Prashantt492: Creating course page via bot

2026-03-04T09:55:47Z

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{{Infobox Course
| code = CLL789
| name = Applied Time Series Analysis for Chemical Engineering
| credits = 3
| credit_structure = 3-0-0
| pre_requisites =
| overlaps = ELL319, ELL720
}}

== CLL789 : Applied Time Series Analysis for Chemical Engineering ==
Models for Discrete-Time Linear Time Invariant (LTI) Systems: Models for Discrete-Time LTI Systems, LTI system in frequency domain, Sampling and discretization of of process data, Time domain analysis of process data, A quick introduction into time series modelling of process data; LTI system in frequency domain: Frequency response function; Z-transforms, initial final value theorems, Properties of z-Transforms, Transfer function and its properties, Empirical transfer function; Sampling and discretization of of process data: Approximate and exact discretization, Zero order hold, Single rate vs. Multi-rate systems, State space approach for discretization, Sampling and reconstruction, Sampling theorem; Time domain analysis of process data: Auto covariance function, Auto correlation function, White noise process, Cross covariance function, Partial auto correlation function, Partial cross correlation function, A quick introduction into time series modelling of process data: Auto Regressive, Moving Average, Auto Regressive Exogeneous family, Auto Regressive Moving Average Exogeneous family, Box-Jenkins, Output-error models; Frequency domain analysis of process data: Fourier Analysis and Spectral Analysis: Fourier series, Power spectrum, Discrete time Fourier transform, Discrete Fourier transform, Spectrum, spectral density and spectral envelope; Introduction to estimation and inference of linear process dynamical systems with Gaussian noise: Kalman filter; Introduction to estimation and inference of nonlinear process dynamical systems with Gaussian noise: Extended Kalman filter, Unscented Kalman filer, Ensemble Kalman filter; Introduction to estimation and inference of nonlinear process dynamical systems with non-Gaussian noise: Particle filter; Process applications and case studies: Continuous Stirred Tank Reactor example, Quadruple tank system and Tennessee-Eastman process case study. Signal processing toolbox and system identification toolbox in MATLAB.

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