scholarly journals Applying artificial neural networks for modelling ship speed and fuel consumption

2020 ◽  
Vol 32 (23) ◽  
pp. 17379-17395 ◽  
Author(s):  
Wieslaw Tarelko ◽  
Krzysztof Rudzki
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Fuel Consumption ◽  
Support System ◽  
Ann Model ◽  
Data Set ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Ship Speed

AbstractThis 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.

An Automated Energy Conservation Decision Support System for Navy Gas Turbines

2010 ◽  
Author(s):  
Michael J. Roemer ◽  
Carl A. Palmer ◽  
Sudarshan P. Bharadwaj ◽  
Chris Savage
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Energy Conservation ◽  
Real Time ◽  
Fuel Consumption ◽  
Support System ◽  
Gas Turbines ◽  
Fuel Management ◽  
Real Time Information ◽  
Time Information

Energy conservation measures currently employed by U.S. Navy surface combatants require labor-intensive, time-consuming data entry from which fuel curves are generated to drive each ship’s propulsion plant machinery alignment. From these rudimentary curves optimal transit speeds, configurations, and refueling requirements are determined for specific operational demands and mission profiles. This paper describes an automated process for optimizing shipboard fuel consumption rates by integrating advanced diagnostic and maintenance optimization techniques with the onboard data information system. The automated energy conservation decision support system described herein addresses fossil fuel propulsion (gas turbines, steam turbines, and diesel engines), power generation and auxiliary systems. The software tool consists of diagnostic, fuel management, and maintenance modules. The diagnostic module tracks and trends the health state of components that use fuel (and their supporting systems) to provide real-time information on the impact of their current condition on fuel consumption. The fuel management module automates data collection and the generation of fuel curves through open-systems architecture communication with ICAS. It also enables planning by recommending an optimal machinery configuration to minimize fuel consumption based on either speed or time to destination constraints. Additionally, a fuel management module provides real-time information on fuel consumption and optimizes the load of each component based on its health condition, operating requirements and the number and condition of similar components. Finally, overall decision support comes from the maintenance management module that tracks the maintenance actions being performed on fuel consuming systems and recommends future maintenance to be performed (from a fuel conservation standpoint) based on current health information.

scholarly journals A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1426
Author(s):  
Mehmet Erkan Yuksel ◽  
Huseyin Fidan
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Text Mining ◽  
Decision Support System ◽  
Support System ◽  
Grey System Theory ◽  
Database Management System ◽  
Data Set ◽  
Relational Analysis ◽  
Grey Relational

Grey relational analysis (GRA) is a part of the Grey system theory (GST). It is appropriate for solving problems with complicated interrelationships between multiple factors/parameters and variables. It solves multiple-criteria decision-making problems by combining the entire range of performance attribute values being considered for every alternative into one single value. Thus, the main problem is reduced to a single-objective decision-making problem. In this study, we developed a decision support system for the evaluation of written exams with the help of GRA using contextual text mining techniques. The answers obtained from the written exam with the participation of 50 students in a computer laboratory and the answer key prepared by the instructor constituted the data set of the study. A symmetrical perspective allows us to perform relational analysis between the students’ answers and the instructor’s answer key in order to contribute to the measurement and evaluation. Text mining methods and GRA were applied to the data set through the decision support system employing the SQL Server database management system, C#, and Java programming languages. According to the results, we demonstrated that the exam papers are successfully ranked and graded based on the word similarities in the answer key.

A DECISION SUPPORT SYSTEM FOR THE ENVIRONMENTAL IMPACT OF ICT AND E-BUSINESS

2009 ◽  
Vol 08 (02) ◽  
pp. 361-377 ◽  
Author(s):  
LAN YI ◽  
HYWEL R. THOMAS
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Information And Communication Technologies ◽  
Support System ◽  
Environmental Indicators ◽  
Process Data ◽  
Post Process ◽  
Artificial Neural Network Ann ◽  
Expert Decision Support System ◽  
Current Area

Information and communication technologies (ICT) and e-business are affecting our economic progress, social development, and the environment profoundly and in a complex manner. As an emerging field of research, significant interests have been aroused but quantitative studies are rather limited. Traditional systematic approaches for impact studies have been found to be insufficient to deal with this research topic. In order to further explore the relationship between ICT/e-business and the environment, the approach adopted in this study aimed to simulate how ICT/e-business indicators interact with environmental indicators quantitatively. Owing to lack of data and information in the current area in government bodies/councils/research institutes, two questionnaire surveys were conducted. Details of the data collection progress are provided. An artificial neural network (ANN) approach, embedded in a more predictive and empirical model, is suggested herein as a new methodology and possible solution. Furthermore an expert decision support system (EDSS), built around these neural networks with a user-friendly interface and being able to post-process data to information, is developed. The system could be used, for example, by an individual company to analyze how its ICT/e-business adoptions influence its environmental performance.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

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