Analysis and prediction of the performance of free- piston Stirling engine using response surface methodology and artificial neural network

Ye, Wenlian; Wang, Xiaojun; Liu, Yingwen

doi:10.1016/j.applthermaleng.2021.116557

Cited by 44 publications

(14 citation statements)

References 32 publications

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“…In the uniaxial compression process, the force-deformation curve characteristics obtained from the compression process are used to determine the maximal compression force and deformation energy for recovering the oil from the bulk oilseeds and to understand the operational safety of the universal compression testing machine in terms of the undulation effect [27]. The above-mentioned processing factors, thus, influence the mechanical oil pressing process in large-scale production, which can be ascertained under the uniaxial compression process using an appropriate experimental design, such as the response surface methodology (RSM) coupled with the Box-Behnken design (BBD) [28][29][30][31][32]. The RSM is a collection of mathematical and statistical techniques useful for examining the effects of several independent factors [33,34].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Uniaxial Oil Extraction of Bulk Rapeseeds: Spectrophotometric and Chemical Analyses of the Extracted Oil under Pretreatment Temperatures and Heating Intervals

et al. 2021

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Optimizing the operating factors in edible oil extraction requires a statistical technique such as a response surface methodology for evaluating their effects on the responses. The examined input factors in this study were the diameter of pressing vessel, VD (60, 80, and 100 mm), temperature, TPR (40, 60, and 80 °C), and heating time, HTM (30, 60 and 90 min). The combination of these factors generated 17 experimental runs where the mass of oil, oil yield, oil extraction efficiency, and deformation energy were calculated. Based on the response surface regression analysis, the combination of the optimized factors was VD: 100 (+1) mm; TPR: 80 °C (+1) and HTM: 60 (0) min); VD: 60 (−1) mm; TPR: 80 °C (+1) and HTM: 75 (+0.5) min and VD: 100 (+1) mm; TPR: 80 °C (+1) and HTM: 90 (+1). The absorbance and transmittance values significantly (p < 0.05) correlated with the wavelength and temperature, but they did not correlate significantly (p > 0.05) with heating time. The peroxide value did not correlate significantly with temperature, however, it correlated significantly with heating time. Neither the acid value nor the free fatty acid value correlated with both temperature and heating time. The findings of the present study are part of our continuing research on oilseeds’ processing optimization parameters.

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Section: Introductionmentioning

confidence: 99%

Optimizing Uniaxial Oil Extraction of Bulk Rapeseeds: Spectrophotometric and Chemical Analyses of the Extracted Oil under Pretreatment Temperatures and Heating Intervals

et al. 2021

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“…The ANN method has proved to be a perfect method to predict the performance of Stirling machines. 31,32 Hence, applying the ANN model to study the Stirling machines' performance was investigated by many researchers. Yang et al 33 have modeled the performance of a Stirling cryocooler using an NN by taking into account these three input parameters: compression stroke, expansion stroke, and phase angle.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, researchers have employed different models to decrease the expensive costs of experimental tests. The ANN method has proved to be a perfect method to predict the performance of Stirling machines 31,32 . Hence, applying the ANN model to study the Stirling machines' performance was investigated by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of a heat‐to‐cool Stirling cycle using artificial neural network

Katooli

Moghadam

Mehrpooya

2022

Intl J of Energy Research

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This study focuses on applying artificial neural network (ANN) to investigate different parameters and operating conditions that affect the cooling flux and efficiency of a heat-to-cool gamma-type Stirling machine. ANN model is developed to predict output parameters of the Stirling machine based on sample data generated by a MATLAB code-named Nlog code. The code was previously developed and validated by the authors. To reach the addressed goal, three input parameters, including Stirling refrigerator frequency (f), displacer stroke (d), and phase angle (φ) were selected. The output parameters are coefficient of performance (COP) and cooling power of the Stirling refrigerator, and efficiency (η) and output power (P e ) of the Stirling engine. The ANNs proved to have the most effective performance with 10 neurons and the ANN structure as 4-10-1 since it could perfectly map the input parameters to the output parameters. The presented ANNs were compared with four different design cases of a Stirling refrigerator against the experimental data. The ANN results had good agreement with experimental data with an average error of less than 1% for the cooling power and an average error of less than 8% for the COP.Besides, the ANN model and Nlog code error for the efficiency and output power of the Stirling engine were 1.33% and 0.21%, respectively.

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“…The conventional method for determining the optimum conditions of the immobilized cell, which involves varying one factor at a time, is time-consuming and cannot identify the interactions between the factors involved [5]. This has led to the application of response surface methodology (RSM), which is able to analyze the effects of several factors with interactions while reducing the number of experiments [6]. RSM has been widely applied in biochemical processes, such as the production of H 2 from xylose [7] and the production of fibrinolytic enzymes with a new Xanthomonas oryzae [8].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Culture Conditions of Immobilized Cells for Enzyme Excretion and Cell Lysis

et al. 2022

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Recombinant enzyme excretion is essential due to stable and active protein production. However, enzyme excretion, especially in the Escherichia coli system, may increase the occurrence of cell lysis. Thus, cell immobilization was used, and optimization of culture conditions was conducted to increase enzyme excretion and limit cell lysis. The culture conditions were optimized using a response surface methodology. CGTase excretion and cell lysis were analyzed by CGTase and b-galactosidase assays, respectively. High CGTase excretion with low cell lysis was achieved after the optimization process. The results suggest that optimization of the cell immobilization system increased CGTase excretion with low cell lysis and made it more valuable for industrial applications.

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Analysis and prediction of the performance of free- piston Stirling engine using response surface methodology and artificial neural network

Cited by 44 publications

References 32 publications

Optimizing Uniaxial Oil Extraction of Bulk Rapeseeds: Spectrophotometric and Chemical Analyses of the Extracted Oil under Pretreatment Temperatures and Heating Intervals

Optimizing Uniaxial Oil Extraction of Bulk Rapeseeds: Spectrophotometric and Chemical Analyses of the Extracted Oil under Pretreatment Temperatures and Heating Intervals

Design optimization of a heat‐to‐cool Stirling cycle using artificial neural network

Optimization of Culture Conditions of Immobilized Cells for Enzyme Excretion and Cell Lysis

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