Cost-effectiveness analysis of energy efficiency measures for maritime shipping using a metamodel based approach with different data sources

Energy 189 (2019) 116205

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Cost-effectiveness analysis of energy efficiency measures for maritime shipping using a metamodel based approach with different data sources Jun Yuan a, Victor Nian b, *, Junliang He a, c, Wei Yan a, c a b c

China Institute of FTZ Supply Chain, Shanghai Maritime University, China Energy Studies Institute, National University of Singapore, Singapore Engineering Research Center of Container Supply Chain Technology, Ministry of Education, Shanghai Maritime University, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 March 2019 Received in revised form 18 September 2019 Accepted 23 September 2019 Available online 24 September 2019

A large number of mitigation strategies including both technical and operational measures have been proposed to reduce ship energy consumption and hence carbon emissions. Under cost and other practical constraints, when prioritizing among mitigation measures, different data sources such as observed data from physical systems and simulated data from simulation models may be used. Data obtained from different sources have different characteristics in terms of accuracy and data volume. Therefore, it is important to integrate different data sources when evaluating alternative mitigation measures in a systematic and systemic manner. In response, a Gaussian process metamodel based method is proposed to evaluate energy saving measures when different data sources are combined synergistically. In addition, a cost-effectiveness analysis is used to prioritize the mitigation strategies based on cost considerations. A case study is developed to demonstrate the advantages of the proposed method in terms of accuracy and efficiency. All mitigation measures selected in the case study are found to have a negative cost which can translate to both energy and cost savings. Among the evaluated measures, speed reduction has shown to be the most plausible measure in terms of energy savings and marginal costeffectiveness. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Ship energy system Gaussian process Metamodel Energy savings Cost-effectiveness Maritime mitigation strategies

1. Introduction Shipping, as a major transportation mode, accounts for 938 million tonnes of CO2 emissions in 2012, which translates to about 2.6% of the total global CO2 emissions [1]. CO2 emissions from shipping are expected to be doubled or even tripled by 2050 without effective emissions mitigation strategies. In response to the need for decarbonizing the shipping industry, the International Maritime Organization (IMO) [2] has identified more than 50 mitigation strategies including both operational and technical strategies for reducing carbon emissions in shipping. In the literature, there is a rich pool of studies on the mitigation measures for shipping. Eide et al. [3] studied 25 mitigation measures including hull design, speed and weather routing. Hoffmann et al. [4] evaluated the cost-effectiveness of 25 measures and found

* Corresponding author. E-mail address: [email protected] (V. Nian). https://doi.org/10.1016/j.energy.2019.116205 0360-5442/© 2019 Elsevier Ltd. All rights reserved.

that a 6% increase in capital expenditure can lead to a reduction of 30% when cost-effective measures are implemented. cost-effective measures are implemented, the CO2 emissions will decrease 30% while the capital expenditure will increase 6%. Heitmann and Peterson [5] assessed the potential contributions of the shipping sector to global carbon dioxide emissions based on a selection of 22 measures. Lindstad et al. [6] studied the CO2 emission reduction potentials for 12 mitigation measures. Based on a review conducted by Bouman et al. [7] there is a vast number of mitigation strategies such as hull design, power and propulsion, speed control and weather routing available for consideration. Despite a rich literature on mitigation measures, it is often plausible to implement all the mitigation strategies at the same time due to cost and other practical constraints. Thus, it is necessary to identify and implement the most appropriate mitigation strategies to achieve emission reductions effectively. Various mitigation strategies have been proposed and studied for maritime shipping. The effectiveness of mitigation measures can be viewed from different perspectives such as the energy saving potential and

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J. Yuan et al. / Energy 189 (2019) 116205

cost of emission reductions. Various methods have been proposed to evaluate ship energy consumptions which can be broadly classified as the top-down and bottom-up approaches. The top-down approach is usually carried out based on the statistical data obtained from fuel delivery reports [8]. The bottom-up approach is usually used to estimate ship energy consumptions based on shipping activities as described by data sources [9,10]. For instance, in the top-down approach, ship energy consumption can be estimated based on the statistics of cargo volumes [11], vessel arrival and/or departure times [12], and Automatic Identification System (AIS) data [13]. In comparison, the bottom-up approach is generally more accurate as the computational results are obtained based on detailed operational data [14]. In the literature, there is a large number of studies on ship energy consumptions and energy saving potentials of mitigation €rtsila € [15] evaluated the energy saving potentials of measures. Wa several mitigation strategies such as the lightweight construction, optimization of hull dimensions, propulsion upgrade and optimization of trim and ballast. The IMO [2] studied the energy savings and hence CO2 emission reductions potentials of 28 mitigation measures including 8 operational measures and 20 technical measures. However, in these studies, the evaluation of the energy saving for these mitigation strategies is based on the physical observations or the experiments conducted on real ships. Such an approach is usually costly and time consuming. In addition, the mitigation measures are evaluated only in a systematic approach rather than a systemic approach. In other words, these measures are evaluated individually without considering the systemic influence of other measures. A ship is a complex energy system with a large number of components interacting with one another. It is very likely for mitigation measures applied to one component to have an influence on other components. As such, a focused evaluation of individual measures without considering the correlations of other interacting measures can lead to an expensive and erroneous exercise. One of the approaches to mitigate such problems is to evaluate the performance of alternative mitigation measures in a systematic and systemic manner. Cichowicz et al. [16] considered the energy flows in a ship’s energy system in the time domain for complete ship energy systems simulation, which takes into account interactions of the various components in the system. Baldi and Gabrielii [17] developed a model to study the feasibility of the waste heat recovery system in a ship. Smith [18] proposed a simulation framework to study the technical and economic interactions of alternative mitigation measures using an optimization algorithm. In comparison, the evaluation of mitigation strategies based on simulation models can be much more efficient than that based on the physical systems as simulation models are usually faster and less expensive than physical examinations. Examinations of physical systems usually suffer from limited volume of data although the data can be fairly accurate (through meter reading or other physical observations). Simulation models are usually constructed based on expert knowledge and huge volume of empirical data which tend to be more readily available as compared to physical data. In general, existing studies tend to focus on either physical systems or simulation models. There is little research in combining the physical and simulation data in a systematic and systemic manner. As a contribution to the literature, a Gaussian process (GP) metamodel based method is proposed to evaluate the energy saving of different mitigation strategies which can simultaneously use different data sources in a systematic and systemic manner. Metamodels are statistical approximations to the physical systems or simulation models. Metamodel based approaches have been

widely applied in practice as they are usually more efficient in conducting modelling analysis as compared to examining the physical systems or detailed engineering simulation models. Various metamodels have been developed in the literature such as the radial basis function [19], neural networks [20] and Gaussian process [21,22]. These metamodels have also been used for energy prediction [23,24]. Among these metamodels, GP is commonly used due to its mathematical convenience and flexibility [25]. Metamodels have been used to evaluate ship energy systems. e et al. [26] proposed a metamodel based method to assess Journe the ship energy performance with collected data. Leifsson et al. [27] combined physical model and artificial neural networks for a systemic evaluation. Pedersen and Larsen [28] applied an artificial neural network method to estimate ship propulsion power. Petersen et al. [29] used an artificial neural network method to predict the main propulsion efficiency. Petersen et al. [30] compared the performance of artificial neural networks and GP models. While GP models can demonstrate efficient methods for evaluating the performance of specific mitigation measures, there are no GP metamodels capable of handling data from different sources effectively. The GP metamodel as proposed in this study is aimed at simultaneously incorporating both physical and simulated data when evaluating ship energy systems and mitigation measures. In addition to the energy saving, cost represents an important factor to be considered in implementing the mitigation strategies. In order to consider both energy saving and cost of decarbonisation, a cost-effectiveness analysis is further proposed in this study. Two criteria are used to evaluate the cost-effectiveness of different mitigation strategies. The first criterion is the cost-effectiveness (CE) value used in marginal abatement cost curve (MACC) [31]. The second criterion is the marginal cost-effectiveness (MCE) proposed in [32]. The rest of the paper is organized as follows. The description of the ship energy systems is presented in Section 2. The development and application of the GP metamodel with different data sources for evaluating mitigation strategies are presented in Section 3. The cost-effectiveness analyses based on the proposed criteria are presented in Section 4. A case study is presented in Section 5. Section 6 concludes the paper with recommendation for future research. 2. Ship energy systems Ship can be considered as a complex energy system. Fuel is consumed in a ship and then converted to different energy forms for different purposes such as mechanical, electrical and thermal energy for propulsion, auxiliaries and heating. An example of a ship energy system for a chemical tanker is shown in Fig. 1. In this energy system, fuel is converted to mechanical energy through the main engines. The mechanical energy is mainly used for propulsion. The propulsion power required for ship movement is also influenced by the resistance. The resistance generated from ship movement depends on various factors such as ship speed, hull specifications and weather conditions. All these factors can be taken as the input variables that influence the propulsion power demand in the system. Electrical energy can be generated from both the main engines and the auxiliary engines. Electricity is required by many components in the ship energy system such as the compressors in heating, air-conditioning and ventilation (HAVC) system, nitrogen compressor and cargo pumps. Thermal power can be generated from boilers, auxiliary engines and also main engines. The thermal energy is mainly used for heating such as accommodation for crews and fuel heating. Given the complexity of a ship energy system, some

J. Yuan et al. / Energy 189 (2019) 116205

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Fig. 1. Ship energy system of a chemical tanker.

components may have interactions with one another. When mitigation measures are applied to one or more components, the energy saving potentials of the mitigation measures can be influenced by the interacting components. These interactions are usually difficult to verify when each component is analysed individually without accounting for the interactions. Therefore, it is necessary to evaluate the mitigation strategies from systemic perspective. The complexities also imply that a direct analysis of a ship’s energy system can be quite expensive and resource consuming. Engineering simulation models can be developed to represent a ship’s energy system for analyzing the energy saving potentials of mitigation measures in a systematic and systemic manner. The advantage of using simulation model is that it can study the behavior of the system without conducting experiment on physical systems [33]. However, there are several disadvantages of using the simulation model. The development of the simulation model is usually expensive and requires expert knowledge. In addition, running a large and complex simulation models can also be time consuming. These shortcomings can be addressed by metamodels which are more simplified and reasonably accurate approximations to the original complex systems or simulation models. The original energy systems and/or simulation models are usually treated as a black box in a typical metamodel. As shown in Fig. 2, the observed data from the physical system and simulated data from simulation models are both used to develop the

Fig. 2. The concept of developing metamodel with different data sources.

metamodels. The simulation models are usually developed to represent the physical systems. Data for the metamodel can be obtained from physical systems and/or simulation models which include both input and output data. The metamodel can be developed to represent the physical system using the observed data or represent the simulation model using the simulated data. In addition, the metamodel can also be developed using both observed data and simulated data. As explained in the introduction, a Gaussian process metamodel is proposed in this study to evaluate the energy performance of different mitigations strategies by using data sources from both the physical energy systems and simulation models. 3. A metamodel based approach to evaluate mitigation strategies with different data sources 3.1. Data sources The data used to evaluate mitigation strategies can be obtained from different sources. One source of data includes real observations from ship such as recorded AIS data and ship onboard measurements. These real observations are treated as same level data to represent the ship performance although they can be obtained from different sources such as AIS or onboard measurements. Generally, this type of data has high level of accuracy as they are directly observed from the ship energy system. However, the volume of this type of data tend to be low. For example, the number of observed ship fuel consumptions data under different weather conditions are limited by the occurrence of different weather conditions on route. It is unlikely for the same weather condition (say strong wind) to keep repeating over a designated shipping route. Another type of data is the simulated data from ship energy simulation models. Compared to the physical energy systems, the evaluation of simulation models is usually faster and less expensive. In addition, the input variables can be set in the simulation models so as to study the ship energy performance under various different input conditions (e.g. different weather conditions). Therefore, there is usually a large number of simulation data available for analysis. However, simulation models are approximations to the physical energy systems. Thus the simulated data are usually less accurate than the real observed data.

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Since the simulation model is an approximation to the physical system, the relationship between the simulation output and the system output can be expressed as

ys ðxÞ ¼ yr ðxÞ þ dðxÞ þ ε

(1)

where x denotes a vector of input variables in the ship energy system such as ship speed, course and trim, yr ðxÞ denotes the observed system output, ys ðxÞ denotes the simulation output, dðxÞ denotes the discrepancy between the simulation model and the energy system, and ε denotes the stochastic error in the simulation model. This model has been commonly used as a form to represent the relationship between different outputs [34]. For the real observations, there is an observation error for the measurements. The relationship between the observed system output and the true system output can be expressed as

yr ðxÞ ¼ zðxÞ þ e

(2)

where zðxÞ denotes the true system output (e.g. actual fuel consumption) and e denotes the observation error in the measurement of system output. Combining Eqs. (1) and (2), the relationship between the simulation output and the true system output can be expressed as

ys ðxÞ ¼ zðxÞ þ dðxÞ þ ε þ e

(3)

In this way, both observed data and simulated data as two different levels of data sources can incorporated in ship energy system evaluation. The objective is to predict true fuel consumptions of a ship under unknown input conditions based on these two levels of data sources.

2

x’ Þ, and the correlation for the discrepancy is Rd ðx; x’ Þ ¼ expð  2 fd x  x’ Þ. Based on Eq. (3), there are also stochastic error ε in the simulation model and the observation error e for the system measurements. It is further assumed that these two terms follow a normal distribution with mean 0 and variances s2ε and s2e respectively. These are reasonable assumptions in practical applications according to the central limit theorem [36]. Based on these assumptions, the unknown parameters can be denoted as {b,f,s2}, where b ¼ {bz, bd} are the unknown constant mean values for GP, f ¼ {fz, fd} are the unknown decaying parameters in the Gaussian correlation functions and s2 ¼ fs2z ; s2d ; s2ε ; s2e g are the unknown variances in the model. With above GP assumptions, the purpose is to derive the predictive distribution of the output (e.g. fuel consumption) given available data sources, as the predictive distribution can be used to assess the ship energy consumption under different conditions. As stated in section 3.1, there are two levels of data sources used for ship energy system evaluation. Let Y r denote the real observed fuel consumptions at input conditions xr in the ship energy system, and Y s denote the simulated fuel consumptions at input settings xs specified in the simulation model. Based on the characteristics of the multivariate normal distribution, the predictive distribution of the true system output z0 at any unknown input x0 can be derived as a conditional normal distribution with following forms.

z0 jY r ; Y s  Nðm0 ; S0 Þ

(4)

with

m0 ¼ b þ Sðx0 ; xD ÞT SðxD ; xD Þ1 ðY D  b1d Þ

(5)

3.2. Development of Gaussian process metamodel with different data sources

S0 ¼ Sðx0 ; x0 Þ  Sðx0 ; xD ÞT SðxD ; xD Þ1 Sðx0 ; xD Þ

(6)

Gaussian process is a stochastic process that any finite collection of random variables has a multivariate normal distribution [35]. Due to its flexibility and mathematical convenience, GP has been widely used as a metamodel to study the complex simulation models and physical systems. Based on model form shown in Eq. (1), both the true system output zðxÞ and the discrepancy dðxÞ can be assumed to be a Gaussian process. This assumption has also been used in Refs. [25,34], which has shown to be reasonable in practice [35]. Specifically, the true system output zðxÞ is assumed to be a GP

where m0 denotes the predictive mean and S0 denotes the predictive variance. xD ¼ ðxr ; xs Þ denotes input settings including both system inputs and simulation inputs. Y D ¼ ðY r ; Y s Þ denotes the overall outputs including both real observations and simulated outputs. 1d denotes d dimensional vector of 1 where d is the number of overall outputs. Sðx0 ; xD Þ ¼ s2z Rz ðx0 ; xD Þ denotes the covariance function between x0 and xD with Gaussian correlation. Similarly, Sðx0 ; x0 Þ denotes the covariance of x0 , and SðxD ; xD Þ denotes the covariance of xD represented as

SðxD ; xD Þ ¼

s2z Rz ðxr ; xr Þ þ s2e Im s2z Rz ðxr ; xs Þ 2 2 sz Rz ðxs ; xr Þ sz Rz ðxs ; xs Þ þ s2d Rd ðxs ; xs Þ þ s2ε In

with mean function bz ðxÞ and covariance function s2z Rz ðx; x’Þ. The discrepancy dðxÞ is assumed to be a GP with mean function bd ðxÞ and covariance function s2d Rd ðx; x’Þ. Different mean and covariance functions can be used to specify the GP, which have been comprehensively studied in Ref. [35]. Here, the GP is assumed to have a constant mean function, which is a reasonable assumption in many practical applications [35]{Santner, 2013 #20; Santner, 2003 #60}. Therefore, the mean functions for zðxÞ and dðxÞ become unknown constants bz and bd respectively. For the correlation function, the commonly used Gaussian correlation is adopted here due to its wider applicability [35]. Specifically, the correlation for the true system output is Rz ðx; x’ Þ ¼ expð  fz x 

! (7)

where Im is the m  m identity matrix and In is the n  n identity matrix. m and n denote the number of real observations and simulated outputs respectively.

3.3. Parameter estimation The predictive distribution given by Eqs. (4)e(6) can be used to predict ship energy consumption at any input setting. However, there are various unknown parameters that have to be estimated before the model can be used for further analysis. These unknown parameters include b, f and s2 . Different methods can be used to

J. Yuan et al. / Energy 189 (2019) 116205

estimate these unknown parameters. In order to account for the uncertainties of these unknown parameters in the analysis, the Bayesian method is often used, where the obtained posterior distribution can be used to make inference about these unknown parameters [37]. However, the close form of the posterior distribution is usually unknown. Therefore, the numerical method such as Markov Chain Monte Carlo (MCMC) is often used to evaluate the posterior, which may increase the computational complexity. Another commonly used method is the maximum likelihood estimation (MLE) method [38], which is to estimate these unknown parameters by maximizing the likelihood function. Then, the estimated parameters are treated as known plugin parameters in the model. Here, the MLE method is applied to estimate these unknown parameters. This is because MLE is simpler and faster than the Bayesian method. In addition, the numerical results indicate that the uncertainties of these unknown parameters do not have significant influence on the predictive performance. The likelihood function of these unknown parameters, lðb; s2 ;fÞ, can be derived as

5

developed GP metamodel, which can be expressed as

ESi ¼ EC before  EC after i i before

after

where EC i and EC i denote the energy consumptions before and after implementing mitigation strategy i respectively. The value of energy consumption for different scenarios can be obtained as predictive mean based on Eq. (5). The total annual cost includes the annual investment cost, annual operational cost, annual opportunity cost, and cost saving from implementing mitigation strategy, which can be expressed as

TCi ¼ ICi þ RCi þ OCi  SCi

4. Cost-effectiveness analysis for mitigation strategies In addition to energy saving, cost is also an important objective that has to be considered in mitigations strategies evaluation. Costeffectiveness analysis (CEA) has been widely used to evaluate the economic feasibility and efficiency of mitigation strategies [31]. Here two criteria are proposed for cost-effectiveness analysis. CEA is usually used to compare the relative costs and energy savings of different mitigations strategies. Typically, it is the ratio of the cost and energy saving, which can be expressed as

CEi ¼

TCi ESi

(9)

where CEi is the cost-effectiveness value for mitigation strategy i. TCi is the total annual cost for implementing mitigation strategy i, and ESi is the annual energy saving amount by using mitigation strategy i. The annual energy saving can be predicted using the

(11)

where ICi is the annual investment cost (i.e. non-recurring cost) of mitigation strategy i, which is obtained over the remaining years (service years of the strategy or lifetime of the ship which is shorter) and discounted by the interest rate. RCi is the annual

i 1   h 1 l b; s2 ; f ¼ ln ð2pÞd=2  ln½jSðxD ; xD Þj  ðY D  b1d ÞT ½SðxD ; xD Þ1 ðY D  b1d Þ 2 2

The parameters b, f and s2 can be estimated by maximizing this likelihood function. Various standard optimization packages can be used for this estimation such as the ‘mle.tools’ in R package and the ‘mle’ function in MATLAB. Here the ‘mle’ function in MATLAB is used. With these estimated parameters, the predictive distribution can be used for further analysis such as to predict the ship energy consumption at any unknown input set. Therefore, the energy consumption for different input sets (e.g. weather conditions) can be assessed. Then, the energy consumption for different mitigation strategies can be evaluated. By using the GP metamodel to predict the ship energy consumption, it is much more efficient than directly using the simulation model or the physical system. Here the GP metamodel is only used to evaluate the energy consumption of different mitigation strategies. Other than energy consumption, the cost should also be considered as an important criterion for mitigation strategies evaluation. The cost effectiveness analysis of mitigation strategies is provided in next section.

(10)

(8)

operational cost or recurring cost increased by implementing mitigation strategy i. OCi is the opportunity cost due to the loss of service when implementing the strategy. SCi is the cost saved from the implementation of the strategy with reduced energy consumption, which can be expressed as

SCi ¼ ESi  FP

(12)

where FP is the fuel price. With the calculated annual cost and energy savings, the cost-effectiveness (ref. Eq. (9)) can be obtained for each mitigation strategy for comparison. The cost-effectiveness given by Eq. (9) is commonly used as a criterion for mitigation strategies evaluation. However, it has been shown that this criterion may not be appropriate to evaluate mitigation strategies when the cost is negative. The negative cost means the implementation of the mitigation strategy can bring additional ‘benefit’ due to the reduced cost from energy savings over life time. When the cost is negative, the criterion in Eq. (9) may prefer the mitigation strategy with less benefit (larger negative cost) and less energy saving other than the strategy with larger benefit and larger energy saving. In order to handle this problem, a new criterion is proposed in Ref. [32] which incorporates the Pareto optimality to evaluate the mitigation strategies. Let TCi and ESi denote the cost and energy saving for strategy i, and TCj and ESj denote the cost and energy saving for strategy j. To compare strategies i and j, different situations have to be considered. The first situation is that strategy i dominates strategy j, which can be defined as



TCi  TCj with at least one TCi < TCj or ESi > ESj ESi  ESj

(13)

In this situation, strategy i is better than strategy j. Another situation is that strategy j dominates strategy i, which can be defined as



TCi  TCj with at least one TCi > TCj or ESi < ESj ESi  ESj

(14)

In this situation, strategy j is better than strategy i. If the

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J. Yuan et al. / Energy 189 (2019) 116205

comparison between strategies i and j does not belong to any one of above two situations, strategies i and j are the Pareto optimal strategies. That is each strategy has one objective (either cost or energy saving) better than the other strategy. In this situation, the criterion proposed in Ref. [32], which is called the marginal costeffectiveness (MCE), can be used to evaluate different strategies. MCE can be defined as

MCEij ¼

TCi  TCj ESi  ESj

(15)

That is, MCE is the increased cost over the increased energy saving from one strategy to another. It is the additional cost per unit of additional energy saving. This criterion can be further compared with the threshold determined by policy makers. For instance, the threshold can be defined as the price of unit energy consumption. Let MCE0 denote the pre-defined threshold. Then, the comparison between Pareto optimal strategies can be defined as



Strategy i is better than strategy j if MCEij  MCE0 Strategy i is better than strategy j if MCEij > MCE0

(16)

Based on rules defined by Eqs. (13), (14) and (16), this new criterion can be used to evaluate the mitigation strategies. In the case study, both the CE criterion and the proposed new criterion are compared in ship mitigation strategies evaluation. 5. Case study An energy system for a chemical tanker is studied in this paper. The characteristics of the studied ship and the considered mitigation strategies are described first. Then, the developed GP metamodel is validated and the predictive performance with different data sources is assessed. After that, the energy saving amount for different mitigation strategies are evaluated. Combined with the cost assessment for different mitigation strategies, the costeffectiveness analysis is further conducted to compare the performance of mitigation strategies. 5.1. Ship descriptions The energy performance of a chemical tanker is analysed in this case study. The basic characteristics of the ship are provided in Table 1. Based on the energy system shown in Fig. 1, five mitigation strategies are considered, including the speed reduction, trim optimization, autopilot adjustment, weather routing and speed control of pumps and fans. The considered mitigation strategies and their corresponding input factors are given in Table 2. Speed reduction or slow steaming is comprehensively studied in recent years as one of the major mitigation strategies [39]. The reducing of the speed can reduce the required power and further reduce the fuel consumption. For this mitigation strategy, the fuel consumption is evaluated for different speed. Different trim and draft can influence the ship resistance and then have effects on the final fuel consumption. Therefore, trim optimization can reduce the fuel consumption by providing appropriate trim during voyage. For this mitigation strategy, the fuel consumption is evaluated for

different trim. Autopilot adjustment is to keep the ship on course which can prevent the unnecessary use of the rudder. Hence, it can also reduce the fuel consumption. For this mitigation strategy, the fuel consumption is evaluated for different course. 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. For this mitigation strategy, the fuel consumption is evaluated by comparing the ship with and without weather routing under given weather conditions. Speed control of pumps and fans is another mitigation strategy that can be applied in all ship types to reduce the fuel consumption. For this mitigation strategy, the fuel consumption is evaluated for different speed of pumps and fans. Two levels of data sources are used in this study. The first level of data source includes the real observations from AIS data, noon reports and weather reports from January 2017 to March 2018. Another level of data source includes the simulated data from the simulation model. A Simulink simulation model is developed to represent the chemical tanker energy system. The simulated fuel consumptions for various different input conditions are obtained from this simulation model.

5.2. Model validation For validation of the proposed GP metamodel, the observed data in 2017 are used to train the model, and the remaining data (from January to March in 2018) is used for validation. The predicted fuel consumptions versus the observed fuel consumptions for both training data and validation data are shown in Fig. 3. The predicted fuel consumptions are obtained from the developed GP metamodel using both data sources (observed data and simulated data). For further comparisons, the predicted fuel consumptions are obtained using the proposed GP metamodel with only the observed data and only the simulated data separately (results shown in Figs. 4 and 5 separately). The RMSE values for all three cases are given in Table 3. It is evident that the predicted fuel consumptions are close to the predicted fuel consumptions for both data sets. The goodnessof-fit test is used to test the difference between the predicted values and the observed values. The results indicate that the difference is not significant at a level of 0.05. The root mean square error (RMSE) for the training data and validation data is 0.1837 and 0.2594 respectively (Table 3). Therefore, the proposed GP metamodel is considered to have been valid as both RMSE values are considered acceptable. The results further indicate that the GP metamodel developed using both observed and simulated data sources would be more reliable since it can lead to the smallest RMSE (Table 3). In addition, the GP metamodel developed with the observed data performs better than that developed with simulated data. This is expected as the observed data are usually more accurate than the simulated data. For additional comparison, the two-sample t-test is conducted to test the differences among three cases. The results show significant differences at a ¼ 0.05. This is a confirmation that it is more reliable to combine different data sources when developing a GP metamodel.

Table 1 The characteristics of the ship. Ship particular

Vessel type

Hull Type

Length (meters)

Wide (meters)

Setting Ship particular Setting

Chemical tanker Maximum draft (meters) 12.4

Double Maximum capacity (m3) 51,000

181 Main engine number 2

31.3 Auxiliary engine number 2

J. Yuan et al. / Energy 189 (2019) 116205

7

Table 2 Mitigation strategies and corresponding input factors. Mitigaton strategies

Speed reduction

Trim optimization

Autopilo adjustment

Speed control of pumps and fans

Input factors Mitigaton strategies Input factors

Vessel speed Weather routing Wind speed

Trim value

Course

Speed of pumps and fans

Wind direction

Wave height

Wave direction

Fig. 3. Observed versus predicted fuel consumptions using GP with all data sources for training data set (left) and validation data set (right).

Fig. 4. Observed versus predicted fuel consumptions using GP with only real observations for training data set (left) and validation data set (right).

5.3. Results and discussions The fuel saving amount for different mitigation strategies are then evaluated. The total fuel consumption for this chemical tanker in 2017 is 2736 metric tons (MT). The predicted annual fuel saving amount and the corresponding percentage of fuel reduction are given in Table 4. The results indicate that speed reduction by 10% has the largest energy saving amount, which accounts for 19.60% of the annual fuel consumption. The other four mitigation strategies have less than 3% of fuel reduction for each of them. Therefore, the speed reduction has the best performance in terms of energy saving. In addition to the energy saving, the costs for different mitigation strategies are further assessed. The total cost for each mitigation strategy includes the annual investment cost, annual

operational cost, annual opportunity cost and the cost saved from fuel reduction. The annual investment cost and annual operational cost are adopted from the IMO MEPC 62 report [2]. The opportunity cost is calculated by multiplying the term-charted rate of the chemical tanker and expected extra dry docking days. The termcharted rate is estimated by build price of the ship over its vessel life. The build price is given in UNCTAD [40] and the vessel life is assumed to be 30 years. The dry docking days for implementing mitigation strategies are provided in the IMO MEPC 62 report [2]. The saved cost from fuel reduction is obtained based on the fuel saving amount and the fuel price. The fuel saving amount is given in Table 4. The average buying price for the fuel is 586 US$ per metric ton. The costs for different mitigation strategies are given in Table 5. With the assessed energy saving and cost, the cost-effectiveness analysis is further conducted to evaluate the mitigation strategies.

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Fig. 5. Observed versus predicted fuel consumptions using GP with only simulated data for training data set (left) and validation data set (right).

Table 3 Root mean square errors for GP model developed with different data sources. Data

Root mean square error GP with all data

GP with observed data

GP with simulated data

Training data Validation data

0.1837 0.2594

0.3193 0.4057

0.4985 0.5216

Table 4 Annual energy saving and percentage of fuel reduction for different mitigation strategies. Mitigation strategies

Speed reduction (10%)

Trim optimization

Autopilot adjustment

Weather routing

Speed control of pumps and fans

Annual energy saving (MT) Percentage of fuel reduction

536.256 19.60%

45.144 1.65%

33.926 1.24%

67.306 2.46%

16.416 0.60%

Table 5 Costs for different mitigation strategies. Mitigation strategies

Annual cost (US$) Investment cost

Operational cost

Opportunity cost

Cost saved from fuel reduction

Total cost

Speed reduction (10%) Trim optimization Autopilot adjustment Weather routing Speed control of pumps and fans

204,634 2658 3710 0 1551

89,210 896 0 1200 0

0 0 0 0 772

314,246 26,454 19,881 39,441 9620

20,402 22,900 16,171 38,241 7297

Table 6 Cost-effectiveness analysis and ranking of mitigation strategies for different criteria. Mitigation strategies

Energy saving (MT)

Rank

Cost (US$)

Rank

CE

Rank

MCE

Rank

Speed reduction (10%) Weather routing Trim optimization Autopilot adjustment Speed control of pumps and fans

536.256 67.306 45.144 33.926 16.416

1 2 3 4 5

20,402 38,241 22,900 16,171 7297

3 1 2 4 5

38.05 568.17 507.27 476.65 444.49

5 1 2 3 4

38.04 692.22 599.91 506.79 e

1 2 3 4 5

The calculated CE values, MCE values and the ranking results for different criteria are given in Table 6. It can be seen that the mitigation strategies are ranked differently according to different criteria. When the energy saving amount is taken as the criterion, speed reduction is the best mitigation strategy which has the largest energy saving. It is followed by weather routing, trim optimization and autopilot adjustment. Speed control of pumps

and fans has the smallest energy saving. When the cost is used as the criterion, weather routing performs best, while speed reduction has changed to the 3rd place. When the cost-effectiveness (CE) value is taken as the criterion, weather routing becomes the best, while speed reduction becomes the worst. We can see that all mitigation strategies have negative cost. Compared speed reduction with autopilot adjustment and speed control of pumps and

J. Yuan et al. / Energy 189 (2019) 116205

fans, it can be found that speed reduction has larger energy saving and smaller negative cost (i.e. larger benefit). Therefore, speed reduction has better performance than the other two strategies in terms of both energy saving and cost. However, autopilot adjustment and speed control of pumps and fans are preferred to speed reduction according to the CE criterion. Hence, the CE criterion is not appropriate to rank mitigation strategies with negative cost. The marginal cost-effectiveness (MCE) is also used as a criterion to rank the mitigation strategies. The MCE value between speed reduction and weather routing is 38.04. It means that the cost will increase 38.04 US$ in order to reduce one additional metric ton (MT) of fuel consumption by implementing speed reduction strategy instead of weather routing strategy. As the average fuel price is 586 US$/MT, the additional cost of 38.04 US$/MT is usually accepted compared to high fuel price. Therefore, speed reduction is the best strategy using the MCE criterion. This is reasonable as the implementation of speed reduction not only has the largest energy saving, but also can earn the benefit due to the negative cost. The MCE values are negative between other strategies as shown in Table 6. The negative MCE value indicates that one strategy has larger energy saving and also smaller negative cost (i.e. larger benefit), which dominates the other strategy. For instance, compared to trim optimization, weather routing with negative MCE value has larger energy saving and also smaller negative cost. Therefore, weather routing dominates trim optimization. Similarly, trim optimization dominates autopilot adjustment, and autopilot adjustment dominates speed control of pumps and fans. Hence, the ranking results obtained using MCE are reasonable in terms of both energy saving and cost. 6. Conclusion In this paper, a Gaussian process metamodel based modelling method is developed for evaluating energy savings of ship mitigation measures. This method allows for different data sources such as real observations from physical systems and simulated data from simulation models to be simultaneously incorporated in a modelling analysis. The ability to incorporate different sources of data can enable a more accurate modelling of energy saving potentials of mitigation measures. In addition to the energy savings, cost is also considered as an objective in evaluating the mitigation measures. Two criteria, namely, cost-effectiveness and marginal cost-effectiveness are used to assess the mitigation measures in terms of cost and energy saving. These criteria can help business decisions when prioritizing mitigation strategies under budget constraints. A case study using a chemical tanker is developed to validate and demonstrate the advantages of the proposed method. The case study results further demonstrate the advantages of using different data sources simultaneously in the modelling analysis. The costeffectiveness analysis shows that the use of different criteria can lead to different priorities for mitigation measures. As an example, speed reduction has the worst performance when measured by cost-effectiveness, but it has the best performance when measured by marginal cost-effectiveness. Since speed reduction has the highest energy saving potential, it is recognised as the best option among the evaluated mitigation measures. Moreover, to prioritize the mitigation measures with negative cost, the marginal costeffectiveness criterion is more appropriate than the costeffectiveness criterion. The proposed method can be used as a tool to support decision making when prioritizing ship mitigation strategies. From a policy perspective, findings from this study can help identify energy savings and associated costs of different mitigation strategies, which can lead to practical targets for decarbonising the shipping

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sector. From a business perspective, the proposed method can help ship owners identify cost-effective mitigation strategies to improve ship energy performance without having to engage in expensive examination of the physical systems or engineering simulation models. In this paper, only five mitigation measures are considered. A possible future work is to consider more mitigation strategies in a ship energy system which can increase the number of input factors making a more complex modelling problem. Next, an extension of the developed Gaussian process metamodel to handle high dimensional problem is recommended as future research on methodology development. Last, it would be interesting to generate a set of comparable mitigation strategies for the entire the global shipping sector by applying the method to different types of vessels. Acknowledgement This work is supported by the National Natural Science Foundation of China (Grant No.71804108) and Science and Technology Commission of Shanghai Municipality (Grant No. 17040501700). References [1] Smith T, Jalkanen J, Anderson B, Corbett J, Faber J, Hanayama S, et al. Third IMO greenhouse gas study 2014. London, United Kingdom: International Maritime Organization; 2015. p. 327. [2] Faber J, Wang H, Nelissen D, Russell B, Amand D. Marginal abatement costs and cost effectiveness of energy-efficiency measures. London, UK: International Maritime Organization; 2011. [3] Eide MS, Longva T, Hoffmann P, Endresen Ø, Dalsøren SB. Future cost scenarios for reduction of ship CO2 emissions. Marit Policy Manag 2011;38(1):11e37. [4] Hoffmann PN, Eide MS, Endresen Ø. Effect of proposed CO2 emission reduction scenarios on capital expenditure. Marit Policy Manag 2012;39(4):443e60. [5] Heitmann N, Peterson S. The potential contribution of the shipping sector to an efficient reduction of global carbon dioxide emissions. Environ Sci Policy 2014;42:56e66. [6] Lindstad H, Verbeek R, Blok M, Zyl S, Hübscher A, Kramer H. GHG emission reduction potential of EU-related maritime transport and on its impacts. Brussels, Belgium: European Commission; 2015. p. 130. CLIMAB3/ETU/2013/ 0015. [7] Bouman EA, Lindstad E, Rialland AI, Strømman AH. State-of-the-art technologies, measures, and potential for reducing GHG emissions from shippingea review. Transp Res D Transp Environ 2017;52:408e21. [8] Corbett JJ, Winebrake JJ, Green EH, Kasibhatla P, Eyring V, Lauer A. Mortality from ship emissions: a global assessment. Environ Sci Technol 2007;41(24): 8512e8. [9] Corbett JJ, Koehler HW. Updated emissions from ocean shipping. J Geophys Res: Atmosphere 2003;108(D20). €hler H, Van Aardenne J, Lauer A. Emissions from international [10] Eyring V, Ko shipping: 1. The last 50 years. J Geophys Res: Atmosphere 2005;110(D17). [11] Schrooten L, De Vlieger I, Panis LI, Chiffi C, Pastori E. Emissions of maritime transport: a European reference system. Sci Total Environ 2009;408(2): 318e23. [12] Whall C, Cooper D, Archer K, Twigger L, Thurston N, Ockwell D, et al. Quantification of emissions from ships associated with ship movements between ports in the European Community. Report for the European Commission. Northwich, United Kingdom: Entec UK Limited; 2002. [13] Yau P, Lee S, Corbett JJ, Wang C, Cheng Y, Ho K. Estimation of exhaust emission from ocean-going vessels in Hong Kong. Sci Total Environ 2012;431:299e306. [14] Eyring V, Isaksen IS, Berntsen T, Collins WJ, Corbett JJ, Endresen O, et al. Transport impacts on atmosphere and climate: Shipping. Atmos Environ 2010;44(37):4735e71. €rtsila €. Boosting energy efficiency. Energy efficiency catalogue. [15] Corporation Wa €rtsila € Coporation; 2008. Helsinki, Finland: Wa [16] Cichowicz J, Theotokatos G, Vassalos D. Dynamic energy modelling for ship life-cycle performance assessment. Ocean Eng 2015;110:49e61. [17] Baldi F, Gabrielii C. A feasibility analysis of waste heat recovery systems for marine applications. Energy 2015;80:654e65. [18] Smith T. Technical energy efficiency, its interaction with optimal operating speeds and the implications for the management of shipping’s carbon emissions. Carbon Manag 2012;3(6):589e600. [19] Ko C-N, Lee C-M. Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 2013;49:413e22. [20] Kalogirou SA. Applications of artificial neural-networks for energy systems. Appl Energy 2000;67(1):17e35.

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