Tata Consultancy Services Limited

Tata Consultancy Services Limiteg

Venkataramana Runkana

Sri Harsha Nistala

Venkata Sudheendra Buddhiraju

Aditya Pareek

Vishnu Swaroopji Masampally

Anirudh Deodhar

Rajan Kumar

Sivakumar Subramanian

Pradeep Rathore

Sagar Srinivas Sakhinana

Kalyani Bharat Zope

Arghya Basak

Shashank Agarwal

Saurabh Jaywant Desai

Ashit Gupta

Nagaravi Kumar Varma Nadimpalli

Beena Rai

Pankaj Malhotra

Rakesh Gupta

Balarama Sridhar Dwadasi

Muralikrishnan Ramanujam

Karundev Premraj

Rohan Pandya

Mukul Patil

Tanmaya Singhal

Abhishek Baikadi

Gautam Shroff

Lovekesh Vig

Vivek Kumar

Raviraju Vysyaraju

Mahesh Mynam

Arjun Kumar Pukkella

Anagha Nikhil Mehrotra

Swati Sahu

Solomon Pushparaj Manuelraj

Naga Ravikumar Varma Nadimpalli

Ratnamala Manna

Vishnu Tankasala Veparala

Nishit Raj

Aswin Vinod Muthachikavil

Pallavi Venugopal Minimol

Kuldeep Singh

Sai Prasad Parameswran

Joel Thomson Diraviam Andrew

Dhandapani Subramaniam

Nital Shah

Charan Theja Dhanda

Manendra Singh Parihar

Vishal Jadhav

Aniruddha Panda

Anshul Agarwal

Amey Ahsok Kulkarni

Keshari Nandan Gupta

Navnath Manohar Deore

Vinesh Balakrishnan Yezhuvath

Anamika Tiwari

Jayasree Biswas

Anurag Singh Rathore

Garima Thakur

Moksha Sunil Padsalgi

Nikita Saxena

Shantanu Banerjee

Lalith Roshanlal Jain

Ankit Bansal

Sriharsha Nistala

Phanibhargava Vakkantham

Duc Doan

Sushanta Majumder

Balaji Selvanathan

Tathagata Mukherjee

Nagaravi Kumarvarma Nadimpalli

Kshitij Saxena

Vishal Sudam Jadhav

Sakhinana Sagar Srinivas

Mohammed Suhail

Akanksha Dhayal

Abhishek Bondalapati

Sai Prasad Parameswaran

Srinivas Prakash Diwanji

Sandipan Maiti

Abhishek Krishnamoorthy Baikadi

Anirudh Makarand Deodhar

Manedra Singh Parihar

Agglomeration process in agglomeration plants is quite sensitive to changes in input feed material characteristics. End-to-end optimization of the agglomerate process by combining all the units is difficult due to unique complexities and challenges associated with combining the individual process outputs. A method and system for optimizing the operation of an agglomeration plant has been provided. The system performs real time optimization on integrated wet agglomeration and thermal agglomeration process which subsequently increases the plant productivity and agglomerate quality and minimizes the operating cost and emissions from the plant. The optimization process involves various steps such as receiving data, pre-processing of data, prediction using physics-based and data-driven models of agglomeration plant, and optimization execution and configuration. The process also involves continuous monitoring of model performance and self-learning of the models in case of a performance drift. The system is also configured to estimate the key performance parameters of agglomeration plant.

Method and system for optimization of agglomeration of ores

This disclosure relates to solvothermal synthesis-based method for nanoparticles production. In conventional methods, precursor loading is limited to below the solubility limit of the precursor and results in low throughput and low yield. The disclosed method increases metal precursor loading up to the solubility limit of the metal precursor solution. The method includes pouring the pH modifier dropwise into the metal precursor solution with vigorous stirring, resulting in formation of metal hydroxide solution. The concentration of metal precursor solution is maintained in range of 0.025 M to 2 M, pH in range of 9 to 12, and stirring speed of 800-1200 rpm. The metal hydroxide solution is heated and temperature of the reaction in the range of 25° C. to 400° C. is maintained with aging time in the range of 6 to 24 hours to obtain the nanoparticle slurry resulting production of high yield and high throughput nanoparticles.

Method for enhancing throughput and yield in nanoparticle production

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

State of the art techniques used for Flue Gas Desulpharization (FGD) process monitoring fail to comprehend the relationship between various process parameters, which is crucial in determining the performance of a FGD process being monitored. The disclosure herein generally relates to industrial process monitoring, and, more particularly, to a method and system for performance optimization of Flue Gas Desulphurization (FGD) Unit. The system identifies Key Performance Indicators (KPIs) associated with the process being monitored, and identifies parameters associated with each KPI. This information is used to generate several predictive models, from which a predictive model having the highest value of composite model score amongst the predictive models is selected as the predictive model for processing the input data, which is then used to perform optimization of the FGD process.

Method and system for performance optimization of flue gas desulphurization (FGD) unit

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

Pulverizers are very critical equipment in overall functioning of a plant. They need to be controlled and monitored properly for the optimized operation of the pulverizers. A system and method for performance and health monitoring to optimize operation of a pulverizer is provided. The system comprises a digital twin that can mimic the performance of the pulverizer in real-time and assist the operators in decision making related to operation, maintenance and scheduling. The digital twin is configured to receives real-time sensor data from a plurality of data sources and provides real-time soft sensing of key health and performance parameters of the pulverizer. One more key aspect of the solution is the advisory system that alerts and recommends corrective actions in terms of parameters controlled through other equipment or changes in operation or design or changes in cleaning schedule.

System and method for performance and health monitoring to optimize operation of a pulverizer mill

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)

In an industrial plant, various equipment are used to handle processing of raw materials. Considering complexities involved in the processes and the equipment, constant monitoring is required to obtain desired results. The disclosure herein generally relates to industrial process and equipment monitoring, and, more particularly, to data analysis for Just In Time (JIT) characterization of raw materials in any process industry. The system collects real-time plant data among other inputs, and performs characterization of raw materials being used in the plant. The characterization involves categorizing the raw materials into different classes. The class information is further used to predict performance of the industrial plant, and in turn to generate recommendations for optimization of the industrial plant.

System and method for just in time characterization of raw materials

This disclosure relates to method and system for training of deep learning based time-series models based on self-supervised learning. The problem of missing data is taken care of by introducing missing-ness masks. The deep learning model for univariate and multivariate time series data is trained with the distorted input data using the self-supervised learning to reconstruct the masked input data. Herein, the one or more distortion techniques include quantization, insertion, deletion, and combination of the one or more such distortion techniques with random subsequence shuffling. Different distortion techniques in the form of reconstruction of masked input data are provided to solve. The deep learning model performs these different distortion techniques, which force the deep learning model to learn better features. It is to be noted that the system uses a lot of unlabeled data available cheaply as compared to the label or annotated data which is very hard to get.

Method and system for self supervised training of deep learning based time series models

The gasoline blending is a critical aspect in oil refinery operations. There are multiple blending rules in prior art to predict the chemical or physical property of the blend. But none of the prior method focuses on obtaining the best blending rule for each feature used in creating the soft-sensor. A method and system for generating a soft-sensor for getting desired property of a blend by optimizing a set of blending rules for each feature used for creating soft-sensor have been provided using data from individual components of the blend. The method comprises automated soft-sensor creation using multiple data sources. Further, the method involves finding best blending rule for each feature used for building the soft-sensor or the blending model to predict property of the blend. The soft-sensor developed using data of components used for blending is adapted to predict mixture or blend property with limited tuning.

Method and system for getting desired property of blend by optimizing blending rules

State-of-the-art systems used for plant monitoring and optimization fail to efficiently monitor and improve the performance of blast furnace ironmaking plants due to complexity of such plants. In addition, they attempt optimization without considering the operational stability of the blast furnace. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a method and system for ironmaking plant optimization. The system determines an operational stability of the plant in terms of value of a determined Blast Furnace Stability Index (BFSI). Further, if the BFSI or one or more Key Performance Indicators (KPIs) of the plant deviates from corresponding threshold, then the optimization is done.

Method and system for ironmaking plant optimization

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

Combined cycle gas turbine (CCGT) power plants have become common for generation of electric power due to their high efficiencies. There are various problem related with improving the efficiency of CCGT plants by optimizing the manipulated variables. The method and system for optimizing the operation of a combined cycle gas turbine has been provided. The system is configured to calculate an optimal value of manipulated variables (MV) with efficiency as one of the key performance parameters. The MVs from the existing CCGT automation system, i.e. a first set of manipulated variables and the manipulated variables from the optimization approach, i.e. a second set of manipulated variables are combined to determine an optimal set of manipulated variables. The method further checks for the anomalous behavior of the system and define the root cause of the identified anomaly and the operational state of the CCGT plant.

Method and system for optimization of combination cycle gas turbine operation

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

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Pune, India

Venkataramana Runkana