Tata Consultancy Services Limited

Sagar Srinivas Sakhinana

Venkataramana Runkana

Sri Harsha Nistala

Venkata Sudheendra Buddhiraju

Vishnu Swaroopji Masampally

Rajan Kumar

Abhishek Baikadi

Pallavi Venugopal Minimol

Duc Doan

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

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.

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Pune, India

Sagar Srinivas Sakhinana