# Identification¶

## Overview - creating a hybrid model¶

After creating a **new project** and adding **data** to it, a **hybrid model** can be set up via PROJECT and Add Hybrid Model.
This opens a new dialog, in which several parameters have to be specified:

Here only a **very short description** is given for each required item, details can be found in the respective subchapters.

**Name**: a model name (see section Model Name).**Train/Valid Data**: the data set(s) to be used for setting up and optimizing the hybrid model.**Test Data**: the data set, on which the final model shall be applied to estimate its preformance.**Outputs**: one or more variables in the data set serving as response, i.e. the quantity to predict.**Outputs (SD)**:**Mass Balance**: to properly setup a hybrid model, quantities like*feed*,*sampling volume*or*reactor volume*are required and need to be specified at this point.**Dependent Variables**: inputs to the artificial neural network (ANN) model**Dependent Variables – scaling**:**Learning Wrapper**: (technical) model details, such as the ANN model complexity (number of hidden layers, number of hidden nodes, …) or the validation method**ANN Tolerances**:**Temp Folder**:**Model Inputs**: specify the variables in the data set to enter the neural network model as inputs. Also simple mathematical functions of those can be used, such as \(ln(x_1 + x_2)\) or similar.**Model Inputs – Derivatives**:**Model Kinetics**: the kinetic expressions in the form of the right hand side of the differential equations for the state variables (model outputs).**Model Kinetics – Derivatives**:

Completing these steps creates a hybrid model and adds it to the current selected project. Selecting it in the corresponding section on the left and pressing Start will start the **optimization process**.

Note

Several parameters requested in the dialog above can still be changed/modified at this step.

Note

If the model creation failed, check the console log for possible errors.