Original Article
Proceedings of IDMME - Virtual Concept 2010 Bordeaux, France, October 20 – 22, 2010
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Inversion of emission model using constraint propagation on tables and intervals - Application to Ship-Ecodesign V. Larroudé 1, P.A. Yvars 2, D.Millet 3, R.Chenouard 4 , A.Bernard 5
(1) : SUPMECA - LISMMA, Quartier Mayol, Maison des Technologies, 83000 Toulon, France E-mail :
[email protected]
(2) : SUPMECA - LISMMA, 3 Rue Fernand Hainaut, 93407 Saint Ouen Cedex, France E-mail :
[email protected]
(3) : SUPMECA - LISMMA, Quartier Mayol, Maison des Technologies, 83000 Toulon, France E-mail :
[email protected]
(4) : ECN - IRCCyN -, 1 Rue de la Noë, 44 321 Nantes, France E-mail :
[email protected]
(5) : ECN - IRCCyN -, 1 Rue de la Noë, 44 321 Nantes, France E-mail : alain.bernard @irccyn.ec-nantes.fr
Abstract: The purpose of this study is to highlight the interest of using the constraint propagation on intervals method for an exhaust emissions model. The emission model used for our researches is resulting from the MOPSEA project (MOnitoring Program on Air Pollution From SEA-going vessels). A usual approach stemming from the literature and presented in the first part is able to quantify the emissions in a given context, for defined control parameters. But setting all control parameters highly limits the use possibilities of a model. The study developed subsequently makes possible to inverse the model : one can identify and define allowed values intervals for each influent control parameter. That is to say, one can fix a maximum emission level and find different solutions to obtain an emission level below this target. The integration of this study in a dynamic behavior model opens new opportunities for eco-design : it will lead to the consideration of minimizing environmental footprint in aid routing systems, maintenance operations management, utilities operation management…, for different means of transport.
As car manufacturers, who have been pioneers in this domain, all industrial sectors of transportation (a major source of emissions), have focused their researches on the energy optimisation of their products. Before now, Eco-Design is only proposing methodologies to evaluate environmental impacts, in postdesign, when the main design parameters are already valued.
Key words: Eco-design, Greenhouse gases, Modelling, Constraint propagation, Intervals 1- Introduction
Nowadays, it is proven that greenhouse gases Figure 1 : Usual process for product design emissions, wastes production and depletion of resources have a significant impact on human health and on the balance of In this paper, we would like to demonstrate that the ecosystems in the coming years; their control needs a inversion of emission models by constraint propagation on significant modification of product design, operations intervals makes possible to obtain quickly an interval of management and end of life processes methods. This topic is possible values for design parameters by ensuring that the the subject of researches in many fields, including transport. environmental impact of the product remains lower than a
Paper Number
-1-
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Inversion of emission model
level defined by the designer or the standards related to the product. Using this approach will break the loop design / environmental evaluation, which, to our knowledge, has never been done in product design [S1], [C1]. The first paragraph describes the state-of-the-art models of emissions, their use and their limits. The second subsection presents the emission model resulting from the MOPSEA project, its advantages as well as its drawbacks. This analysis concludes with this model suitability. In the third paragraph, theoretical notions on Constraint Satisfaction Problem (CSP) representation and bases on the constraint propagation and interval computation will be given. At the end of this part, a state of the art of these tools in design will be made. The fourth part will be devoted to the implementation of the MOPSEA model (MOnitoring Programme on air pollution from SEA-going vessels) [VMJ1] by using the constraint propagation paradigm and then we will see several scenarios and associated results. Finally, we will present a synthesis of our researches and perspectives for future developments. 2- State of the art
For several years, many emissions models have been developed, especially in road transport, because its environment impact is one of the more significant and contemporary in people mind. 2.1 - MOVES2010 [EPA1]
-
An instant model for research use (including corrections for charge, inclination, temperature, ambient humidity, mileage ...)
2.3 - Canadian National Railway Company
CN [CN1] offers the ability to estimate greenhouse gases emissions due to the transport of products. The calculator is a very simple tool which estimates the equivalent of carbon dioxide emission for the entire transportation of your products. There are some important hypotheses: - The emission factors for marine transport have been estimated only for bulk and containers vessels. - The rail emission factor has been calculated after a 3-years study from the association called Canada’s Locomotive Monitoring Program. - The truck’s emission factor was calculated using diesel fuel consumption data from Canadian statistics “Trucking in Canada in 1995”. - Trans-loading is also considered. 2.4 – LIMOBEL
LIMOBEL project [MJP1] aims to develop an operational modelling tool for studying the impact of different policies on economy and environment, in order to help in decision making. LIMOBEL combines three models: - PLANET2 is an economic model for a long term development. - NODUS is a network model for transporting people and merchandises. - E-MOTION evaluates environment impact taking account the type of transport, the evolution of conventional technologies, alternatives fuels and technological development.
MOVES2010 (Acronym for MOtor Vehicle Emission Simulator which is MOBILE6 [MTQ1] update) is an EPA (United States Environmental Protection Agency) modelling tool for estimating on-road mobile source emissions (cars, trucks, motorcycles, buses). It can be used to estimate greenhouse gases emissions but also air mobile source toxics (benzene, formaldehyde, naphthalene, ethanol ...). It includes an improved emission rate calculator that provides “lookup table” results for starts and evaporative emissions as well as Those three models are linked but not working at the same exhaust emissions. time, some Inputs/Outputs exchanges exists between them. 2.2 - ARTEMIS
In ARTEMIS project [BC1] (Assessment and Reliability of Transport Emission Models and Inventory Systems), the emission models for the various transport modes is converted into computer programs. The level of software development varies considerably according to the transport mode. The software package produced for road transport is the most detailed. Specific emission models have been developed for road emissions and are used in the software: - A continuous model based on the average speed - A discrete model based on a hundred of traffic situations - A discrete and aggregate model: average emission factor for 4 situations : urban, road, highway, and all of these situations. - A continuous model function of several kinematic variables (car speed, average acceleration ...)
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2.5 – CELTU
The Emission Calculator for Urban Transport (CELTU) [TC1] is a Canadian tool used to estimate the annual emissions of greenhouse gases and some key air pollutants from vehicles in an urban context. It takes into account the emissions of the production, refining and transport of used fuel, but also the emissions due to the production of electricity (increasing number of electric cars). The inputs are the type of activity, the year of evaluation, the place, the road type, the fleet composition and the expansion factors. 2.6 – IVE Model
The International Vehicle Emission (IVE) Model [IS1] is a java-based stand-alone computer model designed
to estimate emissions from motor vehicles for any area. There are three types of input: 1) the engine technology and add-on control distribution (including maintenance), 2)
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Inversion of emission model
driving behaviour of the different types of on-road vehicles travelling on local road, 3) vehicle emission factors specific to the local vehicles. In order to make this tool adaptable for any international locale, over 700 technologies have been integrated in the IVE model. 2.7 – MOPSEA Model
Because Belgium needed to comply with international and European agreements, Belgian Science Policy financed the MOPSEA project [VMJ1]. After making an inventory of the environmental air legislation and international reporting obligations, the MOPSEA project has developed a new activity based model to map historical emissions and to make projections of the emissions for the near future. This Model will be used in our study and is detailed in the next paragraph.
MEET, ENTEC, EMS, TREMOVE, TRENDS already exist. They provide estimate emissions from sea-going vessels. Unfortunately, most of those methodologies don’t take into account the technological evolution of sea-going vessels. On the other hand, the MOPSEA model is based on the EMS project, which has been improved to go far its weaknesses. The MOPSEA model [VMJ1] enables to calculate the quantity of the most important produced gases and is available for a very large part of sea-going vessels. This model makes the distinction between fuel related emissions (CO2, SO2) and technology related emissions (HC, CO, NOx, PM).
2.8 – Conclusion
The presented models (excepted 2.7 – MOPSEA) cannot be used in our ship-design application but they clearly show that the problem is transverse. All design processes including road transport vehicles design are concerned. The tools introduced in this brief state of the art are using different kind of models, taking into account several parameters. Nevertheless, all of them can only be used to estimate the emissions of various greenhouse gases or their equivalent of carbon dioxide. None of these tools can help designers to make sure that the product is “green-designed” at the first step of the design process (Figure 1). We chose to develop our design approach on the MOPSEA model because it’s the most detailed and representative one : the calculations are not made for a ship type. They are computed for each individual ship and the emission factors (taken from the EMS project) are age dependant, and makes the difference between fuel and technology related emissions. 3- MOPSEA emission model
Since 1958, the International Maritime Organisation has established a framework for governmental regulation and practices related to technical matters of all kinds affecting shipping engaged in international trade. The most important rules on air emissions from ships are in the Annex VI of the MARPOL convention. In order to reduce emissions from sea-going vessel, Annex VI includes limit values on sulphur oxides, nitrogen oxides, and prohibits deliberate emissions of ozone depleting substances. At first, MARPOL convention didn’t mention anything about emissions of greenhouse gases, that’s why in 2003, IMO adopted a resolution in order to reduce emissions from ships. Europe has established directives on emission regulation and air quality. The emission regulation NECdirective is based on the Göteborg protocol and makes the distinction between fuel types. The EU is in favour of more restriction and aims to reduce NOx emissions of all ships entering European seas and not only ships under European flag. On the one hand, some European methodologies as
Paper Number
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Figure 2: Input / Output parameters of MOPSEA model
• Fuel related emissions : CO2 and SO2 These emissions are closely linked to the type of used fuel. There are three types of fuel: Heavy fuel oil, Diesel and Gas oil, Gas boil off. For each type of fuel and each gas, a fuel emission factor is defined (in kg/tonne fuel). • Technology related emissions : HC, CO, NOx, PM These emissions depend on the main engine technology: 2-stroke engine, 4-stroke engine, Steam turbine. Our study is only interested in 4-stroke-engines, the technology emission factor is defined like in the EMS project: it is a combination of a basic emission factor and correction factors for the technology (age and NOx regulation) and the percentage of Maximum Continuous Rate (MCR). (1) technology emission factor (g/kWh) = basic emission factor (g/kWh) x CorrAge x CorrNOx x CorrMCR The basic emission factors are fuel type depending and based on a test cycle: this is an average of all stages of navigation. Therefore, they are not representative of the individual stages of navigation (expressed in % of MCR), that’s why another correction factor has to be implemented. Emissions are dependent on the year of construction of the vessels because of the evolution of engine technology. MARPOL Annex VI has imposed restrictions on NOx emissions for the main engines built after 1999.
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When we dispose of values for the technology emission factors, we are able to calculate the emissions, but we need to know the energy use which can be determined by multiplying the used power by the duration and fuel use: (2) energy used (kWh) = power (kW) x duration (h) With : (3) power (kW) = % of MCR x maximum power installed (kW) In fuel related emissions calculation, we need to know the fuel weight used : (4) fuel use (t) = energy use (kWh) x specific fuel use (g/kWh) x 1,1.10-6
discrete CSP are different from CSP on real variables also called continuous CSP. - On the one hand, for solving discrete CSP, the methods are ones arising from operational research and artificial intelligence. The first work is more than thirty seven years old [MA1]. These discrete CSP methods, of exponential complexity, are based on enumeration and filtering. This filtering, also called constraint propagation, enables the definition domains of variables to be reduced as the resolution process evolves. - On the other hand, CSPs have been developed with real variables with values in intervals. This interval-based resolution technique is a synthesis between interval-based analysis ([MO1] and CSPs in [LH1]. Several techniques have been developed, one of which is presented as an example in [BG1]. In this study we will focus on the use of CSP on intervals. The CSP community has developed work applicable in product and systems design as in [VSAY1, YH1, CSG1, YLZ1].
(5) Finally:
4.3 – Modelling Tables as global constraints
(6) technology related emission (t) = technology emission factor (g/kWh) x energy used (kWh) x 10-6
The MOPSEA model uses several tables of values. Then, the basic emission factors, the correction factor for the age and the correction factor for the % of MCR should be found inside tables of values. As an example, the correction factor for the % of MCR values (Table 1) should be modeled as a constraint table.
(7) fuel related emission (t) = fuel emission factor (g/t) x fuel use (t) x 10-6 4- Using constraint propagation on intervals and tables
MCR
4.1 – Constraint Satisfaction Problem
A CSP [TS1] is defined by a 3-tuple (X, D, C) such that: - X = {x1, x2, x3…, xn} is a finite set of variables which we call constraint variables with n being the integer number of variables in the problem to be solved. - D = {d1, d2, d3…, dn} is a finite set of variable value domains of X such that: A domain should be a real interval or a set of integer values. - C = {c1, c2, c3…, cp} is a finite set of constraints, p being any integer number representing the number of constraints of the problem. Solving a CSP consists in instantiating each variable of X, and at the same time satisfying the set of problem constraints C.
HC
CO
NOx PM
85
0,84
0,7 0,97
0,97
80
0,87
0,76 0,97
0,98
75
0,89
0,82 0,98
0,98
70
0,92
0,88 0,98
0,99
65
0,95
0,94 0,99
0,99
60
0,98
1 0,99
55
1
1
1,06
1
1
50
1,03
1,12
1
1,01
45
1,09
1,23 1,01
1,01
40
1,16
1,38 1,02
1,03
35
1,27
1,56 1,03
1,05
30
1,42
1,8 1,04
1,08
25
1,65
2,14 1,06
1,12
20
2,02
2,66
1,1
1,19
15
2,74
3,51 1,17
1,32
10
4,46
5,22 1,34
1,63
0
0
0
0
0
Table 1: Correction factor for the % of MCR
A constraint table is a global constraint that A constraint should be any type of mathematical relation (linear, quadratic, non-linear, Boolean…) covering the values represents the possible combination values of a set of constraint variables. By global constraint, we mean a of a set of variables constraint that should be propagated on complex data structures. In our case, each line of a constraint table is a 4.2 – CSP solving process tuple of consistent values. If one or several values of a The solving process for a CSP depends on the type of constraint variable become forbidden during a CSP solving the constraint variables. In fact CSP on integer variables called process all the tuples related to this value are removed from the table too.
Paper Number
-4-
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Inversion of emission model
Initial For example, with Table 1, if we decide that PM has Variables Intervals to be greater than 1 and CO has to be different to 1.12 then, the nine first lines are removed from the table. Only the nine last Basic emission factor lines stay inside the constraint table. befHC 0.6 befCO 3 4.4 – Dealing with direct and reverse model befNOx 12 simultaneously befPM {0,5, 0.8} If we consider an input vector and an output vector Ship characteristics we call direct model the relation below: Duration 1 (hour) Power [0, 1e5] (kW) Energy use [0, 1e9] And reverse model the relation below : (kWh) RPM [0, 1e7] (round per minute) [0, 85] and the %ofMCR Now, if we consider the input interval vector oilType {0, 1, 2} output interval vector , any relation between and Date of {0,…, 6} like Building
(1)
(3)
(2)
0.6 3 12 {0,5, 0.8}
0.6 3 12 0.8
0.6 3 12 0.8
1
1
1
[0, 62.05]
[290, 1e7]
[36.5, 62.05] [36.5, 62.05] [2000, 1e7]
[36.5, 62.05] [36.5, 62.05] [2000, 1e7]
[0, 85] {0, 1, 2} {0,…, 6}
[50, 85] 0 5
[0, 62.05]
0 5
Correction
is sufficient to automatically infer a direct and reverse and . That is to say any reduction on model between and vice versa. In other words, involves reduction on constraints are a-causal. 5- Numerical results 5.1 – Modelling the emission problem as a CSP
CorrAgeH C CorrAgeC O CorrAgeNo x CorrAgeP M CorrMCRH C CorrMCRC O CorrMCRN Ox CorrMCRP M CorrNOx
0.67
[0.5, 1]
[0.5, 1]
[0.67, 1]
[0.67, 1]
0.67
0.67
[0.92, 1.33]
[0.92, 1.33]
0.92
0.92
[0.6, 1]
[0.6, 1]
0.88
0.88
[0, 4.46]
[0, 4.46]
[0.84, 1.03]
[0, 5.22]
[0, 5.22]
[0.7, 1.12]
[0, 1.34]
[0, 1.34]
[0.97, 1]
[0.84, 0.87] [0.7, 0.76] 0.97
[0, 1.63]
[0, 1.63]
[0.97, 1.01]
[0.97, 0.98]
[0, +
1
1
1
[0, +
[0, 2.0676]
[0, +
[0, 15.66]
[0.337, 0.414] [1.4, 2.251]
[0, + [0, +
[0, 21.3864] [0, 1.304]
[0.337, 0.3497] [1.4, 1.527] 10.7088
[0, +
[0, 1.66e-4]
[0, +
[0, 9.717e3] [0, 1.327e3] [0, 8.1e-5]
Emission Factors efHC (g/kWh) efCO (g/kWh) efNOx (g/kWh) efPM (g/kWh) emHC (g) emCO (g) emNOx Figure 3: UML class diagram of the MOPSEA model (g) An emission model depends on two components: the emPM fuel and the motorization. The motorization should be (g)
specialized in two subclasses: The engine and the steam Turbine classes. Finally, the engine class should be specialized in two subclasses too: The 2-Stroke-Engine and 4-StrokeEngine classes (Figure 3).
Paper Number
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[0, + [0, +
[10.7, 11.04] [0.68, 0.711] [1.2e-5, 2.57e-5] [5.13e-5, 1.39e4] [3.9e-4, 6.85e-4] [2.49e-5, 4.41e-5]
[0.682, 0.689] [1.97e-5, 2.17e-5] [8.2e-5, 9.48e-5] [6.25e-4, 6.64e-4] [3.98e-5, 4.28e-5]
Table2: CSP numerical results
In this study, we only present the results obtained on a CSP modelling of the 4-Stroke-Engine class. Then all the relations detailed in paragraph 1 have been implemented as
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Inversion of emission model
constraints. Moreover, all the tables are considered as global constraints as shown in paragraph 4.3. We would like now to illustrate our approach with a set of numerical results. The initial domains for the constraint variables are presented on Table 2, first column.
[BG1] Benhamou F., Granvilliers L., Continuous and Interval Constraints. In Handbook of Constraint Programming, Chapter 16:571-604, 2006.
The next propagation step again reduces the intervals and finds a solution as in Table 2, column (2).
line]. URL : http://www.epa.gov (accessed 04/01/2010).
[C1] Chanaron J.J., Life Cycle Assessment Practices: Benchmarking Selected European Automobile After a first propagation, the intervals are reduced as Manufacturers. International Journal of Product Lifecycle in Table 2, column (1). The algorithm tries to reduce as weak Management 2, pp.290-311, 2007. as possible the intervals and ensure that if a solution exist it’s [CN1] Canadian National Railway Company. Site of the inside the resulting domains. Canadian National Railway Company, [On line]. URL : http://www.cn.ca (accessed 04/01/2010). [CSG1] Chenouard R., Sebastian P., Granvilliers L., Solving 5.2 – Direct model an Air Conditioning Problem in an Embodiment Design After adding the specifications: about: Context using Constraint Satisfaction Techniques, CP'2007, - The type of oil: oilType = 0 13th International Conference on Principles and Practice of - The date of building of the ship: DoB = 5 Constraint Programming, 2007. - The minimum power of the engine: P > 35 [EPA1] Environmental Protection Agency of the United - The minimum speed of the ship: RPM > 2000 States. Site of the Environmental Protection Agency, [On
5.3 – Reverse model
Let’s assume now that we want to post constraints on the outputs of the model. For example as setting the minimum of the emission factor of NOx: efNOx < 10.8
[IS1] International Sustainable Systems Research Center. Site of the International Sustainable Systems Research Center. http://www.issrc.org (accessed 04/01/2010). [LH1] Lhomme O., Consistency Techniques for Numeric CSPs, 13th International Conference on Artificial Intelligence, pp 232-238, Chambéry, France, 1993. [MA1] Mackworth A.K., Consistency in networks of relations, Artificial Intelligence 8, 1, pp.99-118, 1977.
Then automatically, intervals are reduced as in Table 2, [MJP1] Mayeres I., Jourquin B., Pietquin F., Letchien J., De Vlieger I., Schrooten L., Vankerkom J., Effets à long terme column (3). de politiques et measures sur la mobilité en Belgique, Belgian Science Policy 2009. 6- Conclusion
[MO1] Moore R.E., Interval Analysis, Prentice-Hall, 1966
As a conclusion, we can outline that CSP method is able to support the inversion of an emission model reducing the size and the complexity of the emission/control space. Here CSP method avoids complex analytical manipulation of function requirements. Users can test several way of constraining the emission by adding or deleting constraints. This incremental process is very useful in this phase of reducing the emission/control space, in an integrated process. Relating to our future prospect, we plan to associate this CSP approach of emission model with an interval based dynamic ship model. The goal is to connect directly the emission interval vector and the control parameters of the ship. Moreover, we plan to introduce the concept of maintenance in our models : we want both to determine when a maintenance operation is relevant and to introduce its impact on vessel characteristics. Finally, we think that our approach should be applied to minimize environmental footprint in aid routing systems, for different transport ways.
[MTQ1] Ministry of Transport of Quebec. Site of the Ministry of Transport of Quebec, [On line]. URL : http://www.mtq.gouv.qc.ca (accessed 04/01/2010). [S1] Sharrat P., Environmental criteria in design, Computers and Chemical Engineering 23, pp.1469-1475, 1999. [TC1] Transports Canada. Site of transports Canada, [On line]. URL : http://www.tc.gc.ca (accessed 04/01/2010). [TS1]Tsang E., Foundations of Constraint Satisfaction, Academic Press London and San Diego, 1993. [VMJ1] Vangheluwe M., Mees J., Janssen C., Monitoring programme on air pollution from sea-going vessels, Belgian science Policy 2007 [VSAY1] Vargas C., Saucier A., Albert P., Yvars P.A., Knowledge Modelisation and Constraint Propagation in a Computer Aided Design System, In Workshop notes Constraint Processing in CAD of the Third International Conference on Artificial Intelligence in Design, Lausanne, Switzerland, August, 1994. [YH1] Yannou B., Harmel G., Use of Constraint Programming for Design, in Advances in Design, ElMaraghy H., ElMaraghy W. Editors, Springer, p. Chapter 12, 2005.
7- References
[BC1] Boulter P., McCrae I., Assessment and reliability of transport emission models and inventory systems. Unpublished [YLZ1] Yvars P.A., Lafon P., Zimmer L., Optimization of Mechanical System: Contribution of Constraint Satisfaction Final report 2007. Method, Proc of International Conference on Computers and Industrial Engineering (CIE’39), Troyes, 2009.
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