Title: The concept of the agro cargo supply chain model using inland navigation

Author/Authors: Łozowicka D., Kaup M., Ślączka W., Kalbarczyk-Jedynak A., Chorab P., Ignalewski W.

Place of publication: Journal of Civil Engineering and Transport

Year: 2022

Keywords: inland waterway transport, modeling, supply chain

Abstract: Making transport sentences requires taking into account many external and internal factors dependent on changes taking place on the shipping market. The place, time and type of transported cargo, etc., have an impact on the complexity of the transport system structure, which is why it is important to explore the possibilities and create transport optimization models throughout the entire cargo supply chain and analyze issues related to their organization, which affects the safety and reliability of this type of systems. The aim of the article is to present and analyze the concept of the agro cargo supply chain model using inland navigation, in the relations between Poland and the Federal Republic of Germany on the example of the Capital Group OT Logistics S.A. (OTL SA Capital Group). Taking into account the complexity and structure of transport tasks, it is important to look for optimal solutions in the field of transport. Each supply chain model includes only certain selected factors that have a limited impact on the volatility of the transport. It is not possible to build a model that would fully recreate the behavior of the object, with all possible external influences. The article considers the optimization model of inland wheat transport to final consumers, which predicts a constant demand for cereal loads in certain time segments. This model does not provide any rapid domestic fluctuations in demand for wheat at certain time intervals.

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Title: Determination of design formulas for container ships at the preliminary design stage using artificial neural network and multiple nonlinear regression

Author/Authors: Cepowski T., Chorab P.

Place of publication: OCEAN ENGINEERING

Year: 2021

Keywords: container ship, main dimensions, regression, ship design, ANN

Abstract: This article presents preliminary design formulas developed using a database of container ships built since 2015. Artificial neural networks and multiple nonlinear regressions with randomly searched functions were used to develop these formulas. The use of random search for nonlinear functions in a Multiple Nonlinear Regression model gave estimates which were just as precise as estimates created by the artificial neural network. All equations presented in this paper could have practical application for the estimation of dimensions, such as: length between perpendiculars, breadth, draught moulded and side depth. The equations were developed in relation to deadweight, TEU capacity and ship speed. These kinds of relationships have not been demonstrated before in ship theory. A statistical analysis showed that the main dimensions of the container ships can be estimated highly accurately by using the equations presented in the paper. The study showed that taking into account deadweight, TEU capacity and ship speed as three input parameters can improve the accuracy of an estimation by up to 44 percent, than when compared to the estimate accuracy of the design equations which are based on one input parameter.

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of the publication: https://www.sciencedirect.com/science/article/pii/S0029801821010969?via%3Dihub

DOI: 10.1016/j.oceaneng.2021.109727

Title: The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships

Author/Authors: Cepowski T., Chorab P.

Place of publication: Energies

Year: 2021

Keywords: air pollution, bulk carrier, container carrier, deadweight, engine power, fuel consumption, sea transport, speed, tanker, ANN

Abstract: The 2007–2008 financial crisis, together with rises in fuel prices and stringent pollution regulation, led to the need to update the methods concerning ship propulsion system design. In this article, a set of artificial neural networks was used to update the design equations to estimate the engine power and fuel consumption of modern tankers, bulk carriers, and container ships. Deadweight or TEU capacity and ship speed were used as the inputs for the ANNs. This study shows that even a linear ANN with two neurons in the input and output layers, with purelin activation functions, offers an accurate estimation of ship propulsion parameters. The proposed linear ANNs have simple mathematical structures and are straightforward to apply. The ANNs presented in the article were developed based on the data of the most recent ships built from 2015 to present, and could have a practical application at the preliminary design stage, in transportation or air pollution studies for modern commercial cargo ships. The presented equations mirror trends found in the literature and offer much greater accuracy for the features of new-built ships. The article shows how to estimate CO2 emissions for a bulk carrier, tanker, and container carrier utilizing the proposed ANNs.

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of the publication: https://www.mdpi.com/1996-1073/14/16/4827

DOI: 10.3390/en14164827

Title: APPLICATION OF AN ARTIFICIAL NEURAL NETWORK AND MULTIPLE NONLINEAR REGRESSION TO ESTIMATE CONTAINER SHIP LENGTH BETWEEN PERPENDICULARS

Author/Authors: Cepowski T., Chorab P., Łozowicka D.

Place of publication: Polish Maritime Research

Year: 2021

Keywords: container ship, regression, ship design, ANN, length

Abstract: Container ship length was estimated using artificial neural networks (ANN), as well as a random search based on Multiple Nonlinear Regression (MNLR). Two alternative equations were developed to estimate the length between perpendiculars based on container number and ship velocity using the aforementioned methods and an up-to-date container ship database. These equations could have practical applications during the preliminary design stage of a container ship. The application of heuristic techniques for the development of a MNLR model by variable and function randomisation leads to the automatic discovery of equation sets. It has been shown that an equation elaborated using this method, based on a random search, is more accurate and has a simpler mathematical form than an equation derived using ANN.

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of the publication: https://sciendo.com/article/10.2478/pomr-2021-0019

DOI: 10.2478/pomr-2021-0019