotone attributes with ∪-shaped and ∩-shaped functions for the construction of the marginal values. There are two forms of marginal value functions: increasing preferences assigned to cases where the environmental impact is decreasing for higher values, and decreasing preferences where the environmental impact is increased for the higher values. In this study, the decreasing preference value functions are suitable for the parameters used, where a higher metal concentration leads to lower preferences as far as the environmental impacts are concerned. The exact estimation of the a, b, and c parameters of the value function is achieved utilizing a modified mid-value split point technique and dialogue with experts to conclude the appropriate value function for one of the attributes.

Actually, in this case, six consecutive ranges of the criterion range were used, and the experts ranked them according to their importance for changes from the lower value to the higher ones. In this way, the curvature of the marginal value functions was assessed, as well as the general form of the marginal value function. Then, utilizing the visual capabilities and artificial intelligence capabilities of the Multicriteria Interactive Intelligence Decision Aiding System (MIIDAS), the exact parameters of a, b, and c were calculated, and the precise form of the marginal value function was determined. Furthermore, interactive and visual processes can be used, where the shape of the value function is changed in controlled steps providing to the experts the marginal values to specific samples with known concentrations. In this way, experts are able to identify the function closer to their attitudes.

3.2.2. Weight Assessment

There are a large number of proposed methods in the literature for the estimation of criteria weights [ 56], such as AHP [ 22], the Simos technique [ 57, 58, 59], the MAUT compensatory technique [ 52], MACBETH (measuring attractiveness through a categorical-based evaluation technique) [ 60], and the weight assessment through prioritization (WAP) method [ 61].

In the proposed methodology, the weights of each attribute were assessed using the WAP method and the relative software developed to support the implementation of the method [ 61]. For every attribute a i, a weight p i is estimated. The method is proceeds in the following steps:

(a)

The experts rank order the attributes and the ex aequo sets of attributes in a pairwise manner, concerning their importance in environmental issues. The attributes are sorted according to their ranking from the most important to the least important and arranged in m classes (m ≤ n). Each class includes one attribute or a set of ex aequo criteria.

(b)

The key point of the WAP method is the utilization of the z indices for every pair of successive attributes or sets of ex aequo attributes that satisfy the following formula:

where m is the number of importance classes for the criteria, z ≥ 1.

The WAP method does not require the precise identification of the z indices but a range [z minr, z maxr] where the value of z r may vary. For two successive attributes or sets of ex aequo attributes (i.e., g r, g r+1), the range [z minr, z maxr] is identified such that z minr ≤ z r ≤ z maxr. In order to facilitate the experts to identify this value system, visual interactive techniques are used. Through this approach, the DM is asked to sort the n attributes into m classes (m ≤ n) and to identify 2(m-− 1) values for the z minr and z maxr indices (r = 1, 2, ..., m-− 1). Scroll bars are used to assist the visualization of the difference of the relative importance between two successive attributes or ex aequo sets of criteria, ordered by their ranking. The z minr and z maxr values are automatically calculated and presented with bars and unique labels.

Having identified the z min and z max for all the pairs of the successive classes, linear programming (LP) techniques are employed in order to estimate the minimum and maximum values of the weights. The following 2n optimization problem describes the estimation of the weight vectors for the maximization and minimization of the criteria weights:

Actually, the solutions emerging from the above LPs will lead to the identification of the minimum and maximum values of the criteria weights into the hyper-polyhedron (infinite solutions).

(c)

The robustness of the estimated hyper-polyhedron is calculated through the utilization of two indices. The first type of index used is the range between the maximum and minimum values of the criteria weights for every criterion, as these values are estimated for each vertex of the hyper-polyhedron. This gives a picture, at first glance, of the extent of robustness in each criterion. For instance, this index for the i-the attribute is calculated as follows:

where n is the number of attributes, m is the number of vertices of the hyper-polyhedron, and p ij is the weight of the i attribute of the j vertex.

The second index used represents the normalized standard deviation of the different solutions corresponding to the hyper-polyhedron vertices, where the value 1 corresponds to the total robustness of the preferred models. This normalized index is called the average stability index (ASI) [ 62, 63, 64].

where n is the number of attributes, and m is the number of vertices of the hyper-polyhedron.

Furthermore, for the set of 2n weight vectors, the barycenter is estimated. If the values of the barycenter are considered as satisfactory by the DM along with the level of robustness, then it can be used as a working vector of weights. Otherwise, there are two main capabilities provided by the proposed approach. The first focuses on updating or altering particular initial preferences, such as the values of the z min and z max or the criteria ranking. The second capability concerns the feedback with which the robustness can be analyzed, utilizing the tomographical approach [ 65] and eliciting additional preference information.

4. Results

4.1. Evaluation Model for Spent Refractories

The additive value model was assessed to fulfil the main objectives of REFRACT project. A further analysis was required concerning the appropriateness of the spent refractories for different types of recycling (open- or closed-loop). Table 2 tabulates the components content of the examined refractory samples used for the validation of the method. S2051, S2052, and S2053 were received from the REFRACT project, while REF01-REF09 were virtual samples.

4.1.1. Estimation of Marginal Value Functions Using the MIIDAS System

The experts utilized the techniques and visual capabilities of the MIIDAS system for the construction of the marginal value function [ 55]. For the components As, Cd, Cr, Sb, and Hg, upward concave functions were selected by the experts, whereby, for these components, lower concentrations were considered significant for environmental impacts. For the other attributes, downward concave functions were used since their impact was only considered significant for higher concentrations. Every marginal value function was refined through the process described above to identify the exact functions corresponding to every attribute.

Table 3 includes the graphs of the marginal value function for the 15 attributes derived from the screen layout of the MIIDAS system used for the construction.

An example for developing the value function of As is demonstrated ( Table 3). Since the leaching limit value for the inert class for As is 0.5 mg·Kg −1 As, it is categorized in Group I. It is well known that As is a highly toxic element in its inorganic form and is widely recognized as carcinogenic, even at low concentrations [ 66, 67]. The symptoms of As toxicity in humans include skin cancer, gangrene, leucomelanosis, keratosis, melanosis, and cancer of lungs, prostate and liver [ 66, 67]. On the basis of the new scientific data correlated to the adverse health effects of As, the European Council and World Health Organization have established 10 μg As·L −1 as the new parametric value for As in potable water, in contrast to the initial parametric value of 50 μg As·L −1.

A hazardous material containing a content of As higher than the leaching limit value of 0.5 mg·Kg −1 ( Table 3) may release this element in the environment, contaminate groundwater, and pose a severe threat to human health. In this case, the slope of the proposed curve demonstrating the value function of As is high, suggesting that the value function of the MCDA approach for this element is rigorous considering the element's toxicity.

4.1.2. Estimation of Criteria Weights Using the WAP Technique

Table 4 provides the assessed ranking. There are various approaches suggesting criteria related to potential toxicity, human health, and ecological risk [ 66, 67, 68]. For the estimation of the criteria weights, the experts ranked the 15 elements and substances according to their potential toxicity. Through this process, five different ranking classes (1-5) were identified for chemical components, where 1 corresponds to high toxicity and 5 corresponds to low toxicity. The term "expert opinion" is used in contrast to "expert judgement" to emphasize an initial state of knowledge. The determination of expertise included professionals such as scientists and managers. Experts replied to the following research question: "What are the environmental quality expert's views on the ranking of chemical components concerning their toxicity?" If the leaching limit value of a chemical component is 4, 3, 2, 1, and 0 orders of magnitude (rounded to the nearest power of 10) greater than the maximum leaching limit value for the inert category (1000 mg SO ₄ 2−·L −1), it belongs to Groups I, II, III, IV, and V, respectively. This classification is also in line with the parametric values for chemical components provided by the Council Directive 98/83/EC on the quality of water intended for human consumption [ 69].

The leaching limit values (L/S = 10 L/Kg) of chemical components for characterization as inert, non-hazardous, or hazardous waste were sorted in decreasing order and grouped in five classes as follows ( Table 4): (a) Group I: Cd, Hg, Sb, Se; (b) Group II: As, Cr total, Mo, Ni, Pb; (c) Group III: Cu, Zn; (d) Group IV: Ba, F; (e) Group V: Cl −, SO ₄ 2−.

The next step of the methodology involved the relation of each group to a rank, as shown in Table 4.

Subsequently, the experts provided the relative importance (z min and z max) of the five classes utilizing the visual techniques of the WAP method and the relative software. Screen layouts are presented in Figure 3 for a comparison of the first and second classes ( Figure 3).

Table 5 presents the results of the relative importance provided by the experts, as well as the weights of the classes estimated by the linear program solution.

The above-presented methodological approach was utilized for the estimation characterization of the samples of spent refractories in the frame of the REFRACT project co-financed by the European Union and Greek national funds.

4.2. Classification of Refractory Spent Materials

The above additive value model was employed to estimate the environmental appropriateness index (EAI) for a set of samples with differentiations of the concentration of the components. Table 6 includes the results of the additive value model for the calculation of the environmental appropriateness index (EAI).

The EAI values for S2051, S2052, S2053, REF01, REF02, and REF03 samples were higher than the EAI value of the inert sample. The EAI value of the inert sample was estimated using the leaching limit values for the inert material ( Table 6).

The component concentrations for samples S2051, S2052 and S2053 fell within the inert range ( Table 2). Their EAI values were also higher than the corresponding inert limit for virtual samples REF01-REF03. The virtual samples (REF01-REF09) were used in order to validate the EAI, and the results can be considered satisfactory, since the EAI of samples REF04-REF06 varied between 0.75179 and 0.81631, which can be attributed to the concentrations exceeding the inert limit for most of the components ( Table 2 and Table 6). Moreover, sample REF07 presented a low value of EAI (0.31926), attributed to the high content of the first-class components (Cd, Hg, Sb, Se), second-class components (As, Cr total, Mo, Pb, Ni), third-class components (Cu, Zn), and fourth-class components (Ba, F) exceeding the upper nonhazardous limit. Furthermore, the lowest EAI value (0.2456) was observed for the REF08 sample which presented high concentrations of Ni, Sb, Se, Cl −, and SO ₄ 2−, while REF09 (0.36595) presented high concentrations for all examined components ( Table 2 and Table 6). Lastly, the above analysis reveals that the proposed index for measuring the appropriateness of the spent material can provide a rational measure for this case study.

5. Conclusions

This study introduced a multicriteria approach for the characterization of refractory material wastes concerning their recycling capabilities. For the above, a two-phase methodological approach was introduced. The first step of the proposed approach is to characterize the refractory material as inert or hazardous according to the composition of its leachate. Further analysis is performed in the second phase, where a multi-attribute index is introduced for the estimation of the environmental appropriateness of refractory spent materials. The critical features of the proposed methodological approach are (a) the utilization of a weight vector for the 15 critical substances, representing their relative importance related to the environmental impacts, and (b) a nonlinear value function encapsulating the variation of the environmental impacts on the scale of the acceptable concentration of the components in the refractory material waste.

The application of the proposed approach to a limited number of samples presented satisfactory results in measuring the environmental impacts of wastes that can be recycled. Engineers, decision-makers, and scientists may apply the proposed methodology by evaluating the different qualities of refractory material to specific parts of industrial systems since specific qualities of spent refractories can be used in several parts of kilns and furnaces exposed in various temperatures. This constitutes a new direction of this research and will be implemented in the next phase of the project, along with industrial experiments.

Lastly, the EAI value of a sample classified as nonhazardous may exceed the EAI value (0.95464) for inert limits. This corresponds to spent refractories with very good environmental appropriateness in terms of most components; thus, the application of further treatment should be investigated.

It is important to conduct further work on investigating the influence of uncertainties on the estimated parameters (e.g., the parameters of the nonlinear function) in the decision problem and to explore the suitability of other MCDA methods for this specific issue. The elicitation of the preference structure from other experts will be beneficial for the investigation of these parameters. The application of robustness analysis techniques, evaluating the preference structure and providing tools to improve it, will also be considered in future work.

Author Contributions

Conceptualization and methodology, A.S., D.E.A. and I.V.; software, A.S. and N.T.; validation, formal analysis, and investigation, A.S., D.E.A., I.V. and N.T.; resources and data curation, A.S., D.E.A. and E.K.; writing-original draft preparation, A.S., D.E.A. and I.V.; writing-review and editing, N.T., G.V. and E.K.; visualization and supervision, G.V.; project administration, A.S. and E.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the European Regional Development Fund of the European Union and Greek national funds (Greek Secretariat for Research and Innovation-GSRI) through the Operational Program Competitiveness, Entrepreneurship, and Innovation, (EPAnEK 2014-2020) under the call RESEARCH-CREATE-INNOVATE. Project: "Recycling of used refractories from various industries to produce alumino-silicate refractories, ceramics, and masses"(project code: T1EDK05442).

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Data were obtained from Mathios Refractories SA and are contained within this article.

Conflicts of Interest

The authors declare no conflict of interest.

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Spyridakos A, Alexakis DE, Vryzidis I, Tsotsolas N, Varelidis G, Kagiaras E. Waste Classification of Spent Refractory Materials to Achieve Sustainable Development Goals Exploiting Multiple Criteria Decision Aiding Approach. Applied Sciences. 2022; 12(6):3016. https://doi.org/10.3390/app12063016

Chicago/Turabian Style

Spyridakos, Athanasios, Dimitrios E. Alexakis, Isaak Vryzidis, Nikolaos Tsotsolas, George Varelidis, and Efthimios Kagiaras. 2022. "Waste Classification of Spent Refractory Materials to Achieve Sustainable Development Goals Exploiting Multiple Criteria Decision Aiding Approach" Applied Sciences 12, no. 6: 3016. https://doi.org/10.3390/app12063016

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Waste Classification of Spent Refractory Materials to Achieve Sustainable Development Goals Exploiting Multiple Criteria Decision Aiding Approach

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The recycling of used refractory materials in the heavy industry constitutes one of the significant environmental problems in the industry related to environmental and financial issues. This study proposes a multicriteria methodological frame to characterize the refractory material waste and identify the recycling capabilities.

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Refractory changes require strict safety protocols

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Refractory changes require strict safety protocols

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Each time a ladle shroud is changed, operators are in the path of danger. While accidents can be prevented, they cannot be eliminated

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KPI's are different for different people

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KPI's are different for different people

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KPI's in any industry vary by hierarchy and how each individual person views a particular product

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What Is a Key Performance Indicator (KPI)?

Key performance indicators (KPIs) refer to a set of quantifiable measurements used to gauge a company's overall long-term performance. KPIs specifically help determine a company's strategic, financial, and operational achievements, especially compared to those of other businesses within the same sector.

Key Takeaways

Understanding Key Performance Indicators (KPIs)

Also referred to as key success indicators (KSIs), KPIs vary between companies and between industries, depending on performance criteria. For example, a software company striving to attain the fastest growth in its industry may consider year-over-year (YOY) revenue growth as its chief performance indicator. Conversely, a retail chain might place more value on same-store sales as the best KPI metric for gauging growth.

At the heart of KPIs lie data collection, storage, cleaning, and synthesizing. The information may be financial or nonfinancial and may relate to any department across the company. The goal of KPIs is to communicate results succinctly to allow management to make more informed strategic decisions.

Categories of KPIs

Most KPIs fall into four different categories, with each category having its own characteristics, time frame, and users.

1. Strategic KPIs are usually the most high-level. These types of KPIs may indicate how a company is doing, although it doesn't provide much information beyond a very high-level snapshot. Executives are most likely to use strategic KPIs, and examples of strategic KPIs include return on investment, profit margin, and total company revenue.

2. Operational KPIs are focused on a much tighter time frame. These KPIs measure how a company is doing month over month (or even day over day) by analyzing different processes, segments, or geographical locations. These operational KPIs are often used by managing staff and to analyze questions that are derived from analyzing strategic KPIs. For example, if an executive notices that company-wide revenue has decreased, they may investigate which product lines are struggling.

3. Functional KPIs hone in on specific departments or functions within a company. For example, the finance department may keep track of how many new vendors they register within their accounting information system each month, while the marketing department measures how many clicks each email distribution received. These types of KPIs may be strategic or operational but provide the greatest value to one specific set of users.

4. Leading/lagging KPIs describe the nature of the data being analyzed and whether it is signaling something to come or something that has already occurred. Consider two different KPIs: the number of overtime hours worked and the profit margin for a flagship product. The number of overtime hours worked may be a leading KPI should the company begin to notice poorer manufacturing quality. Alternatively, profit margins are a result of operations and are considered a lagging indicator.

Types of KPIs

Financial Metrics and KPIs

Key performance indicators tied to the financials typically focus on revenue and profit margins. Net profit, the most tried and true of profit-based measurements, represents the amount of revenue that remains, as profit for a given period, after accounting for all of the company's expenses, taxes, and interest payments for the same period.

Financial metrics may be drawn from a company's financial statements. However, internal management may find it more useful to analyze different numbers that are more specific to analyzing the problems or aspects of the company that management wants to analyze. For example, a company may leverage variable costing to recalculate certain account balances for internal analysis only.

Examples of financial KPIs include:

Liquidity ratios (i.e., current ratios, which divide current assets by current liabilities): These types of KPIs measure how well a company will manage short-term debt obligations based on the short-term assets it has on hand.

Profitability ratios (i.e., net profit margin): These types of KPIs measure how well a company is performing in generating sales while keeping expenses low.

Solvency ratios (i.e., total-debt-to-total-assets ratio): These types of KPIs measure the long-term financial health of a company by evaluating how well a company will be able to pay long-term debt.

Turnover ratios (i.e., inventory turnover): These types of KPIs measure how quickly a company can perform a certain task. For example, inventory turnover measures how quickly a company can convert an item from inventory to a sale. Companies strive to increase turnover to generate faster churn of spending cash to later recover that cash through revenue.

Customer Experience Metrics and KPI

Customer-focused KPIs generally center on per-customer efficiency, customer satisfaction, and customer retention. These metrics are used by customer service teams to better understand the service that customers have been receiving.

Examples of customer-centric metrics include:

Number of new ticket requests: This KPI counts customer service requests and measures how many new and open issues customers are having.

Number of resolved tickets: This KPI counts the number of requests that have been successfully taken care of. By comparing the number of requests to the number of resolutions, a company can assess its success rate in getting through customer requests.

Average resolution time: This KPI is the average amount of time needed to help a customer with an issue. Companies may choose to segment average resolution time across different requests (i.e., technical issue requests vs. new account requests).

Average response time: This KPI is the average amount of time needed for a customer service agent to first connect with a customer after the customer has submitted a request. Though the initial agent may not have the knowledge or expertise to provide a solution, a company may value decreasing the time that a customer is waiting for any help.

Top customer service agent: This KPI is a combination of any metric above cross-referenced by customer service representatives. For example, in addition to analyzing company-wide average response time, a company can determine the three fastest and slowest responders.

Type of request: This KPI is a count of the different types of requests. This KPI can help a company better understand the problems a customer may have (i.e., the company's website gave incorrect or inaccurate directions) that need to be resolved by the company.

Customer satisfaction rating: This KPI is a vague measurement, though companies may perform surveys or post-interaction questionnaires to gather additional information on the customer's experience.

Process Performance Metrics and KPI

Process metrics aim to measure and monitor operational performance across the organization. These KPIs analyze how tasks are performed and whether there are process, quality, or performance issues. These types of metrics are most useful for companies with repetitive processes, such as manufacturing firms or companies in cyclical industries.

Examples of process performance metrics include:

Production efficiency: This KPI is often measured as the production time for each stage divided by the total processing time. A company may strive to spend only 2% of its time soliciting raw materials; if it discovers it takes 5% of the total process, then the company may strive for solicitation improvements.

Total cycle time: This KPI is the total amount of time needed to complete a process from start to finish. This may be converted to average cycle time if management wishes to analyze a process over a period of time.

Throughput: This KPI is the number of units produced divided by the production time per unit, measuring how fast the manufacturing process is.

Error rate: This KPI is the total number of errors divided by the total number of units produced. A company striving to reduce waste can better understand the number of items that are failing quality control testing.

Quality rate: This KPI focuses on the positive items produced instead of the negative. By dividing the successful units completed by the total number of units produced, this percentage informs management of its success rate in meeting quality standards.

Marketing KPIs

Marketing KPIs attempt to gain a better understanding of how effective marketing and promotional campaigns have been. These metrics often measure conversation rates on how often prospective customers perform certain actions in response to a given marketing medium. Examples of marketing KPIs include:

Website traffic: This KPI tracks the number of people who visit certain pages of a company's website. Management can use this KPI to better understand whether online traffic is being pushed down potential sales channels and if customers are not being funneled appropriately.

Social media traffic: This KPI tracks the views, follows, likes, retweets, shares, engagement, and other measurable interactions between customers and the company's social media profiles.

Conversion rate on call-to-action content: This KPI centers around focused promotional programs that ask customers to perform certain actions. For example, a specific campaign may encourage customers to act before a certain sale date ends. A company can divide the number of successful engagements by the total number of content distributions to understand what percent of customers answered the call to action.

Blog articles published per month: This KPI simply counts the number of blog posts a company publishes in a given month.

Click-through rates: This KPI measures the number of specific clicks that are performed on email distributions. For example, certain programs may track how many customers opened an email distribution, clicked on a link, and followed through with a sale.

A company may desire operational excellence; in this case, it may want to track how its internal technology (IT) department is operating. These KPIs may encourage a better understanding of employee satisfaction or whether the IT department is being adequately staffed. Examples of IT KPIs include:

Total system downtime: This KPI measures the amount of time that various systems must be taken offline for system updates or repairs. While systems are down, customers may be unable to place orders or employees are unable to perform certain duties (i.e., when the accounting information system is down).

Number of tickets/resolutions: This KPI is similar to customer service KPIs. However, these tickets and resolutions relate to internal staff requests such as hardware or software needs, network problems, or other internal technology problems.

Number of developed features: This KPI measures internal product development by quantifying the number of product changes.

Count of critical bugs: This KPI counts the number of critical problems within systems or programs. A company will need to have its own internal standards for what constitutes a minor vs. major bug.

Back-up frequency: This KPI counts how often critical data is duplicated and stored in a safe location. In accordance with record retention requirements, management may set different targets for different bits of information.

The ultimate goal of a company is to generate revenue through sales. Though revenue is often measured through financial KPIs, sales KPIs take a more granular approach by leveraging nonfinancial data to better understand the sales process. Examples of sales KPIs include:

Customer lifetime value (CLV): This KPI represents the total amount of money that a customer is expected to spend on your products over the entire business relationship.

Customer acquisition cost (CAC): This KPI represents the total sales and marketing cost required to land a new customer. By comparing CAC to CLV, businesses can measure the effectiveness of their customer acquisition efforts.

Average dollar value for new contracts: This KPI measures the average size of new agreements. A company may have a desired threshold for landing larger or smaller customers.

Average conversion time: This KPI measures the amount of time from first contacting a prospective client to securing a signed contract to perform business.

Number of engaged leads: This KPI counts how many potential leads have been contacted or met with. This metric can be further divided into mediums such as visits, emails, phone calls, or other contacts with customers.

Human Resource and Staffing KPIs

Companies may also find it beneficial to analyze KPIs specific to their employees. Ranging from turnover to retention to satisfaction, a company may have a wealth of information already available about its staff. Examples of human resource or staffing KPIs include:

Absenteeism rate: This KPI is a count of how many dates per year or specific period employees are calling in sick or missing shifts. This KPI may be a leading indicator for disengaged or unhappy employees.

Number of overtime hours worked: This KPI tracks the amount of overtime hours worked to gauge whether employees are potentially facing burnout or if staffing levels are appropriate.

Employee satisfaction: This KPI often requires a company-wide survey to gauge how employees are feeling about various aspects of the company. To get the best value from this KPI, companies should consider hosting the same survey every year to track changes from one year to the next regarding the exact same questions.

Employee turnover rate: This KPI measures how often and quickly employees are leaving their positions. Companies can further break down this KPI across departments or teams to determine why some positions may be leaving faster than others.

Number of applicants: This KPI keeps count of how many applications are submitted to open job positions. This KPI helps assess whether job listings are adequately reaching a wide-enough audience to capture interest and lure strong candidates.

Examples of KPIs

Let's take a look at electric vehicle maker Tesla ( TSLA) for a few examples of KPIs in real life. These numbers are from its fourth quarter (Q4) 2021 earnings release.

Vehicle Production

During the quarter, Tesla produced a record 305,840 vehicles and delivered 308,650 vehicles. Production is a big deal for the company because it has consistently been criticized for being bad at ramping up. Increased manufacturing scale means more market share and profits for Tesla.

Automotive Gross Margin

For the quarter, Tesla's automotive gross margin expanded to 30.6%. Gross margin is one of the best measures of profitability for Tesla because it isolates its vehicle production costs. Tesla managed to expand its gross margin in Q4 even as sales of lower-priced models outpaced its higher-margin models.

Free Cash Flow

Tesla's free cash flow clocked in at $2.8 billion during the quarter. That represented a vast improvement from the $1.9 billion free cash flow in the prior year. Tesla's level of free cash flow production suggested that the company was reaching a scale of profitability without the help of regulatory credits.

Companies can use KPIs across three broad levels:

First, company-wide KPIs focus on the overall business health and performance. These types of KPIs are useful for informing management of how things are going. However, they are often not granular enough to make decisions. Company-wide KPIs often kick off conversations on why certain departments are performing well or poorly.

At this point, companies often begin digging into department-level KPIs. These are more specific than company-wide KPIs. Department-level KPIs are often more informative as to why specific outcomes are occurring. Many of the examples mentioned above are department-level KPIs, as they focus on a very niche aspect of a company.

If a company chooses to dig even deeper, it may engage with project-level or subdepartment-level KPIs. These KPIs are often specifically requested by management as they may require very specific data sets that may not be readily available. For example, management may want to ask very specific questions to a control group about a potential product rollout.

How to Create a KPI Report

With companies seemingly collecting more data every day, it can become overwhelming to sort through the information and determine what KPIs are most useful and impactful for decision making. When beginning the process of pulling together KPI dashboards or reports, consider the following steps:

1. Discuss goals and intentions with business partners. KPIs are only as useful as the users make them. Before pulling together any KPI reports, understand what you or your business partner are attempting to achieve.

2. Draft SMART KPI requirements. KPIs should have restrictions and be tied to SMART (specific, measurable, attainable, realistic, and time-bound) metrics. Vague, hard-to-ascertain, and unrealistic KPIs serve little to no value. Instead, focus on what information you have that is available and meeting the SMART acronym requirements.

3. Be adaptable. As you pull together KPI reports, be prepared for new business problems to appear and for further attention to be given to other areas. As business and customer needs change, KPIs should also adapt with certain numbers, metrics, and goals changing in line with operational evolutions.

4. Avoid overwhelming users. It may be tempting to overload report users with as many KPIs as you can fit on a report. At a certain point, KPIs start to become difficult to comprehend, and it may become more difficult to determine which metrics are important to focus on.

Advantages of KPIs

A company may wish to analyze KPIs for several reasons. KPIs help inform management of specific problems; the data-driven approach provides quantifiable information useful in strategic planning and ensuring operational excellence.

KPIs help hold employees accountable. Instead of relying on feelings or emotions, KPIs are statistically supported and cannot discriminate across employees. When used appropriately, KPIs may help encourage employees as they realize their numbers are being closely monitored.

KPIs are also the bridge that connects actual business operations and goals. A company may set targets, but without the ability to track progress toward those goals, there is little to no purpose in those plans. Instead, KPIs allow companies to set objectives, then monitor progress toward those objectives.

Limitations of KPIs

There are some downsides to consider when working with KPIs. There may be a long time frame required for KPIs to provide meaningful data. For example, a company may need to collect annual data from employees for years to better understand trends in satisfaction rates over long periods of time.

KPIs require constant monitoring and close follow-up to be useful. A KPI report that is prepared but never analyzed serves no purpose. In addition, KPIs that are not continuously monitored for accuracy and reasonableness do not encourage beneficial decision making.

KPIs open up the possibility for managers to "game" KPIs. Instead of focusing on actually improving processes or results, managers may feel incentivized to focus on improving KPIs tied to performance bonuses. In addition, quality may decrease if managers are hyper-focused on productivity KPIs, and employees may feel pushed too hard to meet specific KPI measurements that may simply not be reasonable.

What does KPI mean?

KPI is an abbreviation for key performance indicator, data that has been collected, analyzed, and summarized to help decision making. KPIs may be a single calculation or value that summarizes a period of activity, such as "450 sales in the month of October." By themselves, KPIs do not add any value to a company. However, a company can use this information to make more informed decisions about business operations and strategies.

What is an example of a KPI?

One of the most basic examples of a KPI is revenue per client (RPC). For example, if you generate $100,000 in revenue annually and have 100 clients, then your RPC is $1,000. A company can use this KPI to track its RPC over time.

What are five of the most common key performance indicators (KPIs)?

KPIs vary from business to business, and some KPIs are more suitable for certain companies compared to others. In general, five of the most commonly used KPIs are:

1. Revenue growth

2. Revenue per client

3. Profit margin

4. Client retention rate

5. Customer satisfaction

How do you measure KPIs?

It depends on the actual KPI being measured. Generally speaking, businesses measure and track KPIs through business analytics software and reporting tools. This includes everything from the collection of data via reliable sources, the safe storage of information, the cleaning of data to standardize its format for analysis, and the actual number crunching. Finally, KPIs are often reported using visualization or reporting software.

What makes a KPI good?

A good KPI provides objective and clear information on progress toward an end goal. It tracks and measures factors such as efficiency, quality, timeliness, and performance while providing a way to measure performance over time. The ultimate goal of a KPI is to help management make more informed decisions.

The Bottom Line

KPIs offer an effective way to measure and track a company's performance on a variety of different metrics. By understanding exactly what KPIs are and how to implement them properly, managers are better able to optimize the business for long-term success.

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Key Performance Indicator (KPI): Definition, Types, and Examples

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Key performance indicators (KPIs) are quantifiable measures that gauge a company's performance against a set of targets, objectives, or industry peers.

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Aiming to solve these issues would result in an innovation

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Vesuvius is the world leader in the design, development and supply of high quality refractory products. Last year we supplied close to 400,000 ladle shrouds, and through listening to our customers and understanding their needs we continue to develop better products.

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Vesuvius is the world leader in the design, development and supply of high quality refractory products. Last year we supplied close to 400,000 ladle shrouds, and through listening to our customers and understanding their needs we continue to develop better products.

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The Metal Industry Robotics market in the world is projected to grow by 2.32% (2024-2028) resulting in a market volume of US$0.80bn in 2028.