Optimization for waste coconut and fish oil derived biodiesel with MgO nanoparticle blend: Grey relational analysis, grey wolf optimization, driving training based optimization and election based optimization algorithm

Dharmegowda, Impha Yalagudige; Muniyappa, Lakshmidevamma Madarakallu; Suresh, Ajith Bintravalli; Chandrashekarappa, Manjunath Patel Gowdru; Pradeep, N.B.

doi:10.1016/j.fuel.2022.127249

Cited by 7 publications

(3 citation statements)

References 84 publications

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“…Functionally graded composites are materials that have varying properties and composition along their length or thickness, which can result in improved performance and functionality [55,56]. The GRA method involves comparing the performance of different main parameters against a set of criteria or objectives, such as mechanical properties or cost [57,58]. The method uses grey relational coefficients to quantify the degree of relationship between the main parameters and the criteria, which can then be used to identify the optimal combination of parameters.…”

Section: Grey Relational Analysismentioning

confidence: 99%

Optimization and Characterization of Centrifugal-Cast Functionally Graded Al-SiC Composite Using Response Surface Methodology and Grey Relational Analysis

et al. 2023

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In this study, an optimization approach was employed to determine the optimal main parameters that improve the performance of functionally graded composites manufactured using a combination of stirring and horizontal centrifugal casting. Pure aluminum reinforced with silicon carbide particles was used as the material for the composites. The effects of key input parameters such as mold speed, pouring temperature, stirring speed, and radial distance were optimized using a combination of grey relational analysis and response surface methodology. The statistical significance of the predicted grey relational grade model was assessed through an analysis of variance to identify the appropriate main parameters. The results showed that radial distance had the greatest impact on the performance of the composites, followed by pouring temperature. The optimal combination of main parameters was determined to be a mold speed of 1000 rpm, a pouring temperature of 750 °C, a stirring speed of 150 rpm, and a radial distance of 1 mm. Confirmation tests using these optimal values resulted in a 54.69% improvement in the grey relational grade.

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Section: Grey Relational Analysismentioning

confidence: 99%

Optimization and Characterization of Centrifugal-Cast Functionally Graded Al-SiC Composite Using Response Surface Methodology and Grey Relational Analysis

et al. 2023

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“…The acquired DTBO-based PID controller gain values after the optimization procedure has been successfully completed are Kp = 8.4131, Ki = 961.421, and Kd = 1.97e-08. The transfer function of the PID controller is shown in (30), obtained by substituting these gain values in (13).…”

Section: Implementation Of Dtbo-pid Controller For Speed Control Of B...mentioning

confidence: 99%

“…One recently proposed meta-heuristic algorithm, DTBO, resembles human driving training mathematically [29]. A number of benchmark functions have been used to successfully assess the performance of the aforementioned optimization, but only a handful of real-life applications, such as those for fuel technological advances [30], computational signal processing [31], and detection of gas leaks [32], has been observed. Following a thorough review of the literature, the authors were unable to locate any appropriate applications of the DTBO algorithm in improving the gain parameters of a PID controller intended to drive a BLDC motor.…”

Section: Introductionmentioning

confidence: 99%

An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

Sarma,

Bardalai

2023

IJPEDS

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Tuning of proportional-integral-derivative (PID) controller remains a matter of great concern for the control engineers as it plays a major role to obtain optimal performance of any system Due to their simplicity and excellent efficiency, metaheuristic algorithms have recently become extremely popular among researchers for handling a wide range of real-world optimization challenges. In order to optimize a PID controller for managing the speed of a BLDC motor, this work proposes a novel application of the driving training-based optimization (DTBO) algorithm, one of the latest and most recent human-based metaheuristic algorithms. The purpose of this present study is to optimize a PID controller for a BLDC motor speed control by DTBO method and evaluate its performance with a similar controller tuned by grey wolf optimization (GWO) method. Additionally, the suggested DTBO-PID controller's robustness analysis is being carried out with BLDC motor parameter modifications as well as a comparison to the GWO-PID controller. The comparison is carried out in MATLAB/Simulink, and the results are based on common step response metrics such rise time, settling time, and maximum overshoot. For easier comprehension, the results are presented in tabular and graphical form. The chosen BLDC motor drive system's selected DTBO-PID controller performs better and is more reliable than the GWO-PID controller, according to the final simulation findings.

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Effect of nanoparticles on diesel engines driven by biodiesel and its blends: A review of 10 years of research

Jin

Wei

Chen

et al. 2023

Energy Conversion and Management

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Optimization for waste coconut and fish oil derived biodiesel with MgO nanoparticle blend: Grey relational analysis, grey wolf optimization, driving training based optimization and election based optimization algorithm

Cited by 7 publications

References 84 publications

Optimization and Characterization of Centrifugal-Cast Functionally Graded Al-SiC Composite Using Response Surface Methodology and Grey Relational Analysis

Optimization and Characterization of Centrifugal-Cast Functionally Graded Al-SiC Composite Using Response Surface Methodology and Grey Relational Analysis

An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

Effect of nanoparticles on diesel engines driven by biodiesel and its blends: A review of 10 years of research

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