Differential evolution algorithm with self-adaptive strategy and control parameters for P -xylene oxidation process optimization
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| 024 | 7 | 0 | |a 10.1007/s00500-014-1349-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1349-y | ||
| 245 | 0 | 0 | |a Differential evolution algorithm with self-adaptive strategy and control parameters for P -xylene oxidation process optimization |h [Elektronische Daten] |c [Qinqin Fan, Xuefeng Yan] |
| 520 | 3 | |a Considering that the model of the $$p$$ p -Xylene (PX) oxidation reaction process is a hybrid and highly nonlinear model, a differential evolution algorithm with self-adaptive mutation strategy and control parameters (SSCPDE) was proposed to optimize the operating conditions. In SSCPDE, each individual has its own control parameters and mutation strategies that can be self-adaptively adjusted to different evolution phases and various optimization problems. SSCPDE was compared with 6 state-of-the-art DE variants by 38 different types of benchmark functions. Simulation results show that the average performance of SSCPDE is better than the six famous self-adaptive DE algorithms. Finally, the SSCPDE algorithm was used to optimize the five main operating conditions of the PX oxidation reaction process. Optimization results indicate that the production cost, loss of acetic acid and PX combustion of the PX oxidation reaction process are greatly reduced and that SSCPDE performs better than JADE, EPSDE, SaDE, and the optimizer of Aspen Plus and similar to jDE and CoDE. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Process optimization |2 nationallicence | |
| 690 | 7 | |a $$p$$ p -Xylene oxidation process |2 nationallicence | |
| 690 | 7 | |a Differential evolution algorithm |2 nationallicence | |
| 690 | 7 | |a Parameter adaptation |2 nationallicence | |
| 690 | 7 | |a Mutation strategy adaptation |2 nationallicence | |
| 700 | 1 | |a Fan |D Qinqin |u Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, MeiLong Road NO. 130, P.O. BOX 293, 200237, Shanghai, People's Republic of China |4 aut | |
| 700 | 1 | |a Yan |D Xuefeng |u Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, MeiLong Road NO. 130, P.O. BOX 293, 200237, Shanghai, People's Republic of China |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/5(2015-05-01), 1363-1391 |x 1432-7643 |q 19:5<1363 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1349-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1349-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Fan |D Qinqin |u Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, MeiLong Road NO. 130, P.O. BOX 293, 200237, Shanghai, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Yan |D Xuefeng |u Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, MeiLong Road NO. 130, P.O. BOX 293, 200237, Shanghai, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/5(2015-05-01), 1363-1391 |x 1432-7643 |q 19:5<1363 |1 2015 |2 19 |o 500 | ||