Pathway Guided Deep Neural Network towards Interpretable and Predictive Modeling and Drug Preparation

The evaporator is vital to the pharmaceutical industry as it is used to refine finished pharmaceutical products. The evaporator is used to remove excess water during the manufacturing of pharmaceuticals. The SISO evaporator is used to determine the dry matter content by measuring temperature. System identification is used to create a mathematical model of the evaporator in a pharmaceutical factory. Adjusting the temperature of an evaporator is a laborious process. Thus, we build and implement a Neural network predictive controller for usage in the pharmaceutical industry. To fine-tune the evaporator’s control signal, it can be utilised to predict the device’s future performance. The effectiveness of the controller is evaluated using error metrics like ISE, IAE, ITSE, and ITAE. Time-domain criteria such as rising time, settling time, and overshoot are utilised to better appreciate controller functionality. Based on these analyses, it is clear that the predictive controller is superior than the more common PID controller in use in the pharmaceutical sector.