Maximization of biodiesel production from sunflower and soybean oils and prediction of diesel engine performance and emission characteristics through response surface methodology

Elkelawy, Medhat; Bastawissi, Hagar Alm‐Eldin; Esmaeil, Khaled Khodary; Radwan, Ahmed; Panchal, Hitesh; Sadasivuni, Kishor Kumar; Muthusamy, Suresh; Israr, Mohammad

doi:10.1016/j.fuel.2020.117072

Cited by 171 publications

(42 citation statements)

References 41 publications

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“…As a result, the Box-Behnken (B.B.) design was selected since it required fewer tests than the central composite design (CCD) [28,29]. The engine load, compression ratio, and blending ratio were chosen as control variables.…”

Section: Design Of Experiments and Data Collectionmentioning

confidence: 99%

Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi-Objective Response Surface Methodology

Sharma¹,

Chhillar²,

Said

et al. 2021

Energies

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Sustainable Development Goals were established by the United Nations General Assembly to ensure that everyone has access to clean, affordable, and sustainable energy. Third-generation biodiesel derived from algae sources can be a feasible option in tackling climate change caused by fossil fuels as it has no impact on the human food supply chain. In this paper, the combustion and emission characteristics of Azolla Pinnata oil biodiesel-diesel blends are investigated. The multi-objective response surface methodology (MORSM) with Box–Behnken design is employed to decrease the number of trials to conserve finite resources in terms of human labor, time, and cost. MORSM was used in this study to investigate the interaction, model prediction, and optimization of the operating parameters of algae biodiesel-powered diesel engines to obtain the best performance with the least emission. For engine output prediction, a prognostic model is developed. Engine operating parameters are optimized using the desirability technique, with the best efficiency and lowest emission as the criteria. The results show Theil’s uncertainty for the model’s predictive capability (Theil’s U2) to be between 0.0449 and 0.1804. The Nash–Sutcliffe efficiency is validated to be excellent between 0.965 and 0.9988, whilst the mean absolute percentage deviation is less than 4.4%. The optimized engine operating conditions achieved are 81.2% of engine load, 17.5 of compression ratio, and 10% of biodiesel blending ratio. The proposed MORSM-based technique’s dependability and robustness validate the experimental methods.

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Section: Design Of Experiments and Data Collectionmentioning

confidence: 99%

Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi-Objective Response Surface Methodology

Sharma¹,

Chhillar²,

Said

et al. 2021

Energies

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“…In total, 27 experimental tests were performed (24 tests at factorial points, 3 with repetitions at the central point). According to the author Elkelawy et al 2020 [71], repetitions at the central point are necessary, as they allow the estimation of pure experimental errors and verify the fit of the model. Based on Table 4, it is possible to observe that the highest conversion to fatty acid ethyl esters was found in test 16 with a conversion of 91.12%, at a temperature of 50 • C, for 6 h, in the molar ratio of 1:15 (FFAs/alcohol) and in the biocatalyst content of 0.15 g. Through the software Statistica ® 10 software (Statsoft, Tulsa, OK, USA), it was possible to determine the optimized conditions for the synthesis of biodiesel (0.14 g of biocatalyst, molar ratio of 1:18 (FFAs/alcohol), temperature of 48 • C in 4 h), obtaining a yield theoretical of 95.87%.…”

Section: Physicochemical Characterization Of the Residual Babassu Oilmentioning

confidence: 99%

Optimization of the Production of Enzymatic Biodiesel from Residual Babassu Oil (Orbignya sp.) via RSM

et al. 2020

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Residual oil from babassu (Orbignya sp.), a low-cost raw material, was used in the enzymatic esterification for biodiesel production, using lipase B from Candida antarctica (Novozym® 435) and ethanol. For the first time in the literature, residual babassu oil and Novozym® 435 are being investigated to obtain biodiesel. In this communication, response surface methodology (RSM) and a central composite design (CCD) were used to optimize the esterification and study the effects of four factors (molar ratio (1:1–1:16, free fatty acids (FFAs) /alcohol), temperature (30–50 °C), biocatalyst content (0.05–0.15 g) and reaction time (2–6 h)) in the conversion into fatty acid ethyl esters. Under optimized conditions (1:18 molar ratio (FFAs/alcohol), 0.14 g of Novozym® 435, 48 °C and 4 h), the conversion into ethyl esters was 96.8%. It was found that after 10 consecutive cycles of esterification under optimal conditions, Novozym® 435 showed a maximum loss of activity of 5.8%, suggesting a very small change in the support/enzyme ratio proved by Fourier Transform Infrared (FTIR) spectroscopy and insignificant changes in the surface of Novozym® 435 proved by scanning electron microscopy (SEM) after the 10 consecutive cycles of esterification.

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“…A multiinput and multi-output optimization study, reported by Roy et al [14], employed GEP to optimize the trade-off between performance and emission. Elkelawy et al [15] optimized the experimental and RSM-based engine parameters using sunflower and soybean biodiesel/diesel blends, where engine load and blending ratio were taken as input while BTE, unburnt hydrocarbon (UHC), and NOx were chosen as response variables. Best engine performance was reported with 13.7% BTE, 120.7 ppm UHC, and 234.9 ppm NOx at 2.05 kW engine power at 70% biodiesel.…”

Section: Introductionmentioning

confidence: 99%

AI-Based Prognostic Modeling and Performance Optimization of CI Engine Using Biodiesel-Diesel Blends

Sharma

2021

IJRER

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This article describes aspects of the development of an artificial intelligence (AI)-based prognostic modeling and performance optimization of a single-cylinder CI engine powered by biodiesel-diesel blends. It is a tool based on gene expression programming (GEP) followed by response surface methodology (RSM). RSM is employed to establish an explicit mathematical relationship between input and outputs. A database of experimental data on a computerized engine test bench was collected for model development and its testing. The prognostic ability of the GEP model was verified by error analysis, where the coefficient of determination (R 2 ) and mean absolute percentage error (MAPE) varied marginally within the range of 0.979 ± 0.020 and 2.15 ± 0.25, respectively. The model captures adequate trends. Optimum input conditions of engine load, biodiesel-diesel blending ratio, fuel injection pressure, and fuel injection timing are observed to be 60.49 %, 14.32 %, 231.35 bar, and 23.7° bTDC, respectively, while optimized results of brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and peak in-cylinder pressure (Pmax) are found to be 24.28 %, 0.3135 kg/kWh, and 58.95 bar, respectively. GEP approach followed by RSM is observed to be a robust tool.

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Maximization of biodiesel production from sunflower and soybean oils and prediction of diesel engine performance and emission characteristics through response surface methodology

Cited by 171 publications

References 41 publications

Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi-Objective Response Surface Methodology

Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi-Objective Response Surface Methodology

Optimization of the Production of Enzymatic Biodiesel from Residual Babassu Oil (Orbignya sp.) via RSM

AI-Based Prognostic Modeling and Performance Optimization of CI Engine Using Biodiesel-Diesel Blends

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