WO-2026091270-A1 - CRUDE OIL BLENDING SCHEDULING METHOD AND APPARATUS

WO2026091270A1WO 2026091270 A1WO2026091270 A1WO 2026091270A1WO-2026091270-A1

Abstract

A crude oil blending scheduling method and apparatus. The method comprises: acquiring crude oil blending resource state data; on the basis of the crude oil blending resource state data and a crude oil blending scheduling model, obtaining a crude oil blending scheduling scheme, the crude oil blending scheduling model being obtained by performing training on the basis of crude oil blending sample data and corresponding actual scheduling actions; and in a model training process, optimizing hyperparameters of the model and accelerating model training, and obtaining a model corresponding to the optimized hyperparameters for model training. The apparatus is used to execute the described method.

Inventors

CHEN, SHUAI
Wang, Qinglai
XIE, Hongpeng
WANG, HUA
LIU, JIAN
CUI, ZHENWEI
GUO, Yueming
GAO, Caineng
YANG, HUI
XU, Dingyu
HOU, Shichao

Assignees

昆仑数智科技有限责任公司
中国石油天然气集团有限公司

Dates

Publication Date: 20260507
Application Date: 20241223
Priority Date: 20241030

Claims (17)

A crude oil blending and scheduling method, characterized in that it includes: Obtain crude oil blending resource status data; and Based on the crude oil blending resource status data and the crude oil blending scheduling model, a crude oil blending scheduling scheme is obtained; wherein, the crude oil blending scheduling model is obtained by training based on crude oil blending sample data and corresponding actual scheduling actions; during the training process of the crude oil blending scheduling model, the hyperparameters of the model are optimized and the model training is accelerated, and the model corresponding to the optimized hyperparameters is used for model training.
According to the method of claim 1, the crude oil blending scheduling model is obtained by training based on crude oil blending sample data and corresponding actual scheduling actions, comprising: Obtain crude oil blending sample data and corresponding actual scheduling actions; Initialize the original model and obtain the range of hyperparameter values; Based on the range of hyperparameter values, generate each set of hyperparameters; and The crude oil blending scheduling model is trained based on crude oil blending sample data, corresponding actual scheduling actions, hyperparameters of each group, fitness functions, and the original model; wherein the fitness function is preset.
According to the method of claim 2, the step of training the crude oil blending scheduling model based on crude oil blending sample data and corresponding actual scheduling actions, hyperparameters of each group, fitness functions, and the original model includes: Based on the sample data of this round of crude oil blending and the model to be trained corresponding to each set of hyperparameters, the prediction results of the crude oil blending scheduling action for this round corresponding to each set of hyperparameters are obtained. Based on the prediction results and fitness function of the current round of crude oil blending and scheduling actions corresponding to each set of hyperparameters, the fitness value of the current round corresponding to each set of hyperparameters is obtained; Hyperparameter optimization is performed based on each set of hyperparameters and their corresponding fitness values in the current round, resulting in optimized sets of hyperparameters; and Based on the sample data of crude oil blending in this round and the corresponding actual scheduling actions, a round of model training is carried out on the training models corresponding to each set of optimized hyperparameters.
According to the method of claim 3, the step of optimizing the hyperparameters based on each set of hyperparameters and the fitness of the current round corresponding to each set of hyperparameters to obtain the optimized sets of hyperparameters includes: Based on the fitness value and screening conditions corresponding to each group of hyperparameters in this round, the retained hyperparameters of each group are obtained; Cross-operation is performed based on the retained hyperparameters of each group and the corresponding fitness values of each group to obtain the hyperparameters of the offspring generation; and Mutation operations are performed on the hyperparameters of each group of offspring to obtain the optimized hyperparameters of each group.
According to the method of claim 4, the screening conditions include: If the deviation between the fitness value of a set of hyperparameters in the current round and the optimal fitness value is less than a threshold, then the set of hyperparameters is retained as a set of hyperparameters.
The method according to claim 3 is characterized in that the fitness function is the sum of the tanker's waiting costs at sea, unloading costs, tank inventory costs, and costs incurred in switching the feed to the atmospheric and vacuum distillation unit.
The method according to claim 1, characterized in that it further comprises: Actual crude oil blending and scheduling is carried out according to the aforementioned crude oil blending and scheduling scheme; and If the error between the current actual crude oil blending resource status data and the predicted current crude oil blending resource status data exceeds the error threshold, then a new crude oil blending scheduling plan is obtained based on the current actual crude oil blending resource status data and the crude oil blending scheduling model.
A crude oil blending and scheduling device, characterized in that it comprises: The first acquisition module is used to acquire crude oil blending resource status data; and The prediction module is used to obtain a crude oil blending scheduling scheme based on the crude oil blending resource status data and the crude oil blending scheduling model; wherein, the crude oil blending scheduling model is obtained by training based on crude oil blending sample data and corresponding actual scheduling actions; during the model training process, the hyperparameters of the model are optimized and the model training is accelerated, and the model corresponding to the optimized hyperparameters is used for model training.
The apparatus according to claim 8, characterized in that it further comprises: The second acquisition module is used to acquire crude oil blending sample data and corresponding actual scheduling actions. The initialization module is used to initialize the original model and obtain the range of hyperparameter values; The generation module is used to generate various sets of hyperparameters based on their value ranges; and The training module is used to train the crude oil blending scheduling model based on crude oil blending sample data, corresponding actual scheduling actions, hyperparameters of each group, fitness functions, and the original model; wherein the fitness function is preset.
The apparatus according to claim 9, wherein the training module comprises: The prediction unit is used to obtain the crude oil blending and scheduling scheme for each set of hyperparameters based on the sample data of this round of crude oil blending and the model to be trained for each set of hyperparameters. The acquisition unit is used to obtain the fitness value of each set of hyperparameters in this round based on the current round crude oil blending and scheduling scheme and fitness function corresponding to each set of hyperparameters. The optimization unit is used to optimize hyperparameters based on each set of hyperparameters and the corresponding fitness value in the current round, to obtain the optimized hyperparameters for each set; and The training unit is used to train the model corresponding to each set of optimized hyperparameters in one round based on the sample data of crude oil blending in this round and the corresponding actual scheduling actions.
The apparatus according to claim 10, wherein the optimization unit comprises: The filtering subunit is used to obtain the retained hyperparameters for each group based on the fitness value and filtering conditions corresponding to each group of hyperparameters in this round. The crossover subunit is used to perform crossover operations based on the retained hyperparameters of each group and the fitness values corresponding to the retained hyperparameters of each group to obtain the hyperparameters of each group of offspring; and The mutation operation subunit is used to perform mutation operations on the hyperparameters of each group of offspring to obtain the optimized hyperparameters of each group.
The apparatus according to claim 11 is characterized in that the screening condition includes, if the deviation between the fitness value of a set of hyperparameters in the current round and the optimal fitness value is less than a threshold, then the set of hyperparameters is regarded as a set of retained hyperparameters.
The apparatus according to claim 10 is characterized in that the fitness function is the sum of the tanker's waiting costs at sea, unloading costs, tank inventory costs, and costs incurred in switching the feed to the atmospheric and vacuum distillation unit.
The apparatus according to claim 8, characterized in that it further comprises: The scheduling module is used to perform actual crude oil blending scheduling according to the crude oil blending scheduling scheme; and The judgment module is used to re-obtain the crude oil blending and scheduling scheme based on the current actual crude oil blending and scheduling model after determining that the error between the current actual crude oil blending and scheduling resource status data and the predicted current crude oil blending and scheduling data exceeds the error threshold.
An electronic device includes a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.
A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program/instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.
A computer program product comprising a computer program/instructions, characterized in that, when executed by a processor, the computer program/instructions implement the steps of the method according to any one of claims 1 to 7.

Description

A method and apparatus for blending and scheduling crude oil Related applications This application claims priority to Chinese Patent Application No. 202411537638.9, filed on October 30, 2024, and incorporates the entire contents of the aforementioned patent application as part of this application. Technical Field This application relates to the field of crude oil processing technology, specifically to a crude oil blending and scheduling method and apparatus. Background Technology Crude oil blending is the process of mixing crude oils of different properties in a certain proportion to obtain a blended crude oil that meets specific requirements. With companies increasingly purchasing low-priced and heavy oils, ensuring the stability and efficiency of crude oil blending processing has become an unavoidable issue, thus placing higher demands on crude oil blending scheduling. Currently, crude oil from different sources exhibits significant differences in properties, such as density and viscosity, which directly impact refining processes and product quality. Simultaneously, with the continued growth in global energy demand and the gradual depletion of oil resources, improving crude oil utilization efficiency is crucial. Crude oil blending allows for the mixing and refining of crude oils of different qualities to achieve property homogenization, forming stable blended crude oils. This ensures the stable operation of refinery distillation units, maximizes the utilization of all crude oil resources, and effectively reduces raw material costs. However, current crude oil dispatching schemes often employ rigorous mathematical programming methods, which suffer from problems such as excessively large solution scales, infeasibility, and poor reusability. These methods are ill-suited to the dynamic nature of actual production and fail to effectively fulfill the role of blending and dispatching. Therefore, there is an urgent need to research new crude oil blending and dispatching technologies to more effectively address the challenges of crude oil property differences and resource utilization. Summary of the Invention To address the problems in the prior art, this application provides a crude oil blending and scheduling method and apparatus. Firstly, this application proposes a crude oil blending and scheduling method, including: Obtain data on the status of crude oil blending resources; Based on the crude oil blending resource status data and the crude oil blending scheduling model, a crude oil blending scheduling scheme is obtained; wherein, the crude oil blending scheduling model is obtained by training based on crude oil blending sample data and corresponding actual scheduling actions; during the model training process, the hyperparameters of the model are optimized and the model training is accelerated, and the model corresponding to the optimized hyperparameters is used for model training. Secondly, this application provides a crude oil blending and scheduling device, comprising: The first acquisition module is used to acquire crude oil blending resource status data; The prediction module is used to obtain a crude oil blending scheduling scheme based on the crude oil blending resource status data and the crude oil blending scheduling model; wherein, the crude oil blending scheduling model is obtained by training based on crude oil blending sample data and corresponding actual scheduling actions; during the model training process, the hyperparameters of the model are optimized and the model training is accelerated, and the model corresponding to the optimized hyperparameters is used for model training. Thirdly, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the crude oil blending and scheduling method described in any of the above embodiments. Fourthly, this application provides a computer-readable storage medium storing a computer program/instructions that, when executed by a processor, implement the crude oil blending and scheduling method described in any of the above embodiments. Fifthly, this application provides a computer program product, including a computer program/instructions, which, when executed by a processor, implements the crude oil blending and scheduling method described in any of the above embodiments. The crude oil blending and scheduling method and apparatus provided in this application acquire crude oil blending resource status data; based on the crude oil blending resource status data and the crude oil blending and scheduling model, a crude oil blending and scheduling scheme is obtained; the crude oil blending and scheduling model is obtained by training based on crude oil blending sample data and corresponding actual scheduling actions; during the model training process, the hyperparameters of the model are optimized and the model training is accelerated, and the mo