Showing papers by "Raghunathan Rengaswamy published in 2018"
TL;DR: In this paper, Vanadium Redox Flow Battery (VRFB) is considered as a case study to see the feasibility of this concept and a lumped parameter model of the VRFB system is developed to study the high temperature operation.
25 citations
TL;DR: This work seeks to develop a capacity fade minimizing model predictive control (MPC) framework, which can help in identification and realization of optimum charge-discharge cycles in Lithium-ion (Li-ion) batteries.
15 citations
TL;DR: The design problem considered in this work is to determine pipes where the shut-off valves can be optimally located such that it is possible to prevent the contaminated water from reaching any demand point, regardless of the source node from where the contamination has originated.
13 citations
TL;DR: An investigation and development of strategies for battery modeling and controller implementation, which are two of the essential components of any battery management system, and a generalized approach to incorporate non-linear coupling of various capacity fade mechanisms is proposed.
5 citations
TL;DR: Results reveal that the technique serves as a tool that can quantify the performance and assess the reliability of a state estimator and is developed for linear systems and extended to non-linear systems with single as well as multiple measurable variables.
Abstract: State estimation is a widely adopted soft sensing technique that incorporates predictions from an accurate model of the process and measurements to provide reliable estimates of unmeasured variables. The reliability of such estimators is threatened by measurement related challenges and model inaccuracies. In this article, a method for benchmarking of state estimation techniques is proposed. This method can be used to quantify the performance and hence reliability of an estimator. The Hurst exponents of a posteriori filtering errors are analyzed to characterize a benchmark (minimum mean squared error) estimator, similar to the minimum variance control benchmark developed for control loops. A distance metric is then used to quantify the extent of deviation of an estimator from the benchmark. The proposed technique is developed for linear systems and extended to non-linear systems with single as well as multiple measurable variables. Simulation studies are carried out with Kalman based as well as Monte Carlo based estimators whose computational details are significantly different. Results reveal that the technique serves as a tool that can quantify the performance and assess the reliability of a state estimator. The strengths and limitations of the proposed technique are discussed with guidelines on applications and deployment of the technique in a real life system.
2 citations
TL;DR: The presented integrated model is the first ever quantitative model, providing a mechanistic basis for autism pathogenesis, capturing known biomarkers, as well as, highlighting the potential of novel dietary modifications in alleviating the symptoms of autism.
Abstract: Autism spectrum disorder (ASD) refers to the set of complex neurological disorders characterized by repetitive behaviour The reported occurrence of abnormal gut bacteria, along with prevalence of gastrointestinal disorders in ASD indicate its strong correlation with the gut microflora Our study aims to understand the role of diet and gut bacteria in ASD via an integrated constraint-based and PBPK model Genome scale models of five major gut bacteria, which were reported to be associated with ASD, were integrated with the human host, ie, the combined small intestinal enterocyte and neuronal brain model Simultaneously, a permeability-limited two sub-compartment PBPK model was developed to determine the distribution of bacteria-derived toxins in the body The important results include, (i) inclusion of probiotics into the diet of autistic case restores gut balance, majorly seen as a result of reduced oxidative stress in the brain and the gut, (ii) microbiome and diet together mediate host metabolism in autism, majorly via the nucleotide, central carbon, amino acid, and reactive oxygen species metabolisms, and (iii) gut bacterial-specific secretions contribute to autistic metabotype Thus, the presented integrated model is the first ever quantitative model, providing a mechanistic basis for autism pathogenesis, capturing known biomarkers, as well as, highlighting the potential of novel dietary modifications in alleviating the symptoms of autism
2 citations