Multi-objective online optimization of a marine diesel engine using NSGA-II coupled with enhancing trained support vector machine

Niu, Xuming; Wang, Hechun; Hu, Song; Yang, Chuanlei; Wang, Yinyan

doi:10.1016/j.applthermaleng.2018.03.080

Cited by 53 publications

(22 citation statements)

References 30 publications

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“…31 Efficient optimization algorithms have been reported to be critical to the development of modern engine technology 32 In that report, experimental studies were carried out on optimizing the performance of a diesel engine running with soy biodiesel using hybrid particle swarm optimization (PSO) and GA method. Also, Lotfan et al, 32 investigated the combination of ANN and NSGA-11 for modeling emissions from direct injection dual-fuel (DDF) engine while recently a novel on-line optimization approach based on engine physical model using NSGA-11 coupled with a support vector machine method has been reported 33,34 Considering the optimization and modeling of engine performance, and emission characteristics. major reports from the literature have focused on the application of Taguchi design of experiment (TDOE), convergence computational fluid dynamics (CFD), conventional RSM, particle swarm optimization and genetic algorithm (PSA-GA), combination of ANN, and non-dominated sorting genetic algorithm II (NSGA-II), NSGA-II coupled with supported vector machine (SVM), MIMO-ANN, and Nelder-Meads (NM).…”

Section: Introductionmentioning

confidence: 99%

Combustion exhaust release impact, diesel engine performance, and optimization studies of green diesel-petrodiesel blend in a high compression ratio direct-injection compression-ignition engine

Onukwuli

Esonye

Ofoefule

2021

Advances in Mechanical Engineering

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Reports on the optimum brake-power, surrogate fuel, engine emissions, and efficiency using hybrid model on high compression ratio diesel engines are very imperative for effective application of biodiesel in power and renewable energy generation. This study presents Dyacrodes edulis biodiesel engine performance and combustion release optimization using response surface methodology-genetic algorithm (RSM-GA) as well as the variation of key engine efficiency and exhaust release indices with brake power and fuel blend in a high compression ratio (CR) diesel engine. Combustion emission impacts of the blends with respect to petro-diesel decreased in values except for NOX. Brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), volumetric efficiency, and exhaust temperature increased with brake power while specific energies decreased with load. Optimum conditions obtained using integrated RSM-GA were 40.03%, 0.05 kg/kW-h, 0.03%, 132.30 ppm, and 18.84 ppm for BTE, BSFC, CO, NOx, and HC respectively at low factor (engine load, engine speed, and fuel blend) conditions. At the optimum conditions, the experimental validation results were 44.01%, 0.05 kg/kW-h, 0.04%, 130.05 ppm, and 20.33 ppm for BTE, BSFC, CO, NOx, and HC respectively. The application of the feedstock in compression ignition engine is viable.

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Section: Introductionmentioning

confidence: 99%

Combustion exhaust release impact, diesel engine performance, and optimization studies of green diesel-petrodiesel blend in a high compression ratio direct-injection compression-ignition engine

Onukwuli

Esonye

Ofoefule

2021

Advances in Mechanical Engineering

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“…The third comparative method is the SI-VMD-MFCC-KNN without the process of vector quantization (SVMK) and the fourth comparative method is the SI-VMD-MFCC-VQ-SVM replacing KNN classifiers with SVM classifiers (SVMS). The kernel function of SVM is RBF, and the penalty index and radius of RBF are set to 20 and 0.081, respectively [40]. The single diagnosis results of the contrastive methods depicted in Figure 13 and Table 6 exhibits the average accuracy, average precision, average sensitivity, and calculation time of six experiments.…”

Section: Diagnosis Results Of Valve Clearance Faultmentioning

confidence: 99%

A New Fault Diagnosis Method for a Diesel Engine Based on an Optimized Vibration Mel Frequency under Multiple Operation Conditions

Zhao

Zhang

Jiang

et al. 2019

Sensors

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The diesel engine has been a significant component of large-scale mechanical systems for the intelligent manufacturing industry. Because of its complex structure and poor working environment, it has trouble effectively acquiring the representative fault features. Further, fault diagnosis of the diesel engine faces great challenges. This paper presents a new fault diagnosis method for the detection of diesel engine faults under multiple operation conditions instead of conventional methods confined to a single condition. First, an adaptive correlation threshold process is designed as a preprocessing unit to enhance data quality by weakening non-impact region characteristics. Next, a feature extraction method for sound signals based on the Mel frequency cepstrum (MFC) is improved and introduced into the machinery fault diagnosis. Then, the combination of the improved feature and vibrational mode decomposition (VMD) is proposed to incorporate VMD into an effective adaptive decomposition of non-stationary signals to combine it with an excellent feature representation of the vibration signal. Finally, the vector quantization algorithm is adopted to reduce the feature dimensions and generate codebook model bases, which trains the K-Nearest Neighbor classifiers. Five comparative methods were carried out, and the experimental results show that the proposed method offers a good effect of the common valve clearance fault of diesel engines under different conditions.

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“…The fast, non-dominated sorting, fast crowded distance estimation, and simple crowded comparison operator make NSGA-II an efficient optimization technique. Basic definitions and the flow chart of NSGA-II are available in [39,40] and have not been described in this paper.…”

Section: Non-dominated Sorting Genetic Algorithm (Nsga-ii)mentioning

confidence: 99%

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

Lalwani

Sharma

Pruncu

et al. 2020

JMMP

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This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives.

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Multi-objective online optimization of a marine diesel engine using NSGA-II coupled with enhancing trained support vector machine

Cited by 53 publications

References 30 publications

Combustion exhaust release impact, diesel engine performance, and optimization studies of green diesel-petrodiesel blend in a high compression ratio direct-injection compression-ignition engine

Combustion exhaust release impact, diesel engine performance, and optimization studies of green diesel-petrodiesel blend in a high compression ratio direct-injection compression-ignition engine

A New Fault Diagnosis Method for a Diesel Engine Based on an Optimized Vibration Mel Frequency under Multiple Operation Conditions

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

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