A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets.
The assessment of the overall performance of all the processes was performed using streamflow data, for example, mass flow of the components, pressure, temperature, power, and energy output provided by the simulation of each process configuration.
A detailed comparison of the overall performance indicated that the neuro-fuzzy model performed better than both the Levenberg Marquardt-FFNN and the Bayesian reguLevenberg Marquardt-FFNN
In this case, the absence of autonomous devices alleviated the system from additional control signalling and thus it performed better, resulting to an increased improvement of the overall performance.
A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling.
Comparisons of the overall performance of the models are presented.
Simulation results showed the strong dependence of the overall performance upon the reactor geometrical configuration.
However, the common control channel would become a bottleneck of the overall performance.
Simulation results showed the strong dependence of the overall performance upon the operating conditions examined.
This could be attributed to an increase of N2H4 permeation at the high flow rate, which led to the decline of the overall performance.
To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance.
