Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods

Canbolat, Ahmet Serhan; Bademlioglu, Ali Husnu; Arslanoglu, Nurullah; Kaynaklı, Ömer

doi:10.1016/j.jclepro.2019.05.020

Cited by 104 publications

(62 citation statements)

References 34 publications

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“…The Taguchi analysis and ANOVA method used in this study also ranked the soaking time as the most influential factor, followed by the temperature, while the quenchant was ranked least. The ranking order of both methods was the same, which is a means of confirming the reliability of their use as optimization tool ( Canbolat et al., 2019 ; Celik et al., 2018 ; Chamoli et al., 2016 ).…”

Section: Resultsmentioning

confidence: 57%

“…Though Taguchi is a powerful optimization tool, it is not a suitable method for the simultaneous optimization of multi-objective functions ( Canbolat et al, 2019 ). Therefore, GRA was applied to the mechanical properties of MCS to obtain the order (rank) of the importance of each process factor by maximizing the hardness, YS, and UTS of MCS.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of heat treatment parameters of medium carbon steel quenched in different media using Taguchi method and grey relational analysis

Agboola

Ikubanni

Adeleke

et al. 2020

Heliyon

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Quenching is one of the major processes of heat treatment of medium carbon steel that aims at improving its mechanical properties. However, the effectiveness of this process is dependent on several control factors that must be maximized to obtain optimum results in terms of hardness, yield strength, ultimate tensile strength among others. This study aims at optimizing the process of improving the mechanical properties of medium carbon steel by varying some key factors like the quenchant used (A), heat treatment temperature (B), and soaking time (C). The measured responses in this study were the hardness, yield strength (YS), and ultimate tensile strength (UTS). Optimization was conducted in two stages. The first stage dealt with the mono-optimization of process parameters using Taguchi's Signal-to-Noise (S/N) ratio. A total of nine (9) experiments were performed based on standard L9 orthogonal array because each of the three control factors has three (3) levels. The second stage was multi-objective optimization using Taguchi-based grey relational analysis (GRA). The optimal conditions for hardness, YS, and UTS were obtained at A 2 B 3 C 3 , A 3 B 2 C 3 , and A 3 B 3 C 3 , respectively. Using ANOVA as statistical analysis, it was observed that the soaking time was the main control factor for all three measured responses (31.95% contribution ratio for hardness, 62.46%, and 66.76% for YS and UTS, respectively), while the quenchant had the least contribution. Analysis of the Taguchi-based GRA revealed that the results obtained are in total conformance to that of the Taguchi method, with soaking time having the highest contribution ratio of 69.41%.

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Section: Resultsmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Optimization of heat treatment parameters of medium carbon steel quenched in different media using Taguchi method and grey relational analysis

Agboola

Ikubanni

Adeleke

et al. 2020

Heliyon

View full text Add to dashboard Cite

show abstract

“…An orthogonal array table is created by calculating total degree of freedom (DOF) based on the combined degree of freedom of all parameters (Canbolat et al 2019;Bademlioglu et al 2020).…”

Section: -9-particle Swarm Optimization (Pso)mentioning

confidence: 99%

Hourly soil temperature prediction using integrated machine learning methods, GLUE uncertainty analysis, Taguchi search, and wavelet coherence analysis

Seifi¹,

Ehteram²,

Nayebloei³

et al. 2021

Preprint

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In this study, hourly Ts variations at 5, 10, and 30 cm soil depth were investigated and predicted for an arid site (Sirjan) and a semi-humid site (Sanandaj) in Iran. Standalone machine learning models (adaptive neuron fuzzy interface system (ANFIS), support vector machine model (SVM), radial basis function neural network (RBFNN), and multilayer perceptron (MLP)) were hybridized with four optimization algorithms (sunflower optimization (SFO), firefly algorithm (FFA), salp swarm algorithm (SSA), particle swarm optimization (PSO)) to improve prediction accuracy and reduce uncertainty. Uncertainty analysis was performed using generalized likelihood uncertainty estimation (GLUE), while wavelet coherence was used to assess interactions between Ts and meteorological parameters. For the arid site, ANFIS-SFO (RMSE = 1.18oC, MAE = 1.05oC, NSE = 0.93, PBIAS = 7%, and R2 = 0.9998) produced the most accurate performance at 5 cm soil depth. At best, hybridization with SFO (ANFIS-SFO, MLP-SFO, RBFNN-SFO, SVM-SFO) decreased RMSE by 5.6, 18, 18.3, and 18.18 % compared with the respective standalone model. At the semi-humid site, all integrated models showed most accurate performance at 10 cm soil depth, with RMSE for the best model (ANFIS-SFO) increasing by 10.5%, and MAE by 10.1%, from 10 to 30 cm depth. GLUE analysis confirmed that integrating optimization algorithms with machine learning models decreased the uncertainty in Ts predictions. Wavelet coherence analysis demonstrated that air temperature, relative humidity, and solar radiation, but not wind speed, had high coherence with Ts at different soil depths at both sites, and meteorological parameters mostly influenced Ts in upper soil layers.

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“…The ANOVA was applied to evaluate the importance of each factor. The ANOVA aims to estimate the seriousness of the goal variation caused by the variation of each factor, as observed from the results [28][29][30]. Three factors were analyzed by ANOVA to determine the influence of each factor on the experimental results.…”

Section: Analysis Of Variance (Anova)mentioning

confidence: 99%

Surface Quality Improvement in Machining an Aluminum Honeycomb by Ice Fixation

Wang

Gan

et al. 2020

Chin. J. Mech. Eng.

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A honeycomb structure is widely used in sandwich structure components in aeronautics and astronautics; however, machining is required to reveal some of its features. In honeycomb structures, deficiencies, such as burrs, edge subsiding, and cracking, can easily appear, owing to poor specific stiffness in the radial direction. Some effective fixation methods based on a filling principle have been applied by researchers, including approaches based on wax, polyethylene glycol, iron powder, and (especially) ice. However, few studies have addressed the optimization of the cutting parameters. This study focused on optimizing the cutting parameters to obtain a better surface roughness (calculated as a roughness average or R a ) and surface morphology in the machining of an aluminum alloy honeycomb by an ice fixation method. A Taguchi method and an analysis of variance were used to analyze the effects and contributions of spindle speed, cutting depth, and feed rate. The optimal cutting parameters were determined using the signal-to-noise ratio combined with the surface morphology. An F-value and P-value were calculated for the value of the R a , according to a "smaller is better" model. Additionally, the optimum cutting parameters for machining the aluminum honeycomb by ice fixation were found at different levels. The results of this study showed that the optimal parameters were a feed rate of 50 mm/min, cutting depth of 1.2 mm, and spindle speed of 4000 r/min. Feed rate was the most significant factor for minimizing R a and improving the surface morphology, followed by spindle speed. The cutting depth had little effect on R a and surface morphology. After optimization, the value of R a could reach 0.218 μm, and no surface morphology deterioration was observed in the verified experiment. Thus, this research proposes optimal parameters based on ice fixation for improving the surface quality.

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Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods

Cited by 104 publications

References 34 publications

Optimization of heat treatment parameters of medium carbon steel quenched in different media using Taguchi method and grey relational analysis

Optimization of heat treatment parameters of medium carbon steel quenched in different media using Taguchi method and grey relational analysis

Hourly soil temperature prediction using integrated machine learning methods, GLUE uncertainty analysis, Taguchi search, and wavelet coherence analysis

Surface Quality Improvement in Machining an Aluminum Honeycomb by Ice Fixation

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