Forward and reverse problems of methane dehydro-aromatization based on physics-informed neural network

doi:10.16085/j.issn.1000-6613.2023-1392

Chemical Industry and Engineering Progress ›› 2024, Vol. 43 ›› Issue (9): 4817-4823.DOI: 10.16085/j.issn.1000-6613.2023-1392

• Chemical processes and equipment • Previous Articles

Forward and reverse problems of methane dehydro-aromatization based on physics-informed neural network

LI Yimeng¹(), CHEN Yunquan¹, HE Chang²^,³(), ZHANG Bingjian¹^,³, CHEN Qinglin¹^,³

^1.School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510006, Guangdong, China
^2.School of Chemical Engineering and Technology, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
^3.Guangdong Engineering Center for Petrochemical Energy Conservation, Sun Yat-sen University, Guangzhou 510006, Guangdong, China

Received:2023-08-11 Revised:2023-10-02 Online:2024-09-30 Published:2024-09-15
Contact: HE Chang

基于物理信息神经网络的甲烷无氧芳构化反应的正反问题

李依梦¹(), 陈运全¹, 何畅²^,³(), 张冰剑¹^,³, 陈清林¹^,³

^1.中山大学材料科学与工程学院，广东广州 510006
^2.中山大学化学工程与技术学院，广东珠海 519082
^3.广东省石化过程节能工程技术研究中心，广东广州 510006

通讯作者: 何畅
作者简介:李依梦（1999—），女，硕士研究生，研究方向为过程系统工程。E-mail：liym256@mail2.sysu.edu.cn。
基金资助:
广东省自然科学基金(2022A1515010479);国家自然科学基金(22078373)

Abstract

Abstract:

Research on solving the forward and reverse problems of chemical reaction kinetics modeling can help to gain a deeper understanding of reaction mechanisms and reduce experimental costs. This study took the one-dimensional packed bed methane dehydro-aromatization (MDA) as an example and used a physics-informed neural network to couple the chemical reaction mechanism equations into the loss function. In this way, a solution framework for reaction kinetics modeling and parameter inversion was constructed. Firstly, the optimal neural network hyperparameters were determined by solving the forward problem. The results showed that the constructed model had good predictive performance in solving the MDA reaction kinetics model, with training error and extrapolation error L₂ of 0.19% and 0.95%, respectively. Based on this, the rate constants of MDA were inverted using labeled data under 0, 0.1%, and 0.3% Gaussian noise, and the predicted values obtained from training had a relative error within 0.5% of the true values, demonstrating the ability of physics-informed learning to perform inversion for unknown kinetic parameters under low-quality data.

Key words: methane dehydro-aromatization, physics-informed neural network, reaction kinetic model, inverse problem

摘要：

解决化学反应动力学建模的正问题和反问题研究有助于更深地理解反应机理，降低实验成本。本研究以一维填充床甲烷无氧芳构化（MDA）反应为案例，利用物理信息神经网络（PINN）将化学反应机理方程耦合到损失函数中，以此构建动力学建模和参数反演的求解框架。首先，通过正问题求解确定最佳神经网络超参数方案，结果表明构建的正问题模型在求解MDA反应动力学方程上有良好的预测性能，训练和外推的L₂误差分别为0.19%和0.95%。在此基础上，在0、0.1%、0.3%高斯噪声下，利用标签数据反演反应速率常数，训练得到的预测值与真实值相对误差均在0.5%内，体现出了反问题模型在低质量数据下进行未知动力学参数反演的能力。

关键词: 甲烷无氧芳构化, 物理信息神经网络, 反应动力学模型, 反问题

CLC Number:

TQ013.2

LI Yimeng, CHEN Yunquan, HE Chang, ZHANG Bingjian, CHEN Qinglin. Forward and reverse problems of methane dehydro-aromatization based on physics-informed neural network[J]. Chemical Industry and Engineering Progress, 2024, 43(9): 4817-4823.

李依梦, 陈运全, 何畅, 张冰剑, 陈清林. 基于物理信息神经网络的甲烷无氧芳构化反应的正反问题[J]. 化工进展, 2024, 43(9): 4817-4823.

Figures/Tables 7

反应j	ΔH_j /J·mol^-1	E_j /J·mol^-1	k_j /mol·(h·g)^-1	K_P_j /atm
反应速率		$d F C H 4 / d q = - 2 r 1$ $d F C 2 H 4 / d q = r 1 - 3 r 2 - 2 r 3$ $d F C 6 H 6 / d q = r 2 - r 3$ $d F C 10 H 8 / d q = r 3$ $d F H 2 / d q = 2 r 1 + 3 r 2 + 3 r 3$
1	2.089×10⁵	65580	0.188	6.149×10^-5
2	-58428	30840	0.194	1.234×10⁵
3	-41737	-190740	4.620	9432

反应j	ΔH_j /J·mol^-1	E_j /J·mol^-1	k_j /mol·(h·g)^-1	K_P_j /atm
反应速率		$d F C H 4 / d q = - 2 r 1$ $d F C 2 H 4 / d q = r 1 - 3 r 2 - 2 r 3$ $d F C 6 H 6 / d q = r 2 - r 3$ $d F C 10 H 8 / d q = r 3$ $d F H 2 / d q = 2 r 1 + 3 r 2 + 3 r 3$
1	2.089×10⁵	65580	0.188	6.149×10^-5
2	-58428	30840	0.194	1.234×10⁵
3	-41737	-190740	4.620	9432

m	L₂误差
m	n=2	n=4	n=6	n=8
64	0.3284	0.0019	0.0020	0.0032
128	0.3274	0.0019	0.1400	0.0054
256	0.0023	0.0018	0.1690	0.6616

速率常数	真实值	无噪声		含0.1%高斯噪声		含0.3%高斯噪声
速率常数	真实值	预测值	相对误差/%	预测值	相对误差/%	预测值	相对误差/%
k₁	0.188	0.189	0.5	0.188	0	0.184	2.1
k₂	0.194	0.191	1.5	0.205	5.7	0.184	5.2
k₃	4.620	4.623	0.06	4.760	3.0	4.770	3.2

References 23

1	SKULAN Andrew J, BRUNOLD Thomas C, BALDWIN Jeffrey, et al. Nature of the peroxo intermediate of the W48F/D84E ribonucleotide reductase variant: Implications for O₂ activation by binuclear non-heme iron enzymes[J]. Journal of the American Chemical Society, 2004, 126(28): 8842-8855.
2	LIU Wenchi, RALSTON Walter T, MELAET Gérôme, et al. Oxidative coupling of methane (OCM): Effect of noble metal (M = Pt, Ir, Rh) doping on the performance of mesoporous silica MCF-17 supported Mn _x O _y -Na₂WO₄ catalysts[J]. Applied Catalysis A: General, 2017, 545: 17-23.
3	KOSINOV Nikolay, HENSEN Emiel J M. Reactivity, selectivity, and stability of zeolite-based catalysts for methane dehydroaromatization[J]. Advanced Materials, 2020, 32(44): 935-9648.
4	赵清锐, 韦力, 冯英杰, 等. 甲烷氧化偶联制乙烯反应器的研究进展[J]. 石油化工, 2022, 51(7): 815-822.
	ZHAO Qingrui, WEI Li, FENG Yingjie, et al. Research progress of oxidative coupling of methane reactors[J]. Petrochemical Technology, 2022, 51(7): 815-822.
5	CORREDOR E Camilo, CHITTA Pallavi, Milind DEO. Membrane reactor system model for gas conversion to benzene[J]. Fuel, 2016, 179: 202-209.
6	姚琦敏, 江华东. 甲烷无氧直接转化反应动力学研究及设计模拟[J]. 工业催化, 2017, 25(12): 60-63.
	YAO Qimin, JIANG Huadong. Kinetic study and simulation of methane non-oxidative conversion[J]. Industrial Catalysis, 2017, 25(12): 60-63.
7	JEONG Jaehun, HWANG Ahron, KIM Yong Tae, et al. Kinetic modeling of methane dehydroaromatization over a Mo₂C/H-ZSM5 catalyst: Different deactivation behaviors of the Mo₂C and H-ZSM5 sites[J]. Catalysis Today, 2020, 352: 140-147.
8	TRAORE B B, KAMSU-FOGUEM B, TANGARA F. Deep convolution neural network for image recognition[J]. Ecological Informatics, 2018, 48: 257-268.
9	KRIZHEVSKY A, SUTSKEVER Ilya, HINTON Geoffrey E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25(2): 1097-1105.
10	LI Hang. Deep learning for natural language processing: Advantages and challenges[J]. National Science Review, 2018, 5(1): 24-26.
11	TANG Shaoqiang, YANG Yang. Why neural networks apply to scientific computing?[J]. Theoretical and Applied Mechanics Letters, 2021, 11(3): 100242.
12	RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707.
13	RAISSI M, YAZDANI A, KARNIADAKIS G. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations[J]. Science, 2020, 367(6481): 1026-1030.
14	陆至彬, 李依梦, 何畅, 等. 集成分区耦合策略的物理信息神经网络模拟共轭传热过程研究[J]. 化工学报, 2022, 73(12): 5483-5493.
	LU Zhibin, LI Yimeng, HE Chang, et al. Integrating physics-informed neural networks with partitioned coupling strategy for modeling conjugate heat transfer[J]. CIESC Journal, 2022, 73(12): 5483-5493.
15	陆至彬, 瞿景辉, 刘桦, 等. 基于物理信息神经网络的传热过程物理场代理模型的构建[J]. 化工学报, 2021, 72(3): 1496-1503.
	LU Zhibin, QU Jinghui, LIU Hua, et al. Surrogate modeling for physical fields of heat transfer processes based on physics-informed neural network[J]. CIESC Journal, 2021, 72(3): 1496-1503.
16	WU Zhiyong, ZHANG Bingjian, YU Haoshui, et al. Accelerating heat exchanger design by combining physics-informed deep learning and transfer learning[J]. Chemical Engineering Science, 2023, 282: 119285.
17	SAHLI COSTABAL Francisco, YANG Yibo, PERDIKARIS Paris, et al. Physics-informed neural networks for cardiac activation mapping[J]. Frontiers in Physics, 2020, 8: 42.
18	CHEN Yuyao, LU Lu, KARNIADAKIS George Em, et al. Physics-informed neural networks for inverse problems in nano-optics and metamaterials[J]. Optics Express, 2020, 28(8): 11618.
19	ROJAS Carlos J G, BOLDRINI Jos L, BITTENCOURT Marco L. Parameter identification for a damage phase field model using a physics-informed neural network[J]. Theoretical and Applied Mechanics Letters, 2023, 13(3): 100450.
20	Son Ich NGO, Young-Il LIM. Solution and parameter identification of a fixed-bed reactor model for catalytic CO₂ methanation using physics-informed neural networks[J]. Catalysts, 2021, 11(11): 1304.
21	CAI Shengze, WANG Zhicheng, FUEST Frederik, et al. Flow over an espresso cup: Inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks[J]. Journal of Fluid Mechanics, 2021, 915: A102.
22	KARAKAYA Canan, MOREJUDO Selene Hernández, ZHU Huayang, et al. Catalytic chemistry for methane dehydroaromatization (MDA) on a bifunctional Mo/HZSM-5 catalyst in a packed bed[J]. Industrial & Engineering Chemistry Research, 2016, 55(37): 9895-9906.
23	ZHU Y, AL-EBBINNI N, HENNEY R, et al. Extension to multiple temperatures of a three-reaction global kinetic model for methane dehydroaromatization[J]. Chemical Engineering Science, 2018, 177: 132-138.

[1]	WANG Yuqing, DUAN Yufeng, WANG Rui, LIU Xiaoshuo, SHEN Zhen. Experimental and kinetics analysis of ethanol-hydrated calcium-based adsorbents [J]. Chemical Industry and Engineering Progress, 2023, 42(11): 6053-6063.
[2]	HUA Lei，YANG Fusheng， MENG Xiangyu，BAO Zewei，ZHANG Zaoxiao. Progress in kinetic models for hydrogenation/dehydrogenation of metal hydrides [J]. Chemical Industry and Engineering Progree, 2010, 29(3): 413-.

Forward and reverse problems of methane dehydro-aromatization based on physics-informed neural network

基于物理信息神经网络的甲烷无氧芳构化反应的正反问题

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 23

Related Articles 2

Recommended Articles

Metrics

Comments