Chemicals within a chemical tanker need heating to keep the cargo above a desired temperature. This research focuses on the prediction of the fuel consumption used to heat the cargo. There are three methods discussed in this research: a physics-based model (PBM), a data-driven model (DDM) and a hybrid model (HM). The PBM is a model based on the physical relations defined in the theory of heat transfer. The DDM is a model based on data gathered by a chemical shipping company (Stolt Tankers). Here an algorithm is trained and tested to acquire the model. The last method is a hybrid model. This HM is the combination of both the PBM and the DDM to use the best of both worlds.
The PBM uses ordinary differential equations to model the fuel consumption and uses a numerical approximation. This method is first compared with a simulation in COMSOL Multiphysics and then scaled to an existing vessel. The PBM is able to predict the fuel consumption over a trip where the deviation in fuel consumption is 0.043 tons of fuel per day (5.6% deviation). The temperature deviation averaged over all tanks is 0.68 ∘𝐶. The DDM compares two algorithms: the linear algorithm based on the least squares method and an algorithm based on support vector regression (SVR). The model using SVR acquires the most accurate result, with a mean absolute error (MAE) of 0.0014 ± 1.7880 ∗ 10−4 and a mean absolute percentage error (MAPE) of 5.18616 ± 1.05567. When the PBM and the DDM are combined into one model the HM is formed. The HM is able to predict the fuel consumption of the vessel with a MAE of 0.0001441 ± 0.00010 and a MAPE of 0.8929 ± 1.0955.
The PBM could also be used to calculate the reduction in fuel consumption when insulation is applied. Different types of insulation are simulated, with different thicknesses and thermal conductivity properties. Insulation of the cargo tanks could potentially reduce the cargo heating consumption by 64.8%. This reduction makes it worth looking into the insulation of vessels to reduce the fuel consumption and emissions. The DDM and HM are also tested to see if these models are able to predict values outside the range for the trained data. The DDM is not able to do so, whilst the HM is able to calculate scenarios where the average cargo tank temperature is outside the range for trained data.