Open AccessModeling Based Identifiability and Parametric Estimation of an Enzymatic Hydrolysis Process of Amylaceous Materials
Author(s) -
Daniel Padierna-Vanegas,
Juan Camilo Acosta-Pavas,
Laura María Granados-García,
Héctor Botero-Castro
Publication year - 2022
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.1c06193
Subject(s) - identifiability , unobservable , observability , computer science , experimental data , parametric statistics , process (computing) , property (philosophy) , nonlinear system , estimation theory , parametric model , identification (biology) , mathematical optimization , biological system , algorithm , mathematics , machine learning , econometrics , statistics , philosophy , physics , botany , epistemology , quantum mechanics , biology , operating system
This work presents the modeling of an enzymatic hydrolysis process of amylaceous materials considering the parameter identification problem as a basis for the construction of the model. For this, a modeling methodology is modified in order to apply the identifiability property and improve the proposed model structure. A brief theoretical explanation of the identifiability is described. This concept is based on the observability property of a nonlinear dynamic system. The used methodology is based on the phenomenological based semiphysical model (PBSM). This methodology visualizes that the structure of a dynamic model can only improve with new mass or energy balances suggested by model suppositions. Additionally, a computer algorithm is included in the methodology to validate if the model is structurally locally identifiable or know if the parameters are unidentifiable. Also, an optimization algorithm is used to obtain the numeric values of the identifiable parameters and, hence, guarantee the validity of the result. The methodology focuses on the liquefaction and saccharification stages of an enzymatic hydrolysis process. The results of the model are compared with experimental data. The comparison shows low errors of 7.96% for liquefaction and 7.35% for saccharification. These errors show a significant improvement in comparison with previous models and validate the proposed modeling methodology.