%0 Journal Article %1 odokwo_v_e_2022_7180272 %A E, Odokwo V. %A Nkoi, B %A U, Orji C. %A T, Tamunodukopipi D. %D 2022 %J Global Journal of Engineering and Technology Advances %K Optimization %N 3 %P 042–051 %R 10.30574/gjeta.2022.12.3.0154 %T A generic model towards maximizing the performance of ship propulsion system using artificial neural network %U https://gjeta.com/content/generic-model-towards-maximizing-performance-ship-propulsion-system-using-artificial-neural %V 12 %X In this study, optimization based on artificial neural network (ANN) was established to predict the optimal performance parameters of the system, leading to an optimized propulsion system. Optimization related works on propulsion system investigated by researchers are based on specific fuel consumption, emission characteristics, trim and draft optimization using conventional methods amongst others. This creates a gap for a research window in this direction which this work tries to fill by optimizing overall ship propulsion system efficiency using ANN. The models computational development for this research were actualized using ANN tool box in MATLAB. Attention was given to performance parameter that influences the overall performance of the propulsion system. Hence, the overall ship efficiency, ηsp and energy efficiency design index (EEDI) constitute the parameters of interest to be optimized. A comprehensive ANN program code was developed and implemented to create, configure, train and optimize these parameters of interest. Various ANN-based models of a two layered multilayer perceptron (MLP) structure with different configurations were trained and investigated. Results analysis from MATLAB simulation yields of ηsp 0.507398386 and EEDI of 3.490301699 g of CO2/tm. Results show that the MLP configuration of 14-20-7 gives an optimal model for the ANN. The resulting model could predict the optimized performance output of the system with high degree of accuracy with a minimum mean square error (MSE) at 206 epochs. This point gives the lowest MSE performances value of 3.2923e-10 and regression plot between 0.99999 and 1. The percentage parametric optimization of the propulsion system parameters in ANN gives a 2.4% improvement of ηsp and 3.5% improvement of EEDI. These indicate that the use of ANN for parametric optimization of propulsion system is satisfactory

@article{odokwo_v_e_2022_7180272, abstract = {In this study, optimization based on artificial neural network (ANN) was established to predict the optimal performance parameters of the system, leading to an optimized propulsion system. Optimization related works on propulsion system investigated by researchers are based on specific fuel consumption, emission characteristics, trim and draft optimization using conventional methods amongst others. This creates a gap for a research window in this direction which this work tries to fill by optimizing overall ship propulsion system efficiency using ANN. The models computational development for this research were actualized using ANN tool box in MATLAB. Attention was given to performance parameter that influences the overall performance of the propulsion system. Hence, the overall ship efficiency, ηsp and energy efficiency design index (EEDI) constitute the parameters of interest to be optimized. A comprehensive ANN program code was developed and implemented to create, configure, train and optimize these parameters of interest. Various ANN-based models of a two layered multilayer perceptron (MLP) structure with different configurations were trained and investigated. Results analysis from MATLAB simulation yields of ηsp 0.507398386 and EEDI of 3.490301699 g of CO2/tm. Results show that the MLP configuration of 14-20-7 gives an optimal model for the ANN. The resulting model could predict the optimized performance output of the system with high degree of accuracy with a minimum mean square error (MSE) at 206 epochs. This point gives the lowest MSE performances value of 3.2923e-10 and regression plot between 0.99999 and 1. The percentage parametric optimization of the propulsion system parameters in ANN gives a 2.4% improvement of ηsp and 3.5% improvement of EEDI. These indicate that the use of ANN for parametric optimization of propulsion system is satisfactory}, added-at = {2022-11-15T13:32:25.000+0100}, author = {E, Odokwo V. and Nkoi, B and U, Orji C. and T, Tamunodukopipi D.}, biburl = {https://www.bibsonomy.org/bibtex/2bc24c81a8d5370286504071bedb5dd28/gjetajournal}, doi = {10.30574/gjeta.2022.12.3.0154}, interhash = {1448ca8a63cc99e598e31300fcb00a94}, intrahash = {bc24c81a8d5370286504071bedb5dd28}, issn = {2582-5003}, journal = {{Global Journal of Engineering and Technology Advances}}, keywords = {Optimization}, month = sep, number = 3, pages = {042–051}, timestamp = {2022-11-15T13:32:25.000+0100}, title = {A generic model towards maximizing the performance of ship propulsion system using artificial neural network}, url = {https://gjeta.com/content/generic-model-towards-maximizing-performance-ship-propulsion-system-using-artificial-neural}, volume = 12, year = 2022 }