Title :
Early warning technology for cracking severity based on improved cultural differential algorithm
Author :
Pu, Yunxia ; Liu, Mandan
Author_Institution :
Key Lab. of Adv. Control & Optimization for Chem. Processes, East China Univ. of Sci. & Technol., Shanghai, China
Abstract :
Effective monitoring and early warning for cracking severity are directly related to the ethylene production stability and the overall economic benefits. Elman neural network is used to establish the early warning model of cracking severity, and an improved Cultural Differential Evolution algorithm(ICDE) is proposed to training the model. This hybrid algorithm integrates Cultural algorithm(CA) and Differential Evolution algorithm(DE) together, and increases some measure indicators of evolution direction, to make the algorithm more adaptive. Simulation results show that this method gains accurate warning signals to the cracking working conditions timely and effectively, obtained satisfactory results.
Keywords :
chemical engineering computing; evolutionary computation; neural nets; organic compounds; petrochemicals; production engineering computing; pyrolysis; Elman neural network; cracking working conditions; cultural differential evolution algorithm; early warning technology; ethylene production stability; hybrid algorithm; severity cracking; warning signals; Artificial neural networks; Cultural differences; Furnaces; Next generation networking; Predictive models; Production; Training; Cracking Severity; Cultural Algorithms; Differential Evolution Algorithm; Early Warning; Elman Neural Network; Ethylene;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554552
