Ranking sewage sludge management strategies by means of Decision Support Systems: A case study

Resources, Conservation and Recycling 110 (2016) 1–15

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Ranking sewage sludge management strategies by means of Decision Support Systems: A case study Giorgio Bertanza a,∗ , Pietro Baroni b , Matteo Canato a a b

DICATAM—Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, via Branze 43, I-25123 Brescia, Italy DII—Department of Information Engineering, University of Brescia, via Branze 43, I-25123 Brescia, Italy

a r t i c l e

i n f o

Article history: Received 1 October 2015 Received in revised form 24 December 2015 Accepted 9 March 2016 Keywords: Costs Decision support Environmental impact Biosolids Technical issues Sewage sludge management planning

a b s t r a c t Waste management planning is a complex task involving a variety factors and professional skills. In order to avoid oversimplification leading to the risk of adopting inappropriate solution the relevant decision problems should be tackled with: (a) a comprehensive decision model (including technical, environmental, economic, social, etc. factors) and (b) a suitable decision supporting tool. In this work as far as (a) is concerned we propose a model based on more than 30 parameters for the evaluation of sewage sludge management strategies. As to (b) we implemented this model by employing both a worksheet (“home made” option) and a DSS commercially available (“buy” option). As a case study, we considered the selection of the sewage sludge management strategy in a 500,000 inhabitants area comparing the following alternatives: Agricultural use, Incineration, Wet Oxidation and recovery in Cement Kiln. The assessment of the four alternatives led to the following preference order: Agricultural use  Incineration > Cement Kiln ∼ = Wet Oxidation. Finally, a discussion on the make-or-buy dilemma and on pros and cons of decision support methods is reported. As a result, a paradigm shift defined make-and-buy approach is proposed. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Environmental decision problems can be regarded as “wicked” problems. This notion was introduced for the first time in urban planning and subsequently considered as a pervasive feature of many scientific and technical domains (Churchman, 1967; Rittel and Webber, 1973; Norton, 2012). Wicked problems are described as “ill-formulated, where the information is confusing, where there are many clients and decision makers with conflicting values, and where the ramifications in the whole system are thoroughly confusing”. They are opposed to “tame” or “benign” problems which are clearly “definable and separable and may have solutions that are findable” and where it is easy to check whether or not the problem has been solved. Wickedness refers to the inherent complexity of a decision problem, not only in terms of its size (roughly the number of different elements to be considered), but, more importantly, in terms of its structure, due to conflicting values and interests involved and to a scattered ill-known network of long-lasting interactions with the context where the decision takes place.

∗ Corresponding author. Tel.: +39 030 3711301; fax: +39 030 3711312. E-mail addresses: [email protected] (G. Bertanza), [email protected] (P. Baroni), [email protected] (M. Canato). http://dx.doi.org/10.1016/j.resconrec.2016.03.011 0921-3449/© 2016 Elsevier B.V. All rights reserved.

These general complexity properties are easily recognized within environmental decision problems as they involve several multidisciplinary aspects (related to regulations, social perception, technical constraints, costs, etc.) and stakeholders having conflicting interests (e.g. plant managers, authorities, private companies, citizens). Furthermore, the effects of a decision, like “building an incinerator in a given site” span decades, thus spreading over largely unforeseeable future contexts, possibly, indeed probably, very different from the one where the decision has been made. As a further source of complication, some of the many factors to be considered are quantitative, and can be easily measured or reliably estimated (e.g. energy consumption), while others are completely qualitative and their evaluation involves a lot of subjectivity (e.g. public acceptance of a new sludge treatment plant); it is difficult to take into account all the relevant aspects within an integrated evaluation and comparison. As a reaction to these difficulties, humans may tend to simplify and reduce the dimension of a complex problem (e.g. by focusing on just a few elements and ignoring the others) until it appears to be cognitively more manageable: this tendency causes significant loss of information about the problem, in particular about alternative viewpoints and uncertainty, as pointed out by Linkov and Moberg (2012). Indeed, unaided, humans are quite bad at making complex decisions and, as result, controversies and issues, which can last for years without satisfactory resolution, can arise (McDaniels et al., 1999; Linkov and Moberg, 2012).

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Decision Support Systems (DSSs) are software tools aiming at overcoming these limitations: they are an active investigation field since the mid-1960s (for a historical survey see Power (2007) and the references therein) and have been applied in a rich variety of application areas ranging from corporate management to health care and from education to government (Eom and Kim, 2005). A review of the state-of-the-art of the DSS field is clearly beyond the scope of this paper: the interested reader may refer to fieldcovering books (e.g. Marakas, 2002; Schuff et al., 2011) or more focused surveys (e.g. Foster et al., 2005; Liu and Zaraté, 2014). The benefits of adopting a DSS do not only consist in the availability of computer-based procedures supporting the various phases of a decision process (from problem modelling to visualization and analysis of the results): first, and more importantly, a DSS provides a methodological framework for the analysis and definition of the decision problem at hand and ensures an equitable evaluation of the alternative options considered, through a common formal procedure. In this way, a DSS, if correctly applied, is able to promote both accuracy and accountability of the decision process. Differently from other fields, DSSs in environmental engineering are not widespread: here, decisions are mainly taken based on personal thoughts, views or experiences, political reasons or only considering either costs or environmental impact, instead of conducting holistic integrated evaluations, as underlined in the survey of Achillas et al. (2013). First experience of DSS application in the environmental field dates back nearly 30 years (Saaty and Gholamnezhad, 1982); after this, DSSs were tentatively utilized for supporting decisions related to numerous waste streams (e.g. municipal solid wastes; wastewater; nuclear and radioactive wastes; construction and demolitions wastes; waste electrical and electronic equipment; and hospital wastes). A general conceptual analysis of environmental decision support has been recently provided by Reichert et al. (2015) where, however, an entirely quantitative approach is encompassed involving the estimation of probabilities values and the elicitation of numerical value and utility functions. In the water treatment domain, DSSs were applied for instance for assessing wastewater reuse options (Papa et al., 2015), for helping local authorities to select optimal wastewater treatment units location (Carroll et al., 2004; Vasiloglou et al., 2009; Alemany et al., 2005), or for the comparison of wastewater treatment technologies (Balkema et al., 2001; Garrido-Baserba et al., 2014a). Nevertheless, if DSS applications are anyway scarce and standardized approaches are missing (as pointed out also by Hamouda et al., 2009) in the field of water and wastewater management strategies (water being a central resource in all environmental policies), a fortiori this is even more true for sewage sludge management (a tentative proposal to simultaneously assess technical and economic issues is reported by Bertanza et al. (2015a) while Garrido-Baserba et al. (2014b) proposed the integration of economic and environmental criteria into a single composite indicator), notwithstanding this is becoming a key factor in the EU policies. In fact, the application of Urban Waste Water Treatment Directive 91/271/EC introduced more restrictive standards for effluent quality of municipal WWTPs (Waste Water Treatment Plants); as a consequence, a consistent increase of sewage sludge production has been recorded in last years and it is estimated that sewage sludge production could exceed 13 million tonnes by 2020 (Kelessidis and Stasinakis, 2012). This is a complex technical, economic [sludge management, typically, accounts for up to 30–60% of the total WWTP operating costs: Wei et al. (2003), Neyens et al. (2004)] and environmental issue. Proper reuse/disposal alternatives of growing sludge amounts must be found, while the legislative framework concerning waste and sludge management is still evolving: see for

instance 3rd Draft of Working document on sludge where, for Agricultural use, more intensive pre-treatments finalized to pathogens reduction seem to be necessary and new (organic pollutants) or more stringent (heavy metals) limits are proposed. Furthermore, the developments in scientific research should be taken into account: to this purpose, the following EU programmes have been recently issued for sludge management strategies: ROUTES (http://cordis.europa.eu/project/rcn/98727 it.html) and END-OSLUDG (http://cordis.europa.eu/result/rcn/172107 en.html). As a contribution towards the definition of appropriate decision procedures concerning sewage sludge management, this paper presents an experience of DSS use for the selection of the proper sewage sludge management option at regional level (the case study is a basin of about 560,000 inhabitants in Northern Italy). The experience was aimed both at achieving methodological advancements concerning the specific decision problem considered and at carrying out an empirical comparison of alternative approaches to DSS implementation. In particular, the classical make-or-buy dilemma of software engineering (Buchowicz, 1991) had to be considered, i.e. comparing the pros and cons of building an in-house solution, using basic development tools, versus acquiring a commercial software product available from the market. The work was carried out in cooperation with the public company (District Authority) that manages the water service of the whole district. The overall activity has been articulated in the following main steps: (i) selection of the general decision support methodology to be followed; (ii) analysis and modelling of the decision problem according to the methodology selected in step (i); (iii) selection of two DSS tools for comparison; (iv) implementation of the decision problem model developed in step (ii) within the tools selected at step (iii); (v) analysis and evaluation of the results with comparison between the two tools. Accordingly, on the methodological side the main contribution of the work consists in the outcome of step (ii), namely the definition of a systematic and articulated framework for the evaluation of alternative options for sewage sludge management (also called disposal scenarios). To this purpose more than thirty detailed evaluation criteria (or attributes), were defined and grouped in five categories (technical aspects; administrative aspects and normative constraints; socio-cultural factors; environmental impact; and costs). On the practical side, the comparison between the tools carried out in step (v) provides indications, which can be extended also to other decision problems in environmental engineering, about the pros and cons of either DSS approach and also about their commonalities.

2. Materials and methods 2.1. Methodology and problem analysis This section describes the choice of the methodology [step (i)] and the subsequent problem analysis and modelling [step (ii)]. The choice among the many decision support methodologies depends on the features of the problem at hand. For instance, in contexts where physical models capture all the relevant aspects, model-based decision support can be applied, while OLAP (OnLine Analytical Processing), Data Warehousing and Data Mining methods are appropriate to contexts where large collections of continuously produced operation data (e.g. market sales) are available (Berson and Smith, 1997; Wierzbicki et al., 2000). It goes without saying that the selection of a Sewage Sludge Disposal Scenario (SSDS) does not fall within the above-mentioned contexts. As typical in many environmental engineering decision

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problems, it can be regarded as a knowledge-based task where decision makers, on the basis of their expertise, have to take into account a variety of criteria, ranging from technical and economic considerations to social and legal issues. The assessment of some of these criteria can quantitatively evaluated (e.g. energy balances) while other criteria are qualitative by nature (e.g. the complexity of a legal authorization procedure). This leads to the adoption of the MADM (Multi Attribute Decision Making) methodology for the problem at hand (Yoon and Hwang, 1995). MADM is also known as MADA (Multi Attribute Decision Analysis) and is a branch of the more general field of MCDM/A (Multi Criteria Decision Making/Analysis); however, it must be acknowledged that the use of these acronyms is not always uniform in the literature. The main characteristics of problems suitable for MADM can be summarized as follows (Yoon and Hwang, 1995):

(1) Finite alternatives: the decision space consists of a finite number of alternatives that have to be screened and ranked. (2) Multiple attributes: the evaluation of each alternative involves multiple attributes, also called criteria, which may simply form a list or may be organized in a hierarchy. (3) Incommensurable units: attribute have different (quantitative or qualitative) and non-reconcilable units of measurement. (4) Attribute weights: Attributes may have different importance, expressed by weights on a quantitative or qualitative scale.

The SSDS problem fits all the items in the above list. In particular, as to point (1) the set of alternative technologies that can be considered is of course very small, and we have already commented earlier on points (2) and (3). Finally, the importance of each attribute in a specific SSDS problem may vary depending on context and site specific considerations. As a simple example, social acceptance issues can be much more critical for an installation in a densely populated region rather than in a nearly inhabited area. Following the adoption of MADM, problem analysis and modelling was carried out according to the four main characteristics listed above. As to the identification of the set of alternatives, four final disposal options were considered: Agricultural use, recovery in Cement Kiln, Incineration, and Wet Oxidation (WO). These are the most commonly used options all over the world, so that they can be considered as “consolidated” solutions (Kelessidis and Stasinakis, 2012). Co-treatment/disposal with municipal waste (e.g. co-incineration, co-composting etc.), which can be considered as “consolidated” options as well, was not included in our investigation, based on the a priori choice of the Management Staff of the public company (which in turn represents the decision maker), mainly due to political and administrative reasons. The identification of the relevant evaluation attributes has been much more demanding and is one of the main contributions of this paper. Completeness and non-redundancy are the fundamental properties that a set of attributes should satisfy. To achieve these objectives we adopted a hierarchical modelling approach: we first identified five main categories of attributes and then broke down each category into elementary attributes or further sub-categories if needed, as shown in Table 1. As it can be seen, the category “technical aspects” includes the greater number of attributes, which are grouped in three subcategories. Here, several practical issues have to be taken into account: • The reliability of a solution is assessed by considering not only the inherent reliability of the applied technologies or processes, but

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also based on the number of real scale facilities in operation up to now and the performance stability. • Flexibility and modularity are very important because sludge production changes over time (e.g. by seasons), so that a given option has to tackle variable influent loads. In addition, e.g. due to funding scarcity or predicted time-evolution of the amount of sludge to be dealt with, it may be the case that a plant has to be built step by step, so as to progressively increase the treatment capacity by adding parallel lines: modularity is then a crucial factor. • Complexity and integration with existing structures is another key point: whenever modifications to the existing facilities are required, many factors have to be carefully evaluated. Ten items have been identified as crucial aspects. Note that, at this level, only technical problems are being considered: economic aspects are dealt with elsewhere. Concerning “administrative aspects and normative constraints”, we want to emphasize that the degree of uncertainty of the normative framework has been included. This is a critical point when decisions have to be taken for long-term investments (e.g. 20 years), as in this case. For instance, the EU directive on sludge reuse in agriculture, which is in force up to now, was issued in 1986 (278/86/EEC). From that time, several unsuccessful update attempts were made by the European Commission (the most recent proposals being the 3rd Working document, April 2000, and the Working document sludge and biowaste, September 2010). The new directive has still to come and this poses a high degree of uncertainty for practitioners, because the prevailing orientation of the Commission (e.g. towards an increase of restrictions) has not been officially declared so far. “Socio-cultural aspects” are of course to be carefully considered, as they often represent a severe limitation to the installation of waste or wastewater treatment facilities (see for instance the NIMBY or BANANA syndromes). In this case, instead of real potential effects on human health, either people perception of risk (e.g. hazardous gaseous emissions) and/or disturbance (e.g. odours or noise) have to be pointed out. To this purpose, five attributes have been identified. In contrast with the previous point, “environmental assessment” is meant to quantify the real potential impacts on the ecosystem and human health. This is of course a very complex task. Specific techniques can be employed to this aim (e.g. IPPC 2007; USES-LCA; ReCiPe; WAR), the Life Cycle Assessment (LCA) being the most established and widely used procedure. Details on the main environmental impact assessment methods can be found in Carvalho et al. (2014). Moreover, innovative tools are being proposed: e.g. Papa et al. (2013) developed an assessment tool for applying the cross-media effects principles contained in the Integrated Pollution Prevention and Control—Reference Document on Economics and Cross-Media Effects (2006) for evaluating the ecological sustainability of wastewater treatment processes. Nevertheless, it is recognized that even adopting the most updated and sophisticated methodology, a complete and integrated evaluation is not applicable in practice: too many aspects have to be simultaneously taken into account and too many parameters should be measured or estimated. In this context we propose a simplified approach, by considering the main environmental factors that (1) are relatively easy to be estimated, and (2) highlight significant differences among different solutions. The “economic evaluation” has been performed through a preliminary design process: capital expenditures (CAPEX: depreciation) and operation expenditures (OPEX: Maintenance, Power consumption/production, Heat consumption/production, Reagents, Personnel, Analyses, Transportation of residues, Disposal of residues) were then calculated for each solution. Final cost (obtained as difference between costs and incomes) are expressed

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Table 1 Categories, sub-categories and elementary attributes proposed for the evaluation. Attribute

Abbreviation

Type of parameter

Measurement unit

TechRel

Qualitative

–

Num VarRange

Quantitative Quantitative

– %

Repeat NomSize Overlap

Boolean Boolean Quantitative

– – %

Road Footprint Hydraulic TechElectEn TechThermEn TechReagent AddTreat

Qualitative Quantitative Qualitative Quantitative Quantitative Quantitative Quantitative

– m2 – kW h/d kcal/d – –

Pers InterfLoad

Quantitative Qualitative

Manhours/y –

InterfOther

Qualitative

–

AuthorAdm

Qualitative

–

RegulUncert

Qualitative

–

Odour Traffic SocEmission

Qualitative Qualitative Qualitative

– – –

Noise SocOther

Qualitative Qualitative

– –

Category: environmental aspects Solid residues to be landfilled Recovered materials Variation in electric energy consumption Variation in thermal energy consumption Transportation Variation in reagents consumption (e.g. polyelectrolyte; coagulant; pure oxygen) Emission to air (different from CO2 ) from combustion processes

ResLandfill RecMat EnvElectEn EnvThermEn Transp EnvReagent EnvEmission

Quantitative Quantitative Quantitative Quantitative Quantitative Quantitative Qualitative

t/d t/d kW h/d kcal/d km/d kg/d –

Category: economic aspects Total cost (difference between costs and incomes) under most favourable conditions Total cost (difference between costs and incomes) under worst conditions

CostFav CostWorst

Quantitative Quantitative

D /tdewatered D /tdewatered

Category: technical aspects Sub-category: reliability Technology reliability (e.g. in relation to variability of influent wastewater/sludge characteristics) of the added facilities Number of full scale applications in EU Measured range of variability of process performance under typical working conditions Sub-category: flexibility/modularity Repeatability of functional units (parallel lines/modules) Possibility to choose the nominal size of the unitary module Overlapping between the nominal treatment capacity (range) and the range of variability of the incoming load Sub-category: complexity and integration with existing structures: required modifications, interference Required modifications to (or interference with) the existing road network Additional footprint for installation of new equipment Intervention required on the hydraulic network for installing the new equipment Variation in electric energy consumption Variation in thermal energy consumption Number of reagents used (e.g. polyelectrolyte; coagulant; pure oxygen) Number of additional sludge treatment stages (e.g. biological or chemical stabilization, thermal drying, others) Additional personnel requirement Potential interference of new technologies with the biological process of the existing WWTP in terms of hydraulic and/or biological overloads Potential interference of new technologies with the biological process of the existing WWTP in terms of e.g. effluent colour, inhibition of biological process, effluent refractory COD, odour emission Category: administrative aspects and normative constraints Complexity of authorization/administrative process (authorization for emissions, safety standards, certifications, etc.) Degree of uncertainty of the regulatory framework (vulnerability) Category: socio-cultural aspects Odours Road traffic intensification Emissions to air perceived as dangerous for health [e.g. organic micro-pollutants; fine particles (PM 2.5)] Noise Other

Table 2 Cost/income items: range of variability under the most favourable and worst conditions, respectively. Cost/income items

Measurement unit

Most favourable conditions

Worst conditions

Transportation Cost for Agricultural use of sludge Cost for Landfill disposal of sludge Cost for Cement Kiln use of dried sludge Cost for Landfill disposal of Incineration ashes Cost for Landfill disposal of WO residue Depreciation Unitary cost of power Unitary income from power sale Unitary cost of methane Unitary income from heat sale Unitary cost of pure oxygen

D /km/ttransported 1 D /tdewatered D /tdewatered D /tdewatered D /tdewatered D /tdewatered Years D /kWh D /kWh2 D /m3 3 D /kWh D /kg

0.20 40 60 10 60 20 20 0.145 0.145 0.50 0.025 0.08

0.25 60 100 40 100 60 15 0.185 0.185 0.80 0.00 0.12

1

Reference value for a truck with 30 t capacity. No subsidies have been considered because the combustion of sludge (which is a renewable source) is achieved by means of a considerable consumption of methane (due to the high water content). 3 Gas volume referred to Standard Conditions: 20 ◦ C, 1.013 × 105 Pa. 2

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as D /t of raw (dewatered) sludge. Reference values used for economic assessment were derived either from the analysed situation or from the literature or from previous experiences of the authors. In order to underline the most influential items and because of the intrinsic uncertainties in costs/incomes estimation, the economic assessment was conducted considering two scenarios, corresponding to the most favourable (where costs were minimized and incomes maximized) and worst (where incomes were minimized and costs maximized) conditions, respectively. Accordingly, two different attributes were considered for the economic aspect (see Table 2). The list of parameters proposed here, though developed for assessing sludge management strategies, can be used also for other applications, by adapting some items, as shown in Bertanza et al. (2015b) and Gianico et al. (2015), where the same conceptual frame was used for comparing different options for upgrading wastewater treatment plants. Depending on the scale of the case study, some issues that here have been considered as qualitative (e.g. required modifications to the road network), because we are working on a regional basin, could be numerically quantified for a more precise evaluation (this is possible for instance if the upgrading of a single WWTP is studied). It has to be noted that the same item can be included in different categories, as in the case, for instance, of reagent consumption. Nevertheless, this is not a redundancy: actually these factors do have relevance in several categories: e.g. a higher number or consumption of reagents may yield a higher number of operative tasks to be performed by operators (technical aspect), a greater environmental impact (environmental aspect), a higher cost and, probably, increased administrative duties. As to attribute weights, given that, as already mentioned, they depend on context specific considerations, we did not fix a priori values for them but rather considered several different scenarios of weight assignment in order to compare the corresponding decision outcomes: this will be described in Section 3.1.2. 2.2. Software tools In the context of MADM, alternative DSS approaches and the relevant software tools may differentiate in many properties, among which the following main non-independent aspects can be identified: • weight elicitation method, namely the procedure to acquire the values of attribute weights from domain experts; • ranking method, namely the procedure adopted to derive a score and the corresponding order of the decision options on the basis of the values and weights of their attributes; • analysis tools, namely the availability of post-evaluation functionalities, e.g. supporting the development of what-if scenarios and sensitivity analyses; • user interface, namely the actual interactive procedures offered to users for model definition, data entry, and result visualization and analysis. As mentioned in Section 1, the classical make-or-buy dilemma of software engineering (Buchowicz, 1991) had to be considered, i.e. building an in-house solution versus acquiring a commercial software product. In order to practically evaluate the pros and cons of making or buying in the SSDS context where, as to our knowledge, no previous experiences of DSS use (at least extended to all aspects as in the present work) are available, we explored either direction by both developing a spreadsheet-based DSS tool (in the following called “A DSS”) and by acquiring and applying to the SSDS problem

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a commercial DSS product, namely D-Sight (http://www.d-sight. com). The experiences with the two DSSs, including considerations about the four main items listed above, are described in the next subsections. Note that while our analysis includes a description of the specific tools adopted (namely an Excel® spreadsheet and the DSight web application based on PROMETHEE methods) most of the considerations we draw are tool-independent: we do not compare tool-specific properties but rather methodological and practical issues arising in the two scenarios. Hence the emerging indications have a general nature and are applicable also in other contexts of environmental engineering where in-house versus commercial decision support solutions have to be assessed. 2.2.1. The DSS developed by the authors (A DSS) As for the ‘make’ option, the authors implemented within a spreadsheet (actually Microsoft Excel® , but this detail is immaterial) an adapted and improved version of an evaluation procedure [described in a previous publication (Bertanza et al., 2015a), where principles and many details are reported], devoted to the assessment of technological alternatives for the upgrading of a single WWTP. In the present paper, only the aspects relevant to the comparison between the two DSS approaches are described. To reflect the hierarchy of attributes described in Section 2.1 and in particular in Table 1 a worksheet arranged in a tree structure was created and replicated for each alternative solution considered. Hence, in the case at hand, four worksheets with identical structure were created and their cells were filled in with the data of the corresponding solutions (Agricultural use, Incineration etc.). The general idea consists in providing the decision-maker an intuitive and immediate to grasp view of the evaluation of each solution through a colour representation. In detail, each cell corresponding to an elementary attribute or to a category of attributes may assume one of three colours: - green is used when the value of the attribute under evaluation is not problematic (it is considered positive or its impact can be neglected); - red is used when the value of the attribute raises a potentially serious criticality; - yellow is used to represent an intermediate situation, i.e. an average criticality. Colour assignment is first carried out on elementary attributes and then extended to categories. As to elementary attributes, the colour assignment method depends on the quantitative or qualitative nature of the attribute itself. For quantitative attributes (e.g. power consumption, transportation distance, number of full-scale plants in EU, etc.) a comparison with reference values is carried out: for each attribute, typically, two tolerance thresholds are set (minimum and maximum), so that three ranges originate corresponding to values lower than minimum, greater than maximum and falling in between. A lower than minimum value is assigned the green colour for “the lower the better” attributes (e.g. energy consumption) or the red colour for “the higher the better” attributes (e.g. recovered materials). Conversely, a greater than maximum threshold is assigned a red or green colour, respectively, while an in between value is assigned the yellow colour in every case. Details about the thresholds and ranges used are provided in Table 3. For qualitative attributes (e.g. authorization procedure complexity, public acceptance, etc.) the colour is assigned directly, based on expert assessment and relevant well-known facts. For instance authorization complexity is assigned green when the authorization procedure for the considered solution is light or

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Table 3 Thresholds and criteria for colour attribution adopted for the DSS developed by the authors (A DSS). ATTRIBUTE (abbreviation)

Synthetic statement or thresholds Assigned Colour Red

Yellow

Green

Low 0 >10%

Medium 1–10 5–10%

High >10 1–5%

No No < 90% and >110%

– – 90–95% and 105–110%

Yes Yes 95–105%

Important >15% Important >100% >50% ≥3 ≥2 >20% Important Important

Of little importance 5–15%; Of little importance 20–100% 10–50% 2 1 10–20% Of little importance Of little importance

Negligible <5% Negligible <20% <10% ≤1 None <10% Negligible Negligible

Category: administrative aspects and normative constraints AuthorAdm RegulUncert

Important Important

Of little importance Of little importance

Negligible Negligible

Category: socio-cultural aspects Odour Traffic SocEmission Noise SocOther

Important Important Important Important Important

Of little importance Of little importance Of little importance Of little importance Of little importance

Negligible Negligible Negligible Negligible Negligible

Category: environmental aspects ResLandfill2 RecMat2 EnvElectEn1 EnvThermEn1 Transp1 EnvReagent1 EnvEmission

>20% 0–15% <−400% <−100% <−45%; >100% Important

10–20% 15–50% −400–+400% −100% to −50% −45–0% −100–+100% Of little importance

0–10% >50% >400% >−50% >0% <−100% Negligible

Category: economic aspects CostFav CostWorst

>80 D /tdewatered >125 D /tdewatered

65–80 D /tdewatered 100–125 D /tdewatered

<65 D /tdewatered <100 D /tdewatered

Category: technical aspects Sub-category: reliability TechRel Num VarRange Sub-category: flexibility/modularity Repeat NomSize Overlap Sub-category: complexity and integration with existing structures: required modifications, interference Road Footprint1 Hydraulic TechElectEn1 TechThermEn1 TechReagent AddTreat Pers1 InterfLoad InterfOther

1 2

Percentages refer to the variation with respect to the present situation (e.g. additional footprint required for the installation of new equipment). Percentages refer to the amount of sludge produced.

simply not required, while red is used for those solutions which require specific authorizations, as in case of air emissions, highpressure units, risk of explosion, etc.). In order to derive category aggregate assessments from the assessments of elementary attributes or of subcategories, where present, a simple averaging method was adopted involving the conversion of colours into numbers and then back to colours as follows. First, each colour is converted to a representative number (1 for green, 0 for yellow, −1 for red), then their mean value is computed (for instance aggregating 2 green and 1 yellow yields 0.66), and finally the mean value is converted back to a colour as follows: red for a mean value <−0.33, yellow for a mean value between −0.33 and +0.33, green for a mean value > 0.33. The same procedure is applied to derive the final global score of one disposal option from the scores of each category. Note that this corresponds to assign uniform weights to the categories, and then in turn to sub-categories and elementary attributes. Synthetic results are presented to the user as a coloured table showing the evaluation of each disposal option for the five main categories and the resulting final score. The user can also

inspect the detailed evaluations of each option within the relevant worksheet. We can now give some general comments on the four aspects listed at the beginning of Section 2.2, while specific comments on the analysis of the case study will be given in Section 3. As to weight elicitation, no specific procedure was adopted: as a simple, yet reasonable, starting point, uniform weights were implicitly assigned to all categories. The ranking procedure is based on a simple discretization into three levels of each attribute followed by an averaging operation. While this may potentially suffer from some arbitrariness in the definition of the thresholds and also from the limited discriminating capability of such a rough discretization, it has the advantage of resorting to very simple concepts only, hence the assessment procedure is completely and easily traceable and can be rather easily presented also to third parties with no specific technical background. Clearly, no ready-to-use analysis tools are provided with the spreadsheet-based DSS but, to partially compensate this limitation, an advanced user can of course play at will with the many functionalities available within the spreadsheet, if s/he devises the need for some additional procedure. Similar considerations apply

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to the user interface and result presentation features, the DSS provides a minimal, if not Spartan, user interface which requires only, but necessarily, previous knowledge of basic spreadsheet features, including the ability to work with multiple worksheets. Presentation functionalities are minimal too, but a user mastering spreadsheet development might rather easily extend them. 2.2.2. The commercial DSS (D-Sight) As to the ‘buy’ option, we first carried out a preliminary analysis to choose a DSS software tool among the many available on the market. The analysis started from the list of “Software Related to MCDM” available on the web site of the International MCDM Society (http://www.mcdmsociety.org/content/softwarerelated-mcdm). A first selection phase was based on the set of functionalities offered (in particular concerning problem modelling and sensitivity analysis) and on the requirement of a still active product development, witnessed by the availability of recent upgrades. This led to identify three candidate products, namely 1000Minds, D-Sight, and Promax (Smart Decisions), on which a preliminary experimentation on a small, but realistic environmental decision problem, was carried out for a “hands on” verification of the desired properties in terms of functionalities offered and user friendliness. While all the three considered products showed generally appreciable features, this “small scale” experiment indicated that D-Sight was the best candidate for a “full scale” evaluation, described in the following. Adopting a commercial DSS implies, first of all, also adopting the methodology it is based on. The selected commercial DSS (D-Sight) is a web application based on PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) and GAIA (Geometrical Analysis for Interactive decision Aid) (Brans, 1982). PROMETHEE methods are able to rank a finite set of alternative using criteria, which are often conflicting. GAIA is a visualization tool, also used for sensitivity analysis, by which the decision maker may explore graphically the relative position of the alternatives in terms of contributions to the various criteria. For a detailed description of GAIA module the reader can refer to Mareschal and Brans (1988) and Brans and Mareschal (1994). In the environmental field, PROMETHEE and GAIA was used for the first time by Bellehumeur et al. (1997) and, subsequently, by Khalil et al. (2004) for evaluating the site suitability for sewage effluent renovation. The main advantages of using PROMETHEE methods are basically due to their mathematical properties and to their particular friendliness of use (Brans and Mareschal, 2005). The user interface of D-Sight is organized in sections (selectable through a list of tabs shown at the top of the screen, see Fig. 1) which guide step-by-step the user in the definition of the model of the decision problem and in the application of the PROMETHEE method, as described below. Section “1. Alternatives” basically requires the list of the alternative options to be evaluated; in our case the four disposal solutions previously mentioned were inserted. Section “2. Criteria” requires the list of the criteria to be used for the comparison of alternatives. Criteria can be grouped in categories and, following the model described in Section 2.1, we defined 32 elementary attributes, grouped into five categories, one of which (namely Technical aspects) in turn including three subcategories. Section “3. Users” allows the definition of a set of users that are granted online access to the decision problem (this is possible since D-Sight is a web application) with different roles and permissions. This feature has not been used in our experience but is potentially very useful in case of group collaborations. Section “4. Weights” requires the assignment of a weight to each criterion. As specified in Section 2.1, as a starting point we used uniform weights for the five main criteria and in turn uniform

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weights for the sub-categories, where present, and for the elementary attributes. Uniform weight assignment is made easy by a specific functionality “Equalize weights” within D-Sight. Further, in addition to direct manual assignment of the weights, D-Sight offers a weight elicitation method based on a pairwise comparison of the relative importance of the criteria (this and other details are omitted since they are beyond the scope of the present paper). Section “5. Parameters” requires a longer explanation since it lies at the heart of the PROMETHEE method adopted in D-Sight. The PROMETHEE method bases its evaluations on the differences of attribute values for all the possible pairs of alternatives. Let A1 and A2 be two alternatives (e.g. Wet Oxidation and Incineration), F be an attribute (e.g. Recovered material), and F(A1) and F(A2) be the attribute values for A1 and A2 (in the example the value of Recovered material for Wet Oxidation and Incineration, respectively). Then let us denote the value difference as DF (A1, A2) = F(A1) − F(A2). For simplicity, in the following description we will refer only to “the higher the better” attributes, so that DF (A1, A2) > 0 implies that A1 is preferable to A2 as far as the attribute F is concerned. Of course, a sign change is required in the opposite case. As a starting point, the system computes the value DF (A1, A2) for every elementary attribute F and for every possible pair A1, A2 (in our case with four alternatives, six different pairs have to be considered). Of course, each value DF (A1, A2) is expressed in the measurement unit corresponding to the attribute F if F is a numerical attribute. If the attribute is qualitative, it is required that each value in the scale is associated with a number (for instance if the qualitative scale is “Low”, “Medium”, “High” one may assign “Low = 1 , “Medium = 2 , “High = 3 ) and then the difference between the values is obtained as the difference between the corresponding integer numbers. As a consequence, all these differences are incommensurable and need to be converted to a common scale for the subsequent processing phases. To this purpose, PROMETHEE assumes the existence of an adimensional preference scale that can be expressed through real values in the interval [0,1], where 0 represents no preference and 1 means the maximum possible preference concerning a single attribute. The mapping of each difference value DF (A1, A2) into a preference value is obtained using a preference function associated individually to each attribute F. In detail, the user has to select, for each attribute, one preference function within a list of six predefined ones (Brans and Vincke, 1985). Actually, for our case study we used two of these six functions, namely “Usual” and “V-shape”. “Usual” is a very simple bipolar function: it returns 1 if DF (A1, A2) > 0, otherwise it returns 0. The “Usual” function is appropriate for qualitative attributes. The “V-shape” function has been used for quantitative attributes and is based on the selection of an attribute specific preference threshold (pF ), which represents the smallest deviation that is considered as fully decisive. Accordingly, the V-shape function returns 1 if DF (A1, A2) ≥ pF , while it returns DF (A1, A2)/pF if 0 < DF (A1, A2) < pF , and returns 0 otherwise. Using the preference functions and the criteria weights a positive and negative outranking flows are computed for each alternative A (intuitively they represent how much A is preferable to other alternatives, and how much the other alternatives are preferable to A) and then a final net outranking flow is determined and generates a complete ranking of alternatives. For the mathematical background the reader can refer to Brans (1982) and Brans and Vincke (1985). The preference functions used for the case study are reported in Table 4. For the attributes using the “V-Shape” preference function, the second column specifies the actual extremes of the range where the linear variation of preference from 0 to 1 occurs.

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Fig. 1. D-Sight user interface.

After this important modelling step, Section “6. Evaluations” requires the user to provide the values of the attributes for each alternative. Then Section “7. Analysis” applies the ranking method described above and provides several presentation and further analysis features: those relevant to our case study will be described in Section 3.1.2. Finally, several report generation capabilities are provided in Section “8. Report”. 2.3. Case study description Data used in this work were provided by a District Authority (DA) in Northern Italy, which commissioned a study with the aim of investigating the suitability of different options for sewage sludge management. To this aim, four final disposal options/alternatives were considered: Agricultural use, recovery in Cement Kiln, Incineration, and Wet Oxidation (WO), as anticipated in Section 2.1. The annual amount of sewage sludge to be disposed of (42,395 t/y, mean dry content of 23.5%) was calculated on the basis of the operational data of the 60 WWTPs (largely characterized by a nominal capacity < 100,000 person equivalent, PE; total treated load = 560,000 PE) managed by the DA. Moreover, the main sewage sludge characteristics (e.g. heavy metal content; carbon, nitrogen and phosphorous content; volatile and total solids ratio; etc.) were compared with Regional standards in force for reuse in agriculture. From this comparison, the sludge produced in four WWTPs (2,032 t/y) was found to be unsuitable for agricultural application and landfill disposal was selected. At present, the thickened sludge coming from 28 small plants (<2,000 PE) is transported by track in three larger plants where centralized mechanical dewatering facilities are in operation. This status was agreed with the DA managers to be unchanged also in the studied scenarios. In the following, for each scenario, the main assumptions are reported. 2.3.1. Agricultural use Dewatered sewage sludge collected from the WWTPs was supposed to be transported to three facilities authorized for the

treatment on behalf of third parties (located at a distance lower than 150 km). In this case, no additional treatments are needed before the final disposal and the compliance with the agricultural constraints has to be guaranteed. As mentioned above, the sewage sludge that not match the acceptability limits (500 t of dry matter/y) was supposed to be disposed in a landfill for non-hazardous wastes sited within 100 km. The annual distance covered by the trucks (30 t of capacity) for sludge disposal was calculated to be 113,100 km.

2.3.2. Recovery in a Cement Kiln In this scenario, sewage sludge was supposed to be disposed in a Cement Kiln as an alternative energy source. In order to achieve the required amount of dry content in the sludge (assumed equal to 90%), the installation of a centralized thermal drying system (estimated evaporating capacity of 4,000 kg of water per hour) was considered. This facility was supposed to be sited in a “reference” WWTP (which has a nominal capacity of 120,000 PE and was selected together with the DA managers, based on plant configuration, residual treatment capacity, location etc.), collecting the dewatered sludge produced in the whole district. Four Cement Kilns within 150 km from the reference WWTP are in operation. Their theoretical capacity is largely above the amount of dried sludge to be disposed (11,089 t/y). Under the aforementioned assumptions, 57,400 km/y have to be covered by the trucks for sludge disposal.

2.3.3. Incineration This scenario consists of the installation of a fluidized bed incinerator at the reference WWTP, where, as in the previous case, the dewatered sludge produced in the whole district is transported. Solids residues to be landfilled (4,990 t/y) were calculated assuming, on the basis of real data, a ratio between volatile solids over total solids (VS/TS) equal to 0.65 and a water content of the bottom ashes of 30% (due to their humidification for dusting control). Disposal in a landfill for non-hazardous wastes sited within 100 km was considered and 48,300 km/y were estimated for this purpose.

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Table 4 Description of the preference functions adopted within the commercial DSS. Synthetic statement or thresholds (“Usual”) Preference linear variation range (“V-Shape”)

Preference function

Low, medium, high 0; 1–10; >10 1–5%; 5–10%; >10%

“Usual” “Usual” “Usual”

Yes; no Yes; no Unsatisfactory (<90% and >110%); sufficient (90–95% and 105–110%); optimal (95–105%)

“Usual” “Usual” “Usual”

Sub-category: complexity and integration with existing structures: required modifications, interference Road Footprint1 Hydraulic TechElectEn1 TechThermEn1 TechReagent AddTreat Pers1 InterfLoad InterfOther

Negligible; of little importance; important <5%; 5–15%; >15% Negligible; of little importance; important <+20%; +20–100%; >+100% <+10%; +10–50%; >+50% 0–6 0–5 <+10%; +10–20%; >+20% Negligible; of little importance; important Negligible; of little importance; important

“Usual” “Usual” “Usual” “Usual” “Usual” “V-Shape” “V-Shape” “Usual” “Usual” “Usual”

Category: administrative aspects and normative constraints AuthorAdm RegulUncert

Negligible; of little importance; important Negligible; of little importance; important

“Usual” “Usual”

Category: social aspects Odour Traffic SocEmission Noise SocOther

Negligible; of little importance; important Negligible; of little importance; important Negligible; of little importance; important Negligible; of little importance; important Negligible; of little importance; important

“Usual” “Usual” “Usual” “Usual” “Usual”

Category: environmental aspects ResLandfill RecMat EnvElectEn EnvThermEn Transp EnvReagent EnvEmission

0–100% 0–100% 0–8.4 × 106 kW h/y 0–13 × 106 kW h/y 0–115,000 km/y 0–6 Negligible; of little importance; important

“V-Shape” “V-Shape” “V-Shape” “V-Shape” “V-Shape” “V-Shape” “Usual”

Category: economic aspects CostFav CostWorst

56–83 D /tdewatered 81–133 D /tdewatered

“V-Shape” “V-Shape”

Attribute (abbreviation) Category: technical aspects Sub-category: reliability TechRel Num VarRange Sub-category: flexibility/modularity Repeat NomSize Overlap

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Percentages refer to the variation with respect to the present situation (e.g. additional footprint required for the installation of new equipment).

2.3.4. Wet Oxidation treatment In this scenario, the whole sludge produced in the district was supposed to be treated in a Wet Oxidation plant to be realized at the reference WWTP. According to the data reported in Bertanza et al. (2015b), for WO residue a dry solid content of 60% was assumed after mechanical dewatering; consequently, 6,362 t/y have to be disposed in landfill for non-hazardous wastes and 51,400 km/y have to be covered by the trucks. 3. Results and discussion As reported in Section 1, the decision problem model described in Section 2.1 was implemented within the selected tools (Section 2.2) and the case study (Section 2.3) was analysed. The presentation and evaluation of the obtained results are reported in Section 3.1, while Section 3.2 deals with a comparison between the two tools. 3.1. Assessment of sludge management strategies 3.1.1. Uniform weight results In Fig. 2, a summary of the evaluation results, under the assumption of uniform weights, by the application of A DSS and D-Sight, respectively, is reported. The figure is organized in two columns:

the left one refers to A DSS, the right to D-Sight. Each column shows six bar graphs: the first five ones concern the single categories (technical aspects, environmental aspects etc.); the sixth one shows the final evaluation (total score), which is derived from the previous ones. It has to be underlined that different numerical scales were adopted (−1; +1) for A DSS and (0; 100) for D-Sight. Nevertheless, in both cases, higher score means better result. In addition, for the presentation of results obtained with A DSS, the colour code (as described in Section 2.2.1) has been used. As a first general comment, we can observe that the rankings of the four alternatives are always in agreement between the two tools both concerning the single categories and the final score. Further comments on the comparison between the tools will be presented in Section 3.2. We now provide a discussion on the main outcomes presented in Fig. 2, by briefly analysing each category and then the final evaluation. Additional and more detailed comments are reported in the Supporting Information. Concerning “Technical aspects”, results show that Agricultural use is preferred. It has to be noted, however, that, due to the inherent approximation of the evaluation, small numerical differences should be regarded as negligible. Accordingly, we can say that Agricultural use, Incineration and Cement Kiln disposal are practically equivalent. On the contrary, WO appears less preferable, mainly

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Fig. 2. Comparison among sludge management strategies: results obtained with A DSS (left column) and D-Sight (right column).

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because of criticalities that arose for complexity and integration with existing structures. “Administrative aspects and normative constraints” appear to be crucial for the “Cement Kiln” solution, while the three remaining are equally less problematic in this respect. The evaluation of the “Socio-cultural aspects” evidenced, as expected, that Agricultural use is perceived as the best (being “natural”) choice together with WO (considered as a low impact technology). On the contrary, Cement Kiln and in particular Incineration are seen as polluting and dangerous activities. As far as “Environmental aspects” are concerned, Agriculture appears to be the best solution, essentially due to the almost complete material recovery together with an unmodified power and reagent consumption with respect to the present situation; the greater impact of transportation is then balanced. The three remaining options have some drawbacks (energy consumption for Cement Kiln; missed material recovery and gaseous emissions for Incineration; missed material recovery and power-reagent consumption for WO) leading to a substantially equivalent evaluation. The score of “Economic aspects” is the average of cost estimation carried out under the most favourable and worst conditions, respectively. Concisely, Agriculture is always the most convenient solution, while WO is less favoured. It must be underlined that, under the most favourable conditions, income from residual heat selling (for all solutions except Agriculture) has been included. Note that, in general, this option might not be feasible if infrastructures and users in the surroundings are not available. In our case study this item has a significant positive influence on the ranking of Incineration. Moreover, solid residues recovery (Incineration and WO) and liquid residue anaerobic digestion (WO only, as described in Bertanza et al., 2015c) might further decrease the costs. The “Total Score” represents a synthetic evaluation resulting from the average (A DSS) or sum (D-Sight) of scores attributed to single aspects: the same weight for every item has been adopted, as discussed previously. Agricultural use of sludge is clearly the best option, reaching the highest scores within each of the abovementioned issues. The basic equivalence of the remaining solutions is clearly showed by the colour code that masks slight differences highlighted by numerical scores. It is worth noting that, neglecting economic issues, WO and Incineration (similar scores) are preferable with respect to Cement Kiln.

3.1.2. Sensitivity analysis In order to verify how a different weight assignment would affect the overall evaluation, a further analysis was conducted, using a specific functionality offered by D-Sight for weight adjustment. The idea was to change the weight of each category one by one over the whole range from 0 to 100%, while redistributing the remaining weights uniformly among the other categories. For instance, when technical aspects are given weight 0, each other category has a weight of 25%, while of course when technical aspects are given 100%, all the other categories are weighted 0. As in intermediate example (see Table 5) a weight 11% given to technical aspects leads to a weight of 22.25% to the other categories. We were interested in identifying the values where a change in the preference ordering occurs. These values, along with the extreme values 0 and 100%, are shown for each category in Table 5. Note first that preference order changes never involve the best preferred option, namely Agricultural use, which, as shown previously, is the favourite option for all single aspects. This shows that, in this case study, the indication of the preferred solution is quite robust.

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Results reported in Table 5 additionally show, in particular, that: - by giving a weight to technical aspects at least of 11%, WO is ranked last; this is due to the low score attributed to this solution in technical issues, as shown in Fig. 2 and previously discussed; - the raise of importance of administrative aspects and normative constraints, from 24% on, disadvantages the Cement Kiln scenario; this solution was in fact given the worst score, while the other three were considered to be equivalent; - with the progressive increase of the importance of socio-cultural aspects WO is rewarded at the expense of both Cement Kiln and Incineration; similarly, Cement Kiln prevails over Incineration; - environmental aspects are critical for Incineration and Cement Kiln options, so that increasing their weight above 67% improves the ranking of WO; - finally, a greater weight of costs tends to diminish the ranking of WO. Again, note that small numerical differences (see for instance Cement Kiln versus Incineration when the weight of environmental aspects is 100%) should be considered negligible. 3.2. Comparison between the software tools In this section, we compare the experience with the two software tools first from the viewpoint of the results produced in the specific case study considered and, then more generally, from a methodological perspective. 3.2.1. Comparison of results As to the first point, it is not guaranteed, in general, that even using the same decision problem model, the same weights, and the same data, two different decision support tools provide the same ranking of the considered options. This critical situation did not occur in our case: as shown in Fig. 2, the same ordering is produced by the two tools both at the level of each main category and of the final global assessment. This could be seen as a confirmation that the two tools have common conceptual bases and, in a sense, could be regarded as variations of fundamentally the same approach. However, it has to be remarked that the coincidence of rankings might also be due to the specific features of the case at hand (in particular, the prevalence of agricultural reuse is rather neat) and the commonality mentioned above of course requires further verifications. While comparing the preference orderings produced by the two tools is straightforward, confronting the numerical values requires more caution, as they are expressed in two different scales with different meanings. In A DSS each value in the (−1; +1) interval simply corresponds to the average of a set of qualitative evaluations (green, yellow, or red) previously transformed into numbers through a simple but rather rough conversion method. Thus the values associated with an alternative depend only on the properties of the alternative itself and are not affected by variations in the properties of other alternatives. In D-Sight each value in the (0; 100) interval results from the outranking flow computation (see Section 2.2.2) that can be basically regarded as a sophisticated multiparty relative comparison method. This means that the value associated with an alternative depends also on the properties of the other alternatives, e.g. differently from A DSS, if one changes the value of an attribute of Wet Oxidation, in principle the values of all the four alternatives might be affected.

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Table 5 Variation of the preference order of solutions depending on weights attributed to the evaluated aspects. Data processed with D-Sight.

Given the above considerations, converting one of the scales in the other one for the sake of a detailed numerical comparison would make little sense. Numerical values can be however considered in order to refine the considerations about the rankings in terms of distance between the various options. Roughly this gives an idea of whether the produced ranking can be affected by small variations in the input data or in the problem model: in case the distance is negligible two alternatives should be regarded as equivalent. Developing this analysis in detail requires in turn to define a formal notion of negligible distance (among the many possible ones) and is beyond the scope of the present paper. Some semi-qualitative observations can anyway be drawn. First, with both tools in the total score Agriculture has a definitely better evaluation than all other options, followed by Incineration which less neatly prevails on Cement Kiln and Wet Oxidation, which can be regarded as almost equivalent. Looking at the single category evaluations we may observe similar concordances in most cases and some small difference as detailed below. As to technical and administrative aspects, the two tools fully agree. As to socio-cultural aspects, Agriculture and Wet Oxidation are at the same level according to A DSS, while Agriculture is slightly preferred in the D-Sight evaluation. In both evaluations they are definitely preferred to the other two options, the Cement Kiln being slightly preferred to Incineration.

As to environmental aspects, in A DSS Agriculture is definitely preferred to the other options, which are exactly at the same level. In D-Sight the preference for Agriculture is still clear but less neat, and a very small preference to Wet Oxidation over Incineration and Cement Kiln could be identified. As to economic aspects, in A DSS Agriculture is again definitely preferred, Incineration and Cement Kiln are at the same intermediate level and Wet Oxidation has definitely the last position. In D-Sight the pattern is the same with a slight preference for Cement Kiln over Incineration. The small differences pointed out in some category evaluation (in particular D-Sight has some small but not negligible differences for some pairs of options having exactly equal scores in A DSS) can be explained by the fact that in A DSS several rather rough discretization steps are applied, which may suppress small differences (but may also amplify them) in some cases. Altogether, in our case study it does not seem that this different behaviour may affect the robustness of the final outcome. 3.2.2. The make-or-buy dilemma Concerning the make-or-buy dilemma, with the two options corresponding to A DSS and D-Sight respectively, the following considerations can be drawn, orderly presented in relation to the main phases of our experience. At the starting point, before the specific case study is tackled, the ‘buy’ option requires a learning period of the selected tool (in our case D-Sight) while the ‘make’ option was (and typically is) based

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on tools the domain experts were already familiar with (in our case a spreadsheet). This learning effort can be a serious obstacle in case there are stringent time constraints and/or no reuse of the tool is foreseen in the future. It can be regarded as an investment instead, if one intends to apply the same tool to other decision problems at some later moment. As a matter fact, D-Sight revealed to be well-designed and learning to use it was quite easy also for team members with no specific background in decision support and computer science. Mastering the underlying theory however was a different matter, as commented later. Coming to the modelling phase, namely the definition of the relevant attributes organized into categories and sub-categories to form a hierarchical structure, both tools did not show any specific conceptual or practical difficulty. It has to be remarked however that in the A DSS the hierarchical structure had to be “hardwired” within a template worksheet, to be replicated for each of the four alternatives, while in D-Sight specific functionalities to define the attributes and their hierarchy are available. This means that revising the attribute hierarchy after the first definition can be very laborious, time-consuming and also rather error-prone in the ‘make’ option, while it is straightforward in the ‘buy’ option. Happily, we did not need to revise the attribute hierarchy during our experience. The definition of attribute weights points out another significant difference in the manageability of the two tools. In our case in the ‘make’ option we only considered (implicitly) uniform weights for all categories and for sub-categories and attributes within categories. Embedding within A DSS a mechanism to adjust weights at the level of elementary attributes would have been technically feasible, but, again, time consuming and error-prone, while it would have been easier, but still not totally free of complications, to allow adjustable weights at the level of the five main categories only. On the contrary, arbitrary weight definition is supported seamlessly by specific functionalities in D-Sight. The mechanism to produce the rankings deserves several comments. First, the “home made” mechanism adopted in A DSS is very simple and intuitive but not backed by any previous work in the scientific literature, while the methodology used in D-Sight, which is extensively documented in the literature, is definitely more complex and less immediate to grasp. Further it requires an additional activity, namely the selection of the preference function for each attribute, as explained in Section 2.2.2. As a consequence, domain experts felt definitely more comfortable with the use of A DSS and the interpretation of its results, while D-Sight was rather perceived as a black box, featuring a not easily traceable relationship between the input data and the final rankings. In this sense, at least in this case, ‘make’ appears to be preferable to ‘buy’ in terms of accountability and explainability, two very important properties given that the decision makers bear the responsibility of the final choice and may be required to justify it to other stakeholders. It must be noted however that this observation must be accompanied by some caveats. First, accountability and explainability in A DSS refer to a fixed evaluation scenario: as it will also be commented later, supporting the results with an additional sensitivity analysis is definitely more difficult in A DSS than in D-Sight. Further, the quite drastic discretization corresponding to the thresholds adopted for quantitative attributes in A DSS may raise some accuracy issues: small numerical differences straddling a threshold border may be amplified, while more significant differences inside the range corresponding to one colour are nullified. This problem does not occur in D-Sight, where the use of the “Vshape” preference function ensures that numerical differences have

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a proportional effect. As a matter of fact, during some preliminary tests in preparation to our experience, it emerged that the discretization effects explained above caused differences in the final rankings produced by A DSS and D-Sight. It emerges that the selection of the thresholds in A DSS is very critical for the soundness of the final results and this can be overlooked at the moment of assessing the final results, where attention may be more attracted, e.g. by weights. In D-Sight the process of generation of the final rankings appears to be smoother and no such (possibly hidden) critical choices are present. This led us to devote a special care to the selection of A DSS thresholds and, for a couple of attributes, to revise them in the light of the results of the preliminary tests. Altogether, we may synthesize these comments by stating that while domain experts are definitely more confident with the ‘make’ option, they should be aware of its inherent, but sometimes not apparent, limitations. Finally, concerning the presentation and analysis of results, A DSS offers a synthetic colour based representation which is immediate to grasp and basically nothing else: including additional functionalities would require a significant development effort, with serious issues concerning software robustness and the possible presence of bugs. D-Sight offers a variety of result presentation options, even beyond the needs of the present project, and, more importantly in our case, specific functionalities for result analysis. For instance, the sensitivity analysis presented in Section 3.1.2 was developed using the weight adjustment functionalities of D-Sight, carrying out a similar analysis within A DSS would have required a huge (and practically unfeasible) effort. Having discussed the two tools with reference to all the phases of our experience, we can add some comments on some common aspects that are not phase specific. As to usability, while a detailed analysis is beyond the scope of this paper, it must be acknowledged at a general level that, after the learning phase, the functionalities offered by the user interface of D-Sight are more convenient and less error-prone than directly filling in the cells of a complex set of worksheets as required in A DSS. Some possible usability improvements for D-Sight emerged anyway during our experience. As to reuse in other decision problems, using D-Sight in other contexts appears to require a limited effort after a first experience has been completed. On the contrary, reapplying A DSS to a different problem involves rewriting or heavily modifying the worksheets used, a quite laborious and error-prone activity. In particular, the size of the decision problem may play a crucial role. The case study we have presented, involving a few tens of elementary attributes and a few categories and technical options, turned out to be appropriately manageable with both tools. With a definitely smaller size problem (say, less than ten attributes) D-Sight is of course applicable but might appear to be overkill, while for a definitely bigger size problem (say ten categories and/or more than one hundred attributes) the A DSS approach might reach its practical applicability limits and run into serious problems of effort effectiveness. Finally, since D-Sight is a web application some confidentiality and reliability concerns may arise, given that the decision problem model and the case specific data are stored on a server managed by the application provider. These aspects were not considered critical in our case study, but could be an issue in other situations. Clearly, other commercial DSS tools that are not web-based are not affected by this potential criticality. It must also be noted however that if the decision problem involves, as it is often the case, multiple experts or stakeholders, a web-based system like D-Sight greatly facilitates their cooperative work. Moreover, the immediate availability of the latest software version is automatically guaranteed.

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4. Conclusions We present here the main conclusions of this work, focusing first on the comparison among sludge management strategies and then commenting on the experience with the DSS tools. As regards the four examined sludge recovery and disposal options, it has to be underlined that these findings cannot be applied to any situation, since they are strongly affected by case specificity: depending on local needs, values and importance of single factors could be different, thus affecting the final result. • Agricultural use does not require any investment and, at the same time, allows the complete recovery of material resources; instead, its drawbacks consist of the uncertainty of the regulation framework (modifications are expected in the next future), the strong dependence on market trends (which set the price) and finally the impact of sludge transportation to treatment facilities. Anyway, this is so far the best solution for the studied district. • Material and energy recovery is the main advantage when treating sludge in a cement factory. On the other hand, the feasibility of this option is affected by their receiving capacity/availability; moreover, a centralized plant for thermal drying has to be built and operated (leading to consequent management problems, such as additional authorizations, personnel, energy consumption, etc.). As far as the present market conditions are regarded, the cost of this option is higher than that of agricultural disposal only. • Sludge incineration ensures the certainty of the final disposal for sludge and residues (possibly to be recovered) and energy production (both power and thermal). Drawbacks likely involve population disagreement; moreover, such a plant is characterized by a high technical and operation complexity. Again, total costs are scarcely competitive, now, unless residual heat is recovered, thus leading to economic convenience. The solid residue recovery might also contribute to a slight cost reduction. • Wet Oxidation does not generate adverse gaseous emissions; final disposal of sludge and residues (possibly to be recovered) is guaranteed. Instead, the wastewater treatment plant hosting the WO facility would result highly overloaded due to the need of treating supernatants deriving from Wet Oxidation (an alternative option might consist in anaerobic digestion and biogas production). Unfortunately, also this solution is characterized by plant complexity and high costs. Solid residues recovery together with liquid residues anaerobic digestion might decrease the cost of this technological approach. The above comments actually derived from a detailed analyses of the case study that came up to the ranking of the four alternatives finally leading to this preference order: Agricultural use  Incineration > Cement Kiln ∼ = Wet Oxidation. The assessment framework that was developed is based on the evaluation and processing of about 30 parameters, which were identified considering technical, administrative/regulation, social, environmental, and economic issues. Data processing, according to criteria defined by the authors, was carried out in parallel by means of a DSS implemented on a worksheet and a commercially available software. As far as the use of DSSs within decisional processes similar to the one considered in the case study, the considerations developed in Section 3.2 provide some useful indications but certainly not a definite final answer to the make-or-buy dilemma. As a matter of fact, this is an instance of the well-known decision-making paradox (Triantaphyllou and Mann, 1989): given that there are multiple decision support methods available, one would need a decision support method to select the “best” decision support method. The notion of “best”, however, is case specific given that in different

contexts the pros and cons evidenced above for the two options may have different importance. We would like to promote an alternative perspective however. As a matter of fact, our experience suggests that for MADM problems similar to the one we considered, one may avoid the make-or-buy dilemma by simply following both directions, i.e. adopting a sort of (simple) make-and-buy (then) approach. Let us provide some more specific comments on this. First, while following both directions involves of course an increase of human resources and costs, it must be observed that it may lead to a more informed and more accountable decision, an advantage which, in critical contexts like environmental planning where decision effects have a huge social impact and may span over decades, is definitely worth the additional efforts mentioned above. Second, starting with a relatively simple home-made tool, like a spreadsheet, allows domain experts to focus on problem modelling without being possibly distracted or biased by the complexity of a sophisticated tool and to produce a first tentative ranking whose motivations are well understood. Of course a home-made tool cannot aim at reproducing the advanced features of a commercial DSS, and, in particular, hardly can be used to assess the robustness of the produced ranking or to carry out an extensive sensitivity analysis. Then the application of a commercial DSS on basically the same decision problem can provide a validation of the first tentative ranking and improve its assessment. If, as in our case study, both approaches essentially produce the same results one gets a decision recommendation, which in addition to being well understood, can be considered robust and whose applicability limits (e.g. in terms of which variations of the weights would lead to a different recommendation) are characterized. If instead some significant differences emerge, domain experts are prompted to identify their causes and, then, to select in a more informed way the ranking they consider more appropriate or, possibly, to revise some aspects of the initial model. While we cannot certainly claim universal applicability for the make-and-buy approach in environmental MADM problems, our experience suggests that, at the price of an acceptable additional effort, it can be very fruitful and encourages further practical experimentations in this direction. As a final remark, we want to underline that any DSS tool may represent a significant added value only if properly employed. Clearly, the final recommendation completely depends on model development, data collection process, and result interpretation. In all these phases the domain experts play a fundamental role. On the contrary, inexperienced users could yield meaningless outcomes even if using a DSS tool in a formally correct way. In particular, in our working team the presence of experts from environmental engineering and computer science was very fruitful: this interdisciplinary approach, in the opinion of the authors, should be encouraged whenever complex problems have to be tackled.

Acknowledgments Case study was assessed by means of the author’s DSS (A DSS) built in MS Excel® while the commercial DSS (D-Sight) was used only for the comparison here reported. Authors acknowledge Eng. Giulia Comini for her valuable support in preliminary evaluation of commercial DSS tools.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.resconrec.2016. 03.011.

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