Abb Schweiz Ag

Abb Research Ltd.

Sabic Innovative Plastics IP B.v.

Shrikant Bhat

Senthil Kumar Sundaram

Gopinath Selvaraj

Mohan Kumar Shanmugam

Andries Adriaan Volkers

Sriram Krishnamurthy

Michael Lundh

Roopali Rai

Nandkishor Kubal

Raoul Jetley

Sandeep Tyagi

Babji Buddhi Srinivasa

Abhijit Badwe

Rahul Kumar-Vij

Naresh Naranbhai Nandola

Prasanna Pathath

Andries Andriaan Volkers

In an embodiment, the present disclosure discloses a method and a module for controlling data transfer in a manufacturing plant. The IoT module is configured to receive a request for a plurality of parameters from a second application from one or more applications. The request is made by a first application from the one or more applications. The IoT module determines status of the requested parameters. The status indicates if the requested parameters are authorized for sharing. Further, the IoT module transfers only the authorized parameters to the first module based on the status. Thus, the present disclosure provides a method and a IoT module for sharing data securely within the manufacturing plant.

Method of controlling data transfer in a manufacturing plant and a system thereof

The present invention relates to a method and a system for controlling a process of a process plant. Hie system is configured to control a process in the process plant by recommending control operations and perform the control operations. The system obtains the control operations and detects availability of control room operators using historical data related to actions performed by the control room operators, sensory parameters of the control room operators and a status associated with the process. The system provides queries related to the control operations, the plant parameters, status of the process to the control room operators upon detecting the availability. Hie queries are validated by the control room operators. The system thereafter autonomously controls tire control room based on the validated data.

Method and system for controlling a process in a process plant

A method and system are disclosed for optimizing load scheduling for a power plant having one or more power generator units. An exemplary method and corresponding system can involve detecting an event indicative of a need for adapting one or more constraints for an objective function used in load scheduling. On such detection, the objective function is analysed to determine adaptive constraint values for the one or more constraints for optimally solving the objective function. These adaptive constraint values can be used to solve the objective function and the solution of the objective function with the one or more adapted constraint values can be used to operate the one or more power generation units of the power plant.

Process optimization method and system for a power plant

The present invention discloses a method for controlling a process using a plurality of regulatory controllers () connected to a remote supervisory controller () over a communication link () and to a local supervisory controller () over a process bus (). The remote supervisory controller () controls the regulatory controllers () over the communication link () with the use of a remote process model. During operation of the plant a local process model is configured in the local supervisory controller () with control data send on the communication link (). Upon failure of the communication link () the local supervisory controller () takes over the control through the process bus (), acting as a redundant or a backup controller. The method comprises: monitoring transmission of control data between the remote supervisory controller () and the plurality of controllers () over the communication link (); determining functional correlations between measured process variables and control process variables, based on the control data and a predetermined correlation function; configuring local process model based on the determined functional correlations; and providing intermediate set points using the configured local process model, upon an intermediate failure of the communication link (), where the communication link recovers from the intermediate failure within a predetermined time period.

Method for controlling a process plant using a redundant local supervisory controller

A method is disclosed for optimization of continuous digestion process. Exemplary embodiments can control a delignification process in a continuous pulp digester in an optimal way for different pulp grades taking into account process disturbances and model uncertainties. This can be achieved by customizing a generic mathematical model for a continuous digestion process which runs through a software application on a dedicated electronic device. The model can be updated periodically based on on-line information, and used with a plant optimization component involving multi-objective optimization to evaluate optimal set points for the controllers. A system is also disclosed for optimizing control of a continuous pulp digester.

System and a method for optimization of continuous digestion process

A method and control system are disclosed for optimizing load scheduling for a power plant having one or more generation units. The method can include analyzing the operating state of one or more components of generation units in terms of one or more risk indices associated with one or more components of generation units; updating an objective function that reflects the state of one or more components of generation units; solving the objective function to optimize a schedule of the one or more generation units and operating state of one or more components of generation units; and operating the one or more generation units at optimized schedule and operating state.

Method and control system for scheduling load of a power plant

Exemplary embodiments relate to a method and system for updating a model in a model predictive controller. The system executing a method that includes assessing the deviation of the operating performance level from the desired performance level of the process plant, and diagnosing the model predictive control for the model plant mismatch by updating the model in a model predictive controller. The step of diagnosing the model predictive controller includes determining the model prediction error in relation to model plant mismatch, quantifying the model plant mismatch, and updating the model in the model predictive controller.

Method and system for updating a model in a model predictive controller

A thermoplastic composition comprising, based on the total weight of the thermoplastic composition, from 0.1 to 30 weight percent of an inorganic filler composition comprising an inorganic filler-polyorganosiloxane composite; up to 80 weight percent of a polycarbonate; and from 1 to 25 weight percent of an impact modifier. Methods of preparing the compositions and articles comprising the compositions are described.

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Nagpur, India

Shrikant Bhat