Empirical modeling of ethanol production dynamics using long short-term memory recurrent neural networks
| Autor: | Flávio Vasconcelos da Silva, Felipe Matheus Mota Sousa, Rodolpho Rodrigues Fonseca |
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| Rok vydání: | 2021 |
| Předmět: |
Environmental Engineering
Artificial neural network Renewable Energy Sustainability and the Environment Computer science 020209 energy Dynamics (mechanics) Empirical modelling Inverse Bioengineering 02 engineering and technology 010501 environmental sciences 01 natural sciences Long short term memory Recurrent neural network 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Process optimization Biological system Waste Management and Disposal 0105 earth and related environmental sciences |
| Zdroj: | Bioresource Technology Reports. 15:100724 |
| ISSN: | 2589-014X |
| DOI: | 10.1016/j.biteb.2021.100724 |
| Popis: | Long short-term memory networks (LSTM) were trained to predict the dynamics of a fermentation process with varying kinetic parameters. The training and test database was obtained through simulations using phenomenological equations and an artificial neural network (ANN) to adjust the kinetics according to operational conditions. Results showed that a shallow LSTM was able to predict the dynamics of all endogenous variables accurately using only three timesteps, including the inverse responses observed, a difficult feature to incorporate in empirical models. For tests using the predictions recursively as endogenous input variables, deeper structures were necessary to achieve a reasonably good performance with errors within commercial sensor's accuracy. These results indicate the applicability of LSTMs to model fermentation processes using raw data, abstractly incorporating kinetics variations in the model, and suggest its use as a tool in model predictive controllers or for process optimization. |
| Databáze: | OpenAIRE |
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