Machine Learning Based Approach to Predict ShortTerm Fuel Consumption on Mobile Offshore Drilling Units The application of machine learning models for optimization and improved decisionmaking has great potential in the drilling industry. This paper demonstrates a model for predicting fuel consumption on a Mobile Offshore Drilling Unit (MODU) with a Multilayer Perceptron (MLP) artificial neural network. The model is proposed as a tool for setting fuel consumption related performance goals for offshore personnel on a MODU. Operational and environmental data have been used as input variables for the model, with a dataset split into 80% training set and 20% test set. The highest performance is obtained with three hidden layers with 38 nodes each. The Adam solver performs better than the Stochastic Gradient Descent (SGD) solver for weight optimization, and the best  alpha  parameter for the L2 regularization term is 0.0001 with the Adam solver. The MLP regression model predicts fuel consumption for the test set with a Root Mean Squared Error (RMSE) of 0.0770. This result indicates that artificial neural networks and the MLP regressor is a suitable algorithm for predictive modelling of fuel consumption on a MODU.

Neural Networks Based Prediction Model for Vessel Track Control The problem of neural networks implementation for the construction of a predictive model for vessel track control was studied. It has been shown that the vessel track control problem may be considered as an approximation task, and neural networks may be implemented as universal approximating tools. The general structure of the prediction model, based on neural networks, has been developed. The model consists of several twolayered feedforward neural networks, which architectures satisfy the conditions of universal approximation properties. The analysis of the functions of the different neural networks in the prediction model has been performed. The network predicting WGS84 geodetic latitude as a part of the predictive model has been constructed, trained and validated by using MATLAB software. The validation results show the good prediction precision of the net.