Prediction of PAHs Emitted from Marine Diesel Engine Using Artificial Neural Networks Combining Genetic Algorithms

2014 ◽  
Vol 599-601 ◽  
pp. 1233-1236
Author(s):  
Ming Yu Wang ◽  
Shao Jun Zhang ◽  
Xiao Zhang
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Diesel Engine ◽  
Network Performance ◽  
Experimental Studies ◽  
Marine Diesel Engine ◽  
Marine Diesel ◽  
Electronic Parameters ◽  
Artificial Neural

Experimental studies on operating a marine diesel engine to determine the performance map under different working conditions need to consume a lot of money and labor. To solve this problem, a mathematical model based on Artificial Neural Networks (ANNs) combined genetic algorithms (GA) to predicate the performance emissions of the marine diesel engine is firstly reported in this paper. The predicted result showed that the network performance is sufficient for all target emission outputs. The input layer without transfer function consisted of 11 neurons is used, and output layer predicted 16 polycyclic aromatic hydrocarbons (PAHs). Electronic parameters such as VIC, SOI, CRP, NUN, VEO and VEC have influences on the PAHs emissions. The actual data obtained from the diesel is well agreed with the predicted data. The usage of ANNs is highly recommended to predict engine emissions instead of having to undertake complex and time-consuming experimental studies.

Research of Marine Diesel Engine’s State Prediction Based on Evolutionary Neural Network and Spectrometric Analysis

2011 ◽  
Vol 346 ◽  
pp. 339-345 ◽  
Author(s):  
Hai Jun Wei ◽  
Guo You Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Diesel Engine ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lubricating Oil ◽  
Spectrometric Analysis ◽  
Marine Diesel Engine ◽  
Marine Diesel

In this paper, an evolutionary neural networks model is proposed to predict the content of metal elements contained in marine diesel engine lubricating oil, by fusing genetic algorithms (GAs) and error back propagation neural network (BPNN) to offset the demerits of one paradigm by the merits of another. The input data of metal content was detected by spectrometric analysis. Genetic algorithms are used to globally optimize the weights and threshold of BP neural networks. Moreover, one case study was presented to illustrate the proposed method. The prediction accuracy of the novel method is compared with that of only BPNN method to illustrate the feasibility and effectiveness of the proposed method. The relative error on average of results is 1.52%, it can meet the precision request of state detecting in marine diesel engine.

A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks

2020 ◽  
pp. 39-58
Author(s):  
Tuğba Özge Onur ◽  
Yusuf Aytaç Onur
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Testing ◽  
Steel Wire ◽  
Experimental Studies ◽  
Fatigue Lifetime ◽  
Bending Fatigue ◽  
Wire Ropes ◽  
Significant Case ◽  
Artificial Neural

Steel wire ropes are frequently subjected to dynamic reciprocal bending movement over sheaves or drums in cranes, elevators, mine hoists, and aerial ropeways. This kind of movement initiates fatigue damage on the ropes. It is a quite significant case to know bending cycles to failure of rope in service which is also known as bending over sheave fatigue lifetime. It helps to take precaution in the plant in advance and eliminate catastrophic accidents due to usage of rope when allowable bending cycles are exceeded. To determine bending fatigue lifetime of ropes, experimental studies are conducted. However, bending over sheave fatigue testing in laboratory environments require high initial preparation cost and longer time to finalize the experiments. Due to those reasons, this chapter focuses on a novel prediction perspective to the bending over sheave fatigue lifetime of steel wire ropes by means of artificial neural networks.

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