Adaptive-neuro fuzzy inference system (ANFIS) based prediction of performance and emission parameters of a CRDI assisted diesel engine under CNG dual-fuel operation

被引：22

作者：

Roy, Sumit ^{[1
]}

Das, Ajoy Kumar ^{[1
]}

Bhadouria, Vivek Singh ^{[2
]}

Mallik, Santi Ranjan ^{[1
]}

Banerjee, Rahul ^{[1
]}

Bose, Probir Kumar ^{[3
]}

机构：

[1] NIT Agartala, Dept Mech Engn, Agartala 799046, India

[2] NIT Silchar, Dept Elect & Commun Engn, Silchar, India

[3] NSHM Knowledge Campus, Durgapur, India

来源：

JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING | 2015年 / 27卷

关键词：

ANFIS; CNG; Diesel; Dual-fuel; Engine performance; Engine emission; TRADE-OFF CHARACTERISTICS; NATURAL-GAS; MODEL; OPTIMIZATION; COMBUSTION; STRENGTH; STRATEGY; TOOL; GEP;

D O I：

10.1016/j.jngse.2015.08.065

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In the present study, an adaptive-neuro fuzzy inference system (ANFIS) based model was developed to predict the performance and emission parameters of a CRDI assisted diesel engine under CNG dual-fuel operation. The established model successfully captured the effects of load, fuel injection pressure and CNG energy share on the desired outputs (BSFC, BTE, NOx, PM and HC). From the performance evaluation, it was evident that the ANFIS predicted data matched the experimental data with high overall accuracy with correlation coefficient (R) values ranging from 0.998875 to 0.999989. The mean absolute percentage error (MAPE) scores were observed to be in the range of 0.08-1.84% with the root mean square errors (RMSEs) in acceptable margins. The developed model could consistently emulate actual engine parameters proficiently even under completely different modes of experimentation thereby providing a holistic and robust predictive platform independent of mode of dual fuel operation for a given dual fuel for virtual sensing to be utilized in real time optimization strategies. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：274 / 283

页数：10

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