Evaluation of Emission Pattern of Compression Ignition Engines Fuelled With Blends of Orange Peel Oil Based Biodiesel Using Artificial Neural Network Model

Abstract The objective of study was ultrasound-assisted extraction of pectin from orange peel using response surface method and artificial neural network technique. The following findings are absorbed from the effects of extraction parameters and technique used. The pH solution was highly significant compared to ultrasound power. As well as interaction between ultrasound and pH were found to be strongly influenced the extraction yield of pectin. The optimal parameters for extraction were irradiation time of 22.5min, pH of 1.5, and ultrasound power of 155W and liquid-solid ratio 22.5:1 mL/ g. Under these conditions, yield of pectin was 26.87% experimentally, while 26.74 and 26.93% of yield were predicted by response surface and artificial neural network model respectively.The extracted pectin was categorized as high methoxyl pectin, since it has 63.13% degree of esterification, which is above 50% affirmed by Fourier transform infrared spectroscopy detection. Both response surface methodology and artificial neural network model prediction was in good agreement with experimental data; however, the prediction of artificial neural network prediction was better than artificial neural network. Therefore, artificial neural network model is much more accurate in estimating the values of pectin yield and mean square error when compared with the response surface methodology.

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Application of Artificial Neural Network in Determining Performance Profile of Compression Ignition Engine Operated with Orange Peel Oil-Based Biodiesel

Journal of Engineering Research and Reports ◽

10.9734/jerr/2021/v20i1217416 ◽

2021 ◽

pp. 1-14

Author(s):

Samson Kolawole Fasogbon ◽

Chukwuemeka Uguba Owora

Keyword(s):

Neural Network ◽

Orange Peel ◽

Training Algorithms ◽

Compression Ignition ◽

Performance Indices ◽

Heat Engines ◽

Levenberg Marquardt ◽

Orange Peel Oil ◽

Artificial Neural ◽

Peel Oil

Literature including one of our previous studies have confirmed the environmental friendliness of orange peeled oil biodiesel (OPOB) when applied to run compression ignition (CI) heat engines. There is also high degree of compatibility of physicochemical properties of OPOB with fossil diesel. However, there is limited knowledge on its performance indices in the same heat engines. This perhaps may have been due to few interests shown by researchers in the area or obviously due to difficult time and other quantum resources required in conducting the rigorous engine tests. To this end, this work conducted experimental study of performance profile of OPOB in direct injection CI engine; and afterwards applied artificial neural networks (ANNs) to ascertain the engine brake thermal efficiencies (BTE) and brake specific energy consumptions (BSEC). The ANN utilized the Levenberg Marquardt (LM), Scaled Conjugate Gradient (SCG) and Gradient Descent with Momentum and Adaptive Learning (GDX) training algorithms for the performance prediction. The choice of the three algorithms was to effect better comparative assessment. The input variables of the neural network were brake load, orange oil-diesel mixture percentages and engine speed. Statistical parameters such as correlation coefficient (R), mean absolute percentage error (MAPE) and root mean squared error (RMSE) were employed to investigate the performance of the neural networks. Among the three training algorithms, the Levenberg Marquardt trained algorithm estimated the BTE and BSEC with highest precision and accuracy; and lowest error rates. From the study, it is concluded that the performance profile of compression ignition heat engines operated with orange peel biodiesel compares favourably with fossil diesel. It also affirmed that Artificial Neural Network is a reliable tool in the prediction of performance indices of compression ignition engines when run with orange-peel oil based biodiesel.

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