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Empirical testing of inventories applying on-board measurements of exhaust emissions at port and at sea

Jahangiri S., Nikolova N., Tenekedjiev K.

Journal of Sustainable Development of Transport and Logistics

2018

Vol. 3, No. 2

6--33

We present a comprehensive case study to identify the best vessel-specific inventory family that predicts the primary emissions from an ocean-going vessel when at berth, while maneuvering and while cruising. The main purpose of the paper is to generalize the implication of the case study by advising a novel policy, which will allow different authorities to estimate the shipping emissions in a cost-effective and reliable way. The emissions rates of nitrogen oxides, sulphur oxides, carbon dioxide, carbon monoxide, hydrocarbon, and particulate matter from the main engine and from the auxiliary engines are measured for different modes of ship operations in an on-board experiment campaign. The measured total emission amounts were predicted with 13 families of emission inventories and prediction deviations have been calculated. A procedure was advised for estimating the prediction inventory deviations of the combined hourly emission amounts from the main engine plus the auxiliary engines. Each inventory family has been formalized as a six-dimensional vector of prediction deviations for any mode of operation. The best vessel-specific inventory families were identified using the minimal mean absolute deviation criteria. A more rational procedure to rank inventories is considered, which treats the missing value problem and constructs a six-attribute value function. The use of preferential analysis and value functions further clarifies the recommended choice of inventory method. In this case study we demonstrated that the most suitable inventory families will provide reliable predictions with acceptable deviations from the measured emissions. At berth and for maneuvering, the best inventory family turned out to be MOPSEA (with 32.2 % and 39.6 % mean absolute deviations respectively). For cruising, the most precise inventory family is MEET (with 59.2% mean absolute deviation), whereas MOPSEA being the third best. However, some of the other inventories produce unacceptably high deviation, well above 100%. The practical implication is that while inventory methods can produce precise and cost-effective predictions, they should never be used without experimental verification. That is why, we provide an algorithm to use on-board experimental measurements to identify the best vessel-specific inventory family, which predicts the primary emission of a ship at a given mode of operation. The proposed algorithm and the implications of the case study are utilized to motivate a proposal for a novel future policy for a cost-effective and reliable emission estimation from shipping.

Comparison of linear interpolation and arctan approximation of one-dimensional monotonic utility functions based on experimental data

Nikolova N., Tenekedjiev K., Dong F., Hirota K.

Control and Cybernetics

2009

Vol. 38, no 3

835-861

Elicitation of utilities is among the most time consuming tasks in decision analysis. We search for ways to shorten this phase without compromising the quality of results. We use the results from an empirical experiment with 104 participants. They elicited 9 inner nodes from their one-dimensional utility function over monetary gains and losses using three elicitation techniques. A specific feature of the results is their interval character, as the elicitators are fuzzy rational individuals. The data is used to construct arctan-approximated and linearly interpolated utilities and to compare the results. We form partial samples with 3, 4 and 5 nodes for each participant and each elicitation method, and again interpolate/approximate the utilities. We introduce goodness-of-fit and deterioration measures to analyze the decrease in quality of the utility function due to reduced data nodes. The analysis, using paired-sample tests, leads to the following conclusions: 1) arctan-approximation is more adequate than linear interpolation over the whole samples; 2) 5 inner nodes are sufficient to construct a satisfactory arctan-approximation; 3) arctan-approximation and linear interpolation are almost equal in quality over the partial samples, but the local risk aversion of the linearly interpolated utility function is of poor quality unlike that of the arctan-approximated utility function.