Hybrid neural network model of an industrial ethanol fermentation process considering the effect of temperature

2007 ◽  
Vol 137-140 (1-12) ◽  
pp. 817-833 ◽  
Author(s):  
Ivana C. C. Mantovanelli ◽  
Elmer Ccopa Rivera ◽  
Aline C. da Costa ◽  
Rubens Maciel Filho
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Ethanol Fermentation ◽  
Fermentation Process ◽  
Effect Of Temperature ◽  
Hybrid Neural Network

scholarly journals Artificial neural network model and fuzzy logic control of dissolved oxygen in a bioreactor

2020 ◽  
Vol 17 (3) ◽  
pp. 1289
Author(s):  
Nor Hana Mamat ◽  
Samsul Bahari Mohd Noor ◽  
Laxshan A/L Ramar ◽  
Azura Che Soh ◽  
Farah Saleena Taip ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dissolved Oxygen ◽  
Network Model ◽  
Neural Network Model ◽  
Fermentation Process ◽  
Fuzzy Logic Controller ◽  
Rbf Neural Network ◽  
Aeration Rate ◽  
Process Data

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network model gives better accuracy than MLP neural network. The model is further used in fuzzy logic controller design to simulate control of dissolved oxygen by manipulation of aeration rate.  Simulation result shows that the fuzzy logic controller can control the dissolved oxygen based on the given profile.

scholarly journals PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6512
Author(s):  
Mario Tovar ◽  
Miguel Robles ◽  
Felipe Rashid
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Lasso Regression ◽  
Neural Network Models ◽  
Model Case ◽  
Hybrid Neural Network ◽  
Photovoltaic Power

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression.

scholarly journals Physical Hybrid Neural Network Model to Forecast Typhoon Floods

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 632 ◽  
Author(s):  
You-Da Jhong ◽  
Chang-Shian Chen ◽  
Hsin-Ping Lin ◽  
Shien-Tsung Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Hybrid Neural Network
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