Abb Schweiz Ag

Abb Ag

Abb Technology Ag

Mario Hoernicke

Katharina Stark

Rainer Drath

Dirk Schulz

Nicolai Schoch

Ralf Jeske

Chaojun Xu

Axel Haller

Sten Gruener

Roland Braun

Marcel Dix

Michael Vach

Joerg Schubert

Ben Schroeter

Martin Hollender

Benjamin Kloepper

Benedikt Schmidt

Fan Dai

Thomas Gamer

Sönke Kock

Pablo Rodriguez

Georg Gutermuth

Moncef Chioua

Raoul Jetley

Julius Rueckert

Hao Ding

Jeff Harding

Anders Trosten

Christoph Winterhalter

Jarkko Lalu

Alexander Fay

Dennis Janka

Jan Christoph Schlake

Nicolas Mauser

Peter Weber

Steve Royston

Kurt Langer

Esteban Arroyo

Mike Barth

A method for non-MTP module integration includes receiving by a wrapper unit logic signals, bus signals and material signals from a non-MTP module, wherein the non-MTP module is a physical module of a process plant, wherein the logic signals comprise information of logic connections and/or functions of the non-MTP module, wherein the bus signals comprise information of outputs of a fieldbus of the non-MTP module, wherein the material signals comprise information of material connections of the non-MTP module; converting, by the wrapper unit, the bus signals to open platform communication identifier architecture, OPC UA, nodes; determining, by the wrapper unit an MTP interface using the logic signals and the material signals; and determining, by the wrapper unit, a MTP conform digital black box module, using OPC UA nodes and the MTP interface.

Non-MTP module integration

A modular automation support system for modular plants comprises an automation engineering support system comprising a feedback processing component configured to modify the operation of one or more other components of the automation engineering support system based on user feedback.

Modular automation support system

A computer-implemented method includes obtaining an amount of a resource and/or capability of a process module, and dividing this amount by the maximum amount of the respective resource and/or capability to obtain a theoretical utilization of the resource and/or capability as the theoretical utilization of the process module. A pool of available process modules is searched to identify candidate process modules to replace the process module. The theoretical utilization for each candidate module is determined and an optimized topology of the plant is generated by replacing the process module with a candidate process module that has a same or a higher theoretical utilization than process module.

Computerized optimization of an engineered modular plant topology

A method of generating control code for an industrial plant comprises: defining control logic for controlling the industrial plant by editing a cause-and-effect matrix provided by an engineering tool, wherein the defining comprises defining both instrument-based control logic and service-based control logic using the same cause-and-effect matrix; and generating the control code for controlling the industrial plant on the basis of the defined control logic.

Generating control code for an industrial plant

A modular automation support system for modular plants includes an engineering support system comprising a control and execution engine configured to: receive data from a monitoring infrastructure of a modular plant and convert that data into semantic data which conforms to a semantic data model; use one or more semantic rules or mechanisms relating the semantic data received from the monitoring infrastructure to services provided by modules of a pipeline of the modular plant to control the pipeline in executing a process.

A method for generating a dynamic model of an industrial plant having: a plurality of physical processes that are dependent such that an outcome of at least one first process is fed into at least one second process; a plurality of low-level controllers, each controller acting upon at least one physical process such that at least one process variable of the at least one physical process is controlled to match a set-point of the low-level controller; and a plurality of sensors, each sensor measuring at least one process variable of one of the physical processes, and/or of the plant as a whole, the set-points of the low-level controllers and current values of the process variables measured by the sensors being the inputs of the model, and predicted future values of the process variables that are likely to result from applying the set-points to the low-level controllers being the outputs.

Modularized model predictive control for industrial plants

The present disclosure is directed at a façade server. The façade server provides a configurable façade having a configurable address space and serving as an interface for a client and to provide a mapping between the configurable address space of the façade and a data space and/or an address space of an external server.

Façade server

A computer-implemented resource management method for modular plants may include: receiving data identifying a required module type to be assembled into the modular plant as part of a module pipeline including one or more modules; and executing an optimization algorithm to select, from a plurality of modules having the required module type, a module for inclusion in the module pipeline on the basis of one or more predetermined optimization criteria.

Resource management for modular plants

A method of providing a control software configuration for a module of a modular plant, the method including, in a module engineering phase: receiving a user definition for the module; and automatically generating the control software configuration for the module based on the user definition, the automatically generating including: specifying parameters for the module that are not specific to any target system; and providing the control software configuration as a controller-agnostic configuration file for subsequent binding of controller software instantiated according to the controller-agnostic configuration file to a hardware controller of the module when the module is integrated into a target system during a plant engineering phase.

Configuration of a modular plant

A method for configuring a modular industrial plant using an engineering tool includes using a plant engineering facility of the tool to create a representation of the modular industrial plant identifying modules to be orchestrated in the modular industrial plant. The modules include at least one function module that includes control software for the modular industrial plant. The method further includes using a module engineering facility of the tool to configure the function module for use in the modular industrial plant by editing a placeholder configuration file created by the tool for defining the configuration of the function module. Editing the configuration file causes a representation of the function module in the plant engineering facility to be automatically updated to reflect adaptations made to the function module using the module engineering facility. The method includes instructing the tool to assign the function modules so configured to the modular industrial plant.

Configuring modular industrial plants

An apparatus includes: a process control system; at least one process module; and a process module logic entity associated with the at least one process module. The process control system communicates with the at least one process module via a process module interface. The process module logic entity provides a control logic of the at least one process module.

Apparatus comprising a process control system and at least one process module, related method, computer program product, and data processing device

The disclosure describes a method associated with a simulation model of a process module configured to execute at least one module service. The process module is interlinkable or interlinked with further process modules to carry out a technical process in a cascade of module services, and includes Providing a process logic specification of the technical process, a control logic specification of the process module, a state engine of the process module from a configuration of the at least one service, and a process connection information of the technical process; Generating at least a part of the simulation model from a combination thereof, and Linking at least a part of the generated simulation model to a module type package for the process module, or extending a module type package for the process module by at least a part of the generated simulation model.

Method associated with a simulation model of a process module, computer program product, and non-volatile data storage medium

A computer-implemented method for determining an operational state of an industrial plant includes acquiring alarms raised within the plant and adding them to a pool of important alarms, determining whether a physical state of the plant indicated by a first alarm causes a second alarm or meets a predetermined state-dependent condition and, if so, moving the first alarm to a pool of informative alarms; and determining the operational state of the plant and/or a corrective action for improving this operational state based on the alarms in the pool of important alarms.

Computer-implemented method for determining an operational state of an industrial plant

A method for determining an interdependency between a plurality of elements in an industrial processing system includes: providing a process flow diagram (PFD) of a topology of the processing system; transforming the PFD into a directed graph, each element of the plurality of elements being transformed into a node and each relation between the plurality of elements being transformed into a directed edge; selecting one node of the plurality of nodes as a starting node; and constructing a subgraph, the subgraph including all the nodes that are forward-connected from the starting node so as to show at least one interdependency between the plurality of elements in the subgraph.

Method for analyzing effects of operator actions in industrial plants

An automated maintenance schedule generation method includes: receiving planning data for designing a modular plant comprising physical modules as received planning data; selecting digital modules, correlating to respective physical modules, from a database, depending on the received planning data as respective correlating physical modules, the digital modules including maintenance information of the respective correlating physical modules; associating the selected digital modules depending on the received planning data; determining an expected maintenance need including an expected maintenance issue and/or an expected maintenance issue date for the selected physical modules depending on the received maintenance information as a determined maintenance need; and generating a maintenance schedule for the modular plant depending on the determined maintenance need.

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Landau, Germany

Mario Hoernicke