Green Chemical Engineering最新文献

英文中文

Machine learning models coupled with ionic fragment σ-profiles to predict ammonia solubility in ionic liquids 结合离子碎片σ-谱的机器学习模型预测氨在离子液体中的溶解度

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-08-22 DOI: 10.1016/j.gce.2024.08.005

Kaikai Li , Yuesong Zhu , Sensen Shi , Yongzheng Song , Haiyan Jiang , Xiaochun Zhang , Shaojuan Zeng , Xiangping Zhang

Emitting NH₃ into the atmosphere leads to significant air pollution, while NH₃ itself serves as an essential component for fertilizers and refrigerants in industry. Thus, recovering and reusing NH₃ is highly valuable. Ionic liquids (ILs) have shown great potential for NH₃ capture, where the accurate prediction of solubility is a critical point for selecting ILs and designing a separation process. This work combined the Ionic Fragment Contribution (IFC) strategy with machine learning (ML) to develop four models (IFC-ML) to predict NH₃ solubility in ILs. A dataset containing 785 solubility data points, covering 10 cations and 10 anions, was collected. From this dataset, the S1–S6 descriptors based on the IFC method were used as inputs for the ML models, together with temperature (T) and pressure (P). Among the models, the IFC-GBR model was recommended for predicting NH₃ solubility in ILs due to its higher coefficient of determination (R²) of 0.9945 and lower mean squared error (MSE) of 0.0003 than the others. Additionally, in comparison with previous conductor-like screening model for real solvents (COSMO-RS) and extreme learning machine (ELM) methods, the IFC-GBR (gradient boosting regressor) method showed a more accurate prediction of the NH₃ solubility in ILs over a wider range of temperatures and pressures, providing additional chemical insights into IL-NH₃ system that cations played a more important role for NH₃ solubility. These results highlighted the developed IFC-GBR model offered valuable insights for helping guide the process design of absorbing NH₃ through IL-based technology.

向大气中排放NH3会导致严重的空气污染，而NH3本身是工业中肥料和制冷剂的重要成分。因此，NH3的回收和再利用是非常有价值的。离子液体具有捕获NH3的巨大潜力，其溶解度的准确预测是选择离子液体和设计分离工艺的关键。这项工作将离子片段贡献（IFC）策略与机器学习（ML）相结合，开发了四个模型（IFC-ML）来预测NH3在il中的溶解度。收集了一个包含785个溶解度数据点的数据集，涵盖10个阳离子和10个阴离子。基于IFC方法的S1-S6描述符与温度(T)和压力(P)一起作为ML模型的输入。在这些模型中，IFC- gbr模型具有较高的决定系数（R2）（0.9945）和较低的均方误差（MSE）（0.0003），因此被推荐用于预测NH3在il中的溶解度。此外，与之前的真实溶剂类导体筛选模型（cosmos - rs）和极限学习机（ELM）方法相比，IFC-GBR（梯度增强回归器）方法在更大的温度和压力范围内更准确地预测了NH3在IL-NH3体系中的溶解度，为IL-NH3体系提供了更多的化学见解，即阳离子对NH3溶解度起着更重要的作用。这些结果表明，开发的IFC-GBR模型为帮助指导利用IL-based技术吸收NH3的工艺设计提供了有价值的见解。

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引用次数: 0

Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data 利用迁移学习和标记数据的物理信息机器学习建立变压吸附过程模型

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-08-14 DOI: 10.1016/j.gce.2024.08.004

Zhiqiang Wu , Yunquan Chen , Bingjian Zhang , Jingzheng Ren , Qinglin Chen , Huan Wang , Chang He

Pressure swing adsorption (PSA) modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior. This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process. To approximate the latent solutions of partial differential equations (PDEs) in the specific steps of pressurization, adsorption, heavy reflux, counter-current depressurization, and light reflux, the system's network representation is decomposed into five lightweight sub-networks. On this basis, we propose a parameter-based transfer learning (TL) combined with domain decomposition to address the long-term integration of periodic PDEs and expedite the network training process. Moreover, to tackle challenges related to sharp adsorption fronts, our method allows for the inclusion of a specified amount of labeled data at the boundaries and/or within the system in the loss function. The results show that the proposed method closely matches the outcomes achieved through the conventional numerical method, effectively simulating all steps and cyclic behavior within the PSA processes.

变压吸附（PSA）的建模一直是一项具有挑战性的任务，因为它具有很强的动态和循环特性。本研究提出了一种系统的物理信息机器学习方法，该方法集成了迁移学习和标记数据，以构建PSA过程的时空模型。为了逼近加压、吸附、重回流、逆流减压和轻回流等具体步骤的偏微分方程（PDEs）的潜在解，将系统的网络表示分解为5个轻量级子网络。在此基础上，我们提出了一种基于参数的迁移学习（TL）与域分解相结合的方法来解决周期性偏微分方程的长期集成问题，加快了网络的训练过程。此外，为了解决与尖锐吸附锋相关的挑战，我们的方法允许在损失函数的边界和/或系统内包含指定数量的标记数据。结果表明，该方法与传统数值方法的结果非常接近，可以有效地模拟PSA过程中的所有步骤和循环行为。

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引用次数: 0

Deep learning-based prediction of velocity and temperature distributions in metal foam with hierarchical pore structure 基于深度学习的分层孔结构金属泡沫中速度和温度分布预测

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-08-11 DOI: 10.1016/j.gce.2024.08.003

Yixiong Lin , Zhengqi Wu , Shiqi You , Chen Yang , Qinglian Wang , Wang Yin , Ting Qiu

Constrained by the substantial computational time required for numerical simulation, a deep learning technique is applied to investigate fluid flow and heat transfer processes in metal foam with a hierarchical pore structure. This work adopted a 3D convolutional neural network (CNN) combining U-Net architecture to predict velocity and temperature distributions, alongside corresponding permeability and overall heat transfer coefficient. This approach demonstrates excellent capability in intricate image segmentation. The training sets were acquired by lattice Boltzmann method (LBM) simulations. The CNN model, trained on a substantial amount of data, demonstrates remarkable precision, exhibiting mean relative errors of 0.57% for permeability prediction and 2.27% for overall heat transfer coefficient prediction. Moreover, in CNN prediction, a broader range of structure parameters and boundary conditions beyond those in the training set was used to evaluate the practicability of the trained CNN model. In contrast to numerical simulation, the CNN model economizes approximately 95.41% and 99.57% of computational time for velocity and temperature distribution prediction, respectively, providing a novel approach for exploring transport processes in metal foam with hierarchical pore structure.

由于数值模拟需要大量的计算时间，应用深度学习技术研究了具有分层孔结构的金属泡沫中的流体流动和传热过程。这项工作采用了3D卷积神经网络（CNN）结合U-Net架构来预测速度和温度分布，以及相应的渗透率和总传热系数。该方法在复杂的图像分割中表现出优异的性能。通过晶格玻尔兹曼方法（LBM）模拟获得训练集。CNN模型经过大量数据的训练，显示出显著的精度，渗透率预测的平均相对误差为0.57%，总传热系数预测的平均相对误差为2.27%。此外，在CNN预测中，除了训练集的结构参数和边界条件之外，还使用了更广泛的结构参数和边界条件来评估训练后的CNN模型的实用性。与数值模拟相比，CNN模型在速度和温度分布预测上分别节省了约95.41%和99.57%的计算时间，为探索具有分层孔隙结构的金属泡沫中的输运过程提供了一种新的方法。

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引用次数: 0

Establishing a generalized model for accurate prediction of higher heating values of substances with large ash fractions 建立了准确预测大灰分物质高热值的广义模型

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-08-05 DOI: 10.1016/j.gce.2024.08.002

Peng Jiang , Lin Li , Han Lin , Tuo Ji , Liwen Mu , Yuanhui Ji , Xiaohua Lu , Jiahua Zhu

The higher heating value (HHV) of biomass is a crucial property for design calculations and numerical simulations in bioenergy utilization. However, existing models for HHV prediction faced challenges in terms of predictive accuracy and generalization capability across various solid waste types, especially for those with high ash content. This work proposed a novel HHV prediction model based on its reduction degree (D_R) and ash content (C_ash). First, ultimate analysis of biomass was applied to establish the calculation method of D_R; then, the correlation between D_R, C_ash, and HHV was analyzed using the Pearson Correlation Coefficient; subsequently, the HHV = f (D_R, C_ash) model was developed using regression analysis. Furthermore, the accuracy was compared to previous literature in terms of correlation coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE). Results revealed that this model provided attractive accuracy with R² = 0.854, RMSE = 0.900, and MAE = 0.773 within a wide range of ash content from 0 to 83.32 wt%. Even higher accuracy was achieved with this model in predicting the HHV of coal, biochar, and bio-oil, with R² of 0.961, 0.989, and 0.939, respectively. Conclusively, this work proposed the use of D_R for HHV estimation, which was not only a simple and accurate approach but also widely applicable to various fuels.

生物质较高的热值（HHV）是生物能源利用设计计算和数值模拟的重要特性。然而，现有的HHV预测模型在各种固体废物类型，特别是高灰分固体废物的预测精度和泛化能力方面面临挑战。本文提出了一种基于还原度（DR）和灰分（Cash）的HHV预测模型。首先，采用生物量的极限分析，建立DR的计算方法；利用Pearson相关系数分析DR、Cash与HHV的相关性；随后，利用回归分析建立HHV = f （DR, Cash）模型。此外，在相关系数（R2）、均方根误差（RMSE）和平均绝对误差（MAE）方面与以往文献的准确性进行比较。结果表明，该模型在灰分0 ~ 83.32 wt%范围内具有较好的预测精度，R2 = 0.854, RMSE = 0.900, MAE = 0.773。该模型对煤、生物炭和生物油的HHV预测精度更高，R2分别为0.961、0.989和0.939。最后，本工作提出了将DR用于HHV估算的方法，这不仅是一种简单准确的方法，而且广泛适用于各种燃料。

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引用次数: 0

Oxoammonium salt mediated conversion of cyclohexylamine toward cyclohexanone with water as the oxygen source 氧铵盐介导环己胺向环己酮的转化，以水为氧源

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-08-02 DOI: 10.1016/j.gce.2024.08.001

Yuting Ruan , Yongtao Wang , Jia Yao , Haoran Li

Cyclohexylamine is a key byproduct during the production of cyclohexanone oxime, which is an important bulk chemical in material industry. Here we report a highly efficient approach to oxidize cyclohexylamine toward cyclohexanone with oxoammonium salt as the oxidant and water as the oxygen source, which has non-involvement of metal catalyst. The obtained cyclohexanone is an important raw material for both cyclohexanone oxime and adipic acid production. On basis of control experiments, mass spectrometry, and product analysis, the essential role of water as oxygen source and the reaction mechanism were elucidated. Moreover, the recycling of the oxoammonium salt succeeded to convert another proportion of the substrate. These findings offer new insights and methods for the oxidative conversion of cyclohexylamine.

环己胺是原料工业中重要的大宗化学品环己酮肟生产过程中的重要副产物。本文报道了一种以氧铵盐为氧化剂，水为氧源，无金属催化剂参与的环己胺氧化制环己酮的高效方法。所得环己酮是生产环己酮肟和己二酸的重要原料。通过对照实验、质谱分析和产物分析，阐明了水作为氧源的重要作用和反应机理。此外，氧铵盐的回收成功地转化了另一比例的底物。这些发现为环己胺的氧化转化提供了新的见解和方法。

引用次数: 0

Screening HFC/HFO and ionic liquid for absorption refrigeration at the atomic scale by the prediction model of machine learning 利用机器学习预测模型在原子尺度上筛选用于吸收制冷的 HFC/HFO 和离子液体

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-07-23 DOI: 10.1016/j.gce.2024.07.004

Jianchun Chu , Maogang He , Georgios M. Kontogeorgis , Xiangyang Liu , Xiaodong Liang

Absorption refrigeration is a highly effective method for utilizing renewable energy, as it can be driven by low-grade heat sources such as industrial waste heat, solar energy, and geothermal energy. The development of new working pairs, particularly hydrofluorocarbon/hydrofluoroolefin refrigerants combined with ionic liquids, has been pivotal in enhancing the cooling efficiency of absorption refrigeration systems. These systems rely on the solubility difference between the generator and absorber, making solubility a crucial factor in determining their efficiency. In this context, we have established an advanced solubility estimation model. This model employs the Attention E(n)-equivariant Graph Neural Network (AEGNN) applied to disconnected graphs, enabling comprehensive learning from both topological and Euclidean structural information. Our atomic-scale model demonstrates significantly higher accuracy than traditional group contribution methods, with an average absolute deviation of 0.003 mol/mol from experimental data. Moreover, it encompasses a much broader range of working pairs. Through extensive screening, we have identified working pairs with high estimated solubility differences. Compared to the high-efficiency working pair identified in the literature, the best-screened working pairs exhibit an improvement in solubility differences by more than 0.3 mol/mol under common operating conditions.

吸收式制冷可以利用工业废热、太阳能、地热能等低品位热源，是一种高效利用可再生能源的方法。新型工作对的开发，特别是氢氟烃/氢氟烯烃制冷剂与离子液体的结合，对提高吸收式制冷系统的冷却效率至关重要。这些系统依赖于发生器和吸收器之间的溶解度差异，使溶解度成为决定其效率的关键因素。在此背景下，我们建立了一个先进的溶解度估计模型。该模型将注意力E(n)-等变图神经网络（aegn）应用于断开图，可以从拓扑和欧几里得结构信息中全面学习。我们的原子尺度模型与实验数据的平均绝对偏差为0.003 mol/mol，其精度明显高于传统的群体贡献方法。此外，它涵盖了更广泛的工作组合。通过广泛的筛选，我们已经确定了具有高估计溶解度差异的工作对。与文献中发现的高效工作对相比，在普通操作条件下，最佳筛选的工作对的溶解度差异改善了0.3 mol/mol以上。

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引用次数: 0

Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms 基于三种人工智能算法开发可解释的 QSPR 模型，用于预测辛醇-水分配系数

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-07-22 DOI: 10.1016/j.gce.2024.07.003

Ao Yang , Shirui Sun , Lu Qi , Zong Yang Kong , Jaka Sunarso , Weifeng Shen

This study aims to significantly improve existing quantitative structure-property relationship (QSPR) models for predicting the octanol-water partition coefficient (K_OW). This is because accurate predictions of K_OW are crucial for assessing the environmental behavior and bioaccumulation potential of chemicals. Previous models have reported determination coefficient (R²) values between 0.9451 and 0.9681, and this research seeks to exceed these benchmarks. Three machine learning (ML) models are explored, i.e., feed-forward neural networks (FNN), extreme gradient boosting (XGBoost), and random forest (RF). Using a dataset of 14,610 solvents (14,580 after data cleaning) and 21 molecular descriptors derived from SMILES representations, we rigorously evaluate these models based on R², mean absolute error (MAE), root mean squared error (RMSE), and mean relative error (MRE). Notably, the best model developed, the XGBoost-based QSPR, demonstrated exceptional performance, exhibiting an impressive R² value of 0.9772, surpassing benchmarks set by prior research models. Additionally, shapley additive explanation (SHAP) analysis is also employed for model interpretation, and it is revealed that the top five influential input features include SMR_VSA8, SMR_VSA3, Kappa2, HeavyAtomCount, and fr_furan. This study not only sets a new benchmark for K_OW prediction accuracy but also enhances the interpretability of QSPR models.

本研究旨在显著改进现有的定量构性关系（QSPR）模型预测辛醇-水分配系数（KOW）。这是因为准确的KOW预测对于评估化学品的环境行为和生物积累潜力至关重要。以前的模型报告的决定系数（R2）值在0.9451和0.9681之间，本研究寻求超越这些基准。探索了三种机器学习（ML）模型，即前馈神经网络（FNN），极端梯度增强（XGBoost）和随机森林（RF）。使用14,610种溶剂的数据集（数据清洗后为14,580种）和21种来自SMILES表示的分子描述符，我们基于R2、平均绝对误差（MAE）、均方根误差（RMSE）和平均相对误差（MRE）对这些模型进行了严格评估。值得注意的是，开发的最佳模型，基于xgboost的QSPR，表现出卓越的性能，显示出令人印象深刻的R2值0.9772，超过了先前研究模型设定的基准。此外，还采用shapley加性解释（SHAP）分析对模型进行解释，发现影响最大的5个输入特征包括SMR_VSA8、SMR_VSA3、Kappa2、HeavyAtomCount和fr_furan。该研究不仅为KOW预测精度提供了新的基准，而且提高了QSPR模型的可解释性。

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引用次数: 0

Multi-criteria computational screening of [BMIM][DCA]@MOF composites for CO2 capture 用于二氧化碳捕获的[BMIM][DCA]@MOF 复合材料的多标准计算筛选

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-07-17 DOI: 10.1016/j.gce.2024.07.002

Mengjia Sheng, Xiang Zhang, Hongye Cheng, Zhen Song, Zhiwen Qi

Ionic liquid (IL) can be inserted into metal organic framework (MOF) to form IL@MOF composite with enhanced properties. In this work, hypothetical IL@MOFs were computationally constructed and screened by integrating molecular simulation and convolutional neural network (CNN) for CO₂ capture. First, the IL [BMIM][DCA] with a large CO₂ solubility was inserted into 1631 pre-selected Computational-Ready Experimental (CoRE) MOFs to create hypothetical IL@MOFs. Then, given the temperature and pressure of adsorption and desorption, the CO₂/N₂ selectivity and CO₂ working capacity of 700 representative IL@MOFs were assessed via molecular simulations. Based on the results, two CNN models were trained and used to predict the performance of other IL@MOFs, which reduces the computational costs effectively. By combining the simulation results and CNN model predictions, 22 IL@MOFs with top-ranked performance were identified. Three distinct ones IL@HABDAS, IL@GUBKUL, and IL@MARJAQ were chosen for explicit analysis. It was found that a desired balance between CO₂/N₂ selectivity and CO₂ working capacity can be obtained by inserting the optimal number of IL molecules. This helps guide a novel design of IL@MOF composites with advanced performance on carbon capture.

离子液体（IL）可以插入到金属有机骨架（MOF）中，形成具有增强性能的IL@MOF复合材料。在这项工作中，通过整合分子模拟和卷积神经网络（CNN）进行CO2捕获，计算构建和筛选假设的IL@MOFs。首先，将具有大CO2溶解度的IL [BMIM][DCA]插入到1631个预先选择的计算就绪实验（CoRE） mof中，以创建假设的IL@MOFs。然后，在给定吸附和解吸温度和压力的情况下，通过分子模拟评估了700个具有代表性的IL@MOFs的CO2/N2选择性和CO2工作容量。在此基础上，训练两个CNN模型并用于预测其他IL@MOFs的性能，有效地降低了计算成本。结合仿真结果和CNN模型预测，确定了22个性能排名靠前的IL@MOFs。三个不同的IL@HABDAS， IL@GUBKUL和IL@MARJAQ被选择进行明确的分析。结果表明，通过插入最佳数量的IL分子，可以获得理想的CO2/N2选择性和CO2工作容量之间的平衡。这有助于指导IL@MOF复合材料的新设计，具有先进的碳捕获性能。

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引用次数: 0

Outside Back Cover 封底外侧

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-07-16 DOI: 10.1016/S2666-9528(24)00028-1

引用次数: 0

OFC: Outside Front Cover OFC：封面外侧

IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

Pub Date : 2024-07-16 DOI: 10.1016/S2666-9528(24)00020-7

引用次数: 0

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