JP-2026074528-A - Plant design support device, inference device, machine learning device, plant design support method, inference method, and machine learning method

JP2026074528AJP 2026074528 AJP2026074528 AJP 2026074528AJP-2026074528-A

Abstract

[Problem] To provide a plant design support device that enables the simplification and efficiency of plant design work. [Solution] The plant design support device 4 comprises a data acquisition unit 400 that acquires input data, and a data generation unit 401 that generates output data for the input data acquired by the data acquisition unit 400 by inputting the input data into a learning model 14. The learning model 14 is a trained model that has learned the correlation between input data and output data by machine learning. The input data includes information 11A related to the design requirements required for a newly constructed plant, and the output data is information that can reproduce the three-dimensional shape of each component constituting the plant, and includes plant design information 12 in which the processes realized by each component are recorded in relation to the three-dimensional shape. [Selection Diagram] Figure 6

Inventors

金丸剛久

Assignees

ブラウンリバース株式会社

Dates

Publication Date: 20260507
Application Date: 20241021

Claims (14)

A data acquisition unit that acquires input data, The system includes a data generation unit that inputs the input data acquired by the data acquisition unit into a learning model and generates output data for said input data, The aforementioned learning model, This is a trained model that has learned the correlation between the input data and the output data using machine learning. The aforementioned input data is This includes information on the design requirements for newly constructed plants. The output data is, Information capable of reproducing the three-dimensional shape of each component constituting the plant, including plant design information in which the processes realized by each component are recorded in relation to the three-dimensional shape. Plant design support system.
The information regarding the design requirements included in the input data is: Information regarding the raw materials of the aforementioned plant, Information relating to the products of the aforementioned plant, Information relating to the ancillary equipment of the plant, information relating to the process flow of the process, and At least one piece of information relating to standards or criteria applicable to the aforementioned plant, The plant design support device according to claim 1.
The aforementioned input data is Information regarding the design concept of the aforementioned plant, Information regarding the external conditions of the aforementioned plant, Information regarding the environmental requirements of the aforementioned plant, and Further including at least one piece of information relating to the cost requirements of the plant, The plant design support device according to claim 1.
The information regarding the design concept included in the input data is, Information relating to the design life of the aforementioned plant, Information regarding the design margin of the aforementioned plant, Information regarding the safety factor of the aforementioned plant, Information regarding the operating rate of the aforementioned plant, and At least one piece of information relating to the turndown requirements of the aforementioned plant, The plant design support device according to claim 3.
The information regarding the external conditions included in the input data is, Information regarding the site conditions of the construction site of the aforementioned plant, Information regarding the weather conditions at the construction site of the aforementioned plant, Information regarding the geological conditions of the construction site of the aforementioned plant, and At least one piece of information relating to the marine conditions of the sea area surrounding the plant, The plant design support device according to claim 3.
The information regarding the environmental requirements included in the input data is: Information regarding the pollution control requirements of the aforementioned plant, Information regarding noise regulations for the aforementioned plant, and At least one piece of information relating to the explosion-proof requirements of the aforementioned plant, The plant design support device according to claim 3.
The information regarding the cost requirements included in the input data is: Information relating to the economic useful life of the plant; information relating to the discount rate of the plant; information relating to the capital investment of the plant; At least one piece of information relating to the operating costs of the aforementioned plant, The plant design support device according to claim 3.
The data generation unit, As a design proposal for the aforementioned plant, multiple pieces of plant design information are generated. The plant design support device according to any one of claims 1 to 7.
The system includes a simulation processing unit that performs simulations of the process based on multiple plant design information and outputs the results of the simulations for each of the design proposals. The plant design support device according to claim 8.
An inference device comprising memory and a processor, The aforementioned processor, A data acquisition process that obtains input data including information on the design requirements for a newly constructed plant, When the input data is acquired by the data acquisition process, an inference process is performed on the input data to infer output data which includes plant design information that is capable of reproducing the three-dimensional shape of each component constituting the plant, and in which the processes realized by each component are recorded in relation to the three-dimensional shape. Reasoning device.
A training data acquisition unit that acquires multiple sets of training data consisting of input data and output data, A machine learning unit uses multiple sets of the training data acquired by the training data acquisition unit to train a learning model on the correlation between the input data and the output data using machine learning. The machine learning unit has a memory unit that stores the learning model in which the correlation has been learned, The aforementioned input data is This includes information on the design requirements for newly constructed plants. The output data is, Information capable of reproducing the three-dimensional shape of each component constituting the plant, including plant design information in which the processes realized by each component are recorded in relation to the three-dimensional shape. Machine learning device.
A computer-based plant design support method, The data acquisition process involves obtaining input data, The system includes a data generation step which generates output data for the input data obtained in the data acquisition step by inputting the input data into a learning model, The aforementioned learning model, This is a trained model that has learned the correlation between the input data and the output data using machine learning. The aforementioned input data is This includes information on the design requirements for newly constructed plants. The output data mentioned above is: Information capable of reproducing the three-dimensional shape of each component constituting the plant, including plant design information in which the processes realized by each component are recorded in relation to the three-dimensional shape. Plant design support methods.
An inference method performed by an inference device comprising memory and a processor, The aforementioned processor, A data acquisition process that obtains input data including information on the design requirements for a newly constructed plant, When the input data is acquired by the data acquisition process, an inference process is performed on the input data to infer output data which includes plant design information that is capable of reproducing the three-dimensional shape of each component constituting the plant, and in which the processes realized by each component are recorded in relation to the three-dimensional shape. Reasoning method.
A machine learning method performed by a computer, The training data acquisition process involves acquiring multiple sets of training data consisting of input data and output data, A machine learning step in which a learning model learns the correlation between the input data and the output data using machine learning, using multiple sets of the training data acquired in the training data acquisition step, The system includes a trained model storage step, which stores the trained model, which has learned the correlation relationship through the machine learning step, in a storage unit. The aforementioned input data is This includes information on the design requirements for newly constructed plants. The output data mentioned above is: Information capable of reproducing the three-dimensional shape of each component constituting the plant, including plant design information in which the processes realized by each component are recorded in relation to the three-dimensional shape. Machine learning methods.

Description

This invention relates to a plant design support device, an inference device, a machine learning device, a plant design support method, an inference method, and a machine learning method. When a new plant is designed by a designer, the specifications, arrangement, and connection relationships of each component of the plant are determined to meet the required design requirements. As a result, a wide variety of deliverables are created, such as equipment lists, instrument lists, layout drawings, specifications, and requirements documents, as described in Patent Document 1, for example. Special Publication No. 2013-514597 This is an overall diagram showing an example of plant design support system 1 and plant 10.This is a data configuration diagram showing an example of a plant management database 20.This is a data configuration diagram showing an example of a plant management database 20.This is a data configuration diagram showing an example of a plant management database 20.This is a block diagram showing an example of machine learning device 3.This is an explanatory diagram showing an example of training data 13 and a training model 14.This is a block diagram showing an example of a plant design support system 4.This is a functional diagram illustrating an example of the plant design support device 4.This is a hardware configuration diagram showing an example of computer 900.This flowchart shows an example of a machine learning method using machine learning device 3.This is a flowchart showing an example of a plant design support method using the plant design support device 4.This is a screen configuration diagram showing an example of the plant design support input screen 15.This is a screen configuration diagram showing an example of the plant design support output screen 16. The following describes embodiments for carrying out the present invention with reference to the drawings. The following sections schematically show the scope necessary for explaining the objectives of the present invention, and primarily describe the scope necessary for explaining the relevant parts of the present invention. Sections omitted from the explanation will be based on prior art. (Configuration of Plant Design Support System 1) Figure 1 is an overall diagram showing an example of a plant design support system 1 and a plant 10. The plant design support system 1 functions as a system to support the design of a newly constructed plant 10 and generate design proposals. The plant 10 is any plant, such as a natural gas plant, an oil refinery, a chemical processing plant, a power plant, or a steelmaking plant, and is not limited to these examples. Plant 10 is composed of multiple components, each component performing a predetermined process. Plant 10 includes, for example, various devices 100 for processing any fluid such as gas, liquid, or fluid powder; piping 101 connecting the devices 100 to form fluid pathways; various instruments (not shown) consisting of flow sensors, pressure sensors, temperature sensors, etc.; and various controllers (not shown) consisting of valves, pumps, compressors, controllers, etc. The devices 100 include, for example, towers, tanks, and heat exchangers for performing reactions, distillation, extraction, absorption, washing, flow rate adjustment, temperature adjustment, etc. The plant design support system 1 comprises, as its main components, a plant management device 2 for managing various information about the plant 10, a machine learning device 3 for performing machine learning on a learning model 14, a plant design support device 4 for assisting in the design of the plant 10 using the learning model 14, an administrator terminal device 5A for use by the administrator of the plant design support system 1, and a designer terminal device 5B for use by the designer of the plant 10. Each device 2 to 5B is, for example, composed of a general-purpose or dedicated computer (see Figure 7 below) and connected to a wired or wireless network 6, enabling the mutual transmission and reception of various data. The number of each device 2 to 5B and the connection configuration of the network 6 are not limited to the example in Figure 1 and may be changed as appropriate. The plant management device 2 is a device that manages various types of information related to plant 10 as a database, and includes a plant management database 20. The plant management database 20 stores information 11 (details described later) regarding various requirements for plant 10, as well as plant design information 12 (details described later) designed based on the information 11 regarding various requirements. The plant management database 20 registers and stores information 11 and 12 regarding multiple plants 10 to be managed; the details of the data structure will be described later. The machine learning device 3 is the device that operates as the main component in the learning phase of machine learning. For example, the machine lea