6533b7d4fe1ef96bd1262a4b

RESEARCH PRODUCT

Using neural networks to obtain indirect information about the state variables in an alcoholic fermentation process

Ugo FioreAnca SiposMaria ArsinAdrian Florea

subject

State variableComputer scienceDesign space explorationBioengineering02 engineering and technologyEthanol fermentationFermentation processlcsh:Chemical technology01 natural scienceslcsh:ChemistryControl theoryFermentation process; Neural network; Prediction applicationChemical Engineering (miscellaneous)Process controllcsh:TP1-1185Layer (object-oriented design)Flexibility (engineering)Artificial neural networkProcess Chemistry and Technology010401 analytical chemistryProcess (computing)021001 nanoscience & nanotechnologyNeural network0104 chemical scienceslcsh:QD1-999Prediction application0210 nano-technology

description

This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg&ndash

yearjournalcountryeditionlanguage
2020-12-31
10.3390/pr9010074http://hdl.handle.net/11367/90393