US-12625490-B2 - Process network with several plants

US12625490B2US 12625490 B2US12625490 B2US 12625490B2US-12625490-B2

Abstract

A computer implemented method for generating a problem specific representation of a process network to enable controlling or monitoring a process network with at least two interconnected chemical plants, the method comprising the steps of providing a first digital representation of the process network comprising a digital process representation of each plant, its connections to other plants and sensor elements placed in the process network, generating based on the first digital representation a graph structure including vertices representing unit operations, edges linking unit operations representing at least physico-chemical quantities, wherein the edges include edge meta data representing at least physico-chemical quantities, and a measurable tag, generating based on the graph structure a collapsed graph structure including, vertices representing virtual unit operations, edges linking virtual unit operations representing at least physico-chemical quantities, wherein the edges include edge meta data representing observable physico chemical quantities, and their relation to vertices, deriving a set of balance equations from the collapsed graph structure, providing the set of balance equations, and physico- chemical quantities for monitoring and/or controlling operation of a process network is proposed.

Inventors

Robert Pack

Assignees

BASF SE

Dates

Publication Date: 20260512
Application Date: 20210205
Priority Date: 20200207

Claims (12)

1 . A computer implemented method for generating a model representation of a process network with at least two interconnected chemical plants to enable controlling or monitoring the process network, the method comprising: providing a digital representation of the process network comprising a digital process representation of each plant, its connections to other plants realized by mass or energy flow and sensor elements placed in the process network, generating, based on a first digital representation, a graph structure including vertices representing unit operations, edges representing physico chemical quantities, wherein the physico chemical quantities comprise mass, energy and component flows, the edges linking the vertices, wherein the edges include a measurable tag for each of the represented physico chemical quantities, the measurable tag indicating if the physico chemical quantity may be measured in the process network, or if the physico chemical quantity may not be measured; categorizing the physico chemical quantities that may be measured in the process network as observable physico chemical quantities, categorizing physico chemical quantities that may be calculated from balance equations around vertices as observable physico chemical quantities, generating based on the graph structure a collapsed graph structure by collapsing the edges with physico chemical quantities that are not categorized as observable into collapsed vertices, wherein the collapsed graph structure comprises: collapsed vertices representing virtual unit operations, vertices representing unit operations edges representing only observable physico chemical quantities, linking collapsed vertices and/or vertices, deriving a set of balance equations for each mass, energy or component flow around each vertex, wherein the set of balance equations modeling the process network and are usable for monitoring, controlling, production planning, or prediction models, and providing the set of balance equations to a control device, a monitoring device, a production planner device, or a prediction model generator, and performing a stationary test on the observable selected physico chemical quantities to generate a signal if the stationary test reveals that a current state of the process network is not stationary, wherein the signal shuts down a chemical plant or process network.
2 . The method according to claim 1 , wherein the vertices representing unit operations further represent vertex meta data comprising physical quantities linked to the respective unit operation.
3 . The method according to claim 1 , wherein generating a collapsed graph structure comprises generating a collapsed graph for each physico chemical quantity.
4 . The method according to claim 3 , wherein a set of balance equations is derived from the collapsed graph structure and comprises a set of balance equations for each conserved quantity, wherein the conserved quantity is a quantity following a conservation law.
5 . The method according to claim 1 , wherein the collapsing edges comprises selecting at least two vertices that are connected via edges collapsing edges between the at least two vertices, thereby creating a virtual vertex.
6 . The method according to claim 1 , wherein the generating based on the first digital representation of the graph structure further comprises generating a converged graph structure by attributing labels to all physico chemical quantities dependent on whether they are measured physico-chemical quantities, determined physico chemical quantities, measured and determined physico chemical quantities, or physico chemical quantities that are neither measured physico chemical quantities nor determined physico chemical quantities.
7 . The method according to claim 6 , further comprising receiving a trigger signal, and wherein the generation of the converged graph structure is initiated upon evaluation of the trigger signal indicating that a physico chemical quantity may no longer be measured.
8 . A system for generating a problem specific representation of a process network to enable controlling or monitoring a process network with at least two interconnected chemical plants, the system comprising a processor configured for performing the method according to claim 1 , an output interface configured for providing the set of balance equations for monitoring and/or controlling operation of a process network.
9 . A non-transistory computer readable medium that when run on a computer performs the method of claim 1 .
10 . A method for monitoring a process network with at least two plants, the method comprising: receiving a request for at least one process network operation parameter, via an input interface retrieving via the input interface a set of balance equations, wherein the set of balance equations is derived by the method of claim 1 , retrieving historical data related to observable physico chemical quantities and metadata related to the at least one process network operation parameter from a database, receiving current data related to observable physico-chemical quantities and metadata for observable physico-chemical quantities, determining a value for the at least one process network operation parameter by solving the system of balance equations based on the historical data and the current data, providing via an output interface the value of the at least one process network operation parameter, and performing a stationary test on observable selected physico chemical quantities to generate a signal if the stationary test reveals that a current state of the process network is not stationary, wherein the signal shuts down a chemical plant or process network.
11 . A method for controlling a process network with at least two plants, the method comprising: receiving via an input interface a request for at least one optimization objective by specifying at least one process parameter to be optimized, retrieving via the input interface a set of balance equations, wherein the set of balance equations is derived according to the method of claim 1 , retrieving from a database historical data, the historical data related to observable physico chemical quantities and metadata related to the at least one process network operation parameter to be optimized, determining a value for the at least one process network operation parameter to be optimized by solving the system of balance equations providing via an output interface the value of the at least one process network operation parameter to be optimized, and performing a stationary test on observable selected physico chemical quantities to generate a signal if the stationary test reveals that a current state of the process network is not stationary, wherein the signal shuts down a chemical plant or process network.
12 . A method for generating a hybrid model to monitor and/or control a process network with at least two plants connected to each other, the method comprising: retrieving via the input interface a set of balance equations, wherein the set of balance equations is derived according to the method of claim 1 , receiving via an input interface at least one objective specifying at least one process parameter dependency to be trained retrieving via an input interface historical data of the process network with at least two plants connected to each other, training of a hybrid model, including the system of balance equations and a data-driven model based on the historical data and on the least one objective specifying at least one process parameter dependency to be trained, providing the trained hybrid model via an output interface, and performing a stationary test on observable selected physico chemical quantities to generate a signal if the stationary test reveals that a current state of the process network is not stationary, wherein the signal shuts down a chemical plant or process network.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S) The present application is a national stage entry under 35 U.S.C. § 371 of International Application No. PCT/EP2021/052877, filed on Feb. 5, 2021, which claims priority to European Patent Application No. 20156232.9, filed on Feb. 7, 2020, the contents of which are hereby incorporated by reference in their entirety. FIELD The invention relates to a system and a computer implemented method for generating a problem specific representation of a process network to enable monitoring and or controlling a process network with at least two plants. The system further relates to use cases of the problem specific representation. BACKGROUND Chemical production is a highly complex environment. Especially, when two or more production plants are involved. Chemical plants typically include multiple assets to produce the chemical product. Multiple feeds of pure components or mixtures are present and at various stages energy is provided or withdrawn. Multiple sensors are distributed in such plants for monitoring and control purposes and collect data. As such, chemical production is a data heavy environment. However, to date monitoring and controlling interconnected plants is challenging. In process engineering flowsheet simulators include graph structures to simulate chemical plants. Models in such flowsheet simulators are typically built to solve a given problem and cannot be transferred to other problems easily. Specifically, such simulators are static and cannot be easily adjusted. Additionally, the model design is cumbersome and time consuming. Specifically, each block/node used in a flowsheet model hast a largely fixed, though potentially parametrized, number of unknowns, and thus requires a specific number of specifications or additional con-straints/equations. Using graph theory for describing a single plant were proposed by Preisig (Copmuter and Chem-ical Engineering, 33 (2009), 598-604). Preisig et al. SIMS 2004, Copenhagen, Denmark, 23-24 Sep. 2004, pp 413-420 and Computers and Chemical Engineering 33 (2009) 598-604 describe a modeler is built on implementing a physical view of the world. It constructs an abstract process representation in form of a topology with two levels of refinements. First a physical view of the space occupied by the process and its relevant environment is defined, which is the physical topology. The first refinement is seen as a coloring of the topology by adding the species that are present in the plant. Finally, the second refinement adds the variables and equations describing the behavior of the individual components of the topology. The modeler described by Preisig aims to guarantee structurally solvable simulation problems, namely differential algebraic equations of index 1. The modeler allows to generate models for problems, including dynamic simulation, optimization and control design. Nevertheless, building the physical topology of the process is not an automatic process but a design process, which requires an in-depth understanding of the process being modelled. McCabe et al. describe the concept of unit operations in chemical engineering (“unit operations of chemical engineering”, McGraw Hill, October 2004, 7th edition) Existing modelers are static and generate a set of equations that are tailored to the user specified problem. These models are not dynamic in the sense that they are automatically adaptable to e.g. different optimization problems. Additionally, interdependencies between process components or multiple plants are difficult to capture and lead to less robust results. The object of the present invention relates to a system and a method for generating a problem specific representation of two or more interconnected plants to enable controlling or a process network with at least two plants. The system further relates to use cases of the problem specific representation. SUMMARY The proposed solution provides a more flexible approach for process simulation. Specifically, the processes are mapped and prepared in such a way that the model or optimization can be easily customized to the specific needs of a process user. Additionally, accuracy is enhanced by combining classical balance-equation based models with data-driven models that capture e.g. environmental effects not represented in rigorous models based on the laws of physics. The proposed solution is particularly suited for monitoring, planning or controlling processes in plant networks, such as chemical production parks including downstream and upstream plants, energy generation complexes including or refineries. It allows for a more flexible approach to generate network models and to adapt the network model depending on present conditions. For example, if one plant in the network fails the network model can be adapted accordingly and still provide accurate predictions for monitoring or even controlling the plant network. A computer implemented method for generating a model repre