Stephen Creff
email: stephen.creff@ensta-bretagne.fr

Joël Champeau
email: joel.champeau@ensta-bretagne.fr

PLiMoS, a DSML to Reify Semantics Relationships: An Application to Model-Based Product Lines

Keywords: Model-Based Engineering, Product Lines, Dependencies, Semantics Relationships, DSML

In the Model-Based Product Line Engineering (MBPLE) context, modularization and separation of concerns have been introduced to master the inherent complexity of current developments. With the aim to exploit efficiently the variabilities and commonalities in MBPLs, the challenge of management of dependencies becomes essential (e.g. hierarchical and variability decomposition, inter-dependencies between models). However, one may observe that, in existing approaches, relational information (i) is mixed with other concerns, and (ii) lacks semantics and abstraction level identification. To tackle this issue, we make explicit the relationships and their semantics, and separate the relational concern into a Domain Specific Modeling Language (DSML) called PLiMoS. Relationships are treated as first-class entities and qualified by operational semantics properties, organized into viewpoints to address distinct objectives, e.g. product derivation, variability consistency management, architectural organization. This paper provides a description of the PLiMoS relationships definition and its implementation in a model-based product line process using two variability languages: Feature Model and OVM. The independence with variability and core assets modeling languages provides benefits to cope with the product line maintenance.

Introduction

Product Line Engineering (PLE) [START_REF] Clements | Software Product Lines: Practices and Patterns[END_REF] aims at increasing product quality, enhancing time-to-market, and reducing production costs. Product Lines (PLs) promote intra-organizational reuse through the explicitly planned exploitation of similarities (commonality) among related products. A PL is a family of products built in a prescribed manner from a common set of core development assets [START_REF] Pohl | Software Product Line Engineering: Foundations, Principles and Techniques[END_REF]. The explicit modeling and management of the variability and commonality is a key point in PLE [START_REF] Pohl | Software Product Line Engineering: Foundations, Principles and Techniques[END_REF]. Several techniques, and thus Domain Specific Languages (DSLs), have been defined to explicitly model the variability. Some of them are defined as orthogonal approaches, dealing with the variability as a separated viewpoint connected to core assets to automate the product derivation. The most popular, and historical one introduced by Kang et al. [START_REF] Kang | Feature-oriented domain analysis (foda) feasibility study[END_REF], is the Feature Model (FM), another one, also used later on in this paper, is the Orthogonal Variability Model (OVM) language [START_REF] Pohl | Software Product Line Engineering: Foundations, Principles and Techniques[END_REF].

In the Model-Based Product Line Engineering (MBPLE) context, clear modularization and separation of concerns have been introduced to master the inherent complexity of current developments. With the aim to exploit efficiently the variabilities and commonalities in model-based PLs, the challenge of management of dependencies becomes essential (e.g. hierarchical and variability decomposition, inter-dependencies between models). Efforts have been provided in existing variability modeling techniques to separate concerns, either by defining categories of features and providing classifications (e.g. [START_REF] Pohl | Software Product Line Engineering: Foundations, Principles and Techniques[END_REF][START_REF] Kang | Form: A feature-oriented reuse method with domain-specific reference architectures[END_REF][START_REF] Metzger | Disambiguating the documentation of variability in software product lines: A separation of concerns, formalization and automated analysis[END_REF]), or by defining intra-variability model relations (e.g. [START_REF] Kang | Form: A feature-oriented reuse method with domain-specific reference architectures[END_REF][START_REF] Lee | Feature dependency analysis for product line component design[END_REF]) and inter-model ones (e.g. [START_REF] Sinnema | Covamof: A framework for modeling variability in software product families[END_REF][START_REF] Metzger | Disambiguating the documentation of variability in software product lines: A separation of concerns, formalization and automated analysis[END_REF]). A survey on of the variability modeling approaches in the literature that handle explicit relationships between variable elements [START_REF] Creff | Relationships in variability modeling approaches: A survey and classification[END_REF] shows that many authors faced the need to define and use specific relationships combined to variability languages. Relationships are defined for various purposes such as: Hierarchical refinement, Configuration, Specific Usage, and Inter-concerns management. Nevertheless, the relationships are often buried in the variability model and can lack semantics: their description can be quite vague and imprecise, especially when describing the specific usages. Up to now, existing approaches do not consider relational information as a global preoccupation in the MBPL. However, this information represent the core concepts necessary to improve the management of the MBPL and its evolution. For example to integrate extensions in the variability models, or to take into account evolution of the application models and modeled core assets of the PL. Therefore, efforts have to be provided to (i) clarify all the relationships, (ii) their semantics and (iii) their corresponding usages, in order to federate all the models of the MBPL modeling space.

To tackle this issue, we make explicit the relationships and their semantics, and separate the relational concern into a Domain Specific Modeling Language (DSML) called PLiMoS. Relationships are treated as first-class entities and qualified by operational semantics properties, organized into viewpoints to address distinct objectives, e.g. product derivation, variability consistency management, architectural organization. We consider PLiMos as an independent relational language to federate models of the MBPL, supporting dependency processings and semantic consistency checkings. The language is amalgamated with existing variability modeling languages to introduce model management capabilities, allowing interrelating heterogeneous variability languages. This paper provides a description of the PLiMoS language and an implementation in a model-based product line process using two variability languages (Feature Model, and OVM), and existing modeling core assets (models at different levels of abstraction). The independence with variability and core assets modeling languages provides benefits to cope with the product line maintenance and evolution.

The remainder of this paper is organized as follows. In the second part, as the root of our work, we give a brief overview of the background on relation-ships in PLE, describing the lack of related approaches and our motivations. In the third section, we present and formalize the PLiMoS DSML. In the fourth section, we describe an application of the language to two variability modeling languages (FM and OVM) for illustration purpose. The fifth section outlines the implementation, providing details on the concrete syntax and tooling. The last section draws our conclusion and introduces future works.

Relationships in PLE

Modularization and Separation of Concerns (SoC) has been used as an effective solution to tackle the growing complexity of products and to enhance the understandability of software development. The variability models describe abstractions from core modeling artifacts related through variability mechanisms, and the relational information between these core artifacts is sometimes represented into the variability modeling viewpoint. Moreover, efforts have been made to refine variability and separate concerns, even though sometimes lacking some formalization in relating manipulated artifacts, e.g. FORM [START_REF] Kang | Form: A feature-oriented reuse method with domain-specific reference architectures[END_REF]. Besides, the modularization issue has been pointed out by many authors, e.g. [START_REF] Metzger | Disambiguating the documentation of variability in software product lines: A separation of concerns, formalization and automated analysis[END_REF][START_REF] Reiser | Multi-level feature trees: A pragmatic approach to managing highly complex product families[END_REF][START_REF] Hartmann | Supplier independent feature modelling[END_REF]. A survey of the relationships in the variability models literature reveals some categories [START_REF] Creff | Relationships in variability modeling approaches: A survey and classification[END_REF]:

-Logical group relationships: are found in any variability language and describe the implication/exclusion of variants from a given logical group (e.g. Or, And, Card). -Hierarchical refinement relationships: where initially called Consist-of [START_REF] Kang | Feature-oriented domain analysis (foda) feasibility study[END_REF].

These co-implication relationships are symmetric and transitive, and are specialized, depending on the approaches, into generalization, aggregation, decomposition, and classification, e.g. [START_REF] Kang | Form: A feature-oriented reuse method with domain-specific reference architectures[END_REF][START_REF] Lee | Feature dependency analysis for product line component design[END_REF]. -Configuration dependencies: are mainly based on the two following constraints: Implication (dissymmetric and transitive), and Exclusion (symmetric but not transitive), e.g. [2-4, 6, 11, 12]. -Usage specific relationships: correspond to various viewpoints (e.g. runtime dependencies [START_REF] Lee | Feature dependency analysis for product line component design[END_REF]), and delimits one concern. -Inter-concerns dependencies: can be defined in a single variability model (e.g. [START_REF] Kang | Form: A feature-oriented reuse method with domain-specific reference architectures[END_REF][START_REF] Lee | Feature dependency analysis for product line component design[END_REF]), between variability models (e.g. [START_REF] Pohl | Software Product Line Engineering: Foundations, Principles and Techniques[END_REF][START_REF] Metzger | Disambiguating the documentation of variability in software product lines: A separation of concerns, formalization and automated analysis[END_REF][START_REF] Sinnema | Covamof: A framework for modeling variability in software product families[END_REF][START_REF] Reiser | Multi-level feature trees: A pragmatic approach to managing highly complex product families[END_REF][START_REF] Hartmann | Supplier independent feature modelling[END_REF]), or between variability models and core artifacts (e.g. [START_REF] Pohl | Software Product Line Engineering: Foundations, Principles and Techniques[END_REF][START_REF] Heidenreich | FeatureMapper: Mapping Features to Models[END_REF]). The relationships lack semantics: their description can be quite vague and imprecise, especially when describing the specific usages, e.g. [START_REF] Kang | Form: A feature-oriented reuse method with domain-specific reference architectures[END_REF][START_REF] Reiser | Multi-level feature trees: A pragmatic approach to managing highly complex product families[END_REF]. Some approaches provide formalizations through metamodels (like in [START_REF] Mei | A metamodel for modeling system features and their refinement, constraint and interaction relationships[END_REF][START_REF] Fey | Feature modeling: A meta-model to enhance usability and usefulness[END_REF]), or with mathematical formalisms (e.g. [START_REF] Metzger | Disambiguating the documentation of variability in software product lines: A separation of concerns, formalization and automated analysis[END_REF][START_REF] Zhu | A feature oriented approach to mapping from domain requirements to product line architecture[END_REF]), but do not cover the entire range of relationships. For example, a relationship like recommends (e.g. [START_REF] Riebisch | Modeling variability for object-oriented product lines[END_REF]), which is a weak form of requires has no precise semantics and associated operations. Moreover, existing approaches introduce relationships buried in the variability model, every one extending variability languages with some relationships. Our approach differs in that we do not focus on variability but only on relationships between the variation point and variants, providing a generic language to describe specific relationships adapted to a given PL. The relationships described are not limited to Boolean equivalent relations like in [START_REF] Metzger | Disambiguating the documentation of variability in software product lines: A separation of concerns, formalization and automated analysis[END_REF] and associated to consistency checking, but can be established for various purposes. Moreover, unlike other approaches, relations considered cover the modeling core assets space.

Besides, related to traceability approaches, a recent work [START_REF] Anquetil | A model-driven traceability framework for software product lines[END_REF] identifies four orthogonal traceability dimensions in PLE (refinement, similarity, time and variability) and provides an implementation of traceability mechanisms in a generic framework, which does not enforce the semantics. Some applications of the traceability domain have been made to PLE [START_REF] Jirapanthong | Xtraque: traceability for product line systems[END_REF][START_REF] Lamb | Formalizing traceability relations for product lines[END_REF], with the aim to enforce the relationships semantics. In comparison, our approach is more pragmatic and targets domain concerns first (variability modeling and the PLE process) to extract semantics relationships that can be a basis for classical traceability activities. As a result, our approach goes further in describing the relationships granularity and usage.

The PLiMoS DSML

In this section, to tackle the exposed issue, we make explicit the relationships and their semantics, and separate the relational concern into a Domain Specific Modeling Language called PLiMoS to model the relational information of the Product Line Modeling Space. The section introduces (i) the approach, and provides details on the PLiMoS (specification) generic metamodel, a base to generate the PLiMoS (implementation) specific metamodel.

Overview of the Approach

We define the semantics of the PLiMoS language on the basis of the abstract syntax. The DSML is implemented with Ecore (eclipse modeling framework ) concepts and gathers various concerns. Figure 1 depicts the process associated to each concern; in the process, a PLiMoS specif ication metamodel provides facilities to build a specific part of the DSML (PLiMoS implementation ), dedicated to a particular modeling space, by generating it from a PLiMoS specif ication model. This paper provides a description of the PLiMoS language in this section and its application and implementation to a modeling space, using Feature Model and the OVM language in Section 4: due to space limitation, the model transformation (specification to implementation) stays out of the scope of the paper.

The PLiMoS Specification Language

This section introduces the PLiMoS specif ication metamodel. A separation of concerns is applied to reveal the basic relationships and the associated operational semantics. So, conceptually, the language is based on a set of Basic Relationships (BR), some Semantics Interpretations (SI ), and functions to bind SI to BR.

P LiM oS specif ication

BR, SI, Φ(SI) → BR In traceability approaches, relations are very often deeply qualified by their causality (if not unidirectional), i.e. relationships usually have a source (the cause) and a target (the effect). With the aim to add genericity, we consider a BasicRelationship to be a mapping (symmetrical link) established between two sets of artifacts, i.e. with a Left Hand Side (LHS) and Right Hand Side (RHS). Figure 2 presents an excerpt of the metamodel. One may note that the BasicRelationship associate EObjects as mapping ends; this implementation details allows to associate the links in PLiMoS specif ication models to variability and core assets metamodel elements. Moreover, the BasicRelationships have by default multi-cardinality on both sides and could be constrained later to a specific range. These relationships can be defined to realize a structural infrastructure (serializable) or considered to be derivable (and therefore inferred on-demand for various activities, e.g. consistency checking, viewpoint picturing), volatile and transient (derived property in Fig. 2). Furthermore, a relationship can be characterized by the abstraction level criterion (process Level ). It can be either vertical if the relationships cross levels, or horizontal whether it does not. Another characteristic is the homogeneity/heterogeneity nature of the relationships: within the variability model, within core assets or between the two. No property is associated to such a characteristic in PLiMoS as it is inferred from lhs and rhs relations ends type.

Variability models describe abstractions of core modeling artifacts related through variability mechanisms. Any variability technique defines propositional connectors and associations among variable elements that are translated in the basic types: implication (noted ι, is non-symmetric but transitive), co-implication (noted I, is symmetric and transitive) and exclusion (noted η, is symmetric and non-transitive). For example, the FM language uses "consist-of" (co-implication between feature and sub-feature), and the Or operator (exclusion between subfeatures), or other languages like OVM defines specific constraints equivalent to implication and exclusion (e.g. "requires v vp " or "excludes vp vp "). Some specific rationales and descriptions can refine the relationship to add semantics; these rationale properties are detailed in Section 3.3.

Fig. 2. Excerpt of the PLiMoS language

Every model syntactically correct and conforms to the metamodel must be interpreted as a definition for a dedicated modeling space relational language.

Defining Semantics Relationships

Relationships need semantics connected to specific usage and activities, the language contains some usual PL relationships.

Product Line Relationships Characteristics Based on the observation from the survey [START_REF] Creff | Relationships in variability modeling approaches: A survey and classification[END_REF], the relationships in PL approaches can be classified by five main properties: (1) Concern: the relationships do or do not cross concerns (intra or inter concerns); (2) Artifact Related : the relationships link the same set of artifacts (homogeneous) or different sets (heterogeneous); (3) Abstraction level : the relationship is defined as vertical (crossing abstraction levels) or horizontal; (4) Time: the relationships is temporally related to (i) design time, being structural (established by logical groups or hierarchy) or dependency (basic configuration or usage specific ones), (ii) run-time (operational activities), or (iii) over-time (evolution concerned); (5) Impact: the relationships define (i) induction (implication, exclusion or activation), (ii) modification (interaction, substitution, deletion).

For example, the five main categories of relationships described in Section 2 are combinations of the previous properties:

-Logical group relationships: intra-concern, homogeneous, horizontal, structural design, co-implication; -Hierarchical refinement relationships: intra-concern, homogeneous, vertical, structural design, co-implication;

-Configuration dependencies: intra-concern, homogeneous, horizontal, design dependencies, implication or inclusion impact; -Usage specific relationships: intra-concern, homogeneous/heterogeneous, horizontal, dependencies, various impacts; -Inter-concerns dependencies: inter-concern, homogeneous/heterogeneous, horizontal, dependencies, various impacts.

The PLiMoS language is designed to be generic, allowing the specialization of all existing relationships.

Semantic Properties in a Library PLiMoS specif ication includes predefined rationale properties to reveal the causality of the relationship, and provides semantics for understandability and process enactment. It includes the four hierarchical roles to specialize a co-implication links (viz. generalization, aggregation, decomposition and classification), necessary to connect PLiMoS to feature modeling languages.

Considering a given intention, the dependency relationships (ι, I, and η) can express specific causality, i.e. depending on the usage, as a base for activities like product derivation or consistency checking. The rational property for constraints may offer benefits to various usages, and PLiMoS includes the properties listed in the following. For implication and co-implication relations: NA (Not Applicable by default, whether the causality is not informed), depends-on, requires, resource, includes (implication only), impacts, satisfies, uses, allocate-to, induces, design, core, implemented-by. For exclusion: incompatible, mutex-with, and conflict. The three relationships rational property can be set to "other" and can be filled with the "Description" property. As built upon basic relationships of implication and exclusion, a translation of the implementation language to the propositional logic technological space is eased. Some of the properties are illustrated and described with more details in Section 4. One may note that the PLiMoS language does not consider, up to now, runtime activation dependencies, like those defined in [START_REF] Lee | Feature dependency analysis for product line component design[END_REF].

Relationships are treated as first-class entities and qualified by operational semantics properties. The relationships typed and qualified by their rationales are specified for a given concern; the latter define viewpoints (cf. graphical filters in the Section 5). Typed relationships can be organized into viewpoints to address distinct objectives, e.g. product derivation, variability consistency management, architectural organization. For example, many constraints have to be checked and verified for product derivation: typed "resource" relationships, allocation and implementation, and so forth; on the other side, only operational relationships have to be used to perform a run-time simulation.

The semantics relationships layers provide semantic enrichment of variability modeling languages and consequently of the entire PL. This section illustrates the adaptation (generative realization of the PLiMoS implementation part) of our DSML to a given modeling process introduced by Metzger et al. [START_REF] Metzger | Disambiguating the documentation of variability in software product lines: A separation of concerns, formalization and automated analysis[END_REF]. Briefly, the variability space is modeled with two kinds of variability models, for i) the PL variability, ii) the software design solution. The core assets space, which is composed of Model-Based Engineering (MBE) artifacts, is represented at two levels of abstraction: problem specification (e.g. with Use-Cases), solution design (e.g. Component, class diagrams), as depicted in Fig. 3. In the following, for representation convenience, the proposed formalisation does not reflect the separation between the relational structure and the semantics interpretations (i.e. the main interpretation is used to define the relation).

Introducing the Variability Languages

The variability is modeled with two types: FM for PL variability, and OVM for the Software one. This section describes the two languages used. Every model that is syntactically correct and conforms to the metamodel must be interpreted as a valid variability model (FM or OVM).

Feature Model Starting from the work performed by Schobbens et al. [START_REF] Schobbens | Generic semantics of feature diagrams[END_REF] and the FFD pivot, we derive a metamodel for feature models in Ecore with OCL constraints. A Feature Model f m ∈ L f m is represented in the article as a 8-tuple F, P, C, E, r, λ, CE, Φ where: -F is non-empty and finite set of Features; -P ⊆ F is a set of Primitive features (i.e. leaves); -C ⊆ F is a set of Concrete features (i.e. non abstract, connected to other artifacts, to be consistent Orthogonal Variability Model From the description by Pohl et al. [START_REF] Pohl | Software Product Line Engineering: Foundations, Principles and Techniques[END_REF], we formalize the OVM DSML, considering that an OVM model Ω is a 7-tuple V P, V, λ V P , DE V P , Opt, P arent, CD , such that: -V P ( = ∅) is the non-empty set of Variation Points (VP); -V ( = ∅) is the non-empty set of Variants; V P ∩ V = ∅; the function λ V P : V P → N × N labels each variation point with the possible variant cardinality; -DE V P ⊆ V P × V is the finite set of decomposition relations between a VP and its Variants. -Opt : V → B provides insight on a variant optionality; -P arent : V ∪ V G → V P is the function that returns the parent Variation Point of a given Variant;

P ⊆ C); -E ⊆ F × F is a finite set of Edges. As FMs are directed, features f 1 , f 2 ∈ F, (f 1 , f 2 ) ∈ E will be noted f 1 → f 2 where f 1 is the parent and f 2 the child. E is acyclic: f 1 , . . . , f k ∈ F.f 1 → .. → f k → f 1 . E is a tree: f 1 , f 2 , f 3 ∈ F.f 1 → f 2 ∧ f 3 → f 2 ; -r ∈ F
-CD ⊆ (V P × CT OV M × V P ) ∪ (V P × CT OV M × V ) ∪ (V × CT OV M × V ),
with CT OV M ={implication, exclusion} represents the set of configuration constraints; Some additional constraints are defined to ensure that: each VP is a parent of at least one V, a V has only one parent, cardinalities are correct.

The PLiMoS Implementation Metamodel

PLiMoS implementation makes an overlap with variability languages and core assets ones (cf. Fig. 1). PLiMoS implementation is generated from a specification given by a PLiMoS specif ication model. We consider in the paper, the PLiMoS implementation specific language to be composed of a set of FM relationships (F M R), a set of OVM ones (OV M R), and a set of inter-model relationships (IM R): P LiM oS implementation F M R, OV M R, IM R .

Relationships Specialized for the VMs Feature Model relationships (F M R) are defined as a tuple E, IDC, CIDC, EDC :

-Consist-of (χ): are co-implication and 1-to-1 relationships, homogeneous, non-derived, and vertical. This basic hierarchical relation is refined giving an intention, e.g. part-of, generalization (E ⊆ F × {generalization, decomposition}×F ); -Implication (ι): the "requires" is a non-symmetric but transitive configuration constraint (IDC ⊆ F × F , IDC ⊆ CE); The CoImplication (I) is a symmetric ι (CIDC ⊆ F × F , CIDC ⊆ CE); these relationships are 1-to-1, homogenous, non-derived and horizontal; -Exclusion (η): is a symmetric and non-transitive "mutex" dependency constraint (EDC ⊆ F × F , EDC ⊆ CE); this relationship is 1-to-1, homogenous, non derived and horizontal; And obviously, E ∩ IDC ∩ CIDC ∩ EDC = ∅. Considering a given intention, the dependency relationships (ι, I, η) may express specific causality, i.e. depending on the usage, as a base for activities like product derivation or consistency checking. In the running example we do not define any specific usage.

In order to make the connection with the OVM language, some relationships need to be specified in the PLiMoS specif ication model, those ones are: OV M R DE V P , AD, CD . variability dependencies (DE V P as define before) between VP and V (mandatory and optional), are co-implication and 1-to-1 relationships, homogenous, non-derived, and vertical; artefact dependencies (AD) and VP artefact dependencies, as implication and 1-to-1 relationships, heterogeneous, non-derived, and horizontal (part of the inter-model relationships); constraints dependencies: (CD, as defined before)"requires V V","requires V VP", "requires VP VP", 'excludes V V","excludes V VP","excludes VP VP". Consecutively, implication and exclusion relationships, 1-to-1, homogenous, nonderived and moreover, both horizontal and vertical ones are needed (implied by the modeling space -cf. previous section)

Relationships Specialized to Describe Inter-model dependencies The PLiMoS specif ication model is also concerned with specific modeling space considerations (depicted in Fig. 3), to build operational relationships. In this case, the inter-model Relationship Model rm is a tuple SR, IR, RR where:

-SR ⊆ P(C) × P(V P ) is a finite set of Satisfaction (ζ) relationships. The marketing Features from the PLVM are satisfied by technical VPs from the SVM: the design solution variability models meet the expectations and needs of the PL; -IR ⊆ (V P × V P ) ∪ (V × V ) is a finite set of Identity (Ξ) relationships. The identity mapping between two features means the similarity wrt. variability and concepts; therefore, associated core assets, whether different, must be totally disjoint; Identity is used to separate concerns at the same level of abstraction (horizontal relationships);

-RR ⊆ (C × CA) ∪ (V × CA) ∪ (V P × CA)
is the set of Realization (P) relationships. A variant can be realized by a "Core Assets" (CA) entity reified in the metamodel (CA are specialized for UML or any other DSLs involved). Refinement (ρ) is the relationship that describes technological refinement between abstraction levels in a model-based process. A given variant can be mapped and realized by multiple core assets; however, no n-ary relationships are defined, the CA concept represents a set of assets (Γ relationships make the links between the set entity and the assets); Additional relationships could be introduced as derived, volatile and transient. In the MDPL context, variability models relate elements with variability operators; in a complex modeling space, these relations can be made explicit to help enforce the semantic cohesiveness of the sets of related models.

Implementation

In this section we highlight tooling concerned with Domain Engineering: Feature Model, OVM and relationships representation, giving a concrete syntax to our infrastructure. One common end user request is to have an integrated, end-to-end tool support, instead of having different tools for closely related problems. Existing tools are based on Eclipse and Obeo Designer1 (commercial Model-Based Tool which provides graphical multi-viewpoint support within an Eclipse integrated environment). A viewpoint is defined for variability modeling (for FM and OVM), another for relationship modeling, both based on the previously introduced metamodels. The choice of a graphical syntax was made to be consistent with the selected variability models (FM and OVM are commonly manipulated through a graphical syntax). This section introduces a running example and provides insights on the tooling.

Introducing the Running Example

The running example is simple and abstracts, in some points, real systems at Thales. Anti-aircraft systems communicate with sensors and actuators e.g., radars and consoles (human machine interfaces). In the following we will use two radars: Radar1 which sends speed and position, Radar2 which sends two positions (t, t+1 ). Treatments are position processing and optionally speed processing (whether needed). Resulting data are sent to the user via consoles: Console1 gives polar coordinate, Console2 cartesian ones. Variability comes from a great extent from external actors like in our applications.

Describing the PL variability The Product Line Variability Model (PLVM) (cf. Fig. 3, and Fig. 4-a) can be seen as an interface, as it contains the user-visible variability, in the problem space. The model encompasses variabilities seen from a marketing perspective. This VM is specific to the missions of the family. This Fig. 4-a represents our running example: a simple control system family; variability visible for the market is limited to: type of data to treat (delivered by the external radars), type of processing consequently needed, presence/absence of internal memory and type of data processed and delivered. The variability comes here, for a part, from external actors, e.g. connecting a new radar with different properties; a characteristic of real applications.

Describing the Software variability The Software Variability Model (SVM), expressed in the OVM language, contains the internal variability of the architecture and design. The OVM model is organized upon architectural concerns and related to MBE solution artifacts (Component, class diagrams, etc.).

Note, in the simple example Fig. 4-b, that the architectural decomposition appears, e.g. Fig. 4-b pictures, from left to right, Entities (treatment rules entities and their relationships), Data (data management, including persistence), Controls (interface data consistency and translation into core data) and Boundaries (interface rules) features which come from a logical pattern application. Given a domain thesaurus, features and variants can have different names between the PLVM and the SVM, the tree hierarchy can be modified and variation points can be duplicated (e.g. red shape in Fig. 4 part b that corresponds to the one in part a); co-implication constraints may appear to keep the VM consistent. All the requires constraints are not represented in the figure for readability reason.

Overview of the inter-model relationships Product management for a PL comprises the positioning of the PL, seen in light of the interplay between the technical view of a product line (common technical platform, SVM) and the marketing view of a PL (products targeting a similar market, not necessarily technically related, PLVM). Figure 5 illustrates some Satisfaction and Realization relationships: The "HMIData" feature is satisfied by the "O.B." variation point, the "O.B." Variation Point is identical to "OutputB.", and the "Vel" variant is realized by a core asset.

Variability Models Designer Viewpoints

Over the "classical" FM representation, specific Obeo Designer filters are built for relationship representations: i) features can show/hide satisfaction link presence (Example Fig. 6 a), the "2 Pos." satisfied feature is colored differently) and represent technical constraints into an optional layer; ii) features can show/hide realization and link presence. It provides stakeholders an initial understanding of the relations among features and assets.

The same kind of filters are defined for the OVM viewpoint to show/hide realization relationships.

Dependency Designer Viewpoint

The dark side of features is feature interaction, which is implicit in feature composition and therefore difficult to understand. We define the dependency viewpoint to get the big picture of the overall dependencies. One could choose an epicenter element; dependencies will be extracted, or computed whether necessary, and shown (we define the operator: extract relationships(element,range) ). For example, in Fig. 6 b) the focus is made on the element "O.B.", with a limit of two links visibility. "HMIData" is part of the PLVM; "O.B.", "OutputB." and "HMI Protocol" are elements of the SVM. The set of required features (of a particular feature) -required features(feature) operator -is given by a transitive closure operator.

Graphical filters on relationship types are provided to focus on one specific concern, e.g., away from our example, to only show resource relationships (expressing resource dependencies).

Conclusions and Future Works

This article describes a Domain Specific Modeling Language called PLiMoS. This language does not focus on variability operators but only on relational knowledge. We propose to consider relationships between elements as an independent language to be merged with any variability modeling language to introduce model management capabilities. Relationships are treated as first-class entities and qualified by operational semantics properties, organized into viewpoints to address distinct objectives, e.g. product derivation, variability consistency management, architectural organization. We aim at providing a solution that allows the engineers to focus on their domain and reason about the relationships between the elements of the different spaces, abstraction levels and concerns.

The approach enforces the semantics of the relationships and provides benefits to cope with heterogeneous variability languages focusing on relations between concepts (the paper describes the amalgamation with two languages, a FM and OVM). The independence with variability and core assets modeling languages provides benefits to cope with the product line maintenance and evolution.

Future work will focus on adding automation to the tooling design process in relation with the PLiMoS relationships (design model generation), within a model-based process. Works will also investigate the connection to other technological spaces to perform various activities, but mainly for behavioral composition, and simulation for operational consistency checking.

Fig. 1 .

 1 Fig. 1. Overview of the PLiMoS language

Fig. 3 .

 3 Fig. 3. Applicative Modeling Space Overview

  is the root of the FM, r is unique and has no parent (∀f∈ F.( f ∈ F.f → f ) ⇐⇒ f = r); the function λ : F → O labels each node with an operator -O -from (And ∪ Or ∪ Xor ∪ {Opt 1 }); -CE ⊆ F ×GCT ×F is the set of intra-model constraint edges, GCT -Graph-ical Constraint Type -is {implication, co-implication, exclusion}, relations described hereinafter; -A set Φ ∈ B(F ) of textual constraints in propositional formulae;

Fig. 4 .

 4 Fig. 4. Variability Models: air defense case study

Fig. 5 .

 5 Fig. 5. Some inter-model relationships: air defense case study

Fig. 6 .

 6 Fig. 6. Variability modeling and dependencies

http://obeo.fr/pages/obeo-designer/en


I Saidi

I Soutrel

F Fourcade

A Amrane

D Floner

N Bellakhal

F Geneste

Flow electrolysis on high surface electrode for biodegradability enhancement of sulfamethazine solutions

Keywords: Sulfamethazine, Electrochemical pre-treatment, Biodegradability, Combined process, Graphite felt, Flow cell

The main objective of this study was to examine the feasibility of coupling an electrochemical process with a biological treatment for the degradation of sulfamethazine, a biorecalcitrant antibiotic. The electrochemical behavior of sulfamethazine was examined by cyclic voltammetry, showing an electroactivity in oxidation. The pre-treatment was carried out using an electrochemical flow cell involving a graphite felt electrode of high specific area. After a single pass through the cell, the analysis of the electrolyzed solution showed a promising trend in view of the proposed combined process, namely a high degradation of the target compound (more than 90%) while the mineralization level remained low (it did not exceed 20%). The optimization of the operating conditions, viz. flow rate and applied potential, allowed to improve the biodegradability of sulfamethazine solutions. Indeed, under optimal conditions, the biodegradability based on the BOD 5 on COD ratio measurement was improved from 0.08 to 0.58, namely above the threshold limit value (0.4).

Introduction

Pharmaceuticals have been widely used as human and veterinary medicinal compounds. Due to their intensive consumption worldwide, their occurrence in aquatic environment (typically in the range of ng to µg/L) has been observed [1][2][3][4]. The assessment of the impact to human health from environmental exposure has been the subject of many investigations [5][6][7][8][9]. Even if the amount of pharmaceuticals in aquatic environment is low, indirect effects such as chronic exposure, mixture effects and development of antibiotic resistant bacteria may constitute a risk for aquatic and terrestrial organisms. Thus, several efforts are being made to find out ways of inactivating or eliminating this class of substances in surface or wastewater.

Pharmaceuticals can be removed by physical techniques, such as membrane technologies [10][11] or adsorption processes [12][13]. For example, techniques involving activated carbon [14],

reverse osmosis [15], coagulation [16] and flocculation [17] have been applied. However, these techniques are not destructive and the pollutant is only transferred to another phase [18][19]. Consequently, expensive regeneration and post treatment processes are required.

Oxidation processes have also proven their efficiency in the treatment of toxic organic pollutants and biorecalcitrant compounds. Among them, Advanced Oxidation Processes (AOP) have been widely used, since they involve hydroxyl radicals (OH °), which are very reactive and can attack most of the organic molecules [20][21]. Degradation of pharmaceuticals have been reported by oxidation treatments such as ozonation processes [22] and by AOP processes, such as anodic oxidation on Boron Doped Diamond electrode [20,[23][24], electro-Fenton [20,23], photo-Fenton [25] and photocatalysis [26]. Although complete mineralization of the pollutant can be achieved by this method, they are expensive and not selective.

Even if biological treatment processes are economical, they can be ineffective for the degradation of recalcitrant compounds such as pharmaceuticals [27][28]. The use of integrated processes, such as the coupling of AOP and biological treatment is therefore an interesting alternative to degrade biorecalcitrant compounds at reduced operating costs [18,19,[27][28][29][30].

The pre-treatment is carried out to increase the biodegradability of the effluent and to reduce its toxicity, which lets expect a complete mineralization during the subsequent biological treatment.

Direct electrochemical oxidation/reduction of recalcitrant pollutants has also been recently reported as a possible pre-treatment before a biological mineralization. This method consists in an electrochemical reaction on electroactive functional groups that are susceptible to be responsible of the non-biodegradability of the molecule. Thus, mild degradation of recalcitrant electroactive compounds, reducing their toxicity and enhancing their biodegradability can be achieved without the intervention of highly reactive hydroxyl radicals. The effectiveness of the method has been demonstrated for recalcitrant molecules such as phosmet an organophosphorous insecticide [31][32], a chlorinated phenoxy herbicide, 2, 4-dichlorophenoxyacetic acid [33][34], and an antibiotic, tetracycline [35]. Sulfamethazine (STM) (Fig. 1) is an antibiotic that belongs to the pharmaceutically important group of heterocyclic sulfonamides. It is widely used in medicine and veterinary practice as antibacterial drug in pharmaceutical preparations [26]. Its structure is presented in Figure 1.

Degradation of sulfamethazine has been studied through different methods. However, all studies deals with Advanced Oxidation Processes, such as photo-Fenton [25] and photocatalysis with TiO 2 and ZnO as catalysts [26]. A recent study has also shown the efficacy of an integrated process coupling electro-fenton and biological treatment for sulfamethazine removal [27]. In this work, the feasibility of coupling a direct electrochemical process with a biological treatment to degrade sulfamethazine was examined. The pre-treatment was achieved in an electrochemical flow cell using a graphite felt electrode of high specific surface. Such an electrochemical system presents the advantages to transform large amounts of products in a relatively short reaction time. It can be easily automated and therefore adaptable to industry.

The influence of various parameters, such as the flow rate and the applied potential, on the degradation of the molecule and the biodegradability of by-products was investigated. These latter were also identified by UPLC-MS/MS.

Materials and methods

Chemicals and materials

Sulfamethazine (purity 99 %) was obtained from Alfa Aesar (Schiltigheim, France). Inert supporting electrolyte Na 2 SO 4 (purity 99 %) was purchased from Carlo Erba Reactif-SDS Acetonitrile (purity 99.9%) was HPLC grade obtained from Sigma-Aldrich. Graphite felt (RVG 4000) was supplied by Mersen (France). Its specific area measured by the BET method, its volume density and its carbon content were 0.7 m 2 g -1 , 0.088 g cm -3 and 99.9%, respectively.

Materials for the electrochemical pre-treatment

Electrochemical pre-treatment, in continuous system, was performed in a home-made flow cell [33]. Two interconnected PAPYEX carbon papers supplied by Mersen (France) were used as counter-electrodes (85 mm × 85 mm) and the compartments were separated by cationic exchange membranes (Ionac 3470 -Lanxess SAS, Courbevoie, France). The reference electrode (Saturated Calomel Electrode -SCE) was positioned in the middle of the graphite felt (48 mm diameter and 12 mm width) and the potential control was performed using a potentiostat. To ensure a good homogeneity of the potential distribution in the three dimensional working electrode, the felt was located between the two counter-electrodes [36].

The cell was thoroughly rinsed with distilled water before and after each experiment. The solution (50 mg L -1 sulfamethazine in 0.1 M Na 2 SO 4 ) percolated the porous electrode at various flow rates monitored by a Gilson minipuls 2 peristaltic pump (Middleton, WI, USA).

Analytical procedure

Electrochemical analysis

Electrochemical analysis of sulfamethazine and electrolyzed solutions were performed using a conventional three-electrode cell with a glassy carbon electrode (7 mm 2 ) as the working electrode and a platinum wire as the counter electrode. All potentials were measured with respect to a saturated calomel electrode (SCE) located near the working electrode.

Experiments were performed at room temperature under nitrogen atmosphere to avoid dissolved oxygen. Voltammograms were obtained by cyclic voltammetry using a versaSTAT3

AMETEK Model (Princeton Applied Research) potentiostat/galvanostat. Before each experiment, the glassy carbon electrode was thoroughly polished with Struers waterproof silicon carbide paper.

High Performance Liquid Chromatography (HPLC)

The residual sulfamethazine concentration was determined by HPLC using a Waters 996 system equipped with waters 996 PDA (Photodiode Array Detector) and Waters 600 LCD Pump. The separation was achieved on a Waters C-18 (5 µm; 4.6×250 mm) reversed-phase and the mobile phase consisted of a mixture of acetonitrile/ultra-pure water (35/65, v/v) delivered at a flow rate of 1 mL/min. Detection of sulfamethazine was carried out at 268 nm and the retention time was approximately 5 min.

Liquid chromatography-mass spectrometry (UPLC-MS/MS)

Ultra-pressure liquid chromatography

The devices used are detailed in a previous work [32].

Tandem mass spectrometry

The separated compounds were detected with a Waters Micromass Quattro Premier (Waters Corporation, Manchester, UK) triple quadrupole mass spectrometer. It was operated with an electrospray source in positive ionization mode with a cone potential of 40 V. The ionization source conditions were: capillary voltage of 3.0 kV, source temperature of 120 °C and desolvation temperature of 350 °C. The cone and desolvation gas flows were 50 L h -1 and 750 L h -1 , respectively; they were obtained from an in-house nitrogen source. High-purity argon (99.99%, Air Liquid, Paris, France) was used as collision gas and was regulated at 0.1 mL min -1 . Analyses were performed in full scan and daughter scan modes. Spectra were acquired between 50 and 300 m/z and the data were treated with Micromass MassLynx 4.1 software.

Total Organic Carbon (TOC) measurements

The solutions were filtered on Sartorius Stedim Minisart 0.40 µm GF prefilters (Goettingen, Germany). TOC was measured by means of a TOC-V CPH/CPN Total Organic Analyzer Schimadzu. Organic carbon compounds were combusted and converted to CO 2 , which was detected and measured by a non-dispersive infrared detector (NDIR). Reproducible TOC values were always obtained using the standard NPOC (Non-Purgeable Organic Carbon) method. For each sample, each measurement was triplicated. solution and of nitrification inhibitor (10 mg L -1 solution of N-Allylthiourea) which have to be added in the shake flask of the Oxitop apparatus. A similar protocol was applied for the control flask except that it was replaced by a solution of easily biodegradable compounds, namely glutamic acid (150 mg L -1 ) and glucose (150 mg L -1 ). Before use, KOH was added to achieve neutral pH (7.0 ± 0.2). Similar protocol was also considered for the blank solution, for which the sample was replaced by water to deduce the biological oxygen demand corresponding to the endogenous respiration.

Results and discussion

Electrochemical behavior of sulfamethazine

The good solubility of sulfamethazine in water was advantageously used to determine the electroactivity of sulfamethazine. Thus, its electrochemical behavior (50 mg L -1 ) was studied in neutral salts (Na 2 SO 4 0.1 mol L -1 ) by cyclic voltammetry on a glassy carbon electrode. The analytical study was performed at neutral pH in view of a direct use of the electrolyzed solution as growth medium for subsequent biodegradation experiments.

The voltammogram obtained during the anodic sweep showed an irreversible wave at 1V/SCE (Fig. 2). By contrast, during the cathodic sweep in the same conditions, the voltammogram did not show any signal corresponding to the electrochemical reduction of sulfamethazine. This behavior was in agreement with the profiles reported for sulfamethazine in the literature [27].

These results proved the electroactive behavior of sulfamethazine in neutral medium and allowed to conclude on the feasibility of an electrochemical pre-treatment in oxidation. Voltammograms were recorded at 100 mV s -1 in Na 2 SO 4 (0.1 M) on a glassy carbon electrode, under nitrogen atmosphere

Sulfamethazine electrolysis

The sulfamethazine solution (50 mg L -1 ) was oxidized at 1 V/SCE at various flow rates (from 1 to 4 mL min -1 ) in the electrochemical flow cell.

After a single pass through the flow cell, the voltammogram of the electrolyzed solution showed the disappearance of the wave at 1 V/SCE confirming the oxidation of sulfamethazine at this potential (Fig. 3a). corresponding to a reduction, when compared with the same analysis performed before electrolysis (Fig. 3b). It is an interesting result because if by-products obtained after the anodic treatment are not biodegradable, a second electrolysis in reduction could be tested.

Since cyclic voltammetry analysis does not allow the detection of low concentrations, the residual sulfamethazine concentrations were estimated by HPLC for all studied flow rates (Table 1). Above 3 mL min -1 , the preferential routes of sulfamethazine into the carbon felt did not allow a good electron transfer and high conversion yields. Interestingly, there was no significant impact of the flow rate in the range 1-3 mL min -1 ; conversion yields higher than 90% were obtained with only a single pass through the flow cell. 3 mL min -1 can be therefore considered, reducing the electrolysis time.

This result confirms the feasibility of the electrochemical pre-treatment and suggests that until 3 mL min -1 flow rate, the recycling of the electrolyzed solution through the electrochemical flow-cell was probably not necessary.

Mineralization of sulfamethazine

The mineralization trend of sulfamethazine on a carbon felt electrode was examined at different flow rates (Fig. 4a). Even if HPLC analysis showed that sulfamethazine was almost completely degraded after oxidation (1 V/SCE), the mineralization level remained low since the obtained TOC values were not far from that of untreated sulfamethazine. The mineralization yield did not exceed 20%.

This result is promising in view of the combined process, since the objective of the electrochemical pre-treatment is only an increase of the biodegradability of the solution; the purpose is to degrade the target compound to obtain by-products which were expected to be biologically assimilable by microorganisms from activated sludge. The mineralization of the effluent is expected to be achieved during a subsequent biological treatment and hence significant residual organic carbon is necessary for microbial culture.

COD measurements

The evolution of the chemical oxygen demand showed a decrease after treatment (Fig. 4a).

This reduction was significant for solutions percolated at 1 and 2 mL min -1 (54% and 31.5 % COD decrease, respectively). Consequently, low flow rates appeared advantageous to insure a significant COD decrease, while keeping low mineralization yields.

This result confirmed the sulfamethazine oxidation and therefore a modification of its chemical structure, which let expect a decrease of its toxicity, and hence can be in favor of a subsequent biological treatment.

The relevance of the coupling of an electrochemical pre-treatment and a biological process can be also estimated from the examination of the evolution of the COD/TOC ratio, displayed in Fig. 4b. A favorable trend is a decrease of this ratio [19], which was experimentally observed after the electrochemical pre-treatment in oxidation (1 V/SCE). Indeed, 59% and 25% decrease of COD/TOC were observed for solutions percolated at 1 and 2 mL min -1 , indicating a significant change in the compound structure.

Biodegradability of sulfamethazine

The biodegradability of a compound can be estimated through the BOD 5 on COD ratio; an effluent is considered as biodegradable for values beyond 0.4 [37][38].

Biodegradability was assessed in the most favorable case, namely the flow rate leading to the most significant COD/TOC decrease, viz. 1 mL min -1 ; the observed BOD 5 /COD ratio was 0.14, showing a slight increase of the biodegradability of the sulfamethazine solution (initial ratio: 0.08). This increase may be expected owing to the significant COD decrease observed (54%). However, the ratio remained lower than the limit of biodegradability (0.4). This result indicates the lack of biodegradability of the by-products formed after the electrochemical treatment.

Effect of the oxidation potential on the biodegradability of by-products

In order to improve biodegradability, 1 mL min -1 flow rate was kept but higher oxidation potentials were considered, in the range 1.2-1.6 V/SCE. An improvement of by-products degradation may be expected from higher anodic potential, which may be in favor of an electrolysis pre-treatment. COD/TOC remained lower than the initial value, but if compared to the value found for 1.0 V/SCE no further significant decrease was obtained (Fig. 5).

However, and interestingly, biodegradability increased with the applied potential; an augmentation of the BOD 5 /COD ratio from 0.14 to 0.58 was obtained for increasing potentials from 1 to 1.6 V/SCE respectively (Table 2), and hence at 1.6 V/SCE by-products from sulfamethazine oxidation could be considered as biodegradable (BOD 5 /COD = 0.58 > 0.4). A biological treatment can be therefore envisaged after the electrochemical pre-treatment.

Table 2

Impact of the applied potential on the biodegradability of the electrolyzed solutions.

Electrolysis conditions: [SMT] 0 = 50 mg L -1 , 1mL min

Degradation by-products analysis

Sulfamethazine was oxidized in a flow electrochemical cell at 1 mL min -1 . To allow the determination of by-products that are in very low concentration in the electrolyzed solution, the oxidation was performed at 1.2 V/SCE with a solution of 100 ppm of sulfamethazine in 0.1 M Na 2 SO 4 . For comparison, a blank was performed in the same conditions but without sulfamethazine.

After electrolysis, the sulfamethazine solution was analyzed by UPLC-MS/MS. Comparison with the blank showed the presence of 7 chromatographic peaks corresponding to byproducts. However, since the same molecular mass was obtained for different chromatographic peaks, this information was not sufficient to identify all by-products.

To get insight into the structure of each compound, spectra acquisition of these compounds have been made with collision energy of 25 eV, giving rise to fragmentation peaks. The values obtained for sulfamethazine and by-products are given in Table 3. Retention times and mass spectra were compared with standard compounds (Table 3 and Fig. 6). the mass spectra of the first peak (0.7 min) and the standard 2 can be explained by the high background noise. This compound was probably present but at a very low concentration.

It has been reported that the electrochemical oxidation of sulfamethazine takes place on the amino group and led to the formation of azo and nitroso species [39]. Such compounds have not been observed after electrolysis, neither by cyclic voltammetry nor by UPLC-MS/MS.

According to the by-products identified by UPLC-MS/MS, the key step responsible of the degradation of sulfamethazine was the cleavage of the sulfonamide. It has been reported that the oxidation of sulfadiazine, a sulfonamide antibiotic, on glassy carbon and boron-doped diamond electrodes led to the formation of 3 by cleavage of the sulfonamide group [40]. The authors have proposed a mechanism involving the formation of an anilinium cation (Fig. 7). The presence of by-products 3 and 5 could be explained, according to Fig. 7, by the formation of this reactive intermediate. Further reactions on 3 and 5 probably gave rise to by-products 2 and 4, respectively.

To check the influence of the applied potential on the nature of by-products formed after electrolysis, a solution of 50 ppm of sulfamethazine in 0.1 M Na 2 SO 4 was oxidized at 1.2 and 1.6 V SCE at 1 mL min -1 . Even if the concentrations of by-products were too low to really quantify them, an evolution of the composition of the electrolyzed solution was clearly observed. When a potential of 1.6 V/SCE was applied, the peak corresponding to the unidentified compound of mass 214.1 increased, whereas compounds 3 and 5 were observed in lower amounts and no peak corresponding to 2 was detected. The compound 4 seemed to remain in the same amount. These results show that the identified intermediates 3 and 5 underwent further oxidation at 1.6 V/SCE, giving rise to other by-products. The decrease of these intermediate compounds is probably at the origin of the improvement of the biodegradability of the solution.

Conclusion

This study demonstrated the efficiency of the electrochemical process as a pre-treatment method for enhancing the biodegradability of sulfamethazine. An analytical study was carried out showing the electroactivity of sulfamethazine in oxidation (1 V/SCE), which allows to envisage the coupling between an electrochemical process and a biological treatment.

Sulfamethazine was electrolyzed in an electrochemical flow cell using a graphite felt electrode. After a single pass through the cell, the analysis of the electrolyzed solution showed a high degradation yield while mineralization remained limited.

The influence of different parameters such as the flow rate and the applied potential on the degradation of the molecule and the biodegradability of by-products was examined. It was found that the biodegradability increased with the oxidation potential, until values exceeding the threshold limit (0.4). Indeed, the BOD 5 /COD ratio increased from 0.08 to 0.58 for increasing potentials from 1 to 1.6 V/SCE. To confirm these promising results, a biological treatment should be subsequently carried out to confirm the biodegradability improvement after pre-treatment.

1 Fig. 1 .

 11 Fig.1. Chemical structure of sulfamethazine

  The analytes were separated by a Waters Acquity UPLC system (Waters Corporation, Milford, MA, USA) consisting of an Acquity UPLC binary solvent manager, an Acquity UPLC sample manager and an Acquity UPLC column heater equipped with a Waters Acquity UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 μm particle size) (Milford, MA, USA) ) maintained at 45 °C. Isocratic LC elution was performed with 0.1% formic acid in acetonitrile as mobile phase A and an ultrapure water 9.5:0.5 acetonitrile (v/v) mix, with added 0.1% (v/v) of formic acid as mobile phase B. Separation of the analytes on the column was performed with a mobile phase consisting of a mixture of phase A/phase B (5/95, v/v) delivered at a flow rate of 0.4 mL min -1 .

4 Fig. 2 .

 42 Fig.2. Cyclic voltammetry of a sulfamethazine (SMT) solution (50 mg L -1 ) during anodic sweep.

Fig. 3 .

 3 Fig.3. Cyclic voltammetry of a sulfamethazine solution (50 mg L -1 ) before ( ) and after ( ) electrolysis in the flow cell at 1 V/SCE (1 mL min -1 ). Voltammograms were recorded at 100 mV s -1 in Na 2 SO 4 (0.1 M) on a glassy carbon electrode during anodic a) and cathodic b) sweep, under nitrogen atmosphere

Fig. 4 a

 4 Fig.4 a) Evolution of a) TOC/TOC 0 ratio (■), COD/COD 0 ratio (○) and b) COD/TOC ratio after electrochemical pre-treatment, in oxidation (1 V/SCE) at different flow rates. Initial concentration of sulfamethazine: 50 ppm. Error bars are based on 4 reproducibility measurements for TOC° and COD° and 2-3 for TOC and COD for low flow rates.

Fig. 5 .

 5 Fig.5. Effect of the oxidation potential on the COD/TOC ratio. Initial concentration of sulfamethazine: 50 ppm. Error bars are based on two reproducibility measurements.

5 Fig. 6 :

 56 Fig. 6: Structure of the identified by-products

5 Figure 7 :

 57 Figure7: mechanism proposed for the formation of identified by-products[40]

  Na 2 HPO 4 , 6.80; KH 2 PO 4 , 2.80. The BOD 5 value was initially estimated based on the experimental COD value BOD 5 =COD/1.46. The range of expected BOD 5 values was then deduced giving rise to the volumes of sample and activated sludge

2.3.5 Chemical Oxygen Demand (COD) measurements

Chemical Oxygen Demand was measured by means of a Test Nanocolor ® CSB 160 and 300 from Macherey-Nagel (Düren, Germany). The amount of oxygen required for the oxidation of the organic and mineral matter at 164 °C for 30 min was quantified after oxidation with K 2 Cr 2 O 7 at acidic pH and heating. 2.3.6 Biological Oxygen Demand (BOD 5 ) measurements Biodegradability was deduced from BOD 5 measurements, carried out in Oxitop IS6 (WTW, Alès, France). Activated sludge provided by a wastewater treatment plant (Rennes Beaurade, Bretagne, France) was used to inoculate the flasks and the initial microbial concentration was 0.5 g L -1 . The following mineral basis was used for all experiments (g L -1 ): MgSO 4 .7H 2 O, 22.5; CaCl 2 , 27.5; FeCl 3 , 0.15; NH 4 Cl, 2.0;

Table 1 :

 1 Degradation yield of sulfamethazine after electrochemical pre-treatment, in

	oxidation (1V/SCE) at different flow rates
	Flow rate	(mL min -1 ) 1 1.5 2 2.5 3 3.5 4
	Conversion yield (%)	94 93 92 91 92 75 76

  -1

	Applied potential (V)	BOD 5 (mg O 2 L -1 ) a	COD (mg O 2 L -1 ) a	BOD 5 /COD a
	0	6 ± 0	70 ± 8	0.08 ± 0.01
	1	4	28 ± 0.7	0.14 ± 0.00
	1.2	9 ± 5	53 ± 6	0.17 ± 0.11
	1.4	9 ± 5	56 ± 17	0.16 ± 0.14
	1.6	29 ± 5	50 ± 10	0.58 ± 0.21

a Uncertainties are based on two reproducibility measurements

Table 3 :

 3 UPLC-MS/MS analysis of by-products

	Electrolyzed solution	Standards
	Retention time MH + Ion fragment Products Retention time MH +	Fragment ion
	0.7	125 67	2	0.6	125	66.7
		81.5				81.8
		107.8				106.9
	0.7	124 66.8	3	0.7	124	106.7
		107.1				81.7
		82				66.2
	0.79	215.2 172.8
		107.3
		132.8
		157.6
		197.9
	0.83	216.1 133.8
		174
		82.1
		126.8
		159.1
	0.85	214.1 107.4
		185.8
		198.3
	1.18	215.2 107.8 82 198.1 158.1	4	1.1	215.27 198 107.7 82
	1.59	216.1 82	5	1.56	216	106.8
		106.5				108.7
		158.8				81.8
		198.5				198.5
		108.7				158.7
	2.04	279.2 91.8	1	2.04	279.2 91.9
		123.9				107.8
		107.8				123.9
		185.5				185.9
		155.8				155.8


Organic materials for construction: Questioning the Concept of feedstock energy

Among the various materials used in the construction sector, consumption of organic materials have a large influence on energy accounting due to their inherent energy content, known as feedstock energy. Current Life Cycle Assessment (LCA) standards require that this quantity be reported in the inventory table as consumed energy. The fact that feedstock energy is considered alongside energy stocks and losses may be intuitively necessary, but there are unresolved concepts related to this issue that need further exploration.

This paper aims at re-exploring the way that feedstock energy is handled in LCAs and proposes a new framework. A review of various kinds of energies and their underlying notions is presented and exposes the reasons for ambiguous meanings in the LCA method in order to evaluate the feedstock problem. This analysis shows that resource consumption indicators, inventory flows, and efficiency concepts are all confounded in the same primary energy concept. Furthermore, whereas energy stocks are currently accounted, the consequential combustion emissions are never considered.

From these various observations, a new frame is proposed, individually considering energy resource depletion indicators, energy flows, and energy efficiency, as well as accounting for emission stocks using the so-called stock inventory table.

1.

INTRODUCTION

The European construction technology platform has set the reduction of resource consumption (energy, matter, materials) as its first research priority for sustainable construction [START_REF]Strategic Research Agenda for the European Construction Sector -achieving a sustainable and competitive construction sector by 2030[END_REF]. The domestic material consumption in the European Union is dominated by construction minerals: it approximately ranges from 4.3 tons/capita in the United Kingdom to 18.4 tons/capita in Finland [START_REF] Weisz | The physical economy of the European Union: cross-country comparison and determinants of material consumption[END_REF]. In the United States, civil engineering construction and buildings are estimated to respectively consume 415,000 and 787,000 TJ of total energy per year [START_REF] Hendrickson | Resource use and environmental emissions of US construction sector[END_REF]. In parallel, the European commission points out Life Cycle Assessment (LCA) as a key method for assessing sustainable construction [START_REF]European Commission, Enterprise and industry, sustainable construction[END_REF].

Among the various materials used in the construction sector, consumption of organic materials is relatively low compared to classical mineral materials. However, in LCA, consumption of organic materials has a large influence on energy accounting, because they contain chemical energy resources, called inherent or feedstock energy. This energy is accounted in the total primary energy, and also accounted as a separate quantity in the inventory table as presently recommended to practitioners [START_REF]Environmental management -Life Cycle Assessment[END_REF][START_REF]Life-Cycle Impact Assessment Data Quality: a conceptual Framework[END_REF]. The fact that organics can be considered alongside energy stocks and energy losses makes accounting for feedstock energy intuitively necessary, but some practical concepts are still unclear.

Bitumen is an organic material obtained after crude oil distillation, at the bottom of the distillation tower in the refining plant. The most prevalent use of the bitumen is for pavements, where it is mixed with aggregates to create asphalt concrete (AC) parking lots and roadways. Performing LCA applied to road pavements, several studies have questioned the validity of accounting practices for feedstock energy contained in bitumen [START_REF] Santero | Life Cycle Assessment of Pavements: A Critical Review of Existing Litterature and Research[END_REF][START_REF] Ventura | L'outil logiciel Ecorce: cadre méthodologique et contexte scientifique[END_REF], generally surmising that current practice does not accurately represent the actual flows moving in and out the technical system. Regardless, as a residual product of the crude oil refining process, bitumen contains a significant amount of chemical energy (upper heating value around 40 MJ/kg), which is goes essentially unused when bitumen is used as a construction material. However, fuel oil producers do not consider it a viable energy resource because it contains concentrated amount of pollutants, such as polycyclic aromatic hydrocarbons, sulphur atoms, heavy metals, and others. Its transformation to available fuel oil (or other, lighter petroleum fractions) would require intensive processing, including cracking, hydro-treating and deasphalting. Direct combustion would require a specific technology to prevent from pollutant emissions [START_REF] Ventura | L'outil logiciel Ecorce: cadre méthodologique et contexte scientifique[END_REF] and is thus rarely practiced.

In that context, the present paper re-explores the energy concepts related to feedstock energy in LCA and proposes a new framework. The paper first presents a review of various types of energy and their underlying concepts and exposes the reasons for ambiguous meanings in the LCA method and tries to re-express the feedstock problematic. The second part of the paper proposes a new framework for feedstock accounting and discusses its possible applications.

RE-EXPRESSING THE PROBLEMATIC OF ENERGY

2.1

Definitions of energies in LCA and underlying concepts LCA considers different types of energy within an analysis, as discussed below. Figure 1 illustrates the relationships between these different energy quantities.

-Primary energy refers to the direct source, without transformation, of crude energy.

-Secondary energy refers to all sources of energy that result from extraction and transformation of primary sources by technological processes. Secondary energy is contained into primary energy. -Process energy refers to the energy that is consumed in order to manufacture a good.

Process energy is obligatory issued from a secondary source and is thus contained into secondary energy. Primary energy represents the amount of energy that is removed from the natural environment. LCA standards [START_REF]Environmental management -Life Cycle Assessment[END_REF] consider the total primary energy, which is the cumulative value of the whole system. Primary energy has been defended by the LCA scientific community as the "most meaningful parameter in judging the energy efficiency of systems since losses due to transformation and transport are fully taken into account" [START_REF] Klöpffer | In Defense of the Cumulative Energy Demand[END_REF].

According to [START_REF] Consoli | Guidelines for life cycle assessment. A code of practice[END_REF], "when organics are used as materials, the energy associated with much of this input remains incorporated in the product". This energy is called feedstock energy.

PRIMARY ENERGY

SECONDARY ENERGY PROCESS ENERGY

Energy losses

Energy losses 1. Energy efficiency of the technosphere accounting for the "loss of energy", 2. The valuation of the energy that is taken from the ecosphere. According to [START_REF] Frischknecht | Einstein's lessons for energy accounting in LCA[END_REF], it represents the extractible energy taken from the natural environment, 3. Account for the energy stock contained into primary matter.

2.2

The different methodological status of energy values From this brief review, one can understand that energy is a specific quantity in LCA: it can be an inventory flow, a reference inventory flow, or an indicator.

Process energy is a classical inventory flow, simply related to the energy consumption of a production process.

Secondary energy is a reference flow as defined by the European Platform of LCA [START_REF]International Life Cycle Data System, General guide for Life Cycle Assessment -Detailed guidance First Edition[END_REF]: it is the flow to which all other input and output flows (i.e. all elementary flows and nonreference product and waste flows) quantitatively relate. Secondary energy brings additional information other than just energy consumption; it is necessary information to relate the LCA technological system upstream to the power source, and then account for environmental impacts of these energy producing processes (see Figure 2). Total primary energy is an indicator: it represents a non-renewable energy resource protection as a safeguard subject for human society [START_REF] Frischknecht | Einstein's lessons for energy accounting in LCA[END_REF]. Characterisation factors are applied to various kinds of energy containing matter: upper heating value for fossil resources or unsustainably used renewable resources, and fission energy for nuclear resources [START_REF] Frischknecht | Einstein's lessons for energy accounting in LCA[END_REF].

2.3

Avoiding ambiguity by re-expressing the energy problematic According to the previously mentioned literature, the three notions of energy efficiency, energy resource depletion, and energy stocks, are all included in the primary energy concept. Additionally, in the case of organic materials, another ambiguity is with the stock concept. According to [START_REF] Consoli | Guidelines for life cycle assessment. A code of practice[END_REF], the feedstock energy should be counted as a consumed energy because the organic matter is eventually combusted and, if it is not combusted, it "represents a loss of available resource". From this perspective, what initially is a resource depletion indicator becomes an inventory flow: as total primary energy is expressed in energy units (MJ), it is often confused with an inventory flow and understood as "consumed energy". All these combined observations lead to the lack of understanding of energy accounting in LCA.

To reconsider the problem, the methodological status of the different energy values should be clearly stated and the energy efficiency and the stock concepts should be more clearly defined.

RENEWING THE FRAME FOR ENERGY ACCOUNTING IN LCA

The separate consideration of the notions of non-renewable energy resource depletion indicator, energy efficiency and energy stock, leads to different methodological solutions. These solutions are discussed below.

3.1

Primary energy is not an inventory flow First, primary energy should not be included in the inventory table because it is not an inventory flow, but rather a non-renewable energy resource depletion indicator [START_REF] Frischknecht | Einstein's lessons for energy accounting in LCA[END_REF]. Only process energy and secondary energy should feature in inventory tables. However, once the technological system is defined, total primary energy should be calculated from the inventory table, as it is done for all other environmental impact indicators. Furthermore, primary energy is this resource depletion indicator is only focused on energy resources.

Abiotic Depletion Potential (ADP) is a broader indicator for resource depletion as it gathers both energy resources and mineral resources, and includes a scarcity factor [START_REF] Guinée | Handbook on life cycle assessment. Operational guide to the ISO standards. I: LCA in perspective[END_REF]. ADP (expressed in kg of antimony (Sb) equivalent) reflects the amount and quality of material resources taken in the natural environment in a finite global reserve, but does not include other types of resources, such as land occupation, which are considered in a separate category. Other indicators based on exergy, such as cumulative exergy extraction from the natural environment (CEENE) are proposed (including land occupation) [START_REF] Dewulf | Cumulative exergy extraction from the natural environment (CEENE): a comprehensive life cycle impact assessment method for resource accounting[END_REF][START_REF] Dewulf | An improved calculation of the exergy of natural resources for exergetic life cycle assessment (ELCA)[END_REF], but does not include the reserve notion.

Both ADP and CEENE already include the primary energy concept because they use characterisation factors for organic materials related to the amount of inherent energy. ADP characterisation factors set primary energy equal to the material's upper heating value [START_REF] Guinée | Handbook on life cycle assessment. Operational guide to the ISO standards. I: LCA in perspective[END_REF]; for CEENE, they correspond to the chemical exergy, i.e. the Gibb's free energy value of the combustion chemical reaction. The latter method better reflects the amount of extractible energy from primary matter, but it is in fact of the same order of magnitude than the upper heating value for organic materials [START_REF] Dewulf | Cumulative exergy extraction from the natural environment (CEENE): a comprehensive life cycle impact assessment method for resource accounting[END_REF].

Energy efficiency is not an LCA indicator

The energy efficiency is linked to the loss of energy, i.e. the energy that is not recoverable, nor usable. According to the first law of thermodynamics, mentioning "consumed" energy, as often done in LCA, is not an exact terminology. Energy is never destroyed during a process; it changes from one form to another. According to the second law of thermodynamics, the energy form is always degraded as entropy is growing.

The concept of exergy accounts for these thermodynamic laws. In thermodynamics, the exergy of a system is defined as the maximum useful work possible during a process that brings the system into equilibrium with a heat reservoir. When the surroundings are the heat reservoir, exergy is the potential of a system to cause a change as it achieves equilibrium with its environment. After the system and its surroundings reach equilibrium, the exergy is equal to zero. Exergy is always destroyed when a process involves a temperature change. This destruction is proportional to the entropy increase of the system together with its surroundings.

The following definition for the energy efficiency of a system is generally used in thermodynamics:

ideal system real system system Ex Ex , , = η (1)
where η is the energy efficiency of a system (no unit), real Ex is the real exergy balance of the technological system to evaluate (MJ), ideal Ex is the exergy balance of a system that performs the same task, but works in a reversible way (MJ) i.e. without entropy production.

This energy efficiency concept can be applied to a simple thermodynamic system, or to more complicated system, such as technological systems in LCA: this methodology is called Exergetic Life Cycle Assessment (ELCA) and is a currently be used as an assessment technique in the LCA field [e.g. , 18].

Previous research suggests that the valuation of primary energy is linked to the capability of primary matter to produce usable energy; this "extractible energy" depends on the available technology at the moment of the study [START_REF] Frischknecht | Einstein's lessons for energy accounting in LCA[END_REF]. The purpose of ELCA is to analyse the exergy flows of technological systems, using various technological solutions, in order to maximise their efficiency.

In LCA methodology, indicators reflect the environmental impacts generated by the technosphere in the ecosphere. Energy efficiency is a useful indicator for eco-designing technological systems, but it remains inside the technosphere once natural resources have been removed. Although very useful to ensure a maximal useful energy yield from natural resources in a LCA study [START_REF] Udo De Haes | Life Cycle Assessment for energy analysis and management[END_REF], it is not an indicator reflecting an environmental impact category.

3.3

The energy stock concept The third notion underlying primary and feedstock energies is to consider organic matter as an energy stock. As previously mentioned, convention is to account for the feedstock energy as a consumed energy because the organic matter is eventually combusted and, if it is not combusted, it "represents a loss of available resource" [START_REF] Frischknecht | Einstein's lessons for energy accounting in LCA[END_REF].

This statement means two things: first, the effective availability of the stock for energy production is not important; second, the feedstock energy should be counted as consumed energy, i.e. be included as an inventory flow.

The key notion of the stock is the initial choice for the use of organic primary matter for an energy source or a material. In this latter case, the energy contained into the material is accounted for in the inventory due to the depletion of an available resource. There is, with this precise methodological recommendation, confusion between an inventory flow and a resource depletion indicator. The loss of available resource is already taken into account using one of following indicator: primary energy, Abiotic Depletion Potential, or exergetic resource, as previously discussed in this paper.

The stock represents a scenario for a potential future: the end of life of organic materials. If the inherent energy of matter is rightly considered lost from the ecosphere and included in an indicator, consequently there is no matter to consider whether or not the material is eventually combusted. However, from the inventory perspective, the decision whether or not to account for the production of process energy makes a difference, as well as the emissions issued from the combustion of this stock. These emissions are typically ignored, even though organic materials indeed contain airborne emissions stocks, and, in particular, carbon stocks.

In summary, accounting organic matter stocks should include both energy and emissions, and differentiate stocks from effectively combusted materials.

3.4

The "stock inventory" as a renewed frame We propose to consider stock by introducing a separate inventory table, called stock inventory, which contains the process energy generated by the combustion of the material, as well as the corresponding emissions. As long as the technological system does not include the end of life of the organic material, then the stock inventory remains separated from the main inventory. If the primary organic matter is, during its life cycle, mixed with another material, their stock inventories are added. If the end of life is included in the technological system, then combustion is considered to occur and both stock inventory tables are added. This is similar to established energy accounting approaches (e.g., [START_REF] Guinée | Handbook on life cycle assessment. Operational guide to the ISO standards. I: LCA in perspective[END_REF]), but accounts emission profiles for the feedstock sources. In Figure 3, an organic primary matter, containing E I primary energy is extracted from the ecosphere, transported and transformed. It produces a material used to produce A process energy. If the frontiers of the technological system are set after the production of A, the stock inventory table contains the future use of the process energy A and combustion emissions. If the frontiers of the technological system are set after the production of A, the stock inventory table is added to the main inventory. In either case, E I is accounted as removed from the ecosphere into a chosen indicator: i.e., primary energy, ADP or CEENE.

Figure 4: Technological system of organic primary matter being used as a material

In Figure 4, the same organic primary matter, containing E I primary energy is extracted from the ecosphere, transported and transformed into a material. At its end of life, this material can be combusted (scenario 1) or recycled (scenario 2). The stock inventory will contain the consumed process energy A, as well as corresponding emissions (these can contain emissions from other materials mixed with the considered organic material). This stock inventory will be added to the main inventory if scenario 1 is included in the technological system.

The stock inventory will depend on the technology that is be used to perform combustion. If no technology is readily or practically available, then the stock inventory should be approximated based on uncontrolled combustion emissions.

CONCLUSIONS

The purpose of the present paper was to question the concept of feedstock energy and to propose a new framework for energy accounting in LCA.

The analysis of notions underlying energy concepts in LCA have highlighted that resource consumption indicators, inventory flows and the efficiency concept are all confounded in the same primary energy concept, which has lead to misunderstanding. Furthermore, whereas energy stocks are currently accounted, combustion emissions are never considered. From these various observations, a new framework is proposed that separately considers energy resource depletion indicators, energy flows, energy efficiency, and accounting for emission stocks using the so-called stock inventory table.

As a consequence of this framework, when the feedstock energy is used, for example by recovering energy from waste incinerators, allocation takes place of emissions and resources to, respectively, waste handling and energy produced.

Figure 1 :

 1 Figure 1: Relationships between various energy quantities used in LCA

Figure 2 :

 2 Figure 2: Methodological status of energy quantities in LCA

Figure 3 :

 3 Figure 3: Technological system of organic primary matter being used as an energy resource