composition, for predicting the generation of the different emissions is another important tool for reducing emissions. Tere are a variety of methods available


of varying degrees of sophistication and accuracy and these can be classified into three categories:


• Base-line methods • Intelligent methods


• First principles methods. Base-line methods include the


simplest estimation models in common use. Usually aggregated technical and emissions data is used resulting in highly aggregated predictions with low spatial and temporal resolution. Nevertheless, in the absence of large volumes of detailed data for individual ships, these methods serve to provide a first estimate, or base-line estimate, of the exhaust gas emissions from ships. For example, these methods are


usually used to prepare emission inventories for particular geographical regions such as ports and for making aggregated estimates of the contribution of shipping to the global, continental or national emissions. Furthermore, they are routinely used to generate input data for environmental models which predict


the spread


and evolution of airborne emissions throughout geographical regions. These emission estimation methods


can be divided into three tiers of increasing


levels of complexity,


matched with the quality and amount of ship-specific data available. At Tier 1 the input data is simply a statistical view of


fuel usage (often


determined from bunker sales) coupled with highly aggregated emissions factors, based on the average technology of the global fleet. At Tier 2 the input data requires


that details of the engine technology on board individual ships is known, allowing the inclusion of technology- specific emission factors. At Tier 3 there is a requirement that in


addition to data on shipboard technology, some level of detail of individual ship activity is known. To date, ship activity data is only usually categorised in three phases. Tat is, over the duration of a


The Naval Architect May 2012


complete voyage, ship time is spent in hotelling, manoeuvring or cursing. For each of these modes of operation and for each fuel type, a separate emission factor can be used. Terefore, using the total mass of fuel consumed per engine per activity mode in each phase of the voyage leads to a total emission estimate. Often while attempting to generate


emissions inventories for large numbers of ships over a wide geographical area, the raw data for many of the required parameters is not directly available. Terefore, strategies are also suggested for estimating missing data. For example, it is also common practice to estimate the fuel consumption during particular activities from some level of knowledge of the engines’ rated installed power and multiply this by an estimation of full-load specific fuel consumption and again by an estimated load-factor fraction that accounts fuel consumed by engines operating at part-load. In practice, when using these methods


to prepare emission inventories for large numbers of ships, the data quality across the fleet of interest can be variable, requiring a combination of this tiered approach to be used, applying, for each ship or group of ships, the most sophisticated method relevant to the available data. The


principal challenges with


base-line methods lies in the lack of high quality raw data for any given calculation. Information on technology, ship movements and emissions data is often unreliable. Nevertheless,


these methods are


relatively simple to use and can give aggregated estimates of emissions contributions from large numbers of ships. Tey are also useful for generating a base-line for emissions contributions from shipping against which, for example, comparisons to other emitters can be made and/or allows the formulation of operational and regulatory strategies to mitigate emissions. A more complex calculation comes


from using intelligent methods. That is those which can relate the physical causes of emissions to their rate of production beyond simply attributing a statistically derived emission factor


of fuel used. These methods facilitate more refined predictions for individual ships or engines, or a subset of ships, than is possible by using a base-line approach alone. The application of these methods may eventually lead to prediction of emissions under transient engine conditions – for example, during manoeuvring. Artificial Neural Networks (ANN)


have been investigated as one modelling method which might be able to relate the causes (input layer parameters) to effects (output layer parameters). A recent study focussed on a Wartsila, RT-flex60c slow speed diesel engine. In this study nine causal parameters were used as the input to the model, including engine speed, n, mean effective pressure, Pe


,


injection and exhaust valve timing. Six emission components were identified; CO, CO2


, HC, NO, Soot and PM. And,


while the experiments for training and subsequently testing the ANN were performed under highly controlled conditions on a test-bed, rather than under the less predictable conditions with the engine in-service, the method reportedly shows good potential for making future predictions of marine engine exhaust gas emissions. Another approach that could be used


is regression analysis techniques and response surface methodologies, similar to those used in dimensional analysis and branches of statistics. For example, it can be supposed that


production of NOx is a function of parameters such as engine speed, n, compression ratio, rv pressure, Pe


, mean effective


temperature, Tm NOx=k1(k2na


, maximum combustion , air-fuel ratio, λ, etc. i.e.


.k3rv .k4Pe .k5Tm .k6λe b


c d The unknown coefficients, kn …) , and


indices, a,b,c,…, could then be resolved through a least-squares-errors fitting of sufficient measured data to the formulation. Te advantages of intelligent methods


to the mass


over base-line methods is that predictions of emissions for individual scenarios can be achieved – taking into account the instantaneous operating condition of the engine onboard the ship. Te level of detail that could be included will be


41


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