An online optimization strategy for a fluid catalytic cracking process using a case-based reasoning method based on big data technology-Reference-Cited by-同舟云学术

An online optimization strategy for a fluid catalytic cracking process using a case-based reasoning method based on big data technology

Published:2021 Issue:46 Volume:11 Page:28557-28564
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Ni Peng¹,Liu Bin²,He Ge²^ORCID

Affiliation:

1. China University of Petroleum, Beijing 102249, China

2. Lanzhou Petro of PetroChina Company Limited, Lanzhou 730060, China

Abstract

The case-based reasoning (CBR) method based on big data technology proposed in this study provides a feasible solution for fluid catalytic cracking to achieve online process optimization.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/RA/D1RA03228C

Reference29 articles.

1. Estimation of FCC feed composition from routinely measured lab properties through ANN model

2. Architecture model proposal of innovative intelligent manufacturing in the chemical industry based on multi-scale integration and key technologies

3. Modelling of an Industrial Fluid Catalytic Cracking Unit Using Neural Networks

4. Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data. Part 2: Fluid Catalytic Cracking (FCC) Process

5. Supply Chain Management through Dynamic Model Parameters Optimization

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