DevanshKandpal: Bot: wrap bare course codes in wikilinks

2026-04-14T16:25:10Z

Bot: wrap bare course codes in wikilinks

← Older revision		Revision as of 16:25, 14 April 2026
Line 5:		Line 5:
	\| credit_structure = 3-0-0		\| credit_structure = 3-0-0
	\| pre_requisites =		\| pre_requisites =
	\| overlaps = ELL319, ELL720		\| overlaps = [[ELL319]], [[ELL720]]
	}}		}}

	== CLL789 : Applied Time Series Analysis for Chemical Engineering ==		== 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.		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.

Prashantt492: Creating course page via bot

2026-03-04T09:55:47Z

Creating course page via bot

New page

{{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.

CLL789 - Revision history

DevanshKandpal: Bot: wrap bare course codes in wikilinks

Prashantt492: Creating course page via bot