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Evaluation of Emission Characteristics and Performance of Pomegranate Ethanol Blended S. I. Engine using Artificial Neural Network and Rule Learner Classifier

Evaluation of Emission Characteristics and Performance of Pomegranate Ethanol Blended S. I.... Waste pomegranate fruit is one of the new sources of ethanol. Using four different ethanol mixes, the emission performance of S.I. engine was measured at varied running speeds. Ethanol mixed gasoline enhances the quality of engine exhaust emissions, except for nitrogen oxides. Engine performance was found optimum using a 15% ethanol blend and a 1500 rpm speed. The emission characteristics were further examined using artificial neural network and rule learner classifiers. Experiments yielded data sets in which emission characteristics of engines were mapped in relation to engine speed and ethanol/petrol mixtures. These datasets were utilised to train artificial neural networks and rule learner classifiers to establish relationships among emission characteristics, speeds, and ethanol combinations. Both models were tested, and the rule learner classifier was found to be more accurate than the artificial neural network. Emission characteristics, speed, and ethanol combinations can all be correlated using the proposed rule learner algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India):Series A Springer Journals

Evaluation of Emission Characteristics and Performance of Pomegranate Ethanol Blended S. I. Engine using Artificial Neural Network and Rule Learner Classifier

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Publisher
Springer Journals
Copyright
Copyright © The Institution of Engineers (India) 2022
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-022-00639-z
Publisher site
See Article on Publisher Site

Abstract

Waste pomegranate fruit is one of the new sources of ethanol. Using four different ethanol mixes, the emission performance of S.I. engine was measured at varied running speeds. Ethanol mixed gasoline enhances the quality of engine exhaust emissions, except for nitrogen oxides. Engine performance was found optimum using a 15% ethanol blend and a 1500 rpm speed. The emission characteristics were further examined using artificial neural network and rule learner classifiers. Experiments yielded data sets in which emission characteristics of engines were mapped in relation to engine speed and ethanol/petrol mixtures. These datasets were utilised to train artificial neural networks and rule learner classifiers to establish relationships among emission characteristics, speeds, and ethanol combinations. Both models were tested, and the rule learner classifier was found to be more accurate than the artificial neural network. Emission characteristics, speed, and ethanol combinations can all be correlated using the proposed rule learner algorithm.

Journal

Journal of The Institution of Engineers (India):Series A – Springer Journals

Published: May 3, 2022

Keywords: Biofuel; Emission characteristics; Rule learner; Artificial neural network; Decision tree algorithm; Solid waste management

References

  • Recent trends in global production and utilization of bio-ethanol fuel
    Balat, M; Balat, H
  • When will fossil fuel reserves be diminished?
    Shafiee, S; Topal, E
  • Alternative fuels for internal combustion engines
    Bae, C; Kim, J
  • Ethanol production using corn, switchgrass, and wood; biodiesel production using soybean and sunflower
    Pimentel, D; Patzek, TW
  • Bioethanol from moringa olefira and pithecellobium dulce leafs : Production and characterization
    Nagenderan, S; Rajamamundi, P; Chandran, M; Gopinath, KP
  • Performance analysis of ethanol–gasoline blends on a spark ignition engine: a review
    Thakur, AK; Kaviti, AK; Mehra, R; Mer, KKS
  • Progress in performance analysis of ethanol-gasoline blends on SI engine
    Thakur, AK; Kaviti, AK; Mehra, R; Mer, KKS
  • Ethanol fermentation technologies from sugar and starch feedstocks
    Bai, FW; Anderson, WA; Moo-Young, M
  • Evaluation of pomegranate peel in ethanol production by Saccharomyces cerevisiae and Pichia stipitis
    Demiray, E; Karatay, SE; Dönmez, G
  • The effect of ethanol-gasoline blends on performance and exhaust emissions of a spark ignition engine through exergy analysis
    Doğan, B; Erol, D; Yaman, H; Kodanli, E
  • Effect of ethanol–unleaded gasoline blends on engine performance and exhaust emission
    Al-Hasan, M
  • Investigations on the effects of ethanol–methanol–gasoline blends in a spark-ignition engine: performance and emissions analysis
    Elfasakhany, A
  • The effects of ethanol–unleaded gasoline blends on engine performance and exhaust emissions in a spark-ignition engine
    Koç, M; Sekmen, Y; Topgül, T; Yücesu, HS
  • Investigation into SI engine performance characteristics and emissions fuelled with ethanol/butanol-gasoline blends
    Mourad, M; Mahmoud, K
  • The use of ethanol–gasoline blend as a fuel in an SI engine
    Yüksel, F; Yüksel, B
  • Engine performance and pollutant emission of an SI engine using ethanol–gasoline blended fuels
    Hsieh, W-D; Chen, R-H; Wu, T-L; Lin, T-H
  • Analysis of ethanol blends on spark ignition engines
    Saikrishnan, V; Karthikeyan, A; Jayaprabakar, J
  • Effect of ethanol and gasoline blending on the performance of a stationary small single cylinder engine
    Rao, RN; Silitonga, AS; Shamsuddin, AH; Milano, J; Riayatsyah, TMI; Sebayang, AH; Nur, TB; Sabri, M; Yulita, MR; Sembiring, RW
  • Evaluation on physicochemical properties of iso-butanol additives in ethanol-gasoline blend on performance and emission characteristics of a spark-ignition engine
    Zaharin, MSM; Abdullah, NR; Masjuki, HH; Ali, OM; Najafi, G; Yusaf, T
  • A review on various techniques used in predicting pollutants
    Baby, A; Alexander, AA
  • Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine
    Sugumaran, V; Sabareesh, G; Ramachandran, K
  • Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends
    Mohamed Ismail, H; Ng, HK; Queck, CW; Gan, S
  • Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network
    Roy, S; Banerjee, R; Bose, PK
  • Exhaust emissions prognostication for DI diesel group-hole injectors using a supervised artificial neural network approach
    Taghavifar, H; Taghavifar, H; Mardani, A; Mohebbi, A
  • Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends
    Deh Kiani, MK; Ghobadian, B; Tavakoli, T; Nikbakht, AM; Najafi, G
  • Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network
    Najafi, G; Ghobadian, B; Tavakoli, T; Buttsworth, DR; Yusaf, TF; Faizollahnejad, M
  • Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol–gasoline blend fuel
    Yücesu, HS; Sozen, A; Topgül, T; Arcaklioğlu, E
  • Research of performance on a spark ignition engine fueled by alcohol–gasoline blends using artificial neural networks
    Kapusuz, M; Ozcan, H; Yamin, JA
  • Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings
    Pai, PS; Rao, BS
  • Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks
    Rezaei, J; Shahbakhti, M; Bahri, B; Aziz, AA
  • Artificial neural network based fault diagnostics of rolling element bearings using time-domain features
    Samanta, B; Al-Balushi, KR
  • Machine-learning techniques and their applications in manufacturing
    Pham, DT; Afify, AA