Department of Chemical Engineering

Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

Electrospun fibrous meshes are widely used for tissue repair due to their ability to guide a host of cell responses including phenotypic differentiation and tissue maturation. A critical factor determining the eventual biological outcomes of mesh-based regeneration strategies is the early innate immune response following implantation. The natural healing process involves a sequence of tightly regulated, temporally varying and delicately balanced pro-/anti-inflammatory events which together promote mesh integration with host tissue. Matrix designs that do not account for the immune milieu can result in dysregulation, chronic inflammation and fibrous capsule formation, thus obliterating potential therapeutic outcomes. In this review, we provide systematic insights into the effects of specific fiber/mesh properties and mechanical stimulation on the responses of early innate immune modulators viz., neutrophils, monocytes and macrophages. We identify matrix characteristics that promote anti-inflammatory immune phenotypes, and we correlate such responses with pro-regenerative in vivo outcomes. We also discuss recent advances in 3D fabrication technologies, bioactive functionalization approaches and biomimetic/bioinspired immunomodulatory mesh design strategies for tissue repair and wound healing. The mechanobiological insights and immunoregulatory strategies discussed herein can help improve the translational outcomes of fiber-based regeneration. Statement of significance: The crucial role played by immune cells in promoting biomaterial-based tissue regeneration is being increasingly recognized. In this review focusing on the interactions of innate immune cells with electrospun fibrous meshes, we systematically elucidate the effects of the fiber microenvironment and mechanical stimulation on biological responses, and build upon these insights to inform the rational design of immunomodulatory meshes for effective tissue repair. We discuss state-of-the-art fabrication methods and mechanobiological advances that permit the orchestration of temporally controlled phenotypic switches in immune cells during different phases of healing. The design strategies discussed herein can also be leveraged to target several complex autoimmune and inflammatory diseases. © 2022

World University root node

Acta Biomaterialia

Electrospun fiber-based strategies for controlling early innate immune cell responses: Towards immunomodulatory mesh designs that facilitate robust tissue repair

Bioprocess optimization and control for large scale production of vaccine/protein remain challenging due to the adaptation of experiment-based route which needs numerous expensive and time intensive experiments. The presence of model uncertainties in such a nonlinear system further makes the optimization and scale -up challenging. In this context, we propose a robust framework amalgamating the paradigms of systems biology and dynamic optimization under uncertainty for improving the performance of one of the most widely used vaccine/protein production platform, the Baculovirus expression system [BEVs]. Here, the multi-objective optimal control problem is formulated with an objective of maximizing the productivity and minimizing raw material consumption in a semi-batch baculovirus system considering parametric uncertainty. A comprehensive comparison shows that a multifold increase in the productivity can be obtained using this computational framework considering controlled addition of feed material. This study provides a generic methodology for improving the performance of a bioprocess and represents the first instance where robust optimal control has been applied for optimizing the productivity of a baculovirus-insect cell system. © 2022 IEEE.

2022 8th Indian Control Conference, ICC 2022 - Proceedings

Multi-objective Optimization and control under Uncertainty for performance improvement of a Baculovirus Expression Vector System

Herein, we describe an efficient way to engineer the electronic energy defects and achieve the defect passivation of codoping of two rare-earth (RE) ions, namely, neodymium (Nd3+) and erbium (Er3+), into the TiO2 lattice. It is shown that the PEC performance is significantly improved using the RE-coated TiO2 photoanode. X-Ray diffraction (XRD), transmission electron microscopy (TEM), and Raman study have confirmed the doping concentration's upper limit without modifying the single-phase structure of the TiO2 lattice. The Brunauer-Emmett-Teller (BET) study established the optimal codoping composition of “Er0.003Nd0.001Ti0.996O2” with enriched surface properties. Photoluminescence (PL) investigations indicated defect passivation, which enabled a favourable energy landscape via intermediate trap-sites, improving charge transport characteristics. The photoelectrochemical (PEC) device with the optimized codoped TiO2 photoanode has a higher increased photocurrent density (Jph) of 16 μA cm−2 in comparison to the device with pristine TiO2 (Jph: 7 μA cm−2). As a proof of concept, a simple sensitization of these engineered TiO2 photoanodes with the cadmium sulfide (CdS) quantum dots resulted in a Jph of 2.8 mA cm−2, and with N719 ruthenium dye, a Jph of 0.8 mA cm−2 was obtained, indicating the improved performance of sensitized codoped TiO2 photoanodes for PEC devices. As a proof of concept, N719 dye-based dye-sensitized solar cells with doped TiO2 photoanodes demonstrated an enhanced power conversion efficiency. © 2022 The Royal Society of Chemistry.

Department of Physics

Sustainable Energy and Fuels

Defect engineered (Er3+/Nd3+) codoped TiO2 photoanodes for enhanced photoelectrochemical and photovoltaic applications

Air-core dynamics during the hydrocyclone operation play an essential role in determining the separation efficiency. Precise monitoring of air-core dynamics during operation can suggest possible strategies to prevent underflow discharge problems. Electrical resistance tomography (ERT) facilitates online analysis of the internal behavior of air-core, because of its high temporal resolution. Existing results for ERT-based air-core reconstructions in hydrocyclones employ general algorithms that do not utilize process knowledge. In this paper, two noniterative methods, viz., monotonicity and factorization algorithms, which are process-Aware, are evaluated to estimate air-core diameters in hydrocyclone under feed pressure variations. To further investigate the performance of these algorithms, comparison studies are made with Gauss-Newton (GN) and Total Variation (TV) algorithms. Initially, the algorithms are compared on simulated phantoms for qualitative analysis and to select hyper-parameters. A threshold method is developed for quantitative analysis of experimental data to obtain a crisp radius value. The ground truth is calculated from image processing on images obtained from a video camera. It is observed that GN, monotonicity, and factorization methods result in a similar performance on the experimental data. However, for very fine meshes, monotonicity and factorization algorithms are much faster than GN and TV methods. © 2022 American Chemical Society. All rights reserved.

Industrial and Engineering Chemistry Research

Evaluation of Two Noniterative Electrical Resistance Tomography (ERT) Reconstruction Algorithms for Air-Core Measurements in Hydrocyclone

We experimentally study the dynamics of two identical air bubbles rising side-by-side in water by varying two parameters, namely, the radius of the bubble and center to center distance between them. The bubbles follow a three-dimensional spiraling motion, and their path and shape oscillations are observed in both the front and top views by using a high speed camera with a back-lit illumination and a mirror arrangement. Bubbles of different sizes are created by using a dumping cup mechanism, and the center to center distance between the two bubbles is varied by using telescopic joints. The dynamics of the two side-by-side bubbles is compared and contrasted with that of a single rising bubble. We found that the bubbles act independent of each other, like a single bubble, when the center to center distance is greater than seven times the radius of the bubbles. For similar separation distances, increasing the size of the bubbles results in a smaller terminal velocity and also lesser deviation from a spiral path due to high inertia. © 2021 Author(s).

Department of Mechanical and Aerospace Engineering

Physics of Fluids

An experimental study of two identical air bubbles rising side-by-side in water

While addressing supply chain planning under uncertainty, Robust Optimization (RO) is regarded as an efficient and tractable method. As RO involves calculation of several statistical moments or maximum / minimum values involving the objective functions under realizations of these uncertain parameters, the accuracy of this method significantly depends on the efficient techniques to sample the uncertainty parameter space with limited amount of data. Conventional sampling techniques, e.g. box/budget/ellipsoidal, work by sampling the uncertain parameter space inefficiently, often leading to inaccuracies in such estimations. This paper proposes a methodology to amalgamate machine learning and data analytics with RO, thereby making it data-driven. A novel neuro fuzzy clustering mechanism is implemented to cluster the uncertain space such that the exact regions of uncertainty are optimally identified. Subsequently, local density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling to sample the uncertain parameter space more accurately. The proposed technique is utilized to explore the merits of RO towards addressing the uncertainty issues of product demand, machine uptime and production cost associated with a multiproduct, and multisite supply chain planning model. The uncertainty in supply chain model is thoroughly analysed by carefully constructing examples and its case studies leading to large scale mixed integer linear and nonlinear programming problems which were efficiently solved in GAMS® framework. Demonstration of efficacy of the proposed method over the box, budget and ellipsoidal sampling method through comprehensive analysis adds to other highlights of the current work. © 2021 Elsevier Ltd

Chemical Engineering Science

Data driven robust optimization for handling uncertainty in supply chain planning models

Owing to the generation of vast amount of unlabelled dynamic data and the need to analyze them, deep unsupervised learning based clustering algorithms are gaining importance in the field of data science. Since the task of automated feature extraction is proficiently combined with the machine learning models in deep unsupervised learning algorithms, they are identified to be superior as compared to conventional dynamic similarity measure based clustering methods. In this context, the authors present a recurrent neural network (RNN) based clustering algorithm optimization, where the vital information representing the dynamic data (or time-series data) is extracted first and subsequently clustered using a soft clustering algorithm. This methodology not only ensures dynamic component extraction in terms of static features but also clusters them efficiently using an evolutionary clustering algorithm called Neuro-Fuzzy C-Means (NFCM) clustering, which reduces the large-scale optimization problem of FCM to small-scale along-with identification of optimal number of clusters. The proposed algorithm has been implemented on three different test data sets collected from machine learning repository and it was found that the results are 98-100% accurate. © 2021 IEEE.

2021 7th Indian Control Conference, ICC 2021 - Proceedings

A Deep Unsupervised Learning Algorithm for Dynamic Data Clustering

The attractive and repulsive behaviours of a pair of initially spherical gas bubbles rising side-by-side in a channel with non-uniformly heated walls containing a self-rewetting liquid are investigated numerically. The surface tension of a self-rewetting fluid exhibits a parabolic temperature dependence with a well-defined minimum, as opposed to linear (common) fluids whose surface tension decreases almost linearly with the increasing temperature. It is found that, for low Reynolds numbers, while in an isothermal medium, two gas bubbles display a repulsive behaviour, they attract in non-isothermal systems. The bubbles in the self-rewetting fluid undergo a plastic collision and show a ‘squeezing and relaxing’ behaviour, whereas they attract and then bounce in the linear fluid. A regime map demarcating the repulsive and attractive behaviours for a self-rewetting fluid is plotted in the Weber number (We) and the dimensionless linear component of the surface tension gradient (M1) space. It is found that the bubbles in the self-rewetting fluid remain spherical even for high Weber numbers while they deform considerably in the case of the linear fluid indicating that the attractive behaviour of the bubbles in the self-rewetting fluid is due to the lift force generated by the thermocapillary stresses and not due to the deformation. The mechanism underlying the observed phenomenon is elucidated by studying the drag and lift forces acting on the bubbles, their orientations, and the flow field around them. © 2021 Elsevier Masson SAS

Fulltext

European Journal of Mechanics, B/Fluids

Interaction of two non-coalescing bubbles rising in a non-isothermal self-rewetting fluid

The oblique collision of two unequal-sized liquid droplets in a gaseous environment is investigated numerically. It is found that the asymmetry in the flow field arising due to unequal-sized droplets and oblique collision greatly alters the collision outcomes observed in the case of the head-on collision of identical droplets. Our results reveal that permanent coalescence occurs at intermediate collision angles, but head-on and large-angle collisions result in reflexive separation and stretching separation, respectively. Moreover, we found that the end-pinching mechanism is operational in the case of head-on collision, and the capillary wave instability is responsible for the ligament breakup for large collision angles. It is also observed that the droplets coalesce permanently for low velocity ratios and high radius ratios, but for high velocity ratios and low radius ratios, the droplets coalesce temporarily and then split again. By conducting a large number of numerical simulations, the collision outcomes and the boundary separating them are plotted on R r - W e and θ - W e planes, where We, Rr, and θ represent the Weber number, radius ratio of droplets, and collision angle, respectively. © 2021 Author(s).

A study of two unequal-sized droplets undergoing oblique collision

Ni/γ-Al2O3 wash coated cordierite monolith catalysts are calcined in air at 800 °C for 4, 10, and 20 h in order to study the effect of calcination time on the activity of the catalysts for dry reforming of model biogas. Catalytic activity studies are performed at 800 °C with three different CH4/CO2 ratios of 1.0, 1.5, and 2.0. The catalyst calcined for the longest time (C-20) displays higher stability and activity in terms of CH4 and CO2 conversion compared to those calcined for 4 h (C-4) and 10 h (C-10). XRD data and TPR analysis detect the maximum amount of NiAl2O4/MgAl2O4 phases and strongest metal-support interaction, respectively, for the C-20 sample. FESEM reveals the particle size of the calcined and reduced C-20 sample to be smaller than that of the C-4 and C-10 samples. Whereas, H2 pulse-chemisorption characterization demonstrates the highest metal surface area, metal dispersion, and smallest Ni particle size for the C-20 catalyst. While, no carbon deposition on any catalyst occurs for the CH4/CO2 ratio of one, lowest amount of carbon nanotubes is formed on the C-20 sample for the CH4/CO2 ratio of 1.5 and 2.0, as observe by DTA-TGA. EDX reveals concentration variation of Mg and Si from the cordierite monolith wall along the thickness of the coating for all the samples. In addition, the maximum amount of these elements is observed for the calcined C-20 catalyst coating. These implies that the diffusion of Mg and Si from the cordierite monolith to the catalyst coating during calcination contribute significantly in controlling the physicochemical properties of the catalysts. As a result, the higher stability and activity of the C-20 could be attributed to the formation of higher amount of the Ni– Mg- alumina spinel complex in the catalyst coating during longer calcination time, which leads to the improved metal-support interaction and higher nickel dispersion over monolith. © 2020 Hydrogen Energy Publications LLC