Patents by Inventor Eduardo Gallestey
Eduardo Gallestey has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11566948Abstract: A method, apparatus and system for measuring a temperature can involve measuring a voltage with a resistance temperature detector using a variable excitation current, and deriving a process temperature from the voltage measured by the resistance temperature detector. The process temperature can be further derived by applying a plurality of values of the variable excitation current, measuring corresponding values of voltage, and estimating a resistance by applying a least square estimation. The process temperature can also be derived by applying a different value of the variable excitation current at every iteration, using a recursive least square estimation to measure a resistance, and using confidence intervals for instrument diagnostics.Type: GrantFiled: February 13, 2020Date of Patent: January 31, 2023Assignee: Honeywell International Inc.Inventors: Eduardo Gallestey Alvarez, Sarabjit Singh, Shripad Kumar Pande, Seshagiri Yamarthi
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Publication number: 20210255046Abstract: A method, apparatus and system for measuring a temperature can involve measuring a voltage with a resistance temperature detector using a variable excitation current, and deriving a process temperature from the voltage measured by the resistance temperature detector. The process temperature can be further derived by applying a plurality of values of the variable excitation current, measuring corresponding values of voltage, and estimating a resistance by applying a least square estimation. The process temperature can also be derived by applying a different value of the variable excitation current at every iteration, using a recursive least square estimation to measure a resistance, and using confidence intervals for instrument diagnostics.Type: ApplicationFiled: February 13, 2020Publication date: August 19, 2021Inventors: Eduardo Gallestey Alvarez, Sarabjit Singh, Shripad Kumar Pande, Seshagiri Yamarthi
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Patent number: 9539582Abstract: A system and method are disclosed for observing a change of mass inside a grinding unit as a part of a grinding process with a storing unit. The change of mass is derived from a mass balance for the grinding unit and a mass balance for the storing unit.Type: GrantFiled: November 13, 2013Date of Patent: January 10, 2017Assignee: ABB Research LTDInventors: Eduardo Gallestey Alvarez, Konrad Stadler
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Patent number: 9285787Abstract: A model-based control of an industrial process using a merged MLD system model is provided for the estimation and subsequent control of the process. An optimization of an objective function is performed. The objective function includes a difference between an observed quantity and an output variable of a Mixed Logical Dynamic (MLD) system model of the process. The optimization is performed as a function of state variables of the MLD system model, over a number of time steps in the past, and subject to constraints defined by the MLD system model's dynamics. The optimizing values of the state variables are retained as estimated initial states for subsequent control of the process in a model-based manner including the same MLD system model. The single MLD system model is a combination or merger of individual MLD subsystem models representing the sub-processes of the process, and may be elaborated during a customization step.Type: GrantFiled: December 22, 2011Date of Patent: March 15, 2016Assignee: ABB RESEARCH LTDInventors: Alvarez Eduardo Gallestey, Jan Poland, Konrad Stadler, Sebastian Gaulocher, Hamed Foroush
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Publication number: 20140070034Abstract: A system and method are disclosed for observing a change of mass inside a grinding unit as a part of a grinding process with a storing unit. The change of mass is derived from a mass balance for the grinding unit and a mass balance for the storing unit.Type: ApplicationFiled: November 13, 2013Publication date: March 13, 2014Applicant: ABB RESEARCH LTDInventors: Eduardo GALLESTEY ALVAREZ, Konrad STADLER
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Publication number: 20120150507Abstract: A model-based control of an industrial process using a merged MLD system model is provided for the estimation and subsequent control of the process. An optimization of an objective function is performed. The objective function includes a difference between an observed quantity and an output variable of a Mixed Logical Dynamic (MLD) system model of the process. The optimization is performed as a function of state variables of the MLD system model, over a number of time steps in the past, and subject to constraints defined by the MLD system model's dynamics. The optimizing values of the state variables are retained as estimated initial states for subsequent control of the process in a model-based manner including the same MLD system model. The single MLD system model is a combination or merger of individual MLD subsystem models representing the sub-processes of the process, and may be elaborated during a customization step.Type: ApplicationFiled: December 22, 2011Publication date: June 14, 2012Applicant: ABB RESEARCH LTDInventors: Alvarez Eduardo Gallestey, Jan Poland, Konrad Stadler, Sebastian Gaulocher, Hamed Foroush
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Publication number: 20110208341Abstract: A control system for controlling an industrial process includes an indicator generator configured to determine at least one fuzzy logic based indicator from measured process variables. The control system also includes a state estimator configured to determine estimated physical process states based on the fuzzy indicator. For controlling the industrial process, the process controller is configured to calculate manipulated variables based on (i) defined set-points and (ii) a physical model of the process using the estimated physical process states. Combining a fuzzy logic indicator with a model based process controller provides robust indicators of the process states for controlling an industrial process in a real plant situation in which measured process variables may possibly contradict each other.Type: ApplicationFiled: March 18, 2011Publication date: August 25, 2011Applicant: ABB RESEARCH LTD.Inventors: Konrad STADLER, Eduardo Gallestey Alvarez, Jan Poland
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Patent number: 7426456Abstract: In a method and computer program product for optimal operation of a power plant and a power plant optimising system an optimisation mode (1) minimises a cost function (J[u],J[P]) that comprises a deviation of lifetime of plant components (LTp(?)) from a desired nominal lifetime trajectory (LTn(?)). This is done by, at a given time, determining future values of input values (u(?),P(?)) such as control values (P(?)) or process values (u(?)) to the plant and simulating, in a simulation modulate (2), the behaviour of the plant up to a given future time. Corresponding lifetime values are determined in the simulation, and incorporated in an objective function. The optimisation module (1) minimises the cost function (J[u],J[P]) by varying the input values (u(?),P(?)). As a result, it is possible to operate the plant such that component lifetime (LTp(?)) follows the desired trajectory (LTn(?)).Type: GrantFiled: November 19, 2002Date of Patent: September 16, 2008Assignee: ABB Research LtdInventors: Eduardo Gallestey Alvarez, Alec Stothert, Marc Antoine, Steve Morton
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Publication number: 20070143074Abstract: Modelling of industrial processes is simplified with the use of Mixed Logical Dynamic (MLD) framework. Optimal control problems can be generated for application to industrial processes. For example, two arbitrarily connected MLD blocks are automatically merged to obtain one composite MLD block. Via a repeated use of the procedure, any arbitrarily complex system containing the complete description of an industrial process can be generated from the simplest MLD building blocks. The optimal control problem is generated via adding an MLD block whose unique output becomes the cost functional of the problem. In a graphical environment, any specific industrial process may be reproduced by instantiating blocks from a library of basic MLD elements or atomic MLD blocks and by properly connecting them. In case an appropriate library is available, this process will not require any expert knowledge from the end user apart from the ability to build the graphical interconnections mentioned.Type: ApplicationFiled: December 4, 2006Publication date: June 21, 2007Applicant: ABB Research Ltd.Inventors: Eduardo Gallestey, Dario Castagnoli, Alec Stothert
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Patent number: 7058552Abstract: In a method and computer program product for optimizing power plant control values and a power plant optimizing system an optimization module (1) minimizes total plant operation costs while achieving predetermined required output values for produced power and process steam. This is done by, at a given time, determining future values of control values and simulating, in a simulation module (2), the behavior of the plant up to a given future time. Corresponding fuel costs and generated power are determined in the simulation, and incorporated in an objective function. The optimization module (1) minimizes the objective function by varying the control values. According to the invention, a rate of ageing of plant components is determined when simulating the future behavior of the plant, and the objective function to be minimized comprises said rate of ageing.Type: GrantFiled: December 19, 2001Date of Patent: June 6, 2006Assignee: ABB Research LtdInventors: Alec Stothert, Eduardo Gallestey Alvarez, Markus Ahrens, Marc Antoine, Steve Morton
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Publication number: 20050154476Abstract: The inventive process control system makes use of Optimal control (OC) and model predictive control (MPC) techniques for selection of the Expert Systems (ES) targets U. The ES target U is selected in such a way that the performance criterion J is minimized. In other words, a mathematical model of extended system given by the process P and the ES is developed. This mathematical model has hybrid nature in the sense that both continuous dynamics (mostly process) and logical relationships (mostly ES) appear in it. Controlled variables of the mathematical model are the ES targets U and inputs are the measurements y and the performance criterion J. OC and/or MPC techniques are used to compute U. The optimizer of the OC/MPC selects values of the ES targets U only. This activity has lower sampling rates than selection of C, which makes the design of the OC/MPC controlled easier.Type: ApplicationFiled: December 23, 2004Publication date: July 14, 2005Applicant: ABB Research Ltd.Inventors: Eduardo Gallestey, Alec Stothert
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Publication number: 20040064297Abstract: For estimating a value of a vector of variables p in a mathematical model representing a physical process, where a state vector x of the model is estimated by a State Augmented Extended Kalman Filter (SAEKF), and where that the vector of variables p represents one or more properties of the process and is representable by a function of the state vector x, the following steps are executed:Type: ApplicationFiled: September 29, 2003Publication date: April 1, 2004Applicant: ABB Research LtdInventors: Eduardo Gallestey Alvarez, Geir Hovland, Thomas von Hoff
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Publication number: 20030100974Abstract: In a method and computer program product for optimal operation of a power plant and a power plant optimising system an optimisation module (1) minimises a cost function (J[u],J[P]) that comprises a deviation of lifetime of plant components (LTp(&tgr;)) from a desired nominal lifetime trajectory (LTn(&tgr;)). This is done by, at a given time, determining future values of input values (u(&tgr;),P(&tgr;)) such as control values (P(&tgr;)) or process values (u(&tgr;)) to the plant and simulating, in a simulation module (2), the behaviour of the plant up to a given future time. Corresponding lifetime values are determined in the simulation, and incorporated in an objective function. The optimisation module (1) minimises the cost function (J[u],J[P]) by varying the input values (u(&tgr;),P(&tgr;)). As a result, it is possible to operate the plant such that component lifetime (LTp(&tgr;)) follows the desired trajectory (LTn(&tgr;)).Type: ApplicationFiled: November 19, 2002Publication date: May 29, 2003Inventors: Eduardo Gallestey Alvarez, Alec Stothert, Marc Antoine, Steve Morton
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Publication number: 20020120352Abstract: In a method and computer program product for optimizing power plant control values and a power plant optimizing system an optimization module (1) minimizes total plant operation costs while achieving predetermined required output values for produced power and process steam. This is done by, at a given time, determining future values of control values and simulating, in a simulation module (2), the behavior of the plant up to a given future time. Corresponding fuel costs and generated power are determined in the simulation, and incorporated in an objective function. The optimization module (1) minimizes the objective function by varying the control values. According to the invention, a rate of ageing of plant components is determined when simulating the future behavior of the plant, and the objective function to be minimized comprises said rate of ageing.Type: ApplicationFiled: December 19, 2001Publication date: August 29, 2002Inventors: Alec Stothert, Eduardo Gallestey Alvarez, Markus Ahrens, Marc Antoine, Steve Morton