3.5 | ANN results
The data set consisting of 96 data were used for artificial neural
network modeling. Of this set, 68 were allocated for training, 14 for
testing and 14 for control data. In order for the network not to
memorize, learning should take place properly, the data set should be
blended well, and the training test and control data should be selected
homogeneously. For this purpose, a series of experiments were performed
with different initial condition, and the situation where all of the
R2 were high was investigated. There are 6 input
variables and 1 output variable in the general structure of the network.
The value of hidden layer size was tried to be determined by preliminary
experiments. Conversion was selected as output variable. Network
structure with 2 outputs (conversion and selectivity) and single output
(only selectivity) were also studied, but positive results could not be
obtained from here. It has already been mentioned in the previous
chapters that a significant correlation between the selectivity value
and the feeding modes could not be seen. Here 6 input variables are;
phenol/acetone ratio, co-catalyst usage state (0/1), feed time, waiting
time, acid concentration and reaction time. The regression graphs
obtained by plotting the conversion values in experimental data against
the results of the network are given in Figure S4.
As can be seen, the network performed well for both training, test and
control data, 0.99 R2 values were obtained for all.
High training R2 is important because low means the
network is not well trained. This value indicates the educational
performance of the network, but only if this value is high this does not
make sense. Higher R2 of test and control data are
also desirable, otherwise, the network have memorized and cannot predict
when new situations occur. After this point, 3-D graphics were drawn in
MATLAB using the obtained neural network model. Conversion against time
/HCl (%), conversion against time /mol ratio graphs are shown in Figure
5.