Abb Research Ltd.

The Mathworks, Inc.

Alec Stothert

Pascal Gahinet

Marc Antoine

Eduardo Gallestey Alvarez

Eduardo Alvarez Gallestey

Craig Buhr

Steve Morton

John W Glass

Dario Castagnoli

Anthony Lennon

Markus Ahrens

Md Rajibul Huq

Systems and methods may generate a boundary of a FOU for an interval type-2 MF based on a transformation of another boundary of the FOU. The systems and methods may receive a plurality of parameters for a type-1 MF that defines a boundary of the FOU for the interval type-2 MF and may receive at least one other parameter. The systems and methods may generate, based on a transformation of the type-1 MF utilizing the at least one parameter, a type-1 MF that defines a different boundary of the FOU. The system and methods may adjust the plurality of parameters and the at least one second parameter to adjust the FOU for use in a model representing, for example, a real-world physical system, where execution of the model executes a fuzzy inference system and generates results representing a behavior of the real-world physical system.

Systems and methods for generating a boundary of a footprint of uncertainty for an interval type-2 membership function based on a transformation of another boundary

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.

Method of generating optimal control problems for industrial processes

A method of manipulating a block diagram model with a plurality of graphical modeling components by defining an open loop anywhere on the block diagram model is provided. Graphical modeling components in series with the open loop may be automatically recognized and a plurality of parameters of the graphical modeling components in series with the open loop may be displayed on a display device. A user may simultaneously tune the parameters of the graphical modeling components in series with the open loop. Factorization points breaking the block diagram model in two disconnected parts may be identified in the block diagram model. A virtual graphical model may be generated by replacing a pattern of the graphical modeling components between two factorization points with a single graphical modeling component representing the pattern.

Modeling of control systems with open-loop representations and factorization of components

The present invention provides a method of modifying the behavior of a modeled system from a graphical representation of the behavior of the system. Given a graphical representation of the behavior of a modeled system, one or more bounds may be selected for the behavior on the graphical representation. The necessary parameters for the modeled system are then determined for producing the desired behavior based on the selected bounds for the behavior.

Automatic modification of the behavior of system from a graphical representation of the behavior

In a method and computer program product for optimal operation of a power plant and a power plant optimising system an optimisation mode () minimises a cost function (J[u],J[P]) that comprises a deviation of lifetime of plant components (LTp(&#x3c4;)) from a desired nominal lifetime trajectory (LTn(&#x3c4;)). This is done by, at a given time, determining future values of input values (u(&#x3c4;),P(&#x3c4;)) such as control values (P(&#x3c4;)) or process values (u(&#x3c4;)) to the plant and simulating, in a simulation modulate (), 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 () minimises the cost function (J[u],J[P]) by varying the input values (u(&#x3c4;),P(&#x3c4;)). As a result, it is possible to operate the plant such that component lifetime (LTp(&#x3c4;)) follows the desired trajectory (LTn(&#x3c4;)).

Optimal operation of a power plant

The disclosed process control system makes use of Optimal control (OC) and model predictive control (MPC) techniques for selection of the Expert Systems (ES) targets values U. The ES target values U are selected to minimize the performance criterion J. A mathematical model of an extended system given by the process P and the ES is developed. This hybrid mathematical model has both continuous dynamics and logical relationships. Controlled variables of the mathematical model are the ES target values U and inputs are the measurements y and the performance criterion J. The OC and/or MPC techniques are used to compute values U. An optimizer of the OC/MPC selects values of the ES target values U only. This activity has lower sampling rates than selection of controller values, which simplifies the design of the OC/MPC controller.

Process control system

In a method and computer program product for optimizing power plant control values and a power plant optimizing system an optimization module () 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 (), 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 () 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.

Growing community of inventors

Westborough, MA, United States of America

Alec Stothert