**4. Conclusions**

*o*ð Þ*<sup>t</sup>* Output state

*Biotechnological Applications of Biomass*

observed values. The RMSE, R2

*Parameters for LSTM model development.*

**Table 4.**

**Figure 12.**

**620**

*W*ð Þ*<sup>i</sup>* , *W*ð Þ*<sup>f</sup>* ,*W*ð Þ*<sup>c</sup>* , *W*ð Þ*<sup>o</sup>* Weight vectors connecting previous and current

*U*ð Þ*<sup>f</sup>* ,*U*ð Þ*<sup>c</sup>* , *U*ð Þ*<sup>o</sup>* Vectors connecting inputs to the current hidden layer

Similar to the development of NARX model, the modeling of LSTM network also includes data collection, parameter determination, training, testing, validation. The modeling can be done either in MATLAB or using python coding. As a case Study, consider a LSTM network in which two hidden layers are chosen and the number of neurons in each hidden layer is varied till the MSE reaches minimum value. The parameters chosen to frame the LSTM network are listed in **Table 4**. The training to testing ratio is chosen as 67:33. The predicted values of maximum specific growth

The performance of LSTM model is evaluated by the statistical measures RMSE, R2 and Accuracy factor (Af). The Af averages the distance between every point and the line of equivalence as a measure of finding the closeness between predicted and

0.994 and 1.024 respectively. The RMSE and Af values are minimum which suggests

No. of hidden neurons Hidden layer 1 Hidden layer 2

, Af values of the LSTM model are found to be 0.011,

15 47

hidden layers

*σ* Sigmoid activation function

rate calculated from the LSTM model are presented in **Figure 12**.

that the LSTM predictive model fit well with the experimental data.

**Parameter Choice** Batch size 50 No. of hidden layers 2

Activation function Sigmoid Optimizer Adam Learning rate 0.01 No. of epochs 800

*Comparison of LSTM predicted maximum specific growth rate data with experimental data.*

Several modeling techniques that will aid in the monitoring and estimation of fungal biomass in the presence of lignocellulosic substrates during fed-batch fermentation are discussed in this chapter. Moreover, the bioprocess models are validated with experimental data as discussed in case studies. The use of these soft sensors in industries with accompanying control system will improve the cellulase concentration yield.
