Oil dry point prediction calculation method and device, computer equipment and storage medium

Abstract

The invention provides an oil dry point prediction calculation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring sample data; inputting the sample data into a preset neural network algorithm to generate a neural network model with at least one hidden layer; transforming each hidden layer of the neural network model by using a preset matrix to generate an agent model; the prediction module is used for inputting a calculation result of the agent model into a preset modeling program for packaging to obtain a prediction dry point; and inputting a preset component value in the preset modeling program into a prediction module for calculation to obtain a dry point prediction value. After a neural network model is generated by combining a large amount of sample data with an algorithm of a neural network, a corresponding agent model is developed based on parameters generated by the neural network model, and a corresponding online optimization software module is developed based on the agent model, so that accurate calculation and prediction of a dry point value are realized.

Description

technical field [0001] The invention relates to the technical field of oil refining, in particular to an oil dry point prediction calculation method, device, computer equipment and storage medium. Background technique [0002] The "dry point" is when the oil is distilled to the highest vapor phase temperature, which is called the final boiling point or dry point. The "dry point" properties of plant logistics products are usually obtained through on-site testing, while the online optimization system is obtained through physical property calculations within the software. [0003] At present, the commercialized modeling software in the field of continuous reformer online optimization (RTO) generally has the limitation of inaccurate calculation in the calculation of "dry point". The main reason is that the properties of isomers are very different, especially It is because of the process simulation software that after complex fractionation calculations, many physical property ma...

AI Technical Summary

Problems solved by technology

[0003] At present, the commercialized modeling software in the field of continuous reformer online optimization (RTO) generally has the limitation of inaccurate calculation in the calculation of "dry point". The main reason is that the properties of isomers are very different, especially It is because of the process simulation software that after complex fractionation calculations, many physical property matching results are not ideal. Taking the simulation of the reformer as an example, the dry point of the reformed oil generally has a large gap between the calculated value and the measured value of the real value. , the degree of error has lost the meaning of reference and prediction

However, in the process of online optimization, a relatively accurate dry point value is required, so it is necessary to improve the defects of the current process simulation software

Images