{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:28:20Z","timestamp":1772774900347,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Currently, machine learning (ML) technologies are widely employed in the automotive field for determining physical quantities thanks to their ability to ensure lower computational costs and faster operations than traditional methods. Within this context, the present work shows the outcomes of forecasting activities on the prediction of pollutant emissions from engines using an artificial neural network technique. Tests on an optical access engine were conducted under lean mixture conditions, which is the direction in which automotive research is developing to meet the ever-stricter regulations on pollutant emissions. A NARX architecture was utilized to estimate the engine\u2019s nitrogen oxide emissions starting from in-cylinder pressure data and images of the flame front evolution recorded by a high-speed camera and elaborated through a Mask R-CNN technique. Based on the obtained results, the methodology\u2019s applicability to real situations, such as metal engines, was assessed using a sensitivity analysis presented in the second part of the work, which helped identify and quantify the most important input parameters for the nitrogen oxide forecast.<\/jats:p>","DOI":"10.3390\/info14040224","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T03:59:55Z","timestamp":1680753595000},"page":"224","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Using a Machine Learning Approach to Evaluate the NOx Emissions in a Spark-Ignition Optical Engine"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8449-9202","authenticated-orcid":false,"given":"Federico","family":"Ricci","sequence":"first","affiliation":[{"name":"Engineering Department, University of Perugia, Via Goffredo Duranti, 93, 06125 Perugia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9481-5494","authenticated-orcid":false,"given":"Luca","family":"Petrucci","sequence":"additional","affiliation":[{"name":"Engineering Department, University of Perugia, Via Goffredo Duranti, 93, 06125 Perugia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7942-9720","authenticated-orcid":false,"given":"Francesco","family":"Mariani","sequence":"additional","affiliation":[{"name":"Engineering Department, University of Perugia, Via Goffredo Duranti, 93, 06125 Perugia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"key":"ref_1","first-page":"187","article-title":"Advanced ignition technology for the achievement of high thermal efficiency of internal combustion engine","volume":"8","author":"Takahashi","year":"2015","journal-title":"Synth. 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