Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems

Nataly Azucena Ochoa Durazno; Andrea Karina Bermeo Naula; Néstor Diego Rivera-Campoverde

doi:10.1007/978-3-031-97992-7_24

Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems

Research Group in Transportation Engineering (GIIT)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The main objective of this research is to collect a database on pollutant emissions in internal combustion engines to develop a diagnostic model based on fuzzy logic. Operating parameters of MPFI engines are collected using a gas analyzer, which measures concentrations of CO, HC, CO₂, and O₂. Tests are conducted on vehicles with various ignition point settings, lambda factors, and mileage levels. A fuzzy system is then developed to analyze the collected data and create a diagnostic tool for maintenance and repair. The results show diagnostic errors of 0.46% in Mixture Dosage, 45.1% in Ignition Point, and 27.99% in Mileage. These findings provide a basis for optimizing diagnostics and improving engine performance.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference
Editors	Cengiz Kahraman, Selcuk Cebi, Basar Oztaysi, Sezi Cevik Onar, Cagri Tolga, Irem Ucal Sari, Irem Otay
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	205-211
Number of pages	7
ISBN (Print)	9783031979910
DOIs	https://doi.org/10.1007/978-3-031-97992-7_24
State	Published - 2025
Event	7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025 - Istanbul, Turkey Duration: 29 Jul 2025 → 31 Jul 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1529 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025
Country/Territory	Turkey
City	Istanbul
Period	29/07/25 → 31/07/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

Fuzzy logic
internal combustion engines
machine learning
MPFI
pollutant emissions

Access to Document

10.1007/978-3-031-97992-7_24

Cite this

Ochoa Durazno, N. A., Bermeo Naula, A. K., & Rivera-Campoverde, N. D. (2025). Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems. In C. Kahraman, S. Cebi, B. Oztaysi, S. Cevik Onar, C. Tolga, I. Ucal Sari, & I. Otay (Eds.), Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference (pp. 205-211). (Lecture Notes in Networks and Systems; Vol. 1529 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-97992-7_24

Ochoa Durazno, Nataly Azucena ; Bermeo Naula, Andrea Karina ; Rivera-Campoverde, Néstor Diego. / Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems. Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference. editor / Cengiz Kahraman ; Selcuk Cebi ; Basar Oztaysi ; Sezi Cevik Onar ; Cagri Tolga ; Irem Ucal Sari ; Irem Otay. Springer Science and Business Media Deutschland GmbH, 2025. pp. 205-211 (Lecture Notes in Networks and Systems).

@inproceedings{0492bf8b92aa430d873008ad6d1f4d2c,

title = "Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems",

abstract = "The main objective of this research is to collect a database on pollutant emissions in internal combustion engines to develop a diagnostic model based on fuzzy logic. Operating parameters of MPFI engines are collected using a gas analyzer, which measures concentrations of CO, HC, CO₂, and O₂. Tests are conducted on vehicles with various ignition point settings, lambda factors, and mileage levels. A fuzzy system is then developed to analyze the collected data and create a diagnostic tool for maintenance and repair. The results show diagnostic errors of 0.46\% in Mixture Dosage, 45.1\% in Ignition Point, and 27.99\% in Mileage. These findings provide a basis for optimizing diagnostics and improving engine performance.",

keywords = "Fuzzy logic, internal combustion engines, machine learning, MPFI, pollutant emissions",

author = "\{Ochoa Durazno\}, \{Nataly Azucena\} and \{Bermeo Naula\}, \{Andrea Karina\} and Rivera-Campoverde, \{N{\'e}stor Diego\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025 ; Conference date: 29-07-2025 Through 31-07-2025",

year = "2025",

doi = "10.1007/978-3-031-97992-7\_24",

language = "English",

isbn = "9783031979910",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "205--211",

editor = "Cengiz Kahraman and Selcuk Cebi and Basar Oztaysi and \{Cevik Onar\}, Sezi and Cagri Tolga and \{Ucal Sari\}, Irem and Irem Otay",

booktitle = "Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference",

address = "Germany",

}

Ochoa Durazno, NA, Bermeo Naula, AK & Rivera-Campoverde, ND 2025, Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems. in C Kahraman, S Cebi, B Oztaysi, S Cevik Onar, C Tolga, I Ucal Sari & I Otay (eds), Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference. Lecture Notes in Networks and Systems, vol. 1529 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 205-211, 7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025, Istanbul, Turkey, 29/07/25. https://doi.org/10.1007/978-3-031-97992-7_24

Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems. / Ochoa Durazno, Nataly Azucena; Bermeo Naula, Andrea Karina ; Rivera-Campoverde, Néstor Diego.
Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference. ed. / Cengiz Kahraman; Selcuk Cebi; Basar Oztaysi; Sezi Cevik Onar; Cagri Tolga; Irem Ucal Sari; Irem Otay. Springer Science and Business Media Deutschland GmbH, 2025. p. 205-211 (Lecture Notes in Networks and Systems; Vol. 1529 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems

AU - Ochoa Durazno, Nataly Azucena

AU - Bermeo Naula, Andrea Karina

AU - Rivera-Campoverde, Néstor Diego

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - The main objective of this research is to collect a database on pollutant emissions in internal combustion engines to develop a diagnostic model based on fuzzy logic. Operating parameters of MPFI engines are collected using a gas analyzer, which measures concentrations of CO, HC, CO₂, and O₂. Tests are conducted on vehicles with various ignition point settings, lambda factors, and mileage levels. A fuzzy system is then developed to analyze the collected data and create a diagnostic tool for maintenance and repair. The results show diagnostic errors of 0.46% in Mixture Dosage, 45.1% in Ignition Point, and 27.99% in Mileage. These findings provide a basis for optimizing diagnostics and improving engine performance.

AB - The main objective of this research is to collect a database on pollutant emissions in internal combustion engines to develop a diagnostic model based on fuzzy logic. Operating parameters of MPFI engines are collected using a gas analyzer, which measures concentrations of CO, HC, CO₂, and O₂. Tests are conducted on vehicles with various ignition point settings, lambda factors, and mileage levels. A fuzzy system is then developed to analyze the collected data and create a diagnostic tool for maintenance and repair. The results show diagnostic errors of 0.46% in Mixture Dosage, 45.1% in Ignition Point, and 27.99% in Mileage. These findings provide a basis for optimizing diagnostics and improving engine performance.

KW - Fuzzy logic

KW - internal combustion engines

KW - machine learning

KW - MPFI

KW - pollutant emissions

UR - https://www.scopus.com/pages/publications/105013054202

U2 - 10.1007/978-3-031-97992-7_24

DO - 10.1007/978-3-031-97992-7_24

M3 - Conference contribution

AN - SCOPUS:105013054202

SN - 9783031979910

T3 - Lecture Notes in Networks and Systems

SP - 205

EP - 211

BT - Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference

A2 - Kahraman, Cengiz

A2 - Cebi, Selcuk

A2 - Oztaysi, Basar

A2 - Cevik Onar, Sezi

A2 - Tolga, Cagri

A2 - Ucal Sari, Irem

A2 - Otay, Irem

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025

Y2 - 29 July 2025 through 31 July 2025

ER -

Ochoa Durazno NA, Bermeo Naula AK , Rivera-Campoverde ND. Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems. In Kahraman C, Cebi S, Oztaysi B, Cevik Onar S, Tolga C, Ucal Sari I, Otay I, editors, Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference. Springer Science and Business Media Deutschland GmbH. 2025. p. 205-211. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-97992-7_24

Development of a Fuzzy System for Emission Diagnosis in MPFI Engine Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Cite this