# Shankar NarasimhanIndian Institute of Technology Madras | IIT Madras · Department of Chemical Engineering

Shankar Narasimhan

B.Tech, MS, PhD

## About

147

Publications

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3,162

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Citations since 2016

Introduction

**Skills and Expertise**

Additional affiliations

January 1996 - present

## Publications

Publications (147)

Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted with noise. The recently proposed Dynamic Iterative Principal Components Analysis (DIPCA) technique sol...

Effective control of a blast furnace (BF) process requires accurate estimates of key process indicators (KPIs), namely, productivity, coke rate, direct reduction per cent, adiabatic flame temperature, bosh gas volume and top gas utilization. Some of these KPIs are obtained directly from the measurements, and some are derived by carrying out materia...

Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted with noise. The recently proposed DIPCA technique solves the EIV identification problem but is only app...

The paper is concerned with identifying transfer functions of individual input channels in minimal realization form of a Multi-Input Single Output (MISO) from the input-output data corrupted by the error in all the variables. Such a framework is commonly referred to as error-in-variables (EIV). A common approach in the existing methods for identifi...

This article is concerned with the identification of autoregressive with exogenous inputs (ARX) models. Most of the existing approaches like prediction error minimization and state-space framework are widely accepted and utilized for the estimation of ARX models but are known to deliver unbiased and consistent parameter estimates for a correctly su...

Pure component thermodynamic models are developed for solvent extraction involving ionic reaction equilibrium for distribution of macro concentrations of zirconium and hafnium between an aqueous phase containing HNO3 and an organic solvent phase containing diluted TBP. The concentration range considered is 10⁻⁰³ to 10⁰ M. A framework based on chemi...

Process industries have variables that are measured at different rates, with some measurements, such as composition or quality variables, obtained after associated measurement delays. This work introduces a novel “sampled output augmentation” method for fusing delayed and infrequent primary measurements within a multi-rate Kalman filter (KF) framew...

Dynamic model identification from time series data is a critical component of process control, monitoring and diagnosis. An important adjunct of model identification is the derivation of filtered estimates of the variables and consequent one-step-ahead prediction errors (residuals) which are very useful for model assessment and iterative model iden...

Measuring fluid flow rate in a precise manner plays a vital role in process engineering with important objectives, including monitoring and controlling of process operations to improve productivity and ensure safety. Flow sensing is a well-studied problem to the extent, even there exist many sophisticated sensors. Despite the vast developments, the...

Optimal operation of water distribution networks can be posed as a scheduling problem where the objective is to meet the time varying demand while meeting constraints on supply, pressure etc. In the present work, we propose a robust optimization problem to address uncertainty in the parameters of the model used for optimization. The resulting probl...

The paper is concerned with identifying transfer functions of individual input channels in minimal realization form of a Multi-Input Single Output (MISO) from the input-output data corrupted by the error in all the variables. Such a framework is commonly referred to as error-in-variables (EIV). A common approach in the existing methods for identifi...

Modern cryogenic processes used in air separation and liquefaction of natural gas employ multi-stream heat exchangers in the place of multiple two-stream heat exchangers for better heat integration, as well as lower cost. The first step in any process design is the simulation of alternate processes and the evaluation of operating as well as capital...

Reactive extraction of nuclear elements from aqueous nitric acid solutions using an organic solvent is an important process operation. A thermodynamic model to predict the equilibrium compositions of the two phases will be useful in optimizing the performance of such processes. A general framework is presented in this work for modeling such systems...

Extraction of pure species spectra from mixture spectra is important in characterizing unknown mixtures as well as in the monitoring of chemical reactions. In many designed experimental studies, the mixture concentrations are completely known and partial knowledge of the spectra of some of species in a mixture may also be available. In this study,...

Reconciliation of process data is an important pre-processing technique whose main purpose is to obtain accurate estimates of variables and model parameters. Reconciliation requires a process model which is generally developed using first principles. For many complex processes, the development of such models is difficult and time consuming. In this...

The network reconstruction problem is one of the challenging problems in network science. This work deals with reconstructing networks in which the flows are conserved around the nodes. These networks are referred to as conserved networks. We propose a novel concept of conservation graph for describing conserved networks. The properties of conserva...

The performance of model-based control and optimization depends on the accuracy of process models. However, changes in physiochemical and operational conditions can result in a mismatch between the process and its model. This model-plant mismatch (MPM) must be detected and rectified quickly to achieve the desired performance. In this work, we consi...

Identification of linear dynamic systems from input-output data has been a subject of study for several decades. A broad class of problems in this field pertain to what is widely known as the errors-in-variables (EIV) class, where both input and output are known with errors, in contrast to the traditional scenario where only outputs are assumed to...

The nexus between water and energy reveals that transporting water for end use is a highly energy intensive operation. In this work we consider the optimal operation of a water distribution network consisting of pumps delivering water to different reservoirs, with each reservoir catering to a time varying demand. Pumps and ON/OFF valves are used as...

A rigorous thermodynamic framework based on speciation analysis is developed for an aqueous solution of nitric acid in this work. The model uses experimentally measured ‘extent of dissociation’ for determining the thermodynamic dissociation constant of nitric acid in aqueous solutions at 25°C. The activity coefficients of H+ and NO3- ions are model...

In power networks, where multiple fuel cell stacks are employed in a series-parallel configuration to deliver the required power, optimal sharing of the power demand between different stacks is an important problem. This is because the total current collectively produced by all the stacks is directly proportional to the fuel utilization, through st...

With the demand for water increasing rapidly, optimal operation of Water Distribution Networks (WDNs) is necessary to provide consumers with the maximum amount of water possible in an equitable manner. This paper presents the outcomes of an experimental investigation of supply policies implementable on rural WDNs. Tests conducted on a fully automat...

Water Distribution Networks (WDNs) are vulnerable to accidental or deliberate contamination. Such contamination can be detected and identified by deploying a network of sensors. If the sensor network detects the presence of a contaminant, it is also very important to take corrective response actions to minimize the effects of contamination on the p...

Identification of input-output models from data is of utmost relevance in chemical process industries and has applications in process monitoring, control and fault diagnosis. Input-output data used in such identification exercises often has measurement errors in both the variables. Model identification under such conditions translates to solving an...

Concentrations measurements are typically corrupted by noise. Data reconciliation techniques improve the accuracy of measurements by using redundancies in the material and energy balances expressed as relationships between measurements. Since in the absence of kinetic models these relationships cannot integrate information regarding past measuremen...

Water Distribution Networks (WDNs) are an integral part of society. Deliberate introduction of chemical or biological agents through accessible sites of a WDN can spread through the entire system and cause widespread damage to public health. In order to protect against such deliberate attacks on a WDN, an effective and efficient online monitoring s...

State estimation techniques are used for improving the quality of measured signals and for reconstructing unmeasured quantities. In chemical reaction systems, nonlinear estimators are often used to improve the quality of estimated concentrations. These nonlinear estimators, which include the extended Kalman filter, the receding-horizon nonlinear Ka...

Optimum use of available energy sources is essential for cost effective and sustainable growth. Fuel cells - due to their ability in efficiently extracting energy from fuels - have gained considerable attention among the various energy conversion alternatives. Systems researchers working in the field of fuel cells have been focusing on optimal stac...

A reliable dynamic model is essential for model–based control, monitoring, and optimization of reaction systems. Hence, a change in a part or whole of the reaction kinetics of these systems leads to poor performance. In this work, the problem of model–plant mismatch in open reaction system is studied. We propose an online fault diagnosis and rectif...

The paper is concerned with identifying models from data that have errors in both outputs and inputs, popularly known as the errors-in-variables (EIV) problem. The total least squares formulation of the problem is known to offer a few well-known solutions. In this work, we present a novel and systematic approach to the identification of linear dyna...

Leak detection in urban water distribution networks (WDNs) is challenging given their scale, complexity, and limited instrumentation. We present an algorithm for leak detection in WDNs, which involves making additional flow measurements on-demand, and repeated use of water balance. Graph partitioning is used to determine the location of flow measur...

Water Distribution Networks (WDN) are vulnerable to either intentional or accidental contamination. In order to protect against such intrusions, effective and efficient online monitoring systems are needed. Due to cost and maintenance reasons, it is not possible to locate sensors at each and every potential intrusion point. In this work, we design...

We solve the problem of identifying (reconstructing) network topology from steady state network measurements. Concretely, given only a data matrix X where the X_ij entry corresponds to flow in edge i in steady-state j, we wish to find a network structure for which flow conservation is obeyed at all the nodes. This models many network problems invol...

Concentrations measured during the course of a chemical reaction are corrupted with noise, which reduces the quality of information. When these measurements are used for identifying kinetic models, the noise impairs the ability to identify accurate models. The noise in concentration measurements can be reduced using data reconciliation, exploiting...

Small RO based desalination plants driven by solar power are attractive options, especially in areas with unreliable grid power supply. Batteries or other energy storage devices can be used to buffer against the inherent variability in solar power. However, the capital and maintenance cost of batteries and problems associated with their safe dispos...

An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS) process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR) was developed and the effects of integrating Vector Quantization (VQ) with Principle Component Analysi...

A novel approach based on integration of data rectification techniques and Support Vector Regression (SVR) is proposed to predict the sulfur content of treated product in gas oil hydrodesulfurization (HDS) process. Simultaneous approaches consisting of Robust Estimation Method (REM) and Wavelet Transform (WT) were proposed to reduce outliers and no...

We solve the problem of identifying (reconstructing) network topology from steady state network measurements. Concretely, given only a data matrix X where the X_ij entry corresponds to flow in edge i in steady-state j, we wish to find a network structure for which flow conservation is obeyed at all the nodes. This models many network problems invol...

Optimal operation of water distribution networks (WDNs) is concerned with meeting consumer demands at desired pressures in an efficient and equitable manner while conserving resources. This can be achieved by implementing advanced control schemes such as model predictive control (MPC). If sufficient water is available, the control objective is to m...

Data reconciliation (DR) and Principal Component Analysis (PCA) are two popular data analysis techniques in process industries. Data reconciliation is used to obtain accurate and consistent estimates of variables and parameters from erroneous measurements. PCA is primarily used as a method for reducing the dimensionality of high dimensional data an...

Given a state space model together with the state noise and measurement noise characteristics, there are well established procedures to design a Kalman filter based fault diagnosis scheme. In practice, however, such disturbance models relating the true root cause of the unmeasured disturbances with the states / outputs are difficult to develop. To...

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization (HDS) process was proposed. Therefore, an integrated approach using support vector regression (SVR) based on wavelet transform (WT) and principal component analysis (PCA) was used. Experimental data from the...

A novel data-driven, soft sensor based on support vector regression (SVR) integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization (HDS) process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challe...

Water Distribution Networks (WDN) is often exposed to either intentional or accidental contamination. In order to protect against such intrusions, an effective and efficient online monitoring system through sensors is needed. Detection of contaminants in WDN is challenging and it is not possible to place sensors at each and every potential point of...

Liquefied petroleum products such as propane, butane, LPG and LNG are transported by ships and stored in tanks in storage terminals. These products are conveyed from the jetty by above-ground insulated pipelines to storage terminals that are typically situated 12–20 km inland. Unloading of petroleum products is energy intensive and results in exces...

Prediction of liquid-liquid phase equilibria in reacting systems is important in many applications such as reactive extraction. This problem poses several numerical challenges. These systems are governed by highly nonlinear algebraic equations and are plagued by the issue of nonconvergence of iterative algorithms. They exhibit strong sensitivity to...

This technical note presents a new Receding-horizon Nonlinear Kalman (RNK) filter for state estimation in nonlinear systems with state constraints. Such problems appear in almost all engineering disciplines. Unlike the Moving Horizon Estimation (MHE) approach, the RNK Filter formulation follows the Kalman Filter (KF) predictor-corrector framework....

Optimal operation of municipal Water Distribution Networks (WDNs) is based on optimizing one or more performance metrics while meeting consumer demands and satisfying supply side and storage constraints. This can be achieved by implementing advanced control schemes such as Model Predictive Control (MPC). With the alarming decrease in fresh water su...

Ensuring adequate supply of clean drinking water and electricity in several parts of the world continues to be a formidable challenge. In coastal areas facing this problem, desalination of sea water using Reverse Osmosis (RO) driven by solar power without batteries can be an appropriate technological solution. Variability in incident solar power is...

Online estimation of the internal states is a perquisite for monitoring, control, and fault diagnosis of many engineering processes. A cost effective approach to monitor these variables in real time is to employ model-based state estimation techniques. Dynamic model-based state estimation is a rich and highly active area of research and many novel...

Data reconciliation is a technique which is used to obtain accurate estimates of variables and parameters from measured process data and a process model. The process model used for reconciling the measurements is generally derived from material and energy balances and is assumed to be exact. In this paper, we propose two modified reconciliation app...

Independent component analysis (ICA) is a popular technique for separating sources from observed linear mixtures of the sources. If the measured signals are corrupted by noise, then they are generally preprocessed before applying ICA. We make use of a recently developed technique known as the iterative principal component analysis (IPCA) to preproc...

Municipal water delivery networks face increasing demands due to population growth. We focus on enhancing a water utility's infrastructure to meet its growing demands in a cost effective manner. Specifically, we consider the problem of placing pressure boosting pumps in a water network such that the minimum required delivery pressure is maintained...

Nonlinear constrained state estimation is an important task in performance monitoring, online optimization and control. There has been recent interest in developing estimators based on the idea of unscented transformation for constrained nonlinear systems. One of these approaches is the unscented recursive nonlinear dynamic data reconciliation (URN...

Soft sensors are increasingly being used to estimate difficult to measure variables using using a mathematical model and other easily measured variables. Partial Least Squares and Principal Components Regression are two popular methods for developing the linear models used in soft sensors. However, the optimality of these methods has not been estab...

State estimation is an important problem in process operations. For linear dynamical systems, Kalman Filter (KF) results in optimal estimates. Chemical engineering problems are characterized by nonlinear models and constraints on the states. Nonlinearities in these models are handled effectively by the Extended Kalman Filter (EKF), whereas constrai...

Seaborne trade in LPG or its constituents (liquid propane and butane) has been growing substantially and is projected to rise to 70 Mt by 2012. Loading LPG onto ship tankers, transporting and unloading LPG from tankers to inland terminals are time and energy intensive operations. The purpose of this study is to arrive at an optimal sequence of flow...

In Part-I of this two part paper, a method is proposed for on-line leak detection and identification in gas pipeline networks using flow and pressure measurements. Simulations on two illustrative networks were used to demonstrate the applicability of the proposed method. In this paper, the performance of the proposed leak detection and identificati...

Dynamic simulation models can be used along with flow and pressure measurements, for on-line leak detection and identification in gas pipeline networks. In this two part paper, a methodology is proposed for detecting and localizing leaks occurring in gas pipelines. The main features of the proposed methodology are: (i) it is applicable to both sing...

This work deals with state estimation in the presence of delayed and infrequent measurements. While most measurements (referred to as secondary measurements) are available frequently and instantaneously, there might be a delay associated with acquiring other measurements (primary measurements) due to long analysis times involved. The primary measur...

The amount of current generated in a polymer electrolyte membrane fuel cell (PEMFC) depends strongly on the local conditions in a cathode such as available oxygen, surface area available for the reactions, amount of ionomer, and amount of electro-catalyst. In the present work, design parameters of a cathode catalyst layer are optimized to achieve t...

Process monitoring and control requires estimation of quality variables, which are often not measurable directly. A cost effective approach to monitor these variables in real time is to employ model based soft sensing and state estimation techniques. Dynamic model based state estimation is a rich and highly active area of research and many novel ap...

In this contribution we propose an active Fault Tolerant Control (FTC) strategy which enables the isolation and identification of valve stiction and valve blocking, in addition to the additive faults like sensor and actuator biases. The developed method is an extension of the original method proposed by Prakash et al. (2002). This method is based o...

There has been a substantial growth in seaborne trade in Liquefied Petroleum Gas (LPG) or its constituents, viz., propane, butane, etc. Seaborne petrochemical trade is forecast to rise from 57 million tonnes in 2007 to 70 million tonnes by 2012. There are several challenges in the design and transport of LPG including loading and unloading operatio...

In this contribution, we develop a Bayesian framework for Fault Detection and Isolation (FDI) based on Kalman filtering. The FDI problem is solved in two steps, detection and identification, as is classic within the FDI context. This development is to be part of a supervisory control system aimed at the achievement of increased resiliency in comple...

One of the open problems in the area of state estimation is nonlinear state estimation. Several approaches have been proposed to solve this problem. Moving Horizon Estimation (MHE) is one possible approach and has been shown to provide the most reliable estimates in several example problems; albeit at a high computational price. In this paper, an a...

Optimal synthesis of Heat Exchanger Networks (HENs) allows energy integration in a chemical process plant and reduction in energy costs. Poor HEN design or ineffective control strategies can result in the potential savings not being realized. Typical HEN design algorithms are tailored for a single set of operating conditions which include flow rate...