公开(公告)号：US20220301661A1

公开(公告)日：2022-09-22

申请号：US17631080

申请日：2020-12-02

Applicant: EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Wenli DU ,  Minglei YANG ,  Weimin ZHONG ,  Feng QIAN ,  Zhi LI

IPC: G16C20/30 ,  G16C20/10 ,  G16C20/70

Abstract: A method and system for predicting products of an aromatic hydrocarbon isomerization production process are provided. The method includes generating multiple initial sample points, using a mechanism the actual output response values of all of the initial sample points; establishing an radial basis function (RBF) model according to the initial sample points and actual output response values thereof; using a particle swarm optimization (PSO) algorithm to find expected deviation between nearest neighbors as well as the sampling point having the largest sparsity product, using mechanism model to calculate the output response value of said sampling point on RBF model, adding output response value into the sample points, and reconstructing the surrogate model; repeating the previous step until the upper limit for the number of sample points is reached to obtain a final RBF model; and using the RBF model to predict product yield of aromatic hydrocarbon isomerization.

公开(公告)号：US20250155872A1

公开(公告)日：2025-05-15

申请号：US18833310

申请日：2023-01-13

Applicant: EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Weimin ZHONG ,  Jian LONG ,  Wenli DU ,  Feng QIAN ,  Minglei YANG

IPC: G05B19/4155

Abstract: The present invention provides a multi-objective optimization method and a device for catalytic cracking process, and a computer-readable storage medium. The multi-objective optimization method comprises following steps: determining a plurality of optimization objectives, a plurality of process decision variables corresponding to the plurality of optimization objectives, and a constraint range of each of the process decision variables; determining an objective function according to the plurality of optimization objectives and the plurality of process decision variables; adjusting a value of each of the process decision variables within the constraint range by SPEA2 algorithm, which improves filial generation evolution process through a path-based reproduction operator, thereby determining an operation data of the objective function on each of the process decision variables; determining an optimization objective value of each of the optimization objectives according to the operation data of each of the process decision variables; and determining the operation data as guide values of the plurality of process decision variables, corresponding to the plurality of optimization objectives, according to an optimal optimization objective value solution set.