CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 496-507.DOI: 10.11949/j.issn.0438-1157.20181082
• Process system engineering • Previous Articles Next Articles
Zhencheng YE(),Huanlan ZHOU,Debao RAO
Received:
2018-09-26
Revised:
2018-10-23
Online:
2019-02-05
Published:
2019-02-05
Contact:
Zhencheng YE
通讯作者:
叶贞成
作者简介:
叶贞成(1977—),男,副研究员,<email>yzc@ecust.edu.cn</email>
基金资助:
CLC Number:
Zhencheng YE, Huanlan ZHOU, Debao RAO. Hybrid modeling and optimization of acetylene hydrogenation process[J]. CIESC Journal, 2019, 70(2): 496-507.
叶贞成, 周换兰, 饶德宝. 乙炔加氢反应过程混合建模与优化[J]. 化工学报, 2019, 70(2): 496-507.
Add to citation manager EndNote|Ris|BibTeX
URL: https://hgxb.cip.com.cn/EN/10.11949/j.issn.0438-1157.20181082
Parameters | Hybrid model | Mechanism model |
---|---|---|
acetylene | 1.706 | 7.040 |
ethylene | 0.025 | 1.119 |
temperature | 0.429 | 0.985 |
Table 1 Prediction error of different models/%
Parameters | Hybrid model | Mechanism model |
---|---|---|
acetylene | 1.706 | 7.040 |
ethylene | 0.025 | 1.119 |
temperature | 0.429 | 0.985 |
Level | Factor | ||||
---|---|---|---|---|---|
NP | β | NS | δ | R | |
1 | 70 | 0.1 | 4 | 0.8 | 0.1 |
2 | 80 | 0.2 | 5 | 0.85 | 0.2 |
3 | 90 | 0.3 | 6 | 0.9 | 0.3 |
4 | 100 | 0.4 | 7 | 0.95 | 0.4 |
Table 2 Factors and levels of orthogonal test
Level | Factor | ||||
---|---|---|---|---|---|
NP | β | NS | δ | R | |
1 | 70 | 0.1 | 4 | 0.8 | 0.1 |
2 | 80 | 0.2 | 5 | 0.85 | 0.2 |
3 | 90 | 0.3 | 6 | 0.9 | 0.3 |
4 | 100 | 0.4 | 7 | 0.95 | 0.4 |
Problem | NP | β | NS | δ | R |
---|---|---|---|---|---|
C07 | 0.150 | 0.095 | 0.095 | 0.095 | 0.095 |
C08 | 3.553 | 1.474 | 3.010 | 1.635 | 3.354 |
C13 | 0.252 | 0.134 | 0.140 | 0.035 | 0.071 |
C15 | 3.688 | 3.588 | 3.588 | 3.688 | 3.588 |
C17 | 0.094 | 0.133 | 0.168 | 0.113 | 0.062 |
C18 | 0.307 | 0.446 | 0.240 | 0.409 | 0.252 |
Table 3 Analysis table of standard deviation
Problem | NP | β | NS | δ | R |
---|---|---|---|---|---|
C07 | 0.150 | 0.095 | 0.095 | 0.095 | 0.095 |
C08 | 3.553 | 1.474 | 3.010 | 1.635 | 3.354 |
C13 | 0.252 | 0.134 | 0.140 | 0.035 | 0.071 |
C15 | 3.688 | 3.588 | 3.588 | 3.688 | 3.588 |
C17 | 0.094 | 0.133 | 0.168 | 0.113 | 0.062 |
C18 | 0.307 | 0.446 | 0.240 | 0.409 | 0.252 |
Dim | Algorithm | T 1 | T 2 | (T 2-T 1)/T 1 |
---|---|---|---|---|
10 | TSDE | 0.01240 | 0.18911 | 14.25080 |
RTSDE | 0.01269 | 0.22084 | 16.40268 | |
30 | TSDE | 0.02741 | 0.21881 | 6.98285 |
RTSDE | 0.02786 | 0.24745 | 7.88191 |
Table 4 Time complexity analysis table
Dim | Algorithm | T 1 | T 2 | (T 2-T 1)/T 1 |
---|---|---|---|---|
10 | TSDE | 0.01240 | 0.18911 | 14.25080 |
RTSDE | 0.01269 | 0.22084 | 16.40268 | |
30 | TSDE | 0.02741 | 0.21881 | 6.98285 |
RTSDE | 0.02786 | 0.24745 | 7.88191 |
Pro | TSDE | εDEag | CO-CLPSO | |||
---|---|---|---|---|---|---|
10 | 30 | 10 | 30 | 10 | 30 | |
C01 | + | + | ~ | ~ | ~ | + |
C02 | + | + | ~ | + | ~ | ~ |
C07 | ~ | + | ~ | + | + | + |
C08 | ~ | + | ~ | + | + | + |
C13 | + | + | ~ | + | ~ | + |
C14 | ~ | + | ~ | + | + | + |
C15 | + | + | - | ~ | - | - |
C16 | + | + | + | ~ | ~ | ~ |
C17 | + | + | + | + | + | + |
C18 | + | + | + | + | + | + |
Table 5 Comparison of best values
Pro | TSDE | εDEag | CO-CLPSO | |||
---|---|---|---|---|---|---|
10 | 30 | 10 | 30 | 10 | 30 | |
C01 | + | + | ~ | ~ | ~ | + |
C02 | + | + | ~ | + | ~ | ~ |
C07 | ~ | + | ~ | + | + | + |
C08 | ~ | + | ~ | + | + | + |
C13 | + | + | ~ | + | ~ | + |
C14 | ~ | + | ~ | + | + | + |
C15 | + | + | - | ~ | - | - |
C16 | + | + | + | ~ | ~ | ~ |
C17 | + | + | + | + | + | + |
C18 | + | + | + | + | + | + |
Pro | TSDE | εDEag | ICTLBO | CO-CLPSO | ||||
---|---|---|---|---|---|---|---|---|
10 | 30 | 10 | 30 | 10 | 30 | 10 | 30 | |
C01 | + | + | - | ~ | + | + | + | + |
C02 | + | + | + | + | + | + | + | + |
C07 | ~ | ~ | + | + | + | + | + | + |
C08 | + | - | + | - | + | + | + | + |
C13 | + | + | ~ | + | + | + | + | + |
C14 | + | ~ | + | + | + | + | + | + |
C15 | + | - | + | ~ | + | ~ | + | + |
C16 | + | + | - | ~ | + | + | ~ | + |
C17 | + | + | + | + | + | + | + | + |
C18 | + | + | + | + | + | + | + | + |
Table 6 Comparison of mean values
Pro | TSDE | εDEag | ICTLBO | CO-CLPSO | ||||
---|---|---|---|---|---|---|---|---|
10 | 30 | 10 | 30 | 10 | 30 | 10 | 30 | |
C01 | + | + | - | ~ | + | + | + | + |
C02 | + | + | + | + | + | + | + | + |
C07 | ~ | ~ | + | + | + | + | + | + |
C08 | + | - | + | - | + | + | + | + |
C13 | + | + | ~ | + | + | + | + | + |
C14 | + | ~ | + | + | + | + | + | + |
C15 | + | - | + | ~ | + | ~ | + | + |
C16 | + | + | - | ~ | + | + | ~ | + |
C17 | + | + | + | + | + | + | + | + |
C18 | + | + | + | + | + | + | + | + |
R1 cycle | R2 cycle | DE | TSDE | RTSDE |
---|---|---|---|---|
300 | 100 | 1.038×105 | 1.104×105 | 1.146×105 |
120 | 1.035×105 | 1.107×105 | 1.145×105 | |
140 | 9.911×104 | 1.062×105 | 1.105×105 | |
320 | 100 | 1.014×105 | 1.114×105 | 1.149×105 |
120 | 1.103×105 | 1.081×105 | 1.132×105 | |
140 | 9.646×104 | 1.103×105 | 1.105×105 | |
340 | 100 | 9.783×104 | 1.108×105 | 1.134×105 |
120 | 9.460×104 | 1.075×105 | 1.115×105 | |
140 | 9.272×104 | 1.045×105 | 1.098×105 |
Table 7 Comparison of algorithms results
R1 cycle | R2 cycle | DE | TSDE | RTSDE |
---|---|---|---|---|
300 | 100 | 1.038×105 | 1.104×105 | 1.146×105 |
120 | 1.035×105 | 1.107×105 | 1.145×105 | |
140 | 9.911×104 | 1.062×105 | 1.105×105 | |
320 | 100 | 1.014×105 | 1.114×105 | 1.149×105 |
120 | 1.103×105 | 1.081×105 | 1.132×105 | |
140 | 9.646×104 | 1.103×105 | 1.105×105 | |
340 | 100 | 9.783×104 | 1.108×105 | 1.134×105 |
120 | 9.460×104 | 1.075×105 | 1.115×105 | |
140 | 9.272×104 | 1.045×105 | 1.098×105 |
ai ,aj | ——反应i、j的失活系数 |
---|---|
Ci | ——i组分的工厂实测浓度值 |
Ci sim | ——模型对i组分的模拟浓度值 |
Cpi | ——气体的比定压热容,kJ·kg-1·K-1 |
E | ——相对误差 |
E a | ——失活活化能,kJ·kmol-1 |
Ei | ——反应i的活化能,kJ·kmol-1 |
Fi | ——气体i的摩尔流率,kmol·h-1 |
g(·) | ——约束条件 |
ΔHj | ——反应j焓变,kJ·kmol-1 |
| ——两段反应器总乙烯流率增量,kmol·h-1 |
Ki | ——气体i的吸附常数,kPa-1 |
ka | ——失活指前因子,kmol·kg-1·h-1·kPa-3 |
k 0 ,i | ——反应i的指前因子,kmol·kg-1·h-1·kPa-3 |
| ——乙烯的质量通量,kg·kmol-1 |
ni | ——反应i的失活级数 |
| ——分别表示乙烯的价格和催化剂再生费用 |
pi | ——气体i的分压,kPa |
R | ——理想气体常数,kJ·kmol-1·K-1 |
ri ,rj | ——反应i、j的速率,kmol·kg-1·h-1 |
S | ——反应器横截面积,m2 |
T | ——反应温度,K |
T 1 | ——CEC2010测试函数求解10000次所需的平均计算时间,s |
T 2 | ——算法对CEC2010测试函数求解10000次所需的总平均计算时间,s |
ΔT max | ——工厂实际温度的最大温升,K |
t first,t second | ——分别表示一段、二段反应器总运行时间,d |
t 1,t 2 | ——分别表示一段、二段反应器当前运行时间,d |
z | ——反应器长度,m |
ρ | ——催化剂填充密度,kg·m-3 |
下角标 | |
in,out | ——分别表示反应器进、出口 |
R1,R2 | ——分别代表一、二段反应器 |
ai ,aj | ——反应i、j的失活系数 |
---|---|
Ci | ——i组分的工厂实测浓度值 |
Ci sim | ——模型对i组分的模拟浓度值 |
Cpi | ——气体的比定压热容,kJ·kg-1·K-1 |
E | ——相对误差 |
E a | ——失活活化能,kJ·kmol-1 |
Ei | ——反应i的活化能,kJ·kmol-1 |
Fi | ——气体i的摩尔流率,kmol·h-1 |
g(·) | ——约束条件 |
ΔHj | ——反应j焓变,kJ·kmol-1 |
| ——两段反应器总乙烯流率增量,kmol·h-1 |
Ki | ——气体i的吸附常数,kPa-1 |
ka | ——失活指前因子,kmol·kg-1·h-1·kPa-3 |
k 0 ,i | ——反应i的指前因子,kmol·kg-1·h-1·kPa-3 |
| ——乙烯的质量通量,kg·kmol-1 |
ni | ——反应i的失活级数 |
| ——分别表示乙烯的价格和催化剂再生费用 |
pi | ——气体i的分压,kPa |
R | ——理想气体常数,kJ·kmol-1·K-1 |
ri ,rj | ——反应i、j的速率,kmol·kg-1·h-1 |
S | ——反应器横截面积,m2 |
T | ——反应温度,K |
T 1 | ——CEC2010测试函数求解10000次所需的平均计算时间,s |
T 2 | ——算法对CEC2010测试函数求解10000次所需的总平均计算时间,s |
ΔT max | ——工厂实际温度的最大温升,K |
t first,t second | ——分别表示一段、二段反应器总运行时间,d |
t 1,t 2 | ——分别表示一段、二段反应器当前运行时间,d |
z | ——反应器长度,m |
ρ | ——催化剂填充密度,kg·m-3 |
下角标 | |
in,out | ——分别表示反应器进、出口 |
R1,R2 | ——分别代表一、二段反应器 |
1 | 涂飞, 青红英, 罗雄麟, 等 . 乙炔加氢反应器的先进控制(Ⅰ): 动态机理模型的建立[J]. 化工自动化及仪表, 2003, 30(1): 20-24. |
Tu F , Qing H Y , Luo X L , et al . Advanced process control of acetylene hydrogenation reactor (Ⅰ): Construct dynamic model[J]. Control and Instruments in Chemical Industry, 2003, 30(1): 20-24. | |
2 | 王松汉, 何细藕 . 乙烯工艺与技术[M]. 北京:中国石化出版社, 2000. |
Wang S H , He X O . Ethylene Process and Technology[M]. Beijing:China Petrochemical Press, 2000. | |
3 | 张万钧 . 扬子乙烯装置技术综览. 第一篇:综合技术[M]. 北京:中国石化出版社, 1997. |
Zhang W J . Technology Overview of Yangzi Ethylene Plant. Chapter 1:Integrated Technology[M]. Beijing:China Petrochemical Press, 1997. | |
4 | 谢府命, 许峰, 梁志珊, 等 . 乙炔加氢反应器全周期操作优化[J]. 化工学报, 2018, 69(3): 1081-1091. |
Xie F M , Xu F , Liang Z S , et al . Operation optimization of acetylene hydrogenation reactor on regeneration cycle[J]. CIESC Journal, 2018, 69(3): 1081-1091. | |
5 | 吴斌, 李绍军, 刘漫丹, 等 . 补偿模糊神经网络在乙炔加氢反应器中的作用[J]. 计算技术与自动化, 2003, 22(2): 307-310. |
Wu B , Li S J , Liu M D , et al . Application of compensated fuzzy neural network in acetylene hydrogenation reactor[J]. Computing Technology and Automation, 2003, 22(2): 307-310. | |
6 | Gobbo R , Soares R P , Lansarin M A , et al . Modeling, simulation and optimization of a front-end system for acetylene hydrogenation reactors[J]. Braz.J. Chem. Eng., 2004, 21(4): 545-556. |
7 | Azizi M , Zolfaghari S A , Mousavi S A , et al . Study on the acetylene hydrogenation process for ethylene production: simulation, modification and optimization[J]. Chemical Engineering Communication, 2013, 200(7): 863-877. |
8 | Caetano R , Lemos M A , Lemos F , et al . Modeling and control of an exothermal reaction[J]. Chemical Engineering Journal, 2014, 238(4): 93-99. |
9 | 田亮, 蒋达, 钱锋 . 乙炔加氢反应系统操作优化策略[J]. 化工学报, 2015, 66(1): 373-377. |
Tian L , Jiang D , Qian F . Operation optimization strategy for acetylene hydrogenation reaction system[J]. CIESC Journal, 2015, 66(1): 373-377. | |
10 | Du W L , Bao C Y , Chen X , et al . Dynamic optimization of the tandem acetylene hydrogenation process[J]. Industrial & Engineering Chemistry Research, 2016, 55(46): 11983-11995. |
11 | 田亮, 蒋达, 钱峰 . 钯金属催化剂上的乙炔工业选择性加氢反应动力学比较[J]. 计算机与应用化学, 2012, 29(9): 1031-1035. |
Tian L , Jiang D , Qian F . Reaction kinetic comparisons for industrial selective hydrogenation of acetylene on palladium catalysts[J]. Computers and Applied Chemistry, 2012, 29(9): 1031-1035. | |
12 | Biegler L T . An overview of simultaneous strategies for dynamic optimization[J]. Chem. Eng. Process., 2007, 46(6): 1043−1053. |
13 | Borodzinski A . Selective hydrogenation of ethyne in ethene-rich streams on palladium catalysts(Part 1): Effect of changes to the catalyst during reaction [J]. Cat. Rev., 2006, 48(9): 91-144. |
14 | Schbib N S , García M A , Gigola C E , et al . Kinetics of front-end acetylene hydrogenation in ethylene production[J]. Ind. Eng. Chem. Res., 1996, 35(5): 1496-1505. |
15 | Albers P , Pietsch J , Parker S F . Poisoning and deactivation of palladium catalysts[J]. J. Molec. Catal. A: Chem., 2001, 173(1/2): 275-286. |
16 | Saeedizad M , Sahebdelfar S , Mansourpour Z . Deactivation kinetics of platinum-based catalysts in dehydrogenation of higher alkanes[J]. Chem. Eng. J., 2009, 154(11): 76-81. |
17 | 田亮, 蒋达, 钱锋 . 催化剂失活条件下的碳二加氢反应器模拟与优化[J]. 化工学报, 2012, 63(1): 185-192. |
Tian L , Jiang D , Qian F . Simulation and optimization of acetylene converter with decreasing catalyst activity[J]. CIESC Journal, 2012, 63(1): 185-192. | |
18 | 刘艳, 任章 . 基于神经网络的混合模型建模方法及应用[J]. 计算机仿真, 2007, 24(2): 45-48. |
Liu Y , Ren Z . A mixed model modeling method based on neural network and its application[J]. Computer Simulation,2007, 24(2): 45-48. | |
19 | Tian L , Jiang D , Qian F , et al . Improve acetylene hydrogenation selectivity using dynamic deactivation estimation[J]. Hydrocarbon Processing, 2015, 94(12): 39-44. |
20 | Näsi N , Alikoski M , White D C . Advanced control of acetylene reactor[J]. Hydrocarbon Process, 1985, 6(2): 57-60. |
21 | Liu Z Z , Wang Y , Yang S X , et al . Differential evolution with a two-stage optimization mechanism for numerical optimization[J]. IEEE T. Evolut. Comput., 2016, 2(12): 3170-3177. |
22 | Wang Y , Cai Z , Zhang Q . Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE T. Evolut. Comput., 2011, 15(1): 55-66. |
23 | 张大斌, 江华, 徐柳怡 . 基于两阶段变异交叉策略的差分进化算法[J]. 计算机工程, 2014, 40(8): 183-189. |
Zhang D B , Jiang H , Xu L Y . Differential evolution algorithm based on two-stage mutation and crossing strategy[J]. Computer Engineering, 2014, 40(8): 183-189. | |
24 | Wang Y , Wang B C , Li H X . Incorporating objective function information into the feasibility rule for constrained evolutionary optimization[J]. IEEE Transactions on Cybernetics, 2016, 46(12): 2938-2852. |
25 | Mallipeddi R , Suganthan P N . Problem definitions and evaluation criteria for the CEC2010 competition on constrains real-parameter optimization[R]. Singapore: Nanyang Technological University, 2010. |
26 | Rao R V , Patel V . An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems[J]. Int.J. Ind. Eng. Comput., 2012, 3(4): 535-560. |
27 | Montgomery D C . Design and Analysis of Experiments[M]. 7th ed. New York: John Wiley and Sons, Inc, 2008. |
28 | Takahama T , Sakai S . Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation[J]. IEEE T. Evolut, Comput., 2010, 5(3): 1-9. |
29 | Yu K , Wang X , Wang Z . Constrained optimization based on improved teaching-learning-based optimization algorithm[J]. Information Science, 2016, 352(12): 61-78. |
30 | Chen X , Du W L , Huaglory T , et al . Dynamic optimization of industrial processes with nonuniform discretization-based control vector parameterization[J]. IEEE T. Autom. Sci. Eng., 2014, 11(4): 1289-1299. |
31 | Liang J J , Shang Z G , Li Z H . Coevolutionary comprehensive learning particle swarm optimizer[J]. IEEE T. Evolut, Comput., 2010, 6(2): 1-8. |
[1] | Xin YANG, Wen WANG, Kai XU, Fanhua MA. Simulation analysis of temperature characteristics of the high-pressure hydrogen refueling process [J]. CIESC Journal, 2023, 74(S1): 280-286. |
[2] | Fei KANG, Weiguang LYU, Feng JU, Zhi SUN. Research on discharge path and evaluation of spent lithium-ion batteries [J]. CIESC Journal, 2023, 74(9): 3903-3911. |
[3] | Song HE, Qiaomai LIU, Guangshuo XIE, Simin WANG, Juan XIAO. Two-phase flow simulation and surrogate-assisted optimization of gas film drag reduction in high-concentration coal-water slurry pipeline [J]. CIESC Journal, 2023, 74(9): 3766-3774. |
[4] | Yue CAO, Chong YU, Zhi LI, Minglei YANG. Industrial data driven transition state detection with multi-mode switching of a hydrocracking unit [J]. CIESC Journal, 2023, 74(9): 3841-3854. |
[5] | Lei XING, Chunyu MIAO, Minghu JIANG, Lixin ZHAO, Xinya LI. Optimal design and performance analysis of downhole micro gas-liquid hydrocyclone [J]. CIESC Journal, 2023, 74(8): 3394-3406. |
[6] | Manzheng ZHANG, Meng XIAO, Peiwei YAN, Zheng MIAO, Jinliang XU, Xianbing JI. Working fluid screening and thermodynamic optimization of hazardous waste incineration coupled organic Rankine cycle system [J]. CIESC Journal, 2023, 74(8): 3502-3512. |
[7] | Chengying ZHU, Zhenlei WANG. Operation optimization of ethylene cracking furnace based on improved deep reinforcement learning algorithm [J]. CIESC Journal, 2023, 74(8): 3429-3437. |
[8] | Gang YIN, Yihui LI, Fei HE, Wenqi CAO, Min WANG, Feiya YAN, Yu XIANG, Jian LU, Bin LUO, Runting LU. Early warning method of aluminum reduction cell leakage accident based on KPCA and SVM [J]. CIESC Journal, 2023, 74(8): 3419-3428. |
[9] | Guoze CHEN, Dong WEI, Qian GUO, Zhiping XIANG. Optimal power point optimization method for aluminum-air batteries under load tracking condition [J]. CIESC Journal, 2023, 74(8): 3533-3542. |
[10] | Wenzhu LIU, Heming YUN, Baoxue WANG, Mingzhe HU, Chonglong ZHONG. Research on topology optimization of microchannel based on field synergy and entransy dissipation [J]. CIESC Journal, 2023, 74(8): 3329-3341. |
[11] | Wentao WU, Liangyong CHU, Lingjie ZHANG, Weimin TAN, Liming SHEN, Ningzhong BAO. High-efficient preparation of cardanol-based self-healing microcapsules [J]. CIESC Journal, 2023, 74(7): 3103-3115. |
[12] | Xiaoling TANG, Jiarui WANG, Xuanye ZHU, Renchao ZHENG. Biosynthesis of chiral epichlorohydrin by halohydrin dehalogenase based on Pickering emulsion system [J]. CIESC Journal, 2023, 74(7): 2926-2934. |
[13] | Weiming SHAO, Wenxue HAN, Wei SONG, Yong YANG, Can CHEN, Dongya ZHAO. Dynamic soft sensor modeling method based on distributed Bayesian hidden Markov regression [J]. CIESC Journal, 2023, 74(6): 2495-2502. |
[14] | Xuejin GAO, Yuzhuo YAO, Huayun HAN, Yongsheng QI. Fault monitoring of fermentation process based on attention dynamic convolutional autoencoder [J]. CIESC Journal, 2023, 74(6): 2503-2521. |
[15] | Zedong WANG, Zhiping SHI, Liyan LIU. Numerical simulation and optimization of acoustic streaming considering inhomogeneous bubble cloud dissipation in rectangular reactor [J]. CIESC Journal, 2023, 74(5): 1965-1973. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||