Department Of Chemical Engineering

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Janus particles self-propel by generating local tangential concentration gradients along their surface. These gradients are present in a layer whose thickness is small compared to the particle size. Chemical asymmetry along the surface is a prerequisite to generate tangential chemical gradients, which gives rise to diffusio-osmotic flows in a thin region around the particle. This results in an effective slip on the particle surface. This slip results in the observed "swimming"motion of a freely suspended particle even in the absence of externally imposed concentration gradients. Motivated by the chemotactic behavior of their biological counterparts (such as sperm cells, neutrophils, macrophages, bacteria, etc.), which sense and respond to external chemical gradients, the current work aims at developing a theoretical framework to study the motion of a Janus particle in an externally imposed linear concentration gradient. The external gradient along with the self-generated concentration gradient determines the swimming velocity and orientation of the particle. The dominance of each of these effects is characterized by a non-dimensional activity number A (ratio of applied gradient to self-generated gradient). The surface of Janus particle is modeled as having a different activity and mobility coefficient on the two halves. Using the Lorentz reciprocal theorem, an analytical expression for the rotational and translational velocity is obtained. The analytical framework helps us divide the parameter space of surface activity and mobility into four regions where the particle exhibits different trajectories. © 2021 Author(s).

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Physics of Fluids

Motion of an active particle in a linear concentration gradient

Improving the accuracy of travel time predictions depends on providing the correct inputs as well as the prediction algorithm used. Clustering algorithms can be used to identify the patterns in the data, which can improve the inputs to the prediction algorithm. The feature vectors used for clustering greatly affect the clusters formed and, ultimately, the prediction performance. Clustering being an unsupervised learning technique, the accuracy or correctness of the cluster formed can not be evaluated directly. A possible solution for this would be to link the problem with prediction accuracy and choose the feature vector combination with maximum prediction accuracy. The present study analyses the use of different feature vectors for clustering and the effect on travel time predictions. Here, three cases, namely, travel time alone, travel time along with features such as time of the day, section index, and day of the week as numerical features and as a mix of categorical and numerical feature vectors, are studied. The effects of using each of these cases as clustering feature vectors on travel time predictions are evaluated. It is observed that the prediction accuracy is the highest when only travel times are used as a clustering feature vector. The study demonstrates the importance of choosing the correct feature vectors for clustering and its effect on a final application, namely, travel time prediction. © 2021 IEEE.

Department Of Civil Engineering

2021 International Conference on COMmunication Systems and NETworkS, COMSNETS 2021

Effects of Clustering Feature Vectors on Bus Travel Time Prediction: A Case Study

Learning from past incidents is critical to achieving and maintaining high process safety performance. Accident and incident records provide one way for learning; however, these are usually in the form of unstructured texts, which makes analysis difficult. Recently, text mining methods based on supervised learning have been proposed for analyzing accident reports; however, they require an impractically large number of labeled records as training examples. This paper proposes an automated, semi-supervised, domain-independent approach for analyzing accident reports. Given a set of user-defined classification topics and domain literature such as handbooks, glossaries, and Wikipedia articles, the method can identify domain-specific keywords and group them into topics with minimal expert involvement. These keywords and topics can then be used for various data mining purposes, including classification. The proposed approach is demonstrated using two different case studies across domains: (1) in aviation to identify the stage of flight when an accident occurs, and (2) in the process industry domain to identify the cause of pipeline accidents. The average classification accuracy of the proposed method was 80% which is comparable to that of supervised learning methods. The key benefits of this approach are that it can generate domain-specific predictive models with limited manual intervention. © 2021 The Institution of Chemical Engineers

Process Safety and Environmental Protection

Text mining of accident reports using semi-supervised keyword extraction and topic modeling

We have reported experimental studies on the self-assembly and degree of ordering of a binary mixture of soft colloids in monolayer deposits obtained by controlled evaporation. A sessile drop containing soft colloids is evaporated on a solid surface to achieve a loosely-packed two-dimensional deposit with a hexagonal arrangement. The soft microgel particles possess a hard core with a compliant corona, which plays a crucial role in retaining the crystallinity of the binary particle monolayer. The ordered arrangement of the binary mixture is observed even when the bulk diameter of one type of particle is 25% higher than the other, irrespective of their mixing ratio (1 : 3, 1 : 1, and 3 : 1). The microgel particles of both sizes are found to be homogeneously distributed throughout the deposit, completely suppressing the size-dependent particle segregation. Furthermore, in contrast to the self-assembly of bidisperse hard colloids, wherein the lattice distorts to accommodate particles of disparate sizes, in soft colloids, the particles deform at the interface to preserve the crystalline lattice. Moreover, unlike the gradual order-to-disorder transition observed in the deposits consisting of monodisperse microgel particles, the deposits of a binary mixture of microgels exhibit no noticeable trend. The areal disorder parameter, pair correlation function and the shape factor which quantifies the local ordering of particles in the deposit indicate the absence of a distinct order-to-disorder transition for the binary mixtures. © the Owner Societies 2021.

Department Of Physics

Physical Chemistry Chemical Physics

Evaporative self-assembly of the binary mixture of soft colloids

Triclosan is a commonly used biocide effective against bacterial and fungal infections. However, its overuse in pharmaceutical and personal care products has resulted in its abundance in the natural environment. The detection of triclosan by visual spectroscopy can be carried out using the azo-coupling reaction of diazonium complexes. However, the reaction is also common to other phenolic compounds and aromatic amines, posing significant challenge. In this work, we investigate the azo-coupling reaction of triclosan and several commonly occurring analogous compounds to develop an improved spectroscopic method for the selective determination of triclosan without interference. We find that the azo-coupling reaction between the diazotized derivative and the phenolic compounds is highly dependent on the pH of the reaction media. At pH 7.2, the absorbance of the azo dye product of triclosan shows a peak at 452 nm which has minimal interference from other phenolic azo-dye products with the exception of naphthol. Naphthol shows an interference corresponding to 58% of the analytical signal of equimolar triclosan concentration. To overcome this, we develop an analytical model for the simultaneous determination of triclosan and naphthol from mixed solutions of the compounds. A linear calibration plot from 1.7 to 34 µM was obtained for both triclosan and naphthol with limit-of-detection (LOD) of 0.62 µM and 1.03 µM respectively. The developed protocol was tested for the analysis of water samples collected from various environmental sources spiked with different concentrations of triclosan and naphthol. The samples were enriched by solid-phase-extraction which allowed a 50-fold enhancement in detection of triclosan. The average relative recovery of triclosan in real samples was found to be 98.6%. © 2021 Elsevier B.V.

Department of Aerospace Engineering

Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy

Sensitive and selective determination of triclosan using visual spectroscopy

Selective microwave heating can significantly impact the performance of multiphase reactors, such as slurry reactors, where the dispersed (solid particles) and continuum (liquid) phases have different dielectric properties. Lack of tools to measure and predict the temperature difference between the dispersed and continuum phases hinders the understanding and optimization of microwave-heated slurry reactors. We utilize experiments, theory, and multiscale simulations to investigate microwave heating of slurries consisting of microwave absorbing solid particles dispersed in a non-polar liquid. Using controlled experiments and mathematical modeling, we propose an ideal slurry reactor and develop analytical expressions to predict the temperature evolution of the solid and liquid phases during microwave heating. Our experiments show a strong impact of the solid material and concentration on the heating rate of slurries, whereas no difference is observed in conventionally heated slurries. We also perform multiscale simulations of microwave-heated slurries employing a coarse-graining methodology to handle the large separation of length scales in slurries and compare them against experiments. © 2020 Elsevier B.V.

Chemical Engineering Journal

Microwave heating of slurries

Selective heating of different phases of multiphase systems via microwaves can result in energy savings and suppression of side reactions. However, materials properties and operating conditions that maximize temperature gradients are poorly understood. Here we utilize computational fluid dynamics (CFD) computations and temperature measurements in structured flow reactors (monoliths) in a monomodal microwave cavity to assess the temperature difference between the walls and the fluid and develop a simple lumped model to estimate when temperature gradients exist. We also explore the material's thermal and electrical properties of structured reactors for isothermal catalyst conditions. We propose that CFD simulations can be used as a nonintrusive, predictive tool of temperature homogeneity. Importantly, we demonstrate that localized heating in the bed under several conditions rather than selective heating is responsible for the selectivity enhancement. Our results indicate that structured beds made of high thermal conductivity materials avoid arcing and enable temperature homogeneity and low electrical conductivity materials allow microwaves to penetrate the domain. ©

Industrial and Engineering Chemistry Research

Temperature Homogeneity under Selective and Localized Microwave Heating in Structured Flow Reactors

This work introduces a multi-objective optimization strategy to handle conflicting set-point tracking and economic objectives in a two-layer hierarchical control framework. A dynamic multi-objective real-time optimizer (DMO), incorporated in the upper layer, handles multiple control objectives with set-point tracking being the higher priority objective and computes optimal plant trajectories. This plant-wide trajectory information is communicated to the lower-layer model predictive control (MPC) operating at a faster sampling rate. The conventional weight-based and lexicographical method for the DMO are discussed. A new algorithm is conceptualized based on the lexicographical method to handle prioritized objectives. The proposed algorithm modifies the higher priority tracking objective and establishes improved economic performance compared to the conventional techniques, with minimal effect on the conflicting tracking objective, through a systematic choice of the preferred Pareto solution. The proposed algorithm's efficacy, within the hierarchical framework, is analyzed using two case studies: A polymerization reactor and a multi-unit reactor–separator system. © 2020 Elsevier Ltd

Journal of Process Control

Two-layered dynamic control for simultaneous set-point tracking and improved economic performance

Performance of electrochemical batteries suffers at cold temperatures. In this paper, we report on an experimental investigation of sensitivity of the vanadium redox flow battery (VRFB) to the cell operating temperature. Experiments have been carried out on a VRFB cell with an active area of 426 cm2 at cell temperatures of 25, +10 and −10 °C. Measured parameters include cycle efficiency and discharge capacity at three current densities and two flow rates, peak power and cell impedance obtained through electrochemical impedance spectroscopy (EIS) studies. The influence of electrode compression and electrode thickness has also been studied through comparative experimental protocols. Results indicate significant loss of discharge capacity and cell efficiency at low temperatures; this can be attributed to substantial increase in ohmic resistance and even higher increase in charge transfer resistance of the cell. Two methods known to reduce cell resistance, namely, increasing compression ratio of the electrode and using thinner electrode, have led to restoring much of the lost discharge capacity and energy efficiency. Optimizing the electrode parameters such as its compression, permeability and thickness for industrial scale cells, together with possible use of electrocatalysts, seems to be a promising way of improving low temperature performance of VRFB cells. © 2020 Elsevier B.V.

Journal of Power Sources

Influence of electrode design parameters on the performance of vanadium redox flow battery cells at low temperatures

Flow of fluids through porous media such as soil, rocks, bones, wood and concrete has been an area of interest for engineering communities that deal with porous materials. The tools developed to describe flow through porous regions are generally applied to bulk materials with single composition. In this paper, however, we demonstrate their applicability to an interface of any two contacting surfaces particularly those of the elastomer and metal in the context of sealing applications. We propose a relationship to account for change in the permeability and the thickness of the seal interface with contact pressure at the interface and fluid pressure of fluid flowing through the interface. We hence develop a porous-media-based model to describe leak of gas through metal–elastomer interface. We validate the model using nitrogen gas leak experimental data for different elastomer–metal interfaces. © 2020, Springer Nature B.V.