Applying artificial neural networks for modelling ship speed and fuel consumption

Neural Computing and Applications https://doi.org/10.1007/s00521-020-05111-2 (0123456789().,-volV)(0123456789(). ,- volV) REVIEW Applying artificial neural networks for modelling ship speed and fuel consumption Wieslaw Tarelko1 • Krzysztof Rudzki2 Received: 5 August 2019 / Accepted: 5 June 2020  The Author(s) 2020 Abstract This paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making decisions regarding setting the proper commanded outputs to is extraordinarily difficult. To support such decisions, we have developed a decision support system. Its main elements are the ANN models enabling ship fuel consumption and speed prediction. To collect data needed for building ANN models, sea trials were conducted. In this paper, the decision support system concept, input and variables of the ship driveline system models, and data acquisition methods are presented. Based on them, we developed appropriate ANN models. Subsequently, we performed a quality assessment of the collected data set, data normalization and division of the data set, selection of an ANN model architecture and assessment of their quality. Keywords Artificial neural network  Modelling  Ship speed  Engine fuel consumption 1 Introduction Ship owners and operators of different types of ships are interested in decreasing the costs related to the effectiveness of their operation. These costs are mainly associated with fuel consumption and operational losses, e.g. excessive travel time to the destination. In the case of ships equipped with a combustion engine (CE) coupled to a controllable pitch propeller (CPP), effectively managing both the fuel consumption and travel time to the destination is related to the optimal choice of commanded outputs determining the work of such a driveline system. This system generates thrust to move a ship across the water at the desired speed with different levels of fuel consumption for various combinations of the commanded outputs, & Wieslaw Tarelko 1 Faculty of Ocean Engineering and Ship Technology, Gdansk University of Technology, Narutowicza 11, 80-333 Gdańsk, Poland 2 Faculty of Marine Engineering, Gdynia Maritime University, Morska 81-87, 81-225 Gdynia, Poland namely the driveline shaft speed and the CPP pitch. The optimal combination of speed and pitch depends on several operational conditions and, therefore, must be subjected to dynamic optimization. For this purpose, the most operated ships used speed/pitch ratio controllers. In such a ship driveline system, the commanded torque is controlled to maintain a certain shaft speed. However, there are ships which are not equipped with this kind of controller and the shaft speed or the CPP pitch ratio is used to control the propeller thrust indirectly. In such cases, due to variable environmental conditions (mainly weather conditions at sea), making decisions about setting the commanded outputs to ensure rational fuel use and the desired ship speed is extraordinarily difficult. A literature review carried out in [1] showed that there are some methods that could support selection of the commanded outputs for a ship’s propulsion system equipped with the CPP. They are mainly based on models developed by use of polynomial or regression equations. As a rule, algorithms for solving of such equations in both types of models are too complex. For this reason, many assumptions are used that simplify these models and significantly decrease usefulness of these methods. Moreover, 123 Neural Computing and Applications their disadvantages are the difficulties with estimating sea conditions. Nonetheless, knowledge of these conditions is essential to selecting the appropriate propeller pitches and engine rotational speeds. For this reason, it would be useful to develop computerbased tools to support such decisions. The base of such tools should be mathematical models connecting fuel consumption and travel time to the destination with the commanded outputs and operational conditions subjected to the propulsion thrust. An analysis of bibliographic references concerning methods of setting the commanded outputs of this kind of ship drivelines was presented by Rudzki [1], who showed that the existing methods do not include models that allow formalizing the required heuristic knowledge. In our opinion, artificial neural network (ANN) techniques can be used for obtaining such models and can be used for better predicting both the fuel consumption and travel time to the destination for the selected commanded outputs and the observed parameters of operational conditions owing to their high accuracy, adequacy and quite promising applications in practice. In this case, an important advantage of the ANN method is that it does not require mathematical relations of the input data and output data. Moreover, this technique allows to solve our problem that is not very well formulated formally. This paper deals with the selected issues of developing ANN models combining the mentioned parameters. In our approach, to collect data needed for building ANN models, sea trials were conducted. 2 Literature review Two ANN models are needed to develop a computer-aided system supporting decision-making regarding setting the ship driveline commanded outputs to ensure rational fuel use and the desired ship speed. The first ANN model should connect a fuel consumption process to factors that influence this process, and the second ANN model connects ship speed to factors that influence this speed. In a mathematical description of these kinds of phenomena, two fundamental approaches are used: • White box modelling, • Black box modelling. In practice, most developed models are obtained using gray box modelling that combines a partial theoretical structure with data to complete the models. White box models, also called cause–effect models, deal with the variables impacting the distribution of a phenomenon and describe a physical process. They integrate existing knowledge about processes into a set of 123 relationships (equations) for quantifying those processes. The most commonly used method to develop white box models of dynamic systems is the balance method. In systems where we must deal with physical quantities, balancing is carried out for parameters that are subordinate to the laws of conservation of energy and momentum. Nevertheless, applying the balance method to modelling both fuel combustion and the ship motion processes to obtain a decision-making model supporting decision-making regarding the commanded outputs of the ship driveline system is practically impossible. This is because the equ (...truncated)