Modelling and optimization of shea butter biodiesel engine performance evaluation using response surface methodology

In this study, the performance and emission parameters of compression ignition (CI) diesel engine powered by shea butter biodiesel blends with diesel via clay doped ionic liquid catalyst (CD-IL) were modelled, optimized and predicted using Response surface methodology (RSM) technique. Using the (American Society for Testing Materials) ASTM D 6751 criteria, the produced biodiesel's quality was successfully evaluated. The biodiesel blends (B 0 , B 50 , B 100 ), load (100, 200, 300)kg and speeds (1400, 1800, 2200) rpm were considered as the factors while Brake thermal efficiency, Brake specific consumption, Brake power, Nitrogen oxide and carbon monoxide emission were considered as the responses of the shea butter biodiesel engine. Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF), and Brunaeuer Emmet and Teller (BET) were used to analyze the catalyst before and after treatment to ascertain their suitability for the process. The capability of the models was evaluated using the correlation coefficient (R 2 ) and mean square error (MSE). The second-order polynomial model is shown in the Analysis of Variance (ANOVA) with an (R 2 -0.9948, Adjusted R 2 –0.9880, Predicted R 2 –0.9163) for brake thermal efficiency, (R 2 -0.9908, Adjusted R 2 –0.9790, Predicted R 2 –0.8529) for brake specific consumption,(R 2 -0.9988, Adjusted R 2 –0.9972, Predicted R 2 –0.9807) for brake power, (R 2 -0.9995, Adjusted R 2 –0.9988, Predicted R 2 –0.9917) for Carbon monoxide emission and (R 2 -0.9979, Adjusted R 2 –0.9951, Predicted R 2 –0.9659) Nitrogen dioxide emission demonstrating the model's acceptance. The optimal condition for shea butter biodiesel engine performance of brake thermal efficiency (16.3%), brake specific consumption(0.83),brake power (1.5), Carbon monoxide emission (175 ppm) and Nitrogen oxide (150 ppm) was obtained at a biodiesel blend (B 50 ), load of 300 kg and speed of 2200 rpm. The 3D model graphs showed the process parameters impacted on the response significantly with better combustion, cleaner air.fuel ration blend, lesser friction loss and air consumption. Hence the proposed RSM modelling tool for shea butter biodiesel engine performance prediction had the best performance at (B 50 ) (50% biodiesel and 50% diesel).

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Publication Details

Journal
Cleaner and Circular Bioeconomy
Published
2024-09-02
DOI
https://doi.org/10.1016/j.clcb.2024.100105
Citations
2
Primary Topic
Biodiesel Production and Applications
Type
article
Field-Weighted Citation Impact
0.37
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article

Modelling and optimization of shea butter biodiesel engine performance evaluation using response surface methodology

Kenechi Nwosu‐Obieogu, Onukwuli Dominic Okechukwu, Ude Callistus Nonso, Ezeugo Joseph
2 citations
Cleaner and Circular Bioeconomy
Biodiesel Production and Applications
0.37
article

Modelling and optimization of shea butter biodiesel engine performance evaluation using response surface methodology

Kenechi Nwosu‐Obieogu, Onukwuli Dominic Okechukwu, Ude Callistus Nonso, Ezeugo Joseph
article en
2 citations

Abstract

In this study, the performance and emission parameters of compression ignition (CI) diesel engine powered by shea butter biodiesel blends with diesel via clay doped ionic liquid catalyst (CD-IL) were modelled, optimized and predicted using Response surface methodology (RSM) technique. Using the (American Society for Testing Materials) ASTM D 6751 criteria, the produced biodiesel's quality was successfully evaluated. The biodiesel blends (B 0 , B 50 , B 100 ), load (100, 200, 300)kg and speeds (1400, 1800, 2200) rpm were considered as the factors while Brake thermal efficiency, Brake specific consumption, Brake power, Nitrogen oxide and carbon monoxide emission were considered as the responses of the shea butter biodiesel engine. Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF), and Brunaeuer Emmet and Teller (BET) were used to analyze the catalyst before and after treatment to ascertain their suitability for the process. The capability of the models was evaluated using the correlation coefficient (R 2 ) and mean square error (MSE). The second-order polynomial model is shown in the Analysis of Variance (ANOVA) with an (R 2 -0.9948, Adjusted R 2 –0.9880, Predicted R 2 –0.9163) for brake thermal efficiency, (R 2 -0.9908, Adjusted R 2 –0.9790, Predicted R 2 –0.8529) for brake specific consumption,(R 2 -0.9988, Adjusted R 2 –0.9972, Predicted R 2 –0.9807) for brake power, (R 2 -0.9995, Adjusted R 2 –0.9988, Predicted R 2 –0.9917) for Carbon monoxide emission and (R 2 -0.9979, Adjusted R 2 –0.9951, Predicted R 2 –0.9659) Nitrogen dioxide emission demonstrating the model's acceptance. The optimal condition for shea butter biodiesel engine performance of brake thermal efficiency (16.3%), brake specific consumption(0.83),brake power (1.5), Carbon monoxide emission (175 ppm) and Nitrogen oxide (150 ppm) was obtained at a biodiesel blend (B 50 ), load of 300 kg and speed of 2200 rpm. The 3D model graphs showed the process parameters impacted on the response significantly with better combustion, cleaner air.fuel ration blend, lesser friction loss and air consumption. Hence the proposed RSM modelling tool for shea butter biodiesel engine performance prediction had the best performance at (B 50 ) (50% biodiesel and 50% diesel).

Cleaner and Circular BioeconomyVol. 9
Chukwuemeka Odumegwu Ojukwu University (NG), Nnamdi Azikiwe University (NG), Michael Okpara University of Agriculture (NG)
Affordable and clean energy
Openalex Percentile: Top 44%
Biodiesel Production and Applications
0.37
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