Showing papers by "Raghunathan Rengaswamy published in 2017"
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TL;DR: In-silico validation of the proposed method on few equivalent circuits of electrochemical systems is presented in this work; future work will include experimental validate of the technique on real Electrochemical systems.
37 citations
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TL;DR: In this article, a computationally efficient first principles dynamic model for PEMFC system simulations and concomitant water management studies is developed, and the steady-state version of this model is validated with experimental data.
18 citations
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TL;DR: In this paper, a modified extended Kalman filter (EKF) approach is proposed to handle uncertainties in both differential and algebraic equations, and equality constraints are applied to a water gas shift reactor.
Abstract: State and parameter estimation plays an important role in many different engineering fields. Estimation of systems described by linear and nonlinear differential equations has been very well studied in the literature. Work in the past decade has been geared toward efficiently extending these algorithms to constrained systems. Of recent interest is the evaluation of state estimation techniques for differential-algebraic equation (DAE) systems. The algebraic equations in these studies are exact, an example being the mole fractions adding to unity. However, there are situations where algebraic equations can be of both certain and uncertain types. In this paper, we propose a modified extended Kalman filter (EKF) approach that can handle uncertainties in both differential and algebraic equations, and equality constraints. We also show the importance of this work in estimation of mole fraction, temperature, and pressure profiles in a water gas shift reactor. The impact of location and type of measurements on th...
17 citations
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TL;DR: In this paper, an univariate interval halving technique is fused with Mahalanobis distance to develop a multivariate tool that accounts for interactions between variables, which can be used for reliable CLPA and/or for user defined benchmarking of control loops.
Abstract: Control loop performance assessment (CLPA) techniques assume that the data being analyzed is generated during steady state operation with fixed plant dynamics and controller parameters. However, in industrial settings one often encounters environmental and feedstock variations which can induce significant changes in the plant dynamics. Availability of data sets corresponding to fixed configurations is therefore questionable in industrial scenarios, in which case it becomes imperative to extract the same from routine plant operating data. This article proposes a technique for segmenting multivariate control loop data into portions corresponding to fixed steady state operation of the system. The proposed technique exploits the fact that changes in the operating region of the system lead to changes in variance-covariance matrix of multivariate control loop data. The univariate interval halving technique is fused with Mahalanobis distance to develop a multivariate tool that accounts for interactions between variables. The resulting data segments can be used for reliable CLPA and/or for user defined benchmarking of control loops. A multivariate control loop performance index is also proposed that requires significantly less data as compared to one of the previously proposed techniques. The proposed technique requires only routine operating data from the plant, and is tested on benchmark systems in the literature with simulations. Experimental validation on a model predictive control system aimed at maintaining the temperature profile of a metal plate demonstrates applicability of the technique to industrial systems. The proposed technique acts as a tool for preprocessing data relevant to CLPA and can be applied to large scale interacting multivariate systems. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3311–3328, 2017
12 citations
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24 Aug 2017
TL;DR: In this paper, a cylindrical PEM fuel cell with hollow semi-cylinders acting as the cathode current collector was developed, which allows for increasing MEA compression without increasing cathode collector thickness.
Abstract: Cylindrical PEM fuel cells have higher volumetric and gravimetric power density compared to planar PEM fuel cells because of the absence of graphite bipolar plates. The performance of the cylindrical fuel cell is largely influenced by MEA compression. In this study, we have developed a cylindrical fuel cell with hollow semi-cylinders acting as the cathode current collector. This design allows for increasing MEA compression without increasing cathode current collector thickness. The decrease in hydrogen flow rate and an increase in hydrogen humidification temperature further increased the cylindrical cell performance. The lower limiting current density obtained at lower hydrogen flow rates can be increased by operating the cell in dead-ended mode. The better performing baffle design for anode current collector in terms of efficient hydrogen utilization is determined from CFD simulations.
4 citations
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TL;DR: A one dimensional model for a μPSC based on mass balances of species in the anode and cathode chambers considering unsteady one dimensional diffusion is developed and validated using test v − i characteristic data.
3 citations
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TL;DR: A deterministic model is proposed to explain the average dynamics of the avalanching process and several possible realizations of the coalescence avalanche are generated by incorporating uncertainty into the parameters in the model.
Abstract: A two-dimensional concentrated emulsion exhibits spontaneous rapid destabilization through an avalanche of coalescence events which propagate through the assembly stochastically. We propose a deterministic model to explain the average dynamics of the avalanching process. The dynamics of the avalanche phenomenon is studied as a function of a composite parameter, the decay time ratio, which characterizes the ratio of the propensity of coalescence to cease propagation to that of propagation. When this ratio is small, the avalanche grows autocatalytically to destabilize the emulsion. Using a scaling analysis, we unravel the relation between a local characteristic of the system and a global system wide effect. The anisotropic nature of local coalescence results in a system size dependent transition from nonautocatalytic to autocatalytic behavior. By incorporating uncertainty into the parameters in the model, several possible realizations of the coalescence avalanche are generated. The results are compared with the Monte Carlo simulations to derive insights into how the uncertainty propagates in the system.
2 citations
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01 Jan 2017
TL;DR: In this paper, a capacity fade minimizing model predictive control approach for identification and realization of optimal charge-discharge cycles for Li-ion batteries is proposed. But, the proposed strategy is limited to a single battery.
Abstract: Lithium ion batteries are one of the most commercially used batteries. Though they are widely used in mobile phones, laptops and other consumer electronics, concerns related to their safety and efficient operation still persists. One of the major issues in Li-ion batteries is the capacity degradation with aging due to various mechanisms such as solid electrolyte interphase (SEI) formation and dissolution, thermal runaway, and Li-plating. In this work, we describe a capacity fade minimizing model predictive control approach for identification and realization of optimal charge-discharge cycles for Li-ion batteries. Optimum charging profiles are obtained such that the reduction in charge carrying capacity with cycling is minimized, while still obtaining required charging. We expect the proposed strategy to improve battery capacity and prolong lifespan. Examples that demonstrate the significance of the proposed framework by comparing battery performance with and without the presence of controller are discussed. Extensions to this work in terms of addressing various battery failure mechanisms, on-line identification of failure mechanisms, and designs for on-line implementation in real battery systems are also outlined.
2 citations
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TL;DR: In this article, a systematic approach for handling nonlinear terms is suggested, which results in appropriate scale and reference values that render the dimensionless variable variations to be of order one.
Abstract: Systematic scaling analysis of model equations can be valuable as a tool for developing computationally tractable simulations of physical systems. The scaling analysis methods in literature pose difficulties in the calculation of scale and reference values, when nonlinear terms are involved in the model equations. Further, existing methods involve trial and error procedures in the scaling process. In this paper, a systematic approach for handling nonlinear terms is suggested, which results in appropriate scale and reference values that render the dimensionless variable variations to be of order one. Further, trial and error procedures are avoided through a new approach wherein a set of nonlinear algebraic equations are solved to identify the scale and reference values. The proposed scaling approach is common to any given model equations with fixed parameters. However, it is to be noted that the proposed procedure may not handle situations when model equations exhibit steady state multiplicity and have dynamic multi-mode regimes. The proposed scaling procedure is illustrated through various examples of different complexities. A 1D model of WGS reactor as a case study shows the effectiveness of the obtained scale and reference values in obtaining simplified model which represents the steady state and dynamic variations of the variables.
2 citations
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TL;DR: This work develops a sensor placement framework that combines genetic algorithms and the extended Kalman filter to obtain optimal sensor locations and includes linearized models around the steady-state operating point.
Abstract: Growing complexity of processes necessitates the use of information from sensors along with first-principles mathematical models to ensure safe and optimal operations. Use of sensors in complex processes requires identifying optimal location of sensors that can maximize information from a process. Classical sensor placement approaches for nonlinear systems that use state estimation schemes usually incorporate linearized models around the steady-state operating point. However, such approaches face difficulties when abnormalities or disturbances drift the system away from the normal operating point. Therefore, use of models that can appropriately track the behavior of the system in the sensor placement framework are of interest. However, the computational complexity of the detailed models makes such approaches intractable. In this work, we develop a sensor placement framework that combines genetic algorithms and the extended Kalman filter to obtain optimal sensor locations. Within this framework, we have in...
2 citations
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01 Jan 2017TL;DR: In this article, a framework for quantitatively benchmarking the merits/demerits of potential technologies using a matrix of several factors is presented, viz, batteries, fuel cells and flow batteries.
Abstract: Increasing demand for electricity, depleting reservoirs of fossil fuels and increasing environmental concerns, call for efficient and clean utilization of available energy. Electrochemical technologies such as batteries, fuel cells and flow batteries are promising alternatives for such clean and efficient energy conversion. While extensive heuristics exist to guide the choice of technology, very little work has been done on development of systematic and rational frameworks to quantitatively benchmark the merits/demerits of potential technologies using a matrix of several factors. In this work, we describe a framework that addresses this gap with a focus on three technologies, viz, batteries, fuel cells and flow batteries. The proposed framework currently evaluates two factors: power and energy density. An algorithm that generates gravimetric and volumetric cost comparisons between secondary batteries, flow batteries and fuel cells is presented. Since the available chemistries are numerous, comparisons are made for the most promising current chemistries for each technology, namely, lithium ion battery, hydrogen PEM fuel cells and all vanadium redox flow battery. For a given application, the algorithm also identifies optimal designs for the corresponding technologies. Generalization of the framework for other factors such as reliability, lifespan, etc. and different chemistries (for each technology) will also be outlined.
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TL;DR: Frequency domain analysis is used to provide a theoretical investigation of the identification of stiction in otherwise linear closed-loop systems and the failure of Hammerstein stiction detection techniques to positively identify valve stictions in certain systems in which it known to be present can be explained accordingly.
Abstract: Previous works introduced control valve stiction detection and quantification methods for closed-loop systems based on the identification of a Hammerstein element between the feedback controller and plant output signals. These techniques each rely upon the fact that the presence of valve stiction introduces nonlinearities in the closed-loop system, yet, little theoretical discussion has been presented which explains the conditions under which these methods will succeed or fail in properly detecting valve stiction. Therefore, the present work uses frequency domain analysis to provide a theoretical investigation of the identification of stiction in otherwise linear closed-loop systems. In this way, the failure of Hammerstein stiction detection techniques to positively identify valve stiction in certain systems in which it known to be present can be explained accordingly.