Our Research

India has expressed its commitment to the target of net zero carbon dioxide emissions by 2070. Amongst a range of solutions to achieve this, hydrogen is expected to play an important role as an energy carrier. Meanwhile, technologies to capture and utilize CO2 are required to mitigate the effect of CO2 that will get generated until then.

Our group works on addressing some of these challenges; in developing efficient approaches for hydrogen generation and storage, CO2 capture, and catalytic conversion of CO2 to fuels and chemicals. These applications use solid catalytic or adsorbent materials, which form the focus of our research.

The existing catalytic materials are either too expensive, or are not sufficiently active to achieve the desired conversion. Therefore, new materials – specifically, reducible metal oxides and metal organic frameworks (MOFs) – are focus of our investigation. We investigate the engineering principle underlying these gas-solid reaction processes. We use a multi-scale approach, combining simulations and experiments across multiple scales. Multi-scale implies that we seek to connect information across multiple time and length scales: We aim to understand the molecular-level interactions at the catalyst sites and use this information to analyze the reactor-level performance. The following example highlights our efforts in this direction.

Power to Gas Application Example

Power-to-gas application, where one uses excess renewable energy for catalytic reduction of CO2 to methane, has a potential to simultaneously address energy storage and CO2 mitigation. Experiments and simulations at the molecular level can help understand the catalytic interactions, and thus identify bottlenecks in the reaction network. These can be connected at the reactor level to analyze, design and optimize the overall system. We have used a similar multi-scale approach for adsorption processes as well: For storage of hydrogen on MOFs and selective adsorption for CO2 capture. Unraveling the principles across molecular to reactor scales will contribute to addressing some of the challenges in achieving a net zero future.

Research Overview

Figure: Overview of the current and recent work in our group

We will use the above figure, often taught in undergraduate chemical engineering “Chemical Technology” course, to provide a broad overview of our research. A chemical plant contains multiple processes that are sometimes spread over a hundreds of acres. Our research is motivated by the need to decentralize these chemical processes. Examples include direct air capture (DAC) of CO2, H2 generation or storage on-board a vehicle, or micro-reactors for catalytic conversion. From process and reaction engineering perspective, there are certain common principles across these examples. We use a top-down approach where overall system-level requirements guide integration and intensification across various processes, which, in turn, exploit the molecular level interactions at the interface between bulk gas and the solid catalyst. A few examples of our efforts in catalysis, combustion and control are elaborated below.

Catalysis

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Exemplary Results

Experimental results on CH4 activation

Simulation results on catalytic utilization of CO2 to methane. Thermally integrated micro-reactor concept is exploited for lower thermal input and provide favorable thermodynamic conditions for improving methane yields.
Synthesis conditions affect catalyst morphology and its activity. Here, we combine fixed-bed reactor experiments and kinetic model to elucidate the role of synthesis conditions on catalyst activity.

Adsorption and CO2 Capture

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Exemplary Results

Experimental Results on CO2 adsorption ZIF-7

Single-component gas uptake experiments with ZIF-8 were combined with UNILAN isotherm and density functional (DFT) simulations to analyze adsorptive CO2 capture and H2 storage.

Simulation Results on H2 storage

Combustion and Microreactors

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Exemplary Results

Excess Enthalpy Combustion in a Spiral Microreactor

Coupling of Combustion and Thermoelectric in a coupled Micro-device

Cross-Flow Microreactor for hydrogen synthesis

Control and Optimization

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Exemplary Results

A LSTM-based Recurrent Neural Network model for predicting the gasification reactions taking place in the free-board region of fluidized-bed biomass gasification

Multi-objective Model Predictive Control

Reinforcement Learning