Industries such as, textile, food processing, chemical and water treatment plants are part of our global development. The efficiency of processes used by them is always a matter of great importance. Efficiency can be greatly improved by obtaining an exact model of the process. This paper studies the two main classifications of model development – First-Principles Model and Empirical Model. First-Principles Model can be obtained with an understanding of the basic physics of the system. On the other hand, Empirical Models require only the input-output data and can thus factor in process non-linearity, disturbances and unexpected errors. This paper utilizes the System Identification Toolbox in MATLAB for empirical model development. Models are developed for a single tank system, a classic SISO problem and for the two interacting tank system. Both systems are studied with respect to three operating points, each from a local linear region. The obtained models are validated with the real-time setup. They are satisfactory in their closeness to the real time process and hence deemed fit for use in control algorithms and other process manipulations
Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering