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Evaluation of Mitigation Strategies in Shipping Industry Using a Metamodel Based Method
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Energy Procedia 158 Energy Procedia 00(2019) (2017)4031–4036 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China China
Evaluation of Mitigation Strategies in Shipping Industry Using a EvaluationTheof15th Mitigation Shipping InternationalStrategies Symposium oninDistrict HeatingIndustry and CoolingUsing a Metamodel Based Method Metamodel Based Method Assessing the feasibilityJun of Yuan using a, the heat demand-outdoor * Jun Yuana, * district heat demand forecast temperature China function for a long-term Institute of FZT Supply Chain, Shanghai Maritime University, Shanghai, China a a
China Institute of FZT Supply Chain, Shanghai Maritime University, Shanghai, China
a,b,c
I. Andrić
*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b Veolia Recherche Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Shipping is a cmajor contributor to the global&CO 2 emissions. In order to reduce the ship emissions, various mitigation strategies Département Systèmes Énergétiques et Environnement IMT 4 the ruenot Alfred Kastler, Nantes, Shipping a major contributor to the global CO2 emissions. In- order to isreduce ship emissions, various mitigation strategies have beenisproposed including technical and operational strategies. AsAtlantique, it usually possible to 44300 implement allFrance these mitigation have been it proposed including technical operational strategies. As itFor is usually not strategies possible toevaluation, implementship all these mitigation strategies, is important to evaluate theand efficiency of these strategies. mitigation energy systems strategies, it is for important to evaluate the efficiency of these strategies. Forenergy mitigation strategies evaluation, ship energy systems are often used assessment from a systems perspective. However, ship systems are usually quite complex for analysis. are used for assessment from aby systems ship energy systems usually quite complexInforthis analysis. Thisoften is because they are constructed many perspective. components However, and these components may haveare non-linear correlations. paper, Abstract This is because theymetamodel are constructed many components these components may havestrategies. non-linearThis correlations. this paper, a Gaussian process basedbymethod is proposed and to evaluate the ship mitigation method isInmuch more aefficient Gaussian process metamodel method is proposed evaluate mitigation strategies. methodAis ship muchenergy more than directly analyzingbased the ship energy systems to when they the are ship computationally expensiveThis to assess. District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the efficient directly tanker analyzing the shipinenergy systems are computationally expensive to assess. A shippotential energy system ofthan a chemical is analyzed this paper. The when resultsthey indicate that speed reduction has a large abatement greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat system of aconsidered chemical tanker is analyzed in this paper. The other. results indicate that speed reduction has a large abatement potential and all the strategies have correlations with each sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, and all the considered strategies have correlations with each other. prolonging the investment return period. Copyright © 2018 Elsevier Ltd. All rights reserved. The main scope of this paper isby to Elsevier assess the feasibility of using the heat demand – outdoor temperature function for heat demand © 2019 The Published Ltd. Copyright ©Authors. 2018 Elsevier Ltd. Allresponsibility rights reserved. Selection and peer-review under of the scientific committee of the 10th International Conference on Applied forecast. The access districtarticle of Alvalade, in Lisbonlicense (Portugal), was used as a caseth study. The district is consisted of 665 This is an open under thelocated CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 10 International Conference on Applied Energy (ICAE2018). Peer-review under responsibility of the scientific of ICAE2018 – The 10th International Conference on Applied buildings that vary in both construction periodcommittee and typology. Three weather scenarios (low, medium, high) and threeEnergy. district Energy (ICAE2018). renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: Ship energy system; mitigaion strategies evaluation; Gaussian process; energy consumption. compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: Ship energy system; mitigaion strategies evaluation; Gaussian process; energy consumption. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.scenarios, Introduction the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1. Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Shipping is anumber major transportation over the world also season a major contributor global COof2 emissions. decrease in the of heating hoursmode of 22-139h during theand heating (depending on to thethe combination weather and Shipping is the a major transportation mode the world intercept and(IMO) alsoincreased astudy majorindicate contributor the decade global CO2 emissions. The results scenarios in third International Organization that to shipping contributes around renovation considered). On theMaritime otherover hand, function for 7.8-12.7% per (depending on the The results in the International Maritime (IMO) study indicate thatforshipping around emissions [1]. Without mitigation strategies to reduce shipscenarios COcontributes it and is 3% of global CO2third coupled scenarios). The values suggested couldappropriate beOrganization used to modify the function parameters the considered, 2 emissions, [1]. estimations. Without appropriate mitigation strategies to reduce ship CO2 emissions, it is 3% of global CO2 emissions improve the accuracy of heat demand © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86-21-38283865; . Cooling. address:author. [email protected] * E-mail Corresponding Tel.: +86-21-38283865; . E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. th Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10 International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.836
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Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
expected that they will increase 50% to 250% by 2050. Therefore, it is important to propose and implement mitigation strategies to reduce the ship CO2 emissions. There are various mitigation strategies that have been studied including operational and technical measures for ship emissions reduction [2]. For instance, 50 possible operational and technical mitigation measures have been identified in the IMO MEPC 62 report [3]. Although there are a lot of mitigation strategies have been studied, it is usually not possible to implement all these mitigation measures simultaneously. Hence, it is important and necessary to evaluate the efficiency of these mitigation strategies so as to select proper mitigation strategies for implementation. Various methods have been proposed to evaluate the efficiency of the shipping mitigation strategies. One commonly used way is to plot the marginal abatement cost curve (MACC) for mitigation strategies [4], which represents the relationship between the cost-effectiveness and the abatement amount. Although MACCs are easy for use to support policy making, they have several shortcomings. One significant shortcoming is that all mitigation strategies are assumed to be independent in abatement and cost evaluation. Due to the complexity of the ship energy system, there always are interactions among various mitigation strategies. For example, the technical measures hull coating I and hull coating II are two correlated measures which are not efficient to implement simultaneously. For a better evaluation of the mitigation strategies, a systems approach is often preferred considering the complex relationship within the ship energy system [5]. Different ship energy systems have been developed and studied with different scopes and purposes. [6] considered energy flows onboard ships in the time domain for complete ship energy systems simulation. Their system allows for interactions at system. [5] described a method to perform a feasibility analysis of the waste heat recovery system. [7] developed a modeling framework to examine the technical and economic interaction and simulate what might be optimal solutions in future. Due to the complexity of the ship energy system, several attributes of the system can be founded. The first is that there are usually a large number of components in the ship energy system. The second is that different components generally have significant interactions with each other. The third is that the relationship among different components often exhibits nonlinearity. These attributes are difficult to evaluate while they may have significant effects on the mitigation strategies evaluation results. To deal with these complexities, a systems approach is commonly used which teats the ship energy system as a whole. [8] proposed a model based energy and exergy analysis for ship energy system. [9] developed a systems approach to assess the efficiency of the combined engine-WHR system considering interactions among components. [10] applied a systems approach to evaluate the operational conditions for ship energy system. Systems approach usually requires the comprehensive examination of the physical systems or simulation models. With large and complex ship energy system, this approach may not be efficient as it can be computationally expensive to evaluate different mitigation strategies. To handle this problem, metamodel based approaches are widely proposed. Metamodels are statistical approximations of the ship energy system, which are usually simpler and cheaper for evaluation. There are various metamodels that have been well studied, including the Gaussian process (GP) models [11] and neural networks [12]. Gaussian process model is one of the commonly used models due to its mathematical convenience and flexibility [13]. Metamodels are also widely adopted to assess the ship energy systems. A mathematical model is proposed in [14] to predict the ship energy performance based on the collected data. The combination of physical model and artificial neural networks for system evaluation is investigated in [15]. An artificial neural network is applied in [16] to estimate ship propulsion power and in [17] to predict the main propulsion efficiency. The comparison between the artificial neural networks and GP models is given in [18], which indicate the good performance of Gaussian process models. It can be found that GP models have been used to evaluate the mitigation measures based on ship energy systems. However, these studies currently only consider the ship propulsion system, the using of GP model for mitigation strategies evaluation still lacks of comprehensive study. In addition, the studies apply the deterministic GP model for ship energy system. In practice, most ship energy systems are stochastic. Therefore, the application of stochastic GP model to evaluate mitigation strategies is needed. In this paper, a GP based method is proposed to evaluate the mitigation strategies based on ship energy system. This method is much more efficient than directly analyzing the ship energy system. Furthermore, the method is applied in a real case study to evaluate the mitigation strategies including speed reduction, weather routing and waste heat recovery. The results can be used to support policy making on mitigation strategies selection. The rest of the paper is organized as follows: the studied ship energy system and the considered mitigation strategies are described in Section 2. The application of GP model to evaluate
Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
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the mitigation strategies is given in Section 3. A case study is given in Section 4. The conclusion is provided in Section 5. 2. Mitigation strategies evaluation based on ship energy systems Fuel is consumed in a ship and then converted to different energy forms for final use, including the mechanical power for propulsion, electricity power for auxiliaries and thermal power for heating. The framework of a ship energy system for a chemical tanker is shown in Fig. 1. The mechanical energy supplied by main engines are mainly used for propulsion. The resistance generated from ship movement depends on various factors, such as the ship speed, hull specifies and weather conditions. All these factors have effects on the propulsion power demand. Electricity power is required by many components in the ship energy system, including the compressors in air conditioning (HAVC) system, nitrogen compressor and cargo pumps. Electricity power can be generated from both main engines and auxiliary engines. Thermal power is mainly used for heating, such as the accommodation, fuel heating and fresh water generation. It can be seen that a ship energy system is constructed by many components and some components have interactions with each other. Therefore, some mitigation strategies can be correlated which makes their evaluation much more difficult. To deal with this problem, a systems approach can be used which treats the ship energy system as a whole system. In this paper, three mitigation strategies are considered, which are speed reduction, weather routing and trim optimization. Speed reduction or slow steaming is comprehensively studied in recent years as one of the major mitigation strategies. The reducing of the speed can reduce the required power hence reduce the fuel consumption. Weather conditions are external factors that may have significant effects on the ship fuel consumption. The optimization of the shipping route under different weather conditions can reduce the unnecessary fuel consumption. Different trim and draft can influence the ship resistance and then have effects on the final fuel consumption. Therefore, trim optimization can provide appropriate trim during voyage so as to reduce the fuel consumption.
Fig. 1. Ship energy system framework of a chemical tanker
3. A metamodel based method for mitigation strategies evaluation The GP model is developed to represent the complex ship energy system. For the considered mitigation strategies, the corresponding input factors in the model include ship speed, wind speed, wind direction, wave height, wave direction, trim and draft. The output of the model is the ship fuel consumption. With the developed GP model, the relationship between these input factors and the ship fuel consumption can be analyzed. Then the effects of corresponding mitigation strategies can be evaluated. The real observed data for shipping activities, weather conditions and fuel consumption in 2017 are used for training the model. The data obtained from January to March in 2018 are used for validation. With identified input values and output fuel consumption from the training data, the GP model can be developed.
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Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
Let x={x1,x2 ,...,xn } denote the observed input values for the considered factors and y (x) denote the observed ship energy consumptions at x. y (x) is assumed to be a GP with an unknown constant mean µ and a covariance function Σ with Gaussian correlation, which is represented as: (1) y ( x) = GP( µ , Σ) Various covariance functions can be used in the GP model. Here, the commonly used Gaussian correlation is adopted which has the following form between any two input sets x and x’. (2) k ( x, x' ) = exp(−θ || d ||2 ) where θ is the unknown decaying parameter and ||d|| denote the Euclidean distance between x and x'. Given GP model form, the interest is to predict the output (e.g. fuel consumption) at unknown conditions. Let y * ( x*) denote the ship energy consumption at unobserved condition x*. Given the GP prior and the likelihood function that the model outputs are normally distributed conditional on the observed data, the predictive distribution of y * ( x*) derived as a conditional normal distribution with following form: (3) y* | y ~ N ( µ + ΣT* Σ −1 ( y − µ ), Σ** − ΣT* Σ −1Σ* where Σ* denote a vector of covariance between the training data set x and the unobserved data set x*, and Σ ** denote the covariance for x*. With equation (3), ship fuel consumption at any input condition can be predicted as the given normal distribution. . To develop the GP model and derive the predictive distribution for the model output, there are various unknown parameters including the unknown constant mean µ and the unknown parameters in the covariance functions. These parameters have to be estimated using the training data. Here the maximum likelihood estimation method is used to estimate these parameters. Given the estimated parameters, the ship fuel consumption can be predicted as equation (3). Furthermore, the developed model can be used to assess the effects of different mitigation strategies. As the implementation of a mitigation strategy corresponds to change the values of some input factors. Therefore, the effects of the mitigation strategies can be evaluated by evaluating the model outputs for different input conditions. The evaluation of the mitigation strategies is further illustrated in a chemical tanker energy system. 4. Case study An energy system for a chemical tanker is studied in this paper. The chemical tanker 181 meters long and 31.3 meters wide. The maximum draft is 12.4 meters and the maximum capacity is 51000 m3. The ship has two main engines and two auxiliary engines. The data obtained in 2017 are used to training the model and the data in the first quarter of 2018 are used to validate the model. The observed fuel consumptions and the predicted fuel consumptions at given input conditions are shown in Fig. 2. It can be found from Fig. 2 that most points are around the fitted line (red line). Therefore, most predicted values are close to the observed values. The goodness-of-fit test is further conducted to verify the model. The test results indicate that there are no significant difference between the observed and the predicted outputs. In addition, the calculated root mean square error (RMSE) is 0.3176, which is accepted compared with the observation error.
Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
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Fig. 2. Comparison of observed and prediction fuel consumptions for validation data
The validated model is then used to evaluate the three mitigation strategies, including speed reduction, weather routing and trim optimization. The amount of fuel consumption reduced by different mitigation strategies or combination of mitigation strategies are given in Table 1. As expected, speed reduction has the largest reduction amount with 19% for the fuel consumption. The results also show that the reduction percentage of the combination of mitigation strategies does not equal to the sum of the reduction percentage for these mitigation strategies. Therefore, these three mitigation strategies have positive correlation with each other. To better evaluate the performance of these mitigation strategies, it is important to consider these correlations. Table 1. Reduction of fuel consumption for different mitigation strategies. Mitigation strategies
Percentage of fuel reduction
Mitigation strategies
Percentage of fuel reduction
Speed reduction
19%
Speed reduction & Weather routing
20.7%
Weather routing
2.5%
Weather routing & Trim optimization
3.6%
Trim optimization
1.6%
Trim optimization & Speed reduction
19.9%
Speed reduction & Weather routing & Trim optimization
21.8%
4. Conclusion There are various mitigation strategies can be used to reduce the ship fuel consumption end emissions. Due to different constraints, it is necessary and important to evaluate these mitigation strategies so as to select the most appropriate ones. In this paper, a metamodel based method is proposed to evaluate the ship mitigation strategies. This method is much more efficient when the ship energy system is time consuming to evaluate. A case study of a chemical tanker is given to illustrate the proposed method. Three mitigation strategies are compared including speed reduction, weather routing and trim optimization. The results show that speed reduction has larger reduction amount compared to other two mitigations strategies. The combination of mitigation strategies does not mean that the maximum reduction amount can be achieved. Therefore, there are correlations among the mitigation strategies. The
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Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
evaluation of these mitigation strategies should consider the effects of the correlation so as to provide more reliable results for policy making. In this paper, only three mitigation strategies are considered for comparison. An important future work is to consider more mitigation strategies. Another potential future work is to apply this method to different types of ship energy systems. References [1] Smith T, Jalkanen J, Anderson B, Corbett J, Faber J, Hanayama S, et al. Third imo ghg study 2014. International Maritime Organization (IMO), London, http://www/ iadc org/wp-content/uploads/2014/02/MEPC-67-6-INF3-2014-Final-Report-complete pdf. 2014. [2] Buhaug Ø, Corbett J, Endresen Ø, Eyring V, Faber J, Hanayama S, et al. Second imo ghg study 2009. 2009. [3] Faber J, Wang H, Nelissen D, Russell B, Amand D. Marginal abatement costs and cost effectiveness of energy-efficiency measures. The Society of Naval Architects and Marine Engineers (SNAME) London. 2011. [4] Yuan J, Ng SH. Emission reduction measures ranking under uncertainty. Applied Energy. 2017;188:270-9. [5] Baldi F, Gabrielii C. A feasibility analysis of waste heat recovery systems for marine applications. Energy. 2015;80:654-65. [6] Cichowicz J, Theotokatos G, Vassalos D. Dynamic energy modelling for ship life-cycle performance assessment. Ocean Engineering. 2015;110:49-61. [7] Smith T. Technical energy efficiency, its interaction with optimal operating speeds and the implications for the management of shipping’s carbon emissions. Carbon Management. 2012;3:589-600. [8] Basurko OC, Gabiña G, Uriondo Z. Energy performance of fishing vessels and potential savings. Journal of Cleaner Production. 2013;54:3040. [9] Larsen U, Pierobon L, Baldi F, Haglind F, Ivarsson A. Development of a model for the prediction of the fuel consumption and nitrogen oxides emission trade-off for large ships. Energy. 2015;80:545-55. [10] Dedes EK, Hudson DA, Turnock SR. Assessing the potential of hybrid energy technology to reduce exhaust emissions from global shipping. Energy Policy. 2012;40:204-18. [11] Yuan J, Nian V, Su B, Meng Q. A simultaneous calibration and parameter ranking method for building energy models. Applied Energy. 2017;206:657-66. [12] Kalogirou SA. Applications of artificial neural-networks for energy systems. Energy Systems: Elsevier; 2000. p. 17-35. [13] Yuan J, Ng SH, Tsui KL. Calibration of stochastic computer models using stochastic approximation methods. IEEE Transactions on Automation Science and Engineering. 2013;10:171-86. [14] Journée J, Rijke R, Verleg G. Marine performance surveillance with a personal computer: Technische Universiteit; 1987. [15] Leifsson LÞ, Sævarsdóttir H, Sigurðsson SÞ, Vésteinsson A. Grey-box modeling of an ocean vessel for operational optimization. Simulation Modelling Practice and Theory. 2008;16:923-32. [16] Pedersen BP, Larsen J. Prediction of full-scale propulsion power using artificial neural networks. The 8th Int Conf Computer and IT Applications in the Maritime Industries2009. p. 537-50. [17] Petersen JP, Winther O, Jacobsen DJ. A machine-learning approach to predict main energy consumption under realistic operational conditions. Ship Technology Research. 2012;59:64-72. [18] Petersen JP, Jacobsen DJ, Winther O. Statistical modelling for ship propulsion efficiency. Journal of marine science and technology. 2012;17:30-9.
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Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
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www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)4031–4036 000–000 www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China China
Evaluation of Mitigation Strategies in Shipping Industry Using a EvaluationTheof15th Mitigation Shipping InternationalStrategies Symposium oninDistrict HeatingIndustry and CoolingUsing a Metamodel Based Method Metamodel Based Method Assessing the feasibilityJun of Yuan using a, the heat demand-outdoor * Jun Yuana, * district heat demand forecast temperature China function for a long-term Institute of FZT Supply Chain, Shanghai Maritime University, Shanghai, China a a
China Institute of FZT Supply Chain, Shanghai Maritime University, Shanghai, China
a,b,c
I. Andrić
*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b Veolia Recherche Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Shipping is a cmajor contributor to the global&CO 2 emissions. In order to reduce the ship emissions, various mitigation strategies Département Systèmes Énergétiques et Environnement IMT 4 the ruenot Alfred Kastler, Nantes, Shipping a major contributor to the global CO2 emissions. In- order to isreduce ship emissions, various mitigation strategies have beenisproposed including technical and operational strategies. AsAtlantique, it usually possible to 44300 implement allFrance these mitigation have been it proposed including technical operational strategies. As itFor is usually not strategies possible toevaluation, implementship all these mitigation strategies, is important to evaluate theand efficiency of these strategies. mitigation energy systems strategies, it is for important to evaluate the efficiency of these strategies. Forenergy mitigation strategies evaluation, ship energy systems are often used assessment from a systems perspective. However, ship systems are usually quite complex for analysis. are used for assessment from aby systems ship energy systems usually quite complexInforthis analysis. Thisoften is because they are constructed many perspective. components However, and these components may haveare non-linear correlations. paper, Abstract This is because theymetamodel are constructed many components these components may havestrategies. non-linearThis correlations. this paper, a Gaussian process basedbymethod is proposed and to evaluate the ship mitigation method isInmuch more aefficient Gaussian process metamodel method is proposed evaluate mitigation strategies. methodAis ship muchenergy more than directly analyzingbased the ship energy systems to when they the are ship computationally expensiveThis to assess. District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the efficient directly tanker analyzing the shipinenergy systems are computationally expensive to assess. A shippotential energy system ofthan a chemical is analyzed this paper. The when resultsthey indicate that speed reduction has a large abatement greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat system of aconsidered chemical tanker is analyzed in this paper. The other. results indicate that speed reduction has a large abatement potential and all the strategies have correlations with each sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, and all the considered strategies have correlations with each other. prolonging the investment return period. Copyright © 2018 Elsevier Ltd. All rights reserved. The main scope of this paper isby to Elsevier assess the feasibility of using the heat demand – outdoor temperature function for heat demand © 2019 The Published Ltd. Copyright ©Authors. 2018 Elsevier Ltd. Allresponsibility rights reserved. Selection and peer-review under of the scientific committee of the 10th International Conference on Applied forecast. The access districtarticle of Alvalade, in Lisbonlicense (Portugal), was used as a caseth study. The district is consisted of 665 This is an open under thelocated CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 10 International Conference on Applied Energy (ICAE2018). Peer-review under responsibility of the scientific of ICAE2018 – The 10th International Conference on Applied buildings that vary in both construction periodcommittee and typology. Three weather scenarios (low, medium, high) and threeEnergy. district Energy (ICAE2018). renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: Ship energy system; mitigaion strategies evaluation; Gaussian process; energy consumption. compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: Ship energy system; mitigaion strategies evaluation; Gaussian process; energy consumption. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.scenarios, Introduction the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1. Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Shipping is anumber major transportation over the world also season a major contributor global COof2 emissions. decrease in the of heating hoursmode of 22-139h during theand heating (depending on to thethe combination weather and Shipping is the a major transportation mode the world intercept and(IMO) alsoincreased astudy majorindicate contributor the decade global CO2 emissions. The results scenarios in third International Organization that to shipping contributes around renovation considered). On theMaritime otherover hand, function for 7.8-12.7% per (depending on the The results in the International Maritime (IMO) study indicate thatforshipping around emissions [1]. Without mitigation strategies to reduce shipscenarios COcontributes it and is 3% of global CO2third coupled scenarios). The values suggested couldappropriate beOrganization used to modify the function parameters the considered, 2 emissions, [1]. estimations. Without appropriate mitigation strategies to reduce ship CO2 emissions, it is 3% of global CO2 emissions improve the accuracy of heat demand © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86-21-38283865; . Cooling. address:author. [email protected] * E-mail Corresponding Tel.: +86-21-38283865; . E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. th Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10 International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.836
4032 2
Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
expected that they will increase 50% to 250% by 2050. Therefore, it is important to propose and implement mitigation strategies to reduce the ship CO2 emissions. There are various mitigation strategies that have been studied including operational and technical measures for ship emissions reduction [2]. For instance, 50 possible operational and technical mitigation measures have been identified in the IMO MEPC 62 report [3]. Although there are a lot of mitigation strategies have been studied, it is usually not possible to implement all these mitigation measures simultaneously. Hence, it is important and necessary to evaluate the efficiency of these mitigation strategies so as to select proper mitigation strategies for implementation. Various methods have been proposed to evaluate the efficiency of the shipping mitigation strategies. One commonly used way is to plot the marginal abatement cost curve (MACC) for mitigation strategies [4], which represents the relationship between the cost-effectiveness and the abatement amount. Although MACCs are easy for use to support policy making, they have several shortcomings. One significant shortcoming is that all mitigation strategies are assumed to be independent in abatement and cost evaluation. Due to the complexity of the ship energy system, there always are interactions among various mitigation strategies. For example, the technical measures hull coating I and hull coating II are two correlated measures which are not efficient to implement simultaneously. For a better evaluation of the mitigation strategies, a systems approach is often preferred considering the complex relationship within the ship energy system [5]. Different ship energy systems have been developed and studied with different scopes and purposes. [6] considered energy flows onboard ships in the time domain for complete ship energy systems simulation. Their system allows for interactions at system. [5] described a method to perform a feasibility analysis of the waste heat recovery system. [7] developed a modeling framework to examine the technical and economic interaction and simulate what might be optimal solutions in future. Due to the complexity of the ship energy system, several attributes of the system can be founded. The first is that there are usually a large number of components in the ship energy system. The second is that different components generally have significant interactions with each other. The third is that the relationship among different components often exhibits nonlinearity. These attributes are difficult to evaluate while they may have significant effects on the mitigation strategies evaluation results. To deal with these complexities, a systems approach is commonly used which teats the ship energy system as a whole. [8] proposed a model based energy and exergy analysis for ship energy system. [9] developed a systems approach to assess the efficiency of the combined engine-WHR system considering interactions among components. [10] applied a systems approach to evaluate the operational conditions for ship energy system. Systems approach usually requires the comprehensive examination of the physical systems or simulation models. With large and complex ship energy system, this approach may not be efficient as it can be computationally expensive to evaluate different mitigation strategies. To handle this problem, metamodel based approaches are widely proposed. Metamodels are statistical approximations of the ship energy system, which are usually simpler and cheaper for evaluation. There are various metamodels that have been well studied, including the Gaussian process (GP) models [11] and neural networks [12]. Gaussian process model is one of the commonly used models due to its mathematical convenience and flexibility [13]. Metamodels are also widely adopted to assess the ship energy systems. A mathematical model is proposed in [14] to predict the ship energy performance based on the collected data. The combination of physical model and artificial neural networks for system evaluation is investigated in [15]. An artificial neural network is applied in [16] to estimate ship propulsion power and in [17] to predict the main propulsion efficiency. The comparison between the artificial neural networks and GP models is given in [18], which indicate the good performance of Gaussian process models. It can be found that GP models have been used to evaluate the mitigation measures based on ship energy systems. However, these studies currently only consider the ship propulsion system, the using of GP model for mitigation strategies evaluation still lacks of comprehensive study. In addition, the studies apply the deterministic GP model for ship energy system. In practice, most ship energy systems are stochastic. Therefore, the application of stochastic GP model to evaluate mitigation strategies is needed. In this paper, a GP based method is proposed to evaluate the mitigation strategies based on ship energy system. This method is much more efficient than directly analyzing the ship energy system. Furthermore, the method is applied in a real case study to evaluate the mitigation strategies including speed reduction, weather routing and waste heat recovery. The results can be used to support policy making on mitigation strategies selection. The rest of the paper is organized as follows: the studied ship energy system and the considered mitigation strategies are described in Section 2. The application of GP model to evaluate
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the mitigation strategies is given in Section 3. A case study is given in Section 4. The conclusion is provided in Section 5. 2. Mitigation strategies evaluation based on ship energy systems Fuel is consumed in a ship and then converted to different energy forms for final use, including the mechanical power for propulsion, electricity power for auxiliaries and thermal power for heating. The framework of a ship energy system for a chemical tanker is shown in Fig. 1. The mechanical energy supplied by main engines are mainly used for propulsion. The resistance generated from ship movement depends on various factors, such as the ship speed, hull specifies and weather conditions. All these factors have effects on the propulsion power demand. Electricity power is required by many components in the ship energy system, including the compressors in air conditioning (HAVC) system, nitrogen compressor and cargo pumps. Electricity power can be generated from both main engines and auxiliary engines. Thermal power is mainly used for heating, such as the accommodation, fuel heating and fresh water generation. It can be seen that a ship energy system is constructed by many components and some components have interactions with each other. Therefore, some mitigation strategies can be correlated which makes their evaluation much more difficult. To deal with this problem, a systems approach can be used which treats the ship energy system as a whole system. In this paper, three mitigation strategies are considered, which are speed reduction, weather routing and trim optimization. Speed reduction or slow steaming is comprehensively studied in recent years as one of the major mitigation strategies. The reducing of the speed can reduce the required power hence reduce the fuel consumption. Weather conditions are external factors that may have significant effects on the ship fuel consumption. The optimization of the shipping route under different weather conditions can reduce the unnecessary fuel consumption. Different trim and draft can influence the ship resistance and then have effects on the final fuel consumption. Therefore, trim optimization can provide appropriate trim during voyage so as to reduce the fuel consumption.
Fig. 1. Ship energy system framework of a chemical tanker
3. A metamodel based method for mitigation strategies evaluation The GP model is developed to represent the complex ship energy system. For the considered mitigation strategies, the corresponding input factors in the model include ship speed, wind speed, wind direction, wave height, wave direction, trim and draft. The output of the model is the ship fuel consumption. With the developed GP model, the relationship between these input factors and the ship fuel consumption can be analyzed. Then the effects of corresponding mitigation strategies can be evaluated. The real observed data for shipping activities, weather conditions and fuel consumption in 2017 are used for training the model. The data obtained from January to March in 2018 are used for validation. With identified input values and output fuel consumption from the training data, the GP model can be developed.
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Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
Let x={x1,x2 ,...,xn } denote the observed input values for the considered factors and y (x) denote the observed ship energy consumptions at x. y (x) is assumed to be a GP with an unknown constant mean µ and a covariance function Σ with Gaussian correlation, which is represented as: (1) y ( x) = GP( µ , Σ) Various covariance functions can be used in the GP model. Here, the commonly used Gaussian correlation is adopted which has the following form between any two input sets x and x’. (2) k ( x, x' ) = exp(−θ || d ||2 ) where θ is the unknown decaying parameter and ||d|| denote the Euclidean distance between x and x'. Given GP model form, the interest is to predict the output (e.g. fuel consumption) at unknown conditions. Let y * ( x*) denote the ship energy consumption at unobserved condition x*. Given the GP prior and the likelihood function that the model outputs are normally distributed conditional on the observed data, the predictive distribution of y * ( x*) derived as a conditional normal distribution with following form: (3) y* | y ~ N ( µ + ΣT* Σ −1 ( y − µ ), Σ** − ΣT* Σ −1Σ* where Σ* denote a vector of covariance between the training data set x and the unobserved data set x*, and Σ ** denote the covariance for x*. With equation (3), ship fuel consumption at any input condition can be predicted as the given normal distribution. . To develop the GP model and derive the predictive distribution for the model output, there are various unknown parameters including the unknown constant mean µ and the unknown parameters in the covariance functions. These parameters have to be estimated using the training data. Here the maximum likelihood estimation method is used to estimate these parameters. Given the estimated parameters, the ship fuel consumption can be predicted as equation (3). Furthermore, the developed model can be used to assess the effects of different mitigation strategies. As the implementation of a mitigation strategy corresponds to change the values of some input factors. Therefore, the effects of the mitigation strategies can be evaluated by evaluating the model outputs for different input conditions. The evaluation of the mitigation strategies is further illustrated in a chemical tanker energy system. 4. Case study An energy system for a chemical tanker is studied in this paper. The chemical tanker 181 meters long and 31.3 meters wide. The maximum draft is 12.4 meters and the maximum capacity is 51000 m3. The ship has two main engines and two auxiliary engines. The data obtained in 2017 are used to training the model and the data in the first quarter of 2018 are used to validate the model. The observed fuel consumptions and the predicted fuel consumptions at given input conditions are shown in Fig. 2. It can be found from Fig. 2 that most points are around the fitted line (red line). Therefore, most predicted values are close to the observed values. The goodness-of-fit test is further conducted to verify the model. The test results indicate that there are no significant difference between the observed and the predicted outputs. In addition, the calculated root mean square error (RMSE) is 0.3176, which is accepted compared with the observation error.
Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
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Fig. 2. Comparison of observed and prediction fuel consumptions for validation data
The validated model is then used to evaluate the three mitigation strategies, including speed reduction, weather routing and trim optimization. The amount of fuel consumption reduced by different mitigation strategies or combination of mitigation strategies are given in Table 1. As expected, speed reduction has the largest reduction amount with 19% for the fuel consumption. The results also show that the reduction percentage of the combination of mitigation strategies does not equal to the sum of the reduction percentage for these mitigation strategies. Therefore, these three mitigation strategies have positive correlation with each other. To better evaluate the performance of these mitigation strategies, it is important to consider these correlations. Table 1. Reduction of fuel consumption for different mitigation strategies. Mitigation strategies
Percentage of fuel reduction
Mitigation strategies
Percentage of fuel reduction
Speed reduction
19%
Speed reduction & Weather routing
20.7%
Weather routing
2.5%
Weather routing & Trim optimization
3.6%
Trim optimization
1.6%
Trim optimization & Speed reduction
19.9%
Speed reduction & Weather routing & Trim optimization
21.8%
4. Conclusion There are various mitigation strategies can be used to reduce the ship fuel consumption end emissions. Due to different constraints, it is necessary and important to evaluate these mitigation strategies so as to select the most appropriate ones. In this paper, a metamodel based method is proposed to evaluate the ship mitigation strategies. This method is much more efficient when the ship energy system is time consuming to evaluate. A case study of a chemical tanker is given to illustrate the proposed method. Three mitigation strategies are compared including speed reduction, weather routing and trim optimization. The results show that speed reduction has larger reduction amount compared to other two mitigations strategies. The combination of mitigation strategies does not mean that the maximum reduction amount can be achieved. Therefore, there are correlations among the mitigation strategies. The
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Jun Yuan / Energy Procedia 158 (2019) 4031–4036 Author name / Energy Procedia 00 (2018) 000–000
evaluation of these mitigation strategies should consider the effects of the correlation so as to provide more reliable results for policy making. In this paper, only three mitigation strategies are considered for comparison. An important future work is to consider more mitigation strategies. Another potential future work is to apply this method to different types of ship energy systems. References [1] Smith T, Jalkanen J, Anderson B, Corbett J, Faber J, Hanayama S, et al. Third imo ghg study 2014. International Maritime Organization (IMO), London, http://www/ iadc org/wp-content/uploads/2014/02/MEPC-67-6-INF3-2014-Final-Report-complete pdf. 2014. [2] Buhaug Ø, Corbett J, Endresen Ø, Eyring V, Faber J, Hanayama S, et al. Second imo ghg study 2009. 2009. [3] Faber J, Wang H, Nelissen D, Russell B, Amand D. Marginal abatement costs and cost effectiveness of energy-efficiency measures. The Society of Naval Architects and Marine Engineers (SNAME) London. 2011. [4] Yuan J, Ng SH. Emission reduction measures ranking under uncertainty. Applied Energy. 2017;188:270-9. [5] Baldi F, Gabrielii C. A feasibility analysis of waste heat recovery systems for marine applications. Energy. 2015;80:654-65. [6] Cichowicz J, Theotokatos G, Vassalos D. Dynamic energy modelling for ship life-cycle performance assessment. Ocean Engineering. 2015;110:49-61. [7] Smith T. Technical energy efficiency, its interaction with optimal operating speeds and the implications for the management of shipping’s carbon emissions. Carbon Management. 2012;3:589-600. [8] Basurko OC, Gabiña G, Uriondo Z. Energy performance of fishing vessels and potential savings. Journal of Cleaner Production. 2013;54:3040. [9] Larsen U, Pierobon L, Baldi F, Haglind F, Ivarsson A. Development of a model for the prediction of the fuel consumption and nitrogen oxides emission trade-off for large ships. Energy. 2015;80:545-55. [10] Dedes EK, Hudson DA, Turnock SR. Assessing the potential of hybrid energy technology to reduce exhaust emissions from global shipping. Energy Policy. 2012;40:204-18. [11] Yuan J, Nian V, Su B, Meng Q. A simultaneous calibration and parameter ranking method for building energy models. Applied Energy. 2017;206:657-66. [12] Kalogirou SA. Applications of artificial neural-networks for energy systems. Energy Systems: Elsevier; 2000. p. 17-35. [13] Yuan J, Ng SH, Tsui KL. Calibration of stochastic computer models using stochastic approximation methods. IEEE Transactions on Automation Science and Engineering. 2013;10:171-86. [14] Journée J, Rijke R, Verleg G. Marine performance surveillance with a personal computer: Technische Universiteit; 1987. [15] Leifsson LÞ, Sævarsdóttir H, Sigurðsson SÞ, Vésteinsson A. Grey-box modeling of an ocean vessel for operational optimization. Simulation Modelling Practice and Theory. 2008;16:923-32. [16] Pedersen BP, Larsen J. Prediction of full-scale propulsion power using artificial neural networks. The 8th Int Conf Computer and IT Applications in the Maritime Industries2009. p. 537-50. [17] Petersen JP, Winther O, Jacobsen DJ. A machine-learning approach to predict main energy consumption under realistic operational conditions. Ship Technology Research. 2012;59:64-72. [18] Petersen JP, Jacobsen DJ, Winther O. Statistical modelling for ship propulsion efficiency. Journal of marine science and technology. 2012;17:30-9.