Tata Consultancy Services Limited

Venkata Sudheendra Buddhiraju

Venkataramana Runkana

Vivek Kumar

Sri Harsha Nistala

Vishnu Swaroopji Masampally

Sagar Srinivas Sakhinana

Aditya Pareek

Nagaravi Kumar Varma Nadimpalli

Karundev Premraj

Swati Sahu

Saurabh Jaywant Desai

Shashank Agarwal

Muralikrishnan Ramanujam

Venkatramana Runkana

Aswin Vinod Muthachikavil

Shantanu Banerjee

Nikita Saxena

Navnath Manohar Deore

Garima Thakur

Anurag Singh Rathore

Anamika Tiwari

Vinesh Balakrishnan Yezhuvath

Keshari Nandan Gupta

Amey Ahsok Kulkarni

Anshul Agarwal

Srinivas Prakash Diwanji

This disclosure relates generally to method and system to monitor and control continuous ultrafiltration (UF) process units. In real time, continuous operation of UF to handle variating concentration in feed stream is tedious and complex. The UF plant system receives a plurality of input data configured to UF process units and from the real time data outliers are removed and missing values are imputed. The prediction module predicts a volumetric concentration factor (VCF) value and a throughput value by selecting a model from a model repository. The optimization module optimizes the VCF value, and the throughput value based on a plurality of optimal variables recommended for a given feed concentration. The UF plant system controls the VCF value and the throughput value for a predefined period of a prediction horizon based on a plurality of trajectory profiles recommended for the feed flow rate, the pressure data, and a feed concentration.

Method and system to monitor and control continuous ultrafiltration process units

This disclosure relates generally to system and method for molecular property prediction. The conventional methods for molecular property prediction suffer from inherent limitation to effectively encapsulate the characteristics of the molecular graph. Moreover, the known methods are computationally intensive, thereby leading to non-performance in real-time scenarios. The disclosed method overcomes the limitations of typical dynamic neighborhood aggregation (DNA) method by fusing the static edge attributes in determining the self-attention coefficients. In an embodiment, the disclosed method transforms the hidden state of a sink node by utilizing a neural-net function, which takes as input an aggregated single-message vector obtained by the self-attention mechanism and the self-attention mechanism transformed hidden state of the node.

Edge conditioned dynamic neighborhood aggregation based molecular property prediction

This disclosure relates generally to system and method for molecular property prediction. The disclosed method includes mapping node embeddings of a molecular graph to a graph-level embedding characterizing the molecular graph. The graph level representation is acquired by pooling characteristics of hidden states of the nodes in the molecular graph by performing an iterative content based attention in a plurality of iterations. The content based attention is performed by considering an edge information fused transformed hidden state vector of the nodes of the molecular graph. The graph level embedding is fed through the linear projection to predict the molecular properties of the molecular graphs.

Molecular property prediction using edge-information fused global graph-read neural network function

This disclosure relates generally to system and method for molecular property prediction using hypergraph message passing neural network (HMPNN). Typical MPNN architectures used for chemical graphs representation learning have limitations, including, inefficiency to learn long-range dependencies for homogeneous graphs, ineffectiveness to model topological properties of graphs taking into consideration the multiscale representations, and lack of anti-smoothing weighting mechanism to address graphs random walk limit distribution. Disclosed method and system HyperGraph attention-driven Hypergraph Convolution. The Hypergraph attention driven convolution, on molecular hypergraph results in learning efficient embeddings on the high-order molecular graph-structured data. By taking into account the transient incidence matrix, the induced inductive bias augments the scope of molecular hypergraph representation learning.

System and method for molecular property prediction using hypergraph message passing neural network (HMPNN)

Hydrogen being a clean, highly abundant and renewable fuel, is a promising alternative for conventional energy sources. Mostly, this hydrogen is stored in the form of hydrides. The existing methods for identification of material for hydrogen storage as expensive and time consuming. A method and system of identification of materials for hydrogen storage has been provided. The method provides a machine learning technique to predict the hydrogen storage capacity of materials, using only the compositional information of the compound. A random forest model used in the work was able to predict the gravimetric hydrogen storage capacities of intermetallic compounds. The method and system is also configured to predict the thermodynamic stability of the intermetallic compound.

Method and system for identification of materials for hydrogen storage

The disclosure generally relates to methods and systems for determining multi-column chromatography process configuration for capturing antibodies. Conventional approaches for design of MCC configuration are limited to rule based, either driven by UV spectroscopic measurements or by performing number of experiments, which involves a lot of material costs and time utilization. The present disclosure solves the technical problem of identifying the operational conditions that optimized the MCC process and the MCC configuration. A multi-objective optimization function defined with one or more decision variables associated with the operating conditions is considered to determine the optimal MCC configuration, while satisfying purification goals. The one or more key performance measures of the MCC process comprises a productivity, a capacity utilization, a product yield, and a product purity. The significant amount of time and the material cost invested for designing the optimum MCC configuration is decreased by the present disclosure.

Methods and systems for determining multi-column chromatography process configuration

The efficient operation of an electric vehicle depends greatly on proper functioning of a battery pack in the electric vehicle. A system and method for optimizing the operation of the battery pack in an electric vehicle is provided. The system comprises a digital twin for a battery pack in an electric vehicle. The system determines the state of charge, state of health and temperature distribution in the battery pack using various models. This information can be used to predict optimal charge and discharge profiles of the battery pack for given load conditions, as well as remaining useful life of the battery. The digital twin would require inputs such as battery temperatures from the sensors, coolant flow rates, coolant temperature, ambient temperature, load on the vehicle, current and voltages from the pack and battery characteristics from the manufacturer.

Method and system for optimizing operation of battery pack of an electric vehicle

Process control of continuous production of biomolecules is a major challenge due to complex nature of processes and time scales of operations involved. Availability of key process variables in real-time is one of main requirements. This disclosure relates to a processor implemented method of controlling a continuous bioprocessing plant which includes at least one of: receiving, an input data associated with one or more equipments; generating, by a recipe builder, a sequence of unit operations to determine at least one job order based on the at least input data; obtaining, a control decision associated with a control parameter based on the at least one job order; communicating, via the middleware, the control decision associated with the control parameter to the PLC; and executing, by a control system of the PLC, the control decision on a unit equipment to control: (i) a continuous bioprocessing train, and (ii) an individual unit operation.

Method and system to control a continuous bioprocessing

The present invention relates to a system for optimizing and controlling the particle size distribution and scale-up of production of nanoparticle in an aerosol flame reactor. The method provides nanoparticles with desired, optimized and controlled particle size and the specific surface area in aerosol reactors using a simulation tool with programmed instructions. The simulation tool couples flame dynamics model and particle population balance model.

System for optimizing and controlling particle size distribution and for scale-up of nanoparticle production in an aerosol flame reactor

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Pune, India

Venkata Sudheendra Buddhiraju