000148069 001__ 148069 000148069 005__ 20250110163829.0 000148069 0247_ $$2doi$$a10.1109/ACCESS.2024.3504724 000148069 0248_ $$2sideral$$a141583 000148069 037__ $$aART-2024-141583 000148069 041__ $$aeng 000148069 100__ $$aFañanás-Anaya, Javier$$uUniversidad de Zaragoza 000148069 245__ $$aFood cooking process modeling with neural networks 000148069 260__ $$c2024 000148069 5060_ $$aAccess copy available to the general public$$fUnrestricted 000148069 5203_ $$aFood cooking process are complex dynamical systems to model. In the state of the art we find that a good solution consists of physics-based finite element models (FEM). FEM models, although being very accurate, have a high computational cost making them unfeasible for real-time applications. To solve this problem, we consider neural networks (NN) trained from FEM simulations. Specifically, we propose a Nonlinear AutoRegressive with eXogenous inputs Neural Network (NARX-NN). The main novelty is that we define a novel training algorithm adapted to the modeling of real-time dynamical systems, allowing a NARX-NN with a simple structure to obtain a negligible error compared to the results of the original FEM model. The NARX-NN trained with the proposed training algorithm obtains an R-squared of more than 0.99 in the test simulations, while the same NARX-NN trained with the standard training algorithm obtains an R-squared of 0.78 in the same tests. The proposed NARX-NN achieves a speedup of 8 orders of magnitude compared to the original FEM model. Moreover, the developed NN is able to predict the complete cooking of the food in a few milliseconds without the need of external sensors. Alternatively, our approach can also be used in real time with information captured with sensors. The presented methodology is highly scalable and could be adapted to different types of food and cooking processes, as well as to other dynamical systems in general. 000148069 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/CPP2021-008938$$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-130224B-I00$$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-124137OB-I00 000148069 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000148069 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000148069 700__ $$0(orcid)0000-0001-9347-5969$$aLópez-Nicolás, Gonzalo$$uUniversidad de Zaragoza 000148069 700__ $$0(orcid)0000-0002-3032-954X$$aSagüés, Carlos$$uUniversidad de Zaragoza 000148069 700__ $$aLlorente, Sergio 000148069 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000148069 773__ $$g12 (2024), 175866-175881$$pIEEE Access$$tIEEE Access$$x2169-3536 000148069 8564_ $$s2399035$$uhttps://zaguan.unizar.es/record/148069/files/texto_completo.pdf$$yVersión publicada 000148069 8564_ $$s2465179$$uhttps://zaguan.unizar.es/record/148069/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000148069 909CO $$ooai:zaguan.unizar.es:148069$$particulos$$pdriver 000148069 951__ $$a2025-01-10-14:26:11 000148069 980__ $$aARTICLE