Abstract
The tendency towards a homogenous mode of development modelled on that of Western countries means that sustainable development has become increasingly urgent. It is necessary to thoroughly redefine products and their expected performances in such a way that the consequences are compatible with sustainable development. In the domain of product design, this means that it is no longer sufficient to use assessment tools “after the fact” to check the impact of products whose functional unit (FU) was defined prior to production; it is now necessary to rethink the definition of the FU itself. This article aims to present an approach based on a combination of life cycle analysis methods and problem-solving by constraint satisfaction. This original approach makes it possible to vary the design of the different dimensions of the FUs of a complex system and thus to make it easier to identify the best architecture along with the best functional definition of the system. In this study, the EcoCSP approach is applied to define the functional performances of an ecological passenger ferry. The complexity of couplings between subsystems and the sheer number of those subsystems mean that the designer has to use “intelligent” tools. These simulate a great number of scenarios and help him/her to fine-tune the system and make the right technological choices with regard to the right functional specifications.
1. Introduction
Industrial growth has affected the conditions of life on earth as witnessed by the increase in ecological problems that threaten future generations. In response to this situation, the concept of sustainable development that began in the 80’s, has gradually become mainstream. Sustainable development calls on all actors involved in the evolution of developed societies to balance the environmental, economic and social dimensions of their activity. This means setting a new paradigm where reasonable consumption/production has become essential. In order to reach this objective, products and systems must be designed to be sustainable. Sustainable design is different from Eco design and Design for Environment because it goes beyond the environmental optimization of goods and services (Van Weenen Citation1995): it attempts to incorporate the considerations demanded by the three pillars of sustainable development, namely social, economic and environmental factors.
Various authors (Daly Citation1973; Simonis Citation1985; Williams, Larson, and Ross Citation1987; Herman, Ardekani, and Ausubel Citation1989; Ayeres and Kneese Citation1990; Freeman Citation1992) have mentioned the radical nature of the technological transformation that needs to be effected in order to improve the environmental performance of a product or system: they have recommended reducing the proportion of material in the economy using expressions such as X Factor, eco-efficiency, industrial ecology, functional economy, dematerialization, product service-system, etc.
Today, traditional Eco design approaches either carry out curative environmental assessments (LCA) (Hauschild et al. Citation2005), or lead designers towards improved solutions by providing guidelines (Wimmer and Züst Citation2003). Both of these approaches, used in the design of complex systems, most often result in global under-optimizations that are unsuited to the design of complex systems. It thus appears necessary to implement new Eco design practices that are better suited to designing such systems.
A complex system can be defined (Krob Citation2009), (Cilliers Citation1998) as a system comprising numerous subsets that are interdependent; each of these subsets has several possible alternative solutions. In general, complex systems have different ways of functioning with performances that change according to conditions of use. Finally, the long-term life cycle of a complex system is not easy to predict during the design phase; this is particularly true for life duration, maintenance, component upgrading and end of life.
Any increase in complexity results in the multiplication of technical solutions and thus of possible alternatives. In such cases, design becomes a long process of negotiation within the design team. This negotiation is generally based on an initial definition of the system’s specifications – specifications that are rarely questioned during the design process. In this article, we focus on the necessity for the actors concerned to generate a functional negotiation, that is to say, to select the “right” functions, then the characteristics of the system in order to optimize its environmental performance.
In the following section of this article we deal with the problem of defining functional units (FUs) in assessing the life cycle of complex systems. In the third part of the article we give a theoretical introduction to the EcoCSP method; this method combines CSP and LCA in order to identify the optimal architecture of a system by negotiating the FU so that an environmental optimum may emerge. In Section 4, the EcoCSP approach is applied in the context of designing a new passenger ferry with hybrid technology and the environmental improvement is estimated compared to a system with fixed functionalities. Finally in conclusion section we discuss the results and summarize the contributions of this article to a global approach, part of which includes EcoCSP.
2. The innovative Eco design approach: reassessing functionalities
2.1. Improving environmental performances by reassessing product functions
As underlined by Lagerstedt (Citation2003), environmental performance generally depends on product functionalities. However, from another point of view, the commercial success of a product depends on the functions it offers to users. Lagerstedt (Citation2003) mentions the balance that must be found between the “environmental cost” and the “functional gain”. Few methodological supports exist in the domain of tools/methods for environmental improvement or hierarchization, and amongst those that do exist, even fewer enable early intervention in the design process. Current methods of Eco design such as life cycle analysis (LCA) and other assessment methods derived from this, such as environmental guidelines and checklists, merely identify the causes of environmental problems in order to redesign the product while keeping its functionalities unchanged; this is in contradiction to strategies of radical environmental improvement (X Factor) that necessitate a complete reassessment of product functionalities. Achieving a higher degree of sustainable development requires finding a balance between acceptable impacts and necessary functions. Luttropp (Citation2005) presents different ways of reaching this balance: he favours reducing environmental impacts while increasing the level of the product’s functional performance – a win–win situation that eliminates all unnecessary functions. On the other hand, he is critical of the “green fix” strategy (using new materials while keeping all the functions) that result in short-term, temporary optimizations; he also judges inefficient the “linear down” strategy (improving environmental impact by downgrading or eliminating functions).
2.2. The problem of defining the FU in the LCA method
LCA is a method of environmental assessment of a product or service over the whole of its life cycle, that is, from the phase of extracting the raw materials and manufacturing the product until the end of life (discharge, recycling, reuse, etc.), including distribution, use and maintenance. The methodological framework of LCA is governed by ISO 14040; this distinguishes four phases – defining the objectives and the perimeter of the study, taking the inventory of the life cycle, assessing the impacts of the life cycle and interpreting the results). The phase of defining the objectives and perimeter of the study requires the definition of a FU. The FU is the “quantified performance of a system of products to be used as the unit of reference in a life cycle analysis” (ISO 14040, Citation2000). The definition of this FU is crucial. Indeed, in cases where the LCA study aims to analyse the potential impacts of different options, it is imperative that all the options assessed fulfil the same function in order to be comparable (Jolliet et al. Citation2005). Now, by constraining the designer to reason by iso-functionality, the LCA methods and its derivations naturally hinder thinking about products that might have a better balance between environmental cost and functional gain (Luttropp and Lagerstedt Citation2006). In general, the available tools, amongst them LCA, are based on a single criterion: the main function expressed in the form of a FU (Lagerstedt Citation2003). This means that very different products or concepts can be compared.
Consequently, when comparative LCA’s are undertaken for these types of products (i.e. that have several functions), it is important to consider the other subfunctions. If these functions are not identified, broken down, specified and/or prioritized in the right way with regard to the objectives and perimeter of the study, it could result in a FU that does not reflect reality. As underlined by Reap et al. (Citation2008), these are important questions, for they can downgrade the precision of the reference flows associated with the chosen FU and thus decrease confidence in LCA results.
2.3. State-of-the-art review of environmental assessments (LCA) on systems of transport and vehicles
In the literature, numerous products and systems have been environmentally assessed using LCA. In order to highlight the problems related to the definition of a FU, about 30 LCA studies published by three scientific publishers (Springer, Taylor & Francis and Elsevier) between 2003 and 2013 as well as a few LCA studies presented in the context of doctoral theses were evaluated in the domain of transport systems.
Each of these LCA studies is characterized by the system (mainly vehicles), the FU attributed by the author and the parameters that were modified during the sensitivity test. The results are given in Table . This non-exhaustive state of the art on new technologies in the transport sector (especially cars) shows that the FU is very often assimilated to a principle function: that of transporting a person or an object from A to B over a distance of x thousand kilometres. As stressed in the previous paragraph, and highlighted by Reap et al. (Citation2008), vehicles are complex systems with subfunctions that must be taken into account when two systems are being compared. The variability of FU is not taken into account in thirteen studies. In others LCA studies, modifications of Functional Unit are suggested like the modification of the driving cycle (annual mileage, vehicle lifetime and use), the reduction of the vehicle’s mass, the number of battery charges, etc. All these functional modifications are integrated into the process of sensitivity analysis required by the ISO norm. Now, in actual fact, the designer only looks at the extra gains or impacts generated by the modification of one or several parameters; he/she takes no account of the consequences on the whole of the system. These modifications are not completed by a redefinition of the design parameters. Indeed, if we take the example of the reduction of a vehicle’s mass, this could potentially call for a different distribution of the masses of the vehicle, and thus a modification of the system’s aerodynamic performances that might generate a resizing of the propulsion.
Table 1. State of the art review of LCA studies in the domain of transport.
3. Presentation of the EcoCSP approach
The EcoCSP approach is a further development of the CSP/LCA approach proposed by (Tchertchian, Yvars, and Millet Citation2013). This approach is based on a combination of two methods “Constraint Satisfaction Problem”/Life Cycle Assessment.
3.1. Definition of a CSP
A constraint satisfaction problem (CSP) is defined by (Montanari Citation1974):
X = {x1, x2, x3, … , xn}, a set of variables, n being the number of variables of the problem. To keep the generic element, we say that these variables may relate to design, performance or state. Design parameters structure the design and their values distinguish between two design configurations. The instantiation of all the design parameters defines the complete potential design solution. Performance parameters translate the state or the quality of a design alternative and compare it to a reference from the specifications or one related to the state of the art of the company or sector concerned. These characteristics are linked to the translation of a given configuration in physical terms and are generally directly linked to the design parameters.
D = {d1, d2, d3, … , dn}, a set of domains. Each domain, associated with a variable, can be discrete or continuous.
C = {c1, c2, c3, … , cp}, a set of constraints, p being the number of constraints of the problem. The constraints translate how the structuring functions are carried out by the system during the life situation in question. The constraints take the form of explicit relationships between several variables. These relationships impose restrictions on the domains of possible values for the variables of the problem. More precisely, it can be a logical combination of several elementary constraints, among the following:
| • | Extensive constraints: a constraint in extension describes an explicit and exhaustive list of possible, or on the contrary, impossible combinations – of values (m-tuples) between the m variables at play within the constraint. | ||||
| • | Intensive constraints: a constraint in intension is an explicit equivalence (or non equivalence) linking two variables to each other (equality or inequality). It brings linear and/or non-linear operators into play. | ||||
| • | Logical constraints: conditional constraints (IF … THEN), conjunction of constraints (AND), disjunction of constraints (OR), obtain logical combinations of constraints. In the case of designing a complex system, logical constraints establish composition relationships among the system’s components and define “components” whose state of functioning varies over the life cycle. | ||||
Environmental criteria come into the CSP approach as constraints to be satisfied in order to respond to an objective of environmental optimization. Depending on the objectives required by the designer, the algorithm of resolution indicates system architectures and operation modes (if these exist) that respect these constraints. Figure shows the resolution of CSP.
Figure 1. Process for solving a CSP.
3.2. CSP resolution
Resolution by constraint satisfaction results in a complete set of solutions that enable the design team to choose one suited to a design problem according to specific performance variables and constraints. A CSP is typically solved by reducing the domains. The objective of propagating constraints is to replace an initial CSP by an equivalent one which has a more restricted research space. The most basic way of reducing the area for solution is to proceed by trial and error or by dichotomy. A more systematic method is to use filtering techniques which rely on arithmetic of intervals (Moore Citation1966) and propagation of constraints (Mackworth Citation1977). The most commonly used domain filtering techniques are Arc-Consistency (Mackworth Citation1977; Debruyne and Bessiere Citation2001) for discrete CSP’s, Hull-Consistency (Lhomme Citation1993; Benhamou Citation1995; Benhamou and Older Citation1997) and Box-Consistency (Chiriaev and Walster Citation1997; Benhamou et al. Citation1999) for discrete and continuous CSP’s. As Chenouard (Citation2007) points out, using CSP in preliminary design has the advantage of great flexibility for expressing knowledge and modifying models; it resolves generic problems. This is a sought after characteristic in design, for it expresses knowledge without defining how it should be dealt with. CSP makes it easier to manipulate and reuse such knowledge.
3.3. The EcoCSP approach: a development of the CSP/LCA approach
The methodology of LCA uses a normalized functional unit (UFn), to facilitate comparisons among systems that show unequal performances.
Our state-of-the-art review and the compilation of Reap et al. (Citation2008) of the main problems posed by LCA, show that defining a FU is not sufficient for the radical improvement of the environmental performances of a complex system.
The works of Tchertchian, Yvars, and Millet (Citation2013) demonstrated the relevance of an approach combining CSP solving approach and LCA method to define the best architecture and operation modes of a complex system with respect to environmental constraints. This fruitful research pointed to a way forward. Indeed, the CSP approach allows us to modelize functional requirements as constraints; by exploring these as such, it is then possible to simulate the various architectural alternatives of a complex system while at the same time varying the specifications of that system. We have called this approach EcoCSP.
The general framework of the EcoCSP approach, represented by a flowchart Figure , contains a phase of predefinition of the functional specifications of the system, in which are listed the negotiable functions (NFs), these are flexible functions in the specification of the “Performance level” expected by the system, and the Non Negotiable Functions (NF̅), these are functions whose performances are specified from the beginning (ex: functions related with security).
Figure 2. Flowchart of the EcoCSP approach.
NFi ∈ [Pi min, Pi max], where Pi min is the minimum level of performance of the function i and Pi max the maximum level of performance.
In parallel with the definition of overall performance of the system, the design team defines the range of variation of the FU. The FU defines the quantification of performance characteristics of the product intended to be used as reference. In EcoCSP approach the FU is sought to minimize the environmental burden of the system architecture. The globally optimized performance of the system (FUGO) may vary in the domain [Ps min, Ps max].
Significant Negotiable Function (SNF), in which variation of the performance level led to a significant fluctuation of the environmental impact of the system, is identified through design of experiments (DoEs) via Taguchi orthogonal array Lz (Taguchi, Elsayed, and Hsiang Citation1989). Lz is an array where z is the number of simulations required to represent all the effect of NF on environmental criteria.
A DoEs an efficient way to identify the contribution of each function to the environmental impact. It reduces the number of trials (CSP simulations). Without DoE the number of simulations required to test all functional mixes is mn with n the number of functions and m the number of performance level for each function.
In this first methodological proposal, the functions are not supposed to interact among themselves and each of them have m = 2 levels. Each function NFi whose performance Pi is in the domain [Pi min, Pi max] the two levels are the upper Pi max and lower Pi min bounds; for functions NFi whose performance is in discrete domain {Pi1, … , Pik} the two levels are the minimum and maximum values.
For example, a functional mix composed of n = 7 NF and with m = 2 performance levels would require 27 trials to test all variants of functional mix. The appropriate Taguchi orthogonal array is L8 (27), reducing the number of CSP simulations to z = 8 instead of 128.
Each variant of the functional mix is characterized by a line in DoE (cf. Table ). The results are used to draw the graph of main effect on the average of Environmental Impact (see Figure ), i.e. the effect of each NFi on environmental performance.
Table 2. Taguchi orthogonal array Lz (2n).
Figure 3. Graph of main effect on environmental impact of NFi.
In Figure , for example, the transition from performance level P1 min to P1 max of function F1 causes a significant change in the average environmental impact EIAvg, the function NF1 is a significant NF. The three most “significant” NF are retained in the next step to specify their optimal performance level.
While the previous approach deals with a functional unit normalized (FUn) and functional performance defined (Pi n) according to system specifications defined at the beginning of design process; the EcoCSP approach allows to model the three significant NFs as constraints that operate on domains of performance: [Pi min, Pi max]. Similarly, the functional unit FUGO is defined as a constraint in domain [Ps min, Ps max]. A limited variation in the level of performance of FUGO compared to the baseline performance of the FUn allows a radical improvement in environmental performance system.
The optimal solution is generated by constraint satisfaction (CSP). Design parameters defining technological choices for each subsystem (e.g. motor sizing), the way components operate according to the use phase sequences (e.g. component activated or not) and the performance level of three functions negotiable are specified by propagating the constraints.
A CSP solver is used to instantiate the design variables and the performance Pi of NFs that minimize the performance variables based on environmental criteria. The constraint solver used is ILOG Solver (Ilog Citation2006), developed by IBM. ILOG Solver is a C++ library. In the flowchart, the solver is involved in three activity box labelled CSP to satisfy the constraints; generating technological choices, operation modes of components and the performance level of three NFs.
Finally, we propose an area of improvement to reduce the impact caused by overproduction of a non-suitable components (oversizing, non-mature technology, etc.) towards the NFs performed. Reducing the performance level of the functions of the system creates a need for appropriately sized components to meet performance adapted downwards or upwards. The number of components constituing such databases are not exhaustive, they depend on their availability on the market. Extending the database of components to meet the appropriate performance level should achieve additional environmental benefits. This observation leads to imagine (re) design specific component to achieve the optimal performance level.
EcoCSP allows judgements and choices to be made about functions on the basis of those functions that are deemed negotiable; the approach makes it possible to vary the system’s performance in order to reduce environmental impacts.
4. Case study: designing an eco-compatible hybrid passenger ferry
4.1. Simplified modelization for a complex system – a maritime ferry
The system under study is a maritime passenger ferry that crosses the bay of Toulon. The ferry to be redesigned is equipped with an aluminium hull, two diesel motors and an electric generator to power the auxiliaries. The ferry can transport 100 passengers. The Toulon ferry runs three lines 7 days a week over 300 days per year. Each ferry makes 24 bay crossings daily. The ferry has a lifetime of 20 years. The diesel motors are replaced approximately every 12,500 h (or about 500,000 km).
The design project aims to define the architecture and the state of the systems for each sequence of the use cycle according to pre-established conditions of use and assessment criteria (performance variables, environmental, technical and economic criteria, respectively). In the case of intercity passenger transport, ship performances are strongly related to conditions of use.
For practical reasons, in this article, we have deliberately simplified the system. The passenger ferry is thus broken down into five main subsets (shown in Figure ): Hull & Superstructure, Power Generation, Propulsion and Steering, Energy and Auxiliaries. The main relationships governing the system are shown in the appendices.
Figure 4. Model of hybrid passenger ferry.
4.2. Defining negotiable functional parameters
To simplify the problem, we identified six NFs: maximum cruise speed, maximum passenger capacity, number of daily trips, thermal insulation, air-conditioning system and the number of charging/day.
In the classical approach, the performance of NFs are defined in the specifications, each function is characterized by a nominal performance level Pi nom (Table ). The FU chosen to compare the various systems and functional scenarios is the number of passengers transported per day. The performance corresponding to the nominal functional unit (FUn) is 2400 passengers transported a day.
Table 3. Definition of nominal performence Pi nom and performance level [Pi min, Pi max] of NFi.
Table shows for each NFs the acceptable performance level; e.g. the performance for NF1 “maximum speed” is:
4.3. Defining significant negotiable functional parameters
There are 26 possible functional mix for six NFs with two performance levels. In order to reduce the number of CSP simulation, the appropriate Taguchi orthogonal array is L8 (26) is implemented Table , representing the main effects of each NF on the environmental impact.
Table 4. Design of experiment – Taguchi orthogonal array L8 (26).
The functional mix 0 (FM0) is the nominal functional mix (maximum speed 12 knots, 100 passengers per day, 24 missions daily, no insulation and air-conditioning system, one charge per day).
For each mix, the CSP model is solved using ILOG solver generating architectures and operation modes minimizing environmental impact with Eco Indicator 99 (EI 99) scores (Goedkoop and Spreinsma Citation2001).
For each Functional mix FMj, Table shows the main elements of Architectures Aj (of the simplified model) selected from the component libraries.
Table 5. Specifications of main elements of the system.
The function “objective” is to minimize the environmental impact over the life cycle (raw materials + manufacturing phase, use phase and maintenance phase, the end of life phase is not included).
The diagram (Figure ) shows the distribution of impacts over the three life cycle phases that are assessed. The predominant phase is the use phase with 87% of impacts, then the maintenance phase with 8% and the raw materials + manufacturing phase with 5%.
Figure 5. Average distribution of impacts on life cycle phases.
The eight functional mix scenarios described above are assessed environmentally using the indicator of a single EI99 score (Table ) in order to make the results clearer. It is understood that a multicriteria assessment is recommended in order that the study be robust. We therefore provide a summary with the results of the multicriteria assessment below in Appendix 1 (Table ).
Table 6. Life cycle assessment of architecture Aj.
4.2.1. Assessment of the raw materials + manufacturing phase
Each architecture generated by functional mix described by DoE (Table ) is environmentally assessed (Table ). The graph of main effects (Figure ) illustrates the influence of NFs on the raw materials and manufacturing impact. Functions from 1 to 5 have little impact on raw materials and manufacturing phase. The number of battery charge (NF6), explains largely environmental gains measured for functional mix scenarios 1, 2, 5 and 6. In fact, recharging batteries more often, the amount of energy to be stored becomes less important and the need for batteries is less. Batteries, with the superstructure of the ship, are the main contributors to the impacts in raw materials and manufacturing phase.
Table 7. Impact of architectures for functional mix with design of experiment L8 (26).
Figure 6. Main effect of negotiable function on environmental impact.
4.2.2. Assessment of the use phase
The use phase represents 87% of the impacts generated by the passenger ferry. Moreover, as shown by the various assessments of FM scenarios 1–7 (Table ), the environmental impact of use is sensitive to the variation of negotiable functionalities. The best functional mixes allow more than 10% gains.
The performance level of NFs leads to a variation in the average environmental impact generated by the eight functional mix scenarios of the DoE of:
| • | 4.5% for NF3, | ||||
| • | 3.3% for NF1, | ||||
| • | 2.7% for NF2, | ||||
| • | 2.5% for NF4, | ||||
| • | 1.3% for NF6 | ||||
| • | and 0.9% for NF5. | ||||
The analysis of different scenarios associated with a functional mix explains in part the impacts resulting of the use phase. Reducing the maximum speed or the number of missions per day reduces the fuel consumption.
The increase in the number of batteries charge leads to decrease in the mass of batteries and thus reducing the need of propelling power as system presents less drag. In the same way, reducing passenger capacity per mission also reduces the propulsive power. The combination of air conditioning and insulation of system is more difficult to predict, better insulation improves thermal efficiency but weighed down the system increasing fuel consumption, while air conditioning increases the energy demand of the system.
4.2.3. Assessment of the maintenance phase
The ferry undergoes maintenance throughout its entire life cycle: motors are changed (lifetime of 12,500 running hours) and batteries are charged and uncharged (600 cycles for this study). Initially, five motors are used over the ship’s lifetime involving four changes of motor. For a single charge/uncharge cycle per day, batteries must be replaced nine times. The batteries are the components generating the highest impact. The number of missions per day leads to a variation of 5% in the average environmental impact of maintenance phase. The use of the system is due to the number of mission it performs per day, which generates wear of these main components.
4.2.4. Global assessment of scenarios
The overall impact (raw materials, use and maintenance) of the various scenarios associated with a functional mix of DoE follows the trend drawn by the use phase, in fact 87% of the environmental impacts caused by the use phase. The three significant functions are determined by the functions generating the greatest variation on the overall impact, they are characterized by observation on the graph of main effects (Figure ). The performance level of NF1, NF2 and NF3 leads, respectively, to a variation of 3.3, 2.7 and 4.7% in the average environmental impact generated by the eight functional mix scenarios of the DoE.
In the following, the three significant NFs are modelled as constraints and the three non significant are set at their nominal values.
4.4. Optimization of the system from SNF
After an identification of the significant NFs, these functions are modelled as constraints:
| • | Maximum speed (kts): P1 = [11.5, 12]. | ||||
| • | Passenger capacity per mission: P2 = {97; 98; 99; 100}. | ||||
| • | Number of mission per day: P3 = {23, 24}. | ||||
The FU “Number of passengers transported per day” is also modelled as a constraint to satisfy Ps = [2280, 2400].
Other “less significant” functions are set at their nominal values:
| • | No thermal insulation isolation thermique P4 nom = {0}. | ||||
| • | No air-conditioning system P5 nom = {0}. | ||||
| • | Number of batteries charged per day P6 nom = {1}. | ||||
The performance level of NF is identified by CSP.
The performance of three SNF to optimize the environmental performance of the system is characterized by 11.5 knots as maximum speed, 98 passengers as a maximum capacity and 23 crossings per day (Table ). The main components of the system satisfying the requirements of functional mix globally optimized (FMGO) are defined in Table . The Functional Unit Globally Optimized (FUGO) is 2300 passengers per day. This corresponds to a reduction of about 5% of the nominal performance. However, in line with the trend observed on the effects of SNF (Figure ), environmental impacts compared to the reference system are reduced by approximately 13%.
Table 8. Functional mix globally optimized (FMGO).
Table 9 Specifications of main elements for system with FMGO.
Reducing the system’s performances or eliminating certain functions raises the question of outcomes for the passenger. For example, in this type of intercity transport, the number of passengers is not constant throughout the day. It fluctuates, and there are more people during rush hours. In the above simulations, the environmental gain is achieved to the detriment of “social” considerations; this is in contradiction to the concept of sustainable development. Reducing the amount of space on the ship results in constraints for the user. In parallel with initiatives to define a coherent functional mix (that we suggest with the EcoCSP tool), it is therefore necessary to set up measures to make sure that the system’s ecological performances are not achieved at the expense of the users experience. This means, for example, setting up incentives to obtain a more regular flow of passengers throughout the day, such as preferential tariffs for certain time bands, etc.
4.5. Influence of the exhaustiveness of technological solutions on environmental performance
In this section, we propose to modify the component database “Engine”. The database used to model the system comes from the manufacturers catalogues. In Figure the speed of the nominal functional mix is reduced by 0.5 kts. In the first case, the database “Engine” (DB0) is not modified. In the second case, a new engine of 70 kW is added to the database (DB1) (Figure ). The integration of a 70 kW diesel engine in the component database of CSP model has allowed an environmental gain of 8% compared to the use of the initial database (DB0) using diesel engines from manufacturers catalogues. More the alternatives are important more the opportunities to generate better solutions for environment are high.
Figure 7. Influence of diesel engine database on environmental performance.
Functional negotiations must lead to a questioning about the components to use in the system, which could otherwise limit the benefits of environmental performance.
5. Discussions and conclusions
The CSP/LCA approach relies on the CSP solver makes it possible to instantiate the design variables of the system that optimize the performance variables, in our case it is environmental criteria (but it could be also economic criteria). It allows to identify early in design process what are the best combinations of technologies, among many alternatives, for each subsystem whose functions are already specified in the specifications.
The philosophy behind the use of CSP is to allow the designer to model and identify viable concepts reconciling environmental and economic aspects (CSP/LCA), but also the social aspects by acting on the definition of functional performance to get closer to a model of sustainable development.
In this article we have therefore enriched the CSP/LCA approach by constructing the EcoCSP approach. This enables us to anticipate the configuration of a system’s architecture by adapting the performances of NFs. A complex system such as a passenger ferry has numerous subfunctions. A slight downgrading of the performances related to these functions can generate substantial environmental gains. The complexity of couplings among subsystems and their sheer number obliges the user to make use of “intelligent” tools, that by simulating many different scenarios, help the designer to fine-tune and choose the right technologies for sustainable systems.
This EcoCSP approach breaks with traditional design conventions, and allows us to define firstly combinations of technologies and secondly the functional mix that will significantly reduce environmental impacts. LCA is thus no longer used as a tool for system assessment and comparison, but as a tool for eco-design.
In the experimentation phase, we showed the significance of the number of alternatives suggested by the CSP model for reducing environmental impacts. The greater the number of alternative techniques, the higher the number of possibilities for generating better environmental solutions.
Finally, modifying the functional performances of a system results in new social, economic and environmental constraints. In parallel therefore, it is necessary to reflect on all the consequences of such modifications in order to avoid destabilizing the three mainstays of sustainable development.
The EcoCSP tool allows us to make functional judgements and choices to optimize negotiable functional performances and thereby reduce environmental impacts. Nevertheless, this does not mean we should neglect consideration of the social consequences that these choices have on the system’s use.
Disclosure statement
No potential conflict of interest was reported by the authors.
References
- Ally, J., and T. Pryor. 2007. “Life-cycle Assessment of Diesel, Natural Gas and Hydrogen Fuel Cell Bus Transportation Systems.” Journal of Power Sources 170 (2): 401–411.10.1016/j.jpowsour.2007.04.036
- Ayeres, R. U. and Kneese, A. V. 1990. "Externalities: Economy and Ecology: Towards sustainable development." In: Archibugi F, Nijkamp P (eds), Dordrecht, Netherlands: Kluwer Academic Publishers.
- Baptista, P., J. Ribau, J. Bravo, C. Silva, P. Adcock, and A. Kells. 2011. “Fuel Cell Hybrid Taxi Life Cycle Analysis.” Energy Policy 39 (9): 4683–4691.10.1016/j.enpol.2011.06.064
- Bartolozzi, I., F. Rizzi, and M. Frey. 2013. “Comparison between Hydrogen and Electric Vehicles by Life Cycle Assessment: A Case Study in Tuscany, Italy.” Applied Energy 101: 103–111.10.1016/j.apenergy.2012.03.021
- Benhamou, F. 1995. “Interval Constraint Logic Programming.” In Constraint Programming: Basics and Trends. Volume 910 of Lecture Notes in Computer Science, pp. 1–21, edited by A. Podelski. Springer-Verlag.
- Benhamou, F., F. Goualard, L. Granvilliers, and J. F. Puget. 1999. “Revising Hull and Box Consistency.” In Proceedings of International Conference on Logic Programming: 230–244, edited by D. De Schreye. Las Cruces, NM: The MIT Press.
- Benhamou, F., and W. J. Older. 1997. “Applying Interval Arithmetic to Real, Integer and Boolean Constraints.” The Journal of Logic Programming 32 (1): 1–24.10.1016/S0743-1066(96)00142-2
- Boureima, F., N. Sergeant, V. Wynen, H. Rombaut, J. Matheys, J. Van Mierlo, M. De Vos, et al. 2008. “LCA of Conventional and Alternative Vehicles using a ‘data range-based modeling system’.” Proceedings of Urban Transport XIV: Urban Transport and the Environment in the 21st Century, 301–309, Malta.
- Castro, M. B. G., J. A. M. Remmerswaal, and M. A. Reuter. 2003. “Life Cycle Impact Assessment of the Average Passenger Vehicle in the Netherlands.” The International Journal of Life Cycle Assessment 8 (5): 297–304.10.1007/BF02978922
- Chenouard, R. 2007. “Résolution par satisfaction de contraintes appliquée à l’aide à la décision en conception architecturale [Constraint Satisfaction Problem applied to decision support in architectural design].” Dissertation, ENSAM, Paris.
- Chiriaev, D., and G. W. Walster. “Interval Arithmetic Specification.” Revised March 13, 2000. Siberian Branch of the Russian Academy of Sciences. http://www.nsc.ru/interval/Programing/IASpecfn.pdf
- Cilliers, P. 1998. Complexity and Postmodernism: Understanding Complex Systems. London: Routledge.
- Daly, H. E. 1973. Towards a steady-state economy. San Francisco, CA: W. H. Freeman.
- Debruyne, R., and C. Bessiere. 2001. “Domain Filtering Consistencies.” Journal of Artificial Intelligence Research 14: 205–230.
- Finkbeiner, M., R. Hoffmann, K. Ruhland, D. Liebhart, and B. Stark. 2006. “Application of Life Cycle Assessment for the Environmental Certificate of Mercedes-Benz S-Class.” The International Journal of Life Cycle Assessment 11 (4): 240–246.10.1065/lca2006.05.248
- Freeman, C. 1992. The Economics of Hope: Essays on Technical Change, Economic Growth, and the Environment. London: Pinter Publishers.
- Gao, L., and Z. C. Winfield. 2012. “Life Cycle Assessment of Environmental and Economic Impacts of Advanced Vehicles.” Energies 5 (12): 605–620.10.3390/en5030605
- Goedkoop, M., and R. Spreinsma. 2001. “The Eco-indicator 99 – A Damage Oriented Method for Life Cycle Impact Assessment – Methodology annex.” Amersfoort, The Netherlands: Pré consultants.
- Hauschild, M., Jeswiet, J., and Alting, L. 2005. “From Life Cycle Assessment to Sustainable Production: Status and Perspectives.” CIRP Annals - Manufacturing Technology. 54(2): 535–554.
- Herman, R., S. Ardekani, and J. Ausubel. 1989. “Dematerialization.” In Technology and Environment, edited by J. Ausubel and H. Sladovich. Washington, DC: National Academy Press.
- Horvath, A. 2006. “Environmental Assessment of Freight Transportation in the US.” The International Journal of Life Cycle Assessment 11 (4): 229–239.10.1065/lca2006.02.244
- Hussain, M. M., I. Dincer, and X. Li. 2007. “A Preliminary Life Cycle Assessment of PEM Fuel Cell Powered Automobiles.” Applied Thermal Engineering 27 (13): 2294–2299.10.1016/j.applthermaleng.2007.01.015
- Ilog, Ilog C P. 2006. Reference Manual. Gentilly: Ilog.
- ISO 14040 (International Standard Organization). 2000. “14040 Series: Environmental Management – Life Cycle Assessment – Principles and Framework.”
- Jolliet, O., M. Saadé, P. Grettaz, and S. Shaked. 2005. Analyse du cycle de vie (ACV), Comprendre et réaliser un écobilan [Life cycle Analysis (LCA), Understanding and performing Eco-Assessment]. Presses polytechniques et universitaires romandes`.
- Koffler, C., and K. Rohde-Brandenburger. 2009. “On the Calculation of Fuel Savings through Lightweight Design in Automotive Life Cycle Assessments.” The International Journal of Life Cycle Assessment 15 (1): 128–135.
- Krob, D. 2009. “Eléments d’architecture des systèmes complexes, Gestion de la complexité et de l’information dans les grands systèmes critiques [Architectural elements of complex systems, complexity and information management in large critical systems].” A. Appriou, Ed., 179-207, CNRS editions.
- Lagerstedt, J. 2003. “Functional and Evironmental Factors in Early Phases of Product Development-Eco Functional Matrix.” Dissertation, KTH, Stokholm.
- Laudon, N., and M. I. Soriano. 2012. “Comparative LCA of Electrified Heavy Vehicles in Urban Use.” Master of Science Thesis in the Master’s Degree Programme Technology, Society and the Environment.
- Lee, J., H. Cho, B. Choi, J. Sung, S. Lee, and M. Shin. 2000. “Life cycle Assessment of Tractors.” The International Journal of Life Cycle Assessment 5 (4): 205–208.10.1007/BF02979361
- Lhomme, O. 1993. “Consistency Techniques for Numeric CSPs.” Proceedings of IJCAI 93, Morgan Kaufmann, San Mateo, CA, USA.
- Luttropp, C. 2005. “Strategy and Material Flow in Ecodesign.” 12th CIRP international conference on Life Cycle Engineering, Grenoble, France.
- Luttropp, C., and J. Lagerstedt. 2006. “EcoDesign and the Ten Golden Rules: Generic Advice for Merging Environmental Aspects into Product Development.” Journal of Cleaner Production 14 (15–16): 1396–1408.10.1016/j.jclepro.2005.11.022
- Ma, H., F. Balthasar, N. Tait, X. Riera-Palou, and A. Harrison. 2012. “A New Comparison between the Life Cycle Greenhouse Gas Emissions of Battery Electric Vehicles and Internal Combustion Vehicles.” Energy Policy 44: 160–173.10.1016/j.enpol.2012.01.034
- Mackworth, A. K. 1977. “Consistency in Networks of Relations.” Artificial Intelligence 8 (1): 99–118.10.1016/0004-3702(77)90007-8
- MacLean, H. L., and L. B. Lave. 2003. “Evaluating Automobile Fuel/Propulsion System Technologies.” Progress in Energy and Combustion Science 29 (1): 1–69.10.1016/S0360-1285(02)00032-1
- Mayyas, A. T., A. Qattawi, A. R. Mayyas, and M. A. Omar. 2012. “Life Cycle Assessment-based Selection for a Sustainable Lightweight Body-in-White Design.” Energy 39 (1): 412–425.10.1016/j.energy.2011.12.033
- Montanari, U. 1974. “Networks of Constraints: Fundamental Properties and Applications to Picture Processing.” Inform Science 7(2): 95–132.
- Moore, R. E. 1966. Interval Analysis. Englewood Cliffs, NJ: Prentice-Hall.
- Mousazadeh, H., A. Keyhani, A. Javadi, H. Mobli, K. Abrinia, and A. Sharifi. 2011. “Life-cycle Assessment of a Solar Assist Plug-in Hybrid Electric Tractor (SAPHT) in Comparison with a Conventional Tractor.” Energy Conversion and Management 52 (3): 1700–1710.10.1016/j.enconman.2010.10.033
- Nagatomo, T., T. Miyauchi, and H. Tsuchiya. 1997. “Preliminary Investigation for Life Cycle Assessment (LCA) of Shinkansen Vehicles.” Accessed June 10, 2003. http://www.rtri.or.jp/infoce/wcrr97/E142/E142.html
- Nanaki, E. A., and C. J. Koroneos. 2012. “Comparative LCA of the use of biodiesel, diesel and gasoline for transportation.” Journal of Cleaner Production 20 (1): 14–19.10.1016/j.jclepro.2011.07.026
- Pehnt, M. 2002. “Life Cycle Assessment of Fuel Cell Systems.” Erscheint in Fuel Cell Handbook, fuel cell technology and applications, (Vol. 3). J. Wiley; 2002.
- Querini, F., S. Dagostino, S. Morel, and P. Rousseaux. 2012. “Greenhouse Gas Emissions of Electric Vehicles Associated with Wind and Photovoltaic Electricity.” Energy Procedia 20: 391–401.10.1016/j.egypro.2012.03.038
- Reap, J., F. Roman, S. Duncan, and B. Bras. 2008. “A survey of Unresolved Problems in Life Cycle Assessment.” The International Journal of Life Cycle Assessment 13 (5): 374–388.10.1007/s11367-008-0009-9
- Schmidt, W. P., E. Dahlqvist, M. Finkbeiner, S. Krinke, S. Lazzari, D. Oschmann, S. Pichon, et al. 2004. “Life cycle Assessment of Lightweight and End-of-Life Scenarios for Generic Compact Class Passenger Vehicles.” The International Journal of Life Cycle Assessment 9 (6): 405–416.10.1007/BF02979084
- Schwab Castella, P., I. Blanc, M. Gomez Ferrer, B. Ecabert, M. Wakeman, J. A. Manson, D. Emery, et al. 2009. “Integrating Life Cycle Costs and Environmental Impacts of Composite Rail Car-bodies for a Korean Train.” The International Journal of Life Cycle Assessment 14 (5): 429–442.10.1007/s11367-009-0096-2
- Schweimer, G. W., and M. Levin. 2000. Life Cycle Inventory for the Golf A4. Wolfsburg: Research, Environment and Transport, Volkswagen AG.
- Simonis, U. E., 1985. “Preventative Environmental Policy: Prerequisites, Trends and Prospects.” Ekistic 313: 369–372.
- Spielmann, M., R. Scholz, O. Tietje, and P. Haan. 2005. “Scenario Modelling in Prospective LCA of Transport Systems. Application of Formative Scenario Analysis.” The International Journal of Life Cycle Assessment 10 (5): 325–335.10.1065/lca2004.10.188
- Spielmann, M., P. De Haan, and R. W. Scholz. 2008. “Environmental Rebound Effects of High-speed Transport Technologies: A Case Study of Climate Change Rebound Effects of a Future Underground Maglev Train System.” Journal of Cleaner Production 16 (13): 1388–1398.10.1016/j.jclepro.2007.08.001
- Strazza, C., A. Del Borghi, P. Costamagna, A. Traverso, and M. Santin. 2010. “Comparative LCA of Methanol-fuelled SOFCs as Auxiliary Power Systems On-board Ships.” Applied Energy 87 (5): 1670–1678.10.1016/j.apenergy.2009.10.012
- Subic, A., and F. Schiavone. 2010. “Comparative Life Cycle Assessment (LCA) of passenger seats and their impact on different vehicle models.” International Journal of Vehicle Design 53 (1–2): 89–109.10.1504/IJVD.2010.032985
- Sweeting, W. J., and P. H. Winfield. 2012. “Future Transportation: Lifetime Considerations and Framework for Sustainability Assessment.” Energy Policy 51: 927–938.10.1016/j.enpol.2012.09.055
- Taguchi, G., E. Elsayed, and T. Hsiang. 1989. “Quality Engineering in Production System”, New York: McGraw Hill.
- Takeda, K., S. Sugioka, and Y. Shimada. 2008. “LCA of the Various Vehicles in Environment and Safety Aspect.” Proceedings of the 12th international conference on Knowledge-based Intelligent Information and Engineering Systems, Pt 3, 9–16, Zagreb, Croatia.
- Tchertchian, N., P. A. Yvars, and D. Millet. 2013. “Benefits and Limits of a Constraint Satisfaction Problem/Life Cycle Assessment Approach for the Ecodesign of Complex Systems: A Case Applied to a Hybrid Passenger Ferry.” Journal of Cleaner Production 42: 1–18.10.1016/j.jclepro.2012.10.048
- Tharumarajah, A., and P. Koltun. 2007. “Is There an Environmental Advantage of using Magnesium Components for Light-weighting Cars?” Journal of Cleaner Production 15 (11–12): 1007–1013.10.1016/j.jclepro.2006.05.022
- Van Mierlo, J., G. Maggetto, and P. Lataire. 2006. “Which Energy Source for Road Transport in the Future? A Comparison of Battery, Hybrid and Fuel Cell Vehicles.” Energy Conversion and Management 47 (17): 2748–2760.10.1016/j.enconman.2006.02.004
- Van Weenen, J. C. 1995. “Towards Sustainable Product Development.” Journal of Cleaner Production 3 (1–2): 95–100.10.1016/0959-6526(95)00062-J
- Wagner, U., R. Eckl, and P. Tzscheutschler. 2006. “Energetic Life Cycle Assessment of Fuel Cell Powertrain Systems and Alternative Fuels in Germany.” Energy 31 (14): 3062–3075.10.1016/j.energy.2005.10.031
- Williams, R. E., E. D. Larson, and M. H. Ross. 1987. “Materials, Affluence, and Industrial Energy Use.” Annual Review of Energy 12: 99–144.10.1146/annurev.eg.12.110187.000531
- Wimmer, W., and R. Züst. 2003. “ECODESIGN Pilot, Product-Investigation-, Learning- and Optimization-Tool for Sustainable Product Development.” Dordrecht, Netherlands: Kluwer Academic Publishers.
- Zackrisson, M., L. Avellán, and J. Orlenius. 2010. “Life Cycle Assessment of Lithium-Ion Batteries for Plug-in Hybrid Electric Vehicles – Critical Issues.” Journal of Cleaner Production 18 (15): 1519–1529.10.1016/j.jclepro.2010.06.004
- Zamel, N., and X. Li. 2006. “Life Cycle Comparison of Fuel Cell Vehicles and Internal Combustion Engine Vehicles for Canada and the United States.” Journal of Power Sources 162 (2): 1241–1253.10.1016/j.jpowsour.2006.08.007