ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel

Amit  Tiwari; Pooja  Varshney; Harshita  Swarnkar; Neny  Pandel; Himanshu  Vasnani; Muhammad Akram

doi:10.4018/979-8-3693-7250-0.ch011

ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel

Amit Tiwari
, Pooja Varshney
, Harshita Swarnkar
, Neny Pandel
, Himanshu Vasnani
, Muhammad Akram

Dundee Business School

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

34 Downloads (Pure)

Abstract

Artificial Neural Network a soft tool enabled by Artificial intelligence was utilized to accurately predict engine performance and emissions analysis using a new set of plastic pyrolysis oil blended with pure diesel. Under various loads, blends, and speeds, this chapter analyzed the effect of plastic pyrolysis oil (PPO) on a diesel engine's performance and emissions, which were used to forecast performance and emissions. The ANN models have the potential to produce a more accurate estimate of the brake thermal efficiency, brake-specific fuel consumption, and emission performance parameters.

Original language	English
Title of host publication	Expert artificial neural network applications for science and engineering
Editors	Lingala Syam Sundar, Deepanraj Balakrishnan, Antonio C. M. Sousa
Place of Publication	Hershey, PA
Publisher	IGI Global Scientific Publishing
Chapter	11
Pages	317-358
Number of pages	42
ISBN (Electronic)	9798369372524
ISBN (Print)	9798369372500, 9798369372517
DOIs	https://doi.org/10.4018/979-8-3693-7250-0.ch011
Publication status	Published - 14 Feb 2025

Publication series

Name	Advances in chemical and materials engineering
Publisher	IGI Global Scientific Publishing
ISSN (Print)	2327-5448
ISSN (Electronic)	2327-5456

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.4018/979-8-3693-7250-0.ch011

Akram_ANNPredictionAnalysisOfEnginePerformance_Published_2025Final published version, 3.09 MB

Cite this

Tiwari , A., Varshney, P., Swarnkar, H., Pandel, N., Vasnani, H., & Akram, M. (2025). ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel. In L. S. Sundar, D. Balakrishnan, & A. C. M. Sousa (Eds.), Expert artificial neural network applications for science and engineering (pp. 317-358). (Advances in chemical and materials engineering). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7250-0.ch011

Tiwari , Amit ; Varshney, Pooja ; Swarnkar, Harshita et al. / ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel. Expert artificial neural network applications for science and engineering. editor / Lingala Syam Sundar ; Deepanraj Balakrishnan ; Antonio C. M. Sousa. Hershey, PA : IGI Global Scientific Publishing, 2025. pp. 317-358 (Advances in chemical and materials engineering).

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title = "ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel",

abstract = "Artificial Neural Network a soft tool enabled by Artificial intelligence was utilized to accurately predict engine performance and emissions analysis using a new set of plastic pyrolysis oil blended with pure diesel. Under various loads, blends, and speeds, this chapter analyzed the effect of plastic pyrolysis oil (PPO) on a diesel engine's performance and emissions, which were used to forecast performance and emissions. The ANN models have the potential to produce a more accurate estimate of the brake thermal efficiency, brake-specific fuel consumption, and emission performance parameters.",

author = "Amit Tiwari and Pooja Varshney and Harshita Swarnkar and Neny Pandel and Himanshu Vasnani and Muhammad Akram",

year = "2025",

month = feb,

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doi = "10.4018/979-8-3693-7250-0.ch011",

language = "English",

isbn = "9798369372500",

series = "Advances in chemical and materials engineering",

publisher = "IGI Global Scientific Publishing",

pages = "317--358",

editor = "Sundar, \{Lingala Syam\} and Deepanraj Balakrishnan and Sousa, \{Antonio C. M.\}",

booktitle = "Expert artificial neural network applications for science and engineering",

}

Tiwari , A, Varshney, P, Swarnkar, H, Pandel, N, Vasnani, H & Akram, M 2025, ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel. in LS Sundar, D Balakrishnan & ACM Sousa (eds), Expert artificial neural network applications for science and engineering. Advances in chemical and materials engineering, IGI Global Scientific Publishing, Hershey, PA, pp. 317-358. https://doi.org/10.4018/979-8-3693-7250-0.ch011

ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel. / Tiwari , Amit ; Varshney, Pooja ; Swarnkar, Harshita et al.
Expert artificial neural network applications for science and engineering. ed. / Lingala Syam Sundar; Deepanraj Balakrishnan; Antonio C. M. Sousa. Hershey, PA: IGI Global Scientific Publishing, 2025. p. 317-358 (Advances in chemical and materials engineering).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

TY - CHAP

T1 - ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel

AU - Tiwari , Amit

AU - Varshney, Pooja

AU - Swarnkar, Harshita

AU - Pandel, Neny

AU - Vasnani, Himanshu

AU - Akram, Muhammad

PY - 2025/2/14

Y1 - 2025/2/14

N2 - Artificial Neural Network a soft tool enabled by Artificial intelligence was utilized to accurately predict engine performance and emissions analysis using a new set of plastic pyrolysis oil blended with pure diesel. Under various loads, blends, and speeds, this chapter analyzed the effect of plastic pyrolysis oil (PPO) on a diesel engine's performance and emissions, which were used to forecast performance and emissions. The ANN models have the potential to produce a more accurate estimate of the brake thermal efficiency, brake-specific fuel consumption, and emission performance parameters.

AB - Artificial Neural Network a soft tool enabled by Artificial intelligence was utilized to accurately predict engine performance and emissions analysis using a new set of plastic pyrolysis oil blended with pure diesel. Under various loads, blends, and speeds, this chapter analyzed the effect of plastic pyrolysis oil (PPO) on a diesel engine's performance and emissions, which were used to forecast performance and emissions. The ANN models have the potential to produce a more accurate estimate of the brake thermal efficiency, brake-specific fuel consumption, and emission performance parameters.

U2 - 10.4018/979-8-3693-7250-0.ch011

DO - 10.4018/979-8-3693-7250-0.ch011

M3 - Chapter (peer-reviewed)

SN - 9798369372500

SN - 9798369372517

T3 - Advances in chemical and materials engineering

SP - 317

EP - 358

BT - Expert artificial neural network applications for science and engineering

A2 - Sundar, Lingala Syam

A2 - Balakrishnan, Deepanraj

A2 - Sousa, Antonio C. M.

PB - IGI Global Scientific Publishing

CY - Hershey, PA

ER -

Tiwari A, Varshney P, Swarnkar H, Pandel N, Vasnani H, Akram M. ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel. In Sundar LS, Balakrishnan D, Sousa ACM, editors, Expert artificial neural network applications for science and engineering. Hershey, PA: IGI Global Scientific Publishing. 2025. p. 317-358. (Advances in chemical and materials engineering). Epub 2025 Feb 14. doi: 10.4018/979-8-3693-7250-0.ch011

ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel

Abstract

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