Machine learning drives system optimization of liquid organic hydrogen storage technology

doi:10.11949/0438-1157.20250289

Abstract

Abstract:

Under the dual challenges of global climate change and energy structure transformation, the development of efficient and clean hydrogen storage and transportation technology has become a key path to achieve the goal of carbon neutrality. Liquid organic hydrogen carriers (LOHCs) technology has become a research hotspot in the field of hydrogen energy storage and transportation due to its high safety and the ability to utilize existing infrastructure. However, the slow dehydrogenation kinetics and strong catalyst dependence limit the industrialization process. In recent years, breakthroughs in machine learning (ML) in new material design, reaction optimization, and data-driven modeling have injected new momentum into LOHCs technology. This paper focuses on the latest research of ML in aspects such as the molecular screening of LOHCs, catalyst design, and reaction condition optimization, points out the current research shortcomings, and prospects the future development directions.

Key words: organic compounds, hydrogen production, machine learning, optimal design, dehydrogenation catalyst

摘要：

在全球气候变化加剧与能源结构转型的双重挑战下，发展高效清洁的氢能储运技术已成为实现碳中和目标的关键路径。液态有机氢载体（LOHCs）储氢技术因其高安全性、可利用现有基础设施等优势成为氢能储运领域的研究热点，但其产业化受制于脱氢动力学缓慢、催化剂依赖性强等问题。近年来机器学习（ML）在新材料设计、反应优化以及数据驱动建模中的突破为LOHCs技术注入了新动能。本文聚焦于ML在LOHCs分子设计与筛选、催化剂设计和反应条件优化等方面的最新研究，提出了目前研究的短板，并展望了未来发展方向。

关键词: 有机化合物, 制氢, 机器学习, 优化设计, 脱氢催化剂

CLC Number:

TQ 031.4

Xiayu FAN, Jianchen SUN, Keying LI, Xinya YAO, Hui SHANG. Machine learning drives system optimization of liquid organic hydrogen storage technology[J]. CIESC Journal, 2025, 76(8): 3805-3821.

范夏雨, 孙建辰, 李可莹, 姚馨雅, 商辉. 机器学习驱动液态有机储氢技术的系统优化[J]. 化工学报, 2025, 76(8): 3805-3821.

Figures/Tables 19

References 71

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储氢体系	目标值	特征值	最优ML模型	精度
甲酸^[50]	甲酸在M@g-C₃N₄上的吸附能	M—N键长、电荷转移量、M-g-C₃N₄内聚能、金属原子d带中心、电负性、电子亲和力、第一电离能、共价半径、荣格半径、d轨道电子数和原子序数等	GBR模型	训练集R²=1，RMSE=0.00 eV；测试集R²=0.92，RMSE=0.07 eV
甲醇	筛选催化剂^[54]	原子序数、原子量、电负性、电离能、电子亲和能、原子半径、离子半径、氧化态、电子构型、价电子数、金属性、非金属性、电导率、热导率、硬度、熔点、沸点、密度、磁性等	PMC-IGM模型	准确率61%，兰德指数97%
甲醇	Cu/ZnO/Al₂O₃的铜表面积^[55]	Cu/Zn、Al%、老化时间、老化温度、pH、沉淀剂、煅烧时间、煅烧温度等	RF分类模型	训练集准确率90.0%，测试集准确率94.7%
MCH^[56]	脱氢中间体的吸附能	配位数、内聚能、电负性以及所有不饱和碳与其最近的金属原子之间的平均键长（d̅_C—Pt/M）	GPR模型	训练集MAE=0.05 eV，验证集MAE=0.13 eV，测试集MAE=0.12 eV
12H-NECZ^[60]	反应的Gibbs自由能	各种二维和三维描述符，包括OpenBabel 描述符、库仑矩阵、Bag of bonds、Fractional buried volumes等	深度神经网络	—

储氢体系	目标值	特征值	最优ML模型	精度
甲酸^[50]	甲酸在M@g-C₃N₄上的吸附能	M—N键长、电荷转移量、M-g-C₃N₄内聚能、金属原子d带中心、电负性、电子亲和力、第一电离能、共价半径、荣格半径、d轨道电子数和原子序数等	GBR模型	训练集R²=1，RMSE=0.00 eV；测试集R²=0.92，RMSE=0.07 eV
甲醇	筛选催化剂^[54]	原子序数、原子量、电负性、电离能、电子亲和能、原子半径、离子半径、氧化态、电子构型、价电子数、金属性、非金属性、电导率、热导率、硬度、熔点、沸点、密度、磁性等	PMC-IGM模型	准确率61%，兰德指数97%
甲醇	Cu/ZnO/Al₂O₃的铜表面积^[55]	Cu/Zn、Al%、老化时间、老化温度、pH、沉淀剂、煅烧时间、煅烧温度等	RF分类模型	训练集准确率90.0%，测试集准确率94.7%
MCH^[56]	脱氢中间体的吸附能	配位数、内聚能、电负性以及所有不饱和碳与其最近的金属原子之间的平均键长（d̅_C—Pt/M）	GPR模型	训练集MAE=0.05 eV，验证集MAE=0.13 eV，测试集MAE=0.12 eV
12H-NECZ^[60]	反应的Gibbs自由能	各种二维和三维描述符，包括OpenBabel 描述符、库仑矩阵、Bag of bonds、Fractional buried volumes等	深度神经网络	—

储氢体系	目标值	特征值	最优ML模型	精度
甲醇	产氢速率^[61]	反应条件（GHSV、温度、S/C、O₂/C、催化剂质量等）、元素以及元素描述符	DTR模型	R²=0.99762 MSE=2.8150×10^-7 MAE=4.6309×10^-5
	技术性能：氢气产率	反应器数量、操作温度、氢气渗透率、膜面积、吹扫气流量、S/C 反应器成本、压缩机成本、人工成本、反应物成本、天然气成本、电力成本	DTR	RMSE=0.00132
	环境性能：二氧化碳排放率		SVR、DTR、GPR	均能较好地拟合数据
	经济性能：单位氢气生产成本^[62]		GPR
	CH₃OH转化率和H₂产率^[63]	进料气体温度、S/C和Reynolds数等	基于反向传播网络的神经网络（NN）模型	H₂产率和CH₃OH转化率的预测误差分别为0.206%和1.004%
甲酸^[64]	甲酸转化率	温度、时间、甲酸浓度、催化剂尺寸、催化剂质量、甲酸钠浓度和溶液体积等	ET模型	RMSE=3.16，R²=0.97，MAE=0.75
DBT/H18-DBT	DBT储氢能力预测	各种反应参数（如底物、催化剂、试剂、添加剂、溶剂、浓度、温度以及反应器类型等）	① HSP-SVM模型^[20] ② HSPS-WFML模型^[69] ③ HSPSML模型^[70]	① HV方法准确率97.0% ② 总体准确率96.40%,误分类率3.60% ③ BR和SCG准确率均为98.70%
DBT/H18-DBT	H18-DBT脱氢反应^[71]	温度、压力、催化剂用量、搅拌速率和反应物浓度等	HPPSML模型	整体准确率89.80%

储氢体系	目标值	特征值	最优ML模型	精度
甲醇	产氢速率^[61]	反应条件（GHSV、温度、S/C、O₂/C、催化剂质量等）、元素以及元素描述符	DTR模型	R²=0.99762 MSE=2.8150×10^-7 MAE=4.6309×10^-5
	技术性能：氢气产率	反应器数量、操作温度、氢气渗透率、膜面积、吹扫气流量、S/C 反应器成本、压缩机成本、人工成本、反应物成本、天然气成本、电力成本	DTR	RMSE=0.00132
	环境性能：二氧化碳排放率		SVR、DTR、GPR	均能较好地拟合数据
	经济性能：单位氢气生产成本^[62]		GPR
	CH₃OH转化率和H₂产率^[63]	进料气体温度、S/C和Reynolds数等	基于反向传播网络的神经网络（NN）模型	H₂产率和CH₃OH转化率的预测误差分别为0.206%和1.004%
甲酸^[64]	甲酸转化率	温度、时间、甲酸浓度、催化剂尺寸、催化剂质量、甲酸钠浓度和溶液体积等	ET模型	RMSE=3.16，R²=0.97，MAE=0.75
DBT/H18-DBT	DBT储氢能力预测	各种反应参数（如底物、催化剂、试剂、添加剂、溶剂、浓度、温度以及反应器类型等）	① HSP-SVM模型^[20] ② HSPS-WFML模型^[69] ③ HSPSML模型^[70]	① HV方法准确率97.0% ② 总体准确率96.40%,误分类率3.60% ③ BR和SCG准确率均为98.70%
DBT/H18-DBT	H18-DBT脱氢反应^[71]	温度、压力、催化剂用量、搅拌速率和反应物浓度等	HPPSML模型	整体准确率89.80%