Problem statement:The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with the use of additional meteorological parameters. After having simulated many different structures of neural networks and trained using measurements as training data, the best structures were selected in order to evaluate their performance in relation with the performance of a neuro-fuzzy system. As the alternative system, ANFIS neuro-fuzzy system was considered, because it combines fuzzy logic and neural network techniques that are used in order to gain more efficiency. ANFIS is trained with the same data. Results: The comparison and the evaluation of both of the systems were done according to their predictions, using several error metrics. Fuzzy model was trained using data of daily solar radiation recorded on a horizontal surface in National Research Institute of Astronomy and Geophysics, Helwan, Egypt (NARIG) at ten years (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). Conclusion: The predicting conclusion indicated that the TS fuzzy model gave a good accuracy of approximately 96% and a root mean square error lower than 6%.
Fermentation process is a time-varying, nonlinear and multivariable dynamic coupling system. Therefore, it is difficult to directly measure the key biological variables using traditional physical sensors during the process of fermentation, which makes the monitoring and real-time control impossible. To resolve this problem, a data-driven soft sensor modeling method based on deep neural network (DNN) is proposed in this paper. This method is suitable for large amount of data and it enjoys high efficiency and robustness. At the same time, an adaptive moment estimation (Adam) algorithm is used to optimize the hyper-parameters of the DNN model, which is a technique for efficient stochastic optimization that only requires first-order gradients with little memory requirement. The consistent correlation method is used to determine the auxiliary variables of the soft sensor model. The penicillin and l-lysine fermentation processes are taken as the research object, substrate concentration, cell concentration, and product concentration are selected as a target variable. The performance of established soft sensor model is evaluated through the indexes of mean square error (MSE), root-mean-square error (RMSE), and mean absolute error (MAE). The simulation results show that the prediction performance of the soft sensor model based on DNN-Adam is good and compared with model based on stochastic gradient descent (SGD) with momentum optimization algorithm. It is verified that the proposed method can make a more accurate real-time prediction of quality variables in the fermentation process, and it has higher prediction accuracy than DNN-SGD method.
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