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Optimization of Performance Characteristics of Homogeneous Charge Compression Ignition Engine with Biodiesel using Artificial Neural Network (ANN) and Response Surface Methodology (RSM)
https://orcid.org/http://orcid.org/0000-0002-2332-576X
In the present investigation, the effect of air intake temperature (Ti) on the performance characteristics of the homogeneous charge compression ignition (HCCI) engine was studied using pig animal fat oil biodiesel (PAFO) and its blends as fuel. A fuel–air mixing unit was used as an external mixing device in the HCCI engine. Experiments were conducted at a constant speed of the engine (1500 rpm) with variation in Ti (100 °C, 120 °C and 140 °C) and blends of biodiesel at the ratios B10, B20, B30 and B50. Experimental results show that the Ti and blends have a major effect on fuel–air mixing. It was observed that the engine operated smoothly up to 120 °C of air intake temperature and later an excessive knocking was observed beyond 120 °C which results in increased emissions. In the HCCI engine, early start combustion was observed for the duration of the 3° crank angle (CA) before TDC (top dead center) at 30° CA. It is observed that the brake thermal efficiency (BTE) of the HCCI engine at 120 °C was reduced when compared to conventional CI engine. But NOx was greatly reduced up to 71% compared to the CI engine with diesel as a fuel. The smoke density was reduced by 48.7% for PAFO B20-fuelled HCCI engine when compared to conventional CI engine. In the HCCI engine using PAFO B20 blend fuel, the HC emissions resulted in high HC emission and CO emission about 40% higher when compared to conventional CI engine. Artificial neural networks (ANNs) model was developed to predict the performance characteristics of HCCI engines with diesel and biodiesel. The results of the ANN model were compared to experimental values and show that the error is within the acceptable limits. The optimum condition was obtained using the response surface method (RSM). The optimized conditions are at the load of 28.77%, a fuel blend of 19.30 and with air intake temperature of 387.788 K, the RSM gives BTE of 20.3659%, NOx emission of 373.118PPM, smoke density of 10.6562 HSU, HC emission of 36.4981 ppm and CO emission of 0.41333%.
Optimization of Performance Characteristics of Homogeneous Charge Compression Ignition Engine with Biodiesel using Artificial Neural Network (ANN) and Response Surface Methodology (RSM)
https://orcid.org/http://orcid.org/0000-0002-2332-576X
In the present investigation, the effect of air intake temperature (Ti) on the performance characteristics of the homogeneous charge compression ignition (HCCI) engine was studied using pig animal fat oil biodiesel (PAFO) and its blends as fuel. A fuel–air mixing unit was used as an external mixing device in the HCCI engine. Experiments were conducted at a constant speed of the engine (1500 rpm) with variation in Ti (100 °C, 120 °C and 140 °C) and blends of biodiesel at the ratios B10, B20, B30 and B50. Experimental results show that the Ti and blends have a major effect on fuel–air mixing. It was observed that the engine operated smoothly up to 120 °C of air intake temperature and later an excessive knocking was observed beyond 120 °C which results in increased emissions. In the HCCI engine, early start combustion was observed for the duration of the 3° crank angle (CA) before TDC (top dead center) at 30° CA. It is observed that the brake thermal efficiency (BTE) of the HCCI engine at 120 °C was reduced when compared to conventional CI engine. But NOx was greatly reduced up to 71% compared to the CI engine with diesel as a fuel. The smoke density was reduced by 48.7% for PAFO B20-fuelled HCCI engine when compared to conventional CI engine. In the HCCI engine using PAFO B20 blend fuel, the HC emissions resulted in high HC emission and CO emission about 40% higher when compared to conventional CI engine. Artificial neural networks (ANNs) model was developed to predict the performance characteristics of HCCI engines with diesel and biodiesel. The results of the ANN model were compared to experimental values and show that the error is within the acceptable limits. The optimum condition was obtained using the response surface method (RSM). The optimized conditions are at the load of 28.77%, a fuel blend of 19.30 and with air intake temperature of 387.788 K, the RSM gives BTE of 20.3659%, NOx emission of 373.118PPM, smoke density of 10.6562 HSU, HC emission of 36.4981 ppm and CO emission of 0.41333%.
Optimization of Performance Characteristics of Homogeneous Charge Compression Ignition Engine with Biodiesel using Artificial Neural Network (ANN) and Response Surface Methodology (RSM)
https://orcid.org/http://orcid.org/0000-0002-2332-576X
In the present investigation, the effect of air intake temperature (Ti) on the performance characteristics of the homogeneous charge compression ignition (HCCI) engine was studied using pig animal fat oil biodiesel (PAFO) and its blends as fuel. A fuel–air mixing unit was used as an external mixing device in the HCCI engine. Experiments were conducted at a constant speed of the engine (1500 rpm) with variation in Ti (100 °C, 120 °C and 140 °C) and blends of biodiesel at the ratios B10, B20, B30 and B50. Experimental results show that the Ti and blends have a major effect on fuel–air mixing. It was observed that the engine operated smoothly up to 120 °C of air intake temperature and later an excessive knocking was observed beyond 120 °C which results in increased emissions. In the HCCI engine, early start combustion was observed for the duration of the 3° crank angle (CA) before TDC (top dead center) at 30° CA. It is observed that the brake thermal efficiency (BTE) of the HCCI engine at 120 °C was reduced when compared to conventional CI engine. But NOx was greatly reduced up to 71% compared to the CI engine with diesel as a fuel. The smoke density was reduced by 48.7% for PAFO B20-fuelled HCCI engine when compared to conventional CI engine. In the HCCI engine using PAFO B20 blend fuel, the HC emissions resulted in high HC emission and CO emission about 40% higher when compared to conventional CI engine. Artificial neural networks (ANNs) model was developed to predict the performance characteristics of HCCI engines with diesel and biodiesel. The results of the ANN model were compared to experimental values and show that the error is within the acceptable limits. The optimum condition was obtained using the response surface method (RSM). The optimized conditions are at the load of 28.77%, a fuel blend of 19.30 and with air intake temperature of 387.788 K, the RSM gives BTE of 20.3659%, NOx emission of 373.118PPM, smoke density of 10.6562 HSU, HC emission of 36.4981 ppm and CO emission of 0.41333%.
Optimization of Performance Characteristics of Homogeneous Charge Compression Ignition Engine with Biodiesel using Artificial Neural Network (ANN) and Response Surface Methodology (RSM)
J. Inst. Eng. India Ser. C
Moulali, P. (Autor:in) / Tarigonda, Hariprasad (Autor:in) / Prasad, B. D. (Autor:in)
Journal of The Institution of Engineers (India): Series C ; 103 ; 875-888
01.08.2022
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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