Green Chemical Engineering最新文献_第3页

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

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

{"title":"Machine learning models coupled with ionic fragment σ-profiles to predict ammonia solubility in ionic liquids","authors":"Kaikai Li , Yuesong Zhu , Sensen Shi , Yongzheng Song , Haiyan Jiang , Xiaochun Zhang , Shaojuan Zeng , Xiangping Zhang","doi":"10.1016/j.gce.2024.08.005","DOIUrl":"10.1016/j.gce.2024.08.005","url":null,"abstract":"<div><div>Emitting NH<sub>3</sub> into the atmosphere leads to significant air pollution, while NH<sub>3</sub> itself serves as an essential component for fertilizers and refrigerants in industry. Thus, recovering and reusing NH<sub>3</sub> is highly valuable. Ionic liquids (ILs) have shown great potential for NH<sub>3</sub> 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 NH<sub>3</sub> solubility in ILs. A dataset containing 785 solubility data points, covering 10 cations and 10 anions, was collected. From this dataset, the <em>S</em>1–<em>S</em>6 descriptors based on the IFC method were used as inputs for the ML models, together with temperature (<em>T</em>) and pressure (<em>P</em>). Among the models, the IFC-GBR model was recommended for predicting NH<sub>3</sub> solubility in ILs due to its higher coefficient of determination (R<sup>2</sup>) 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 NH<sub>3</sub> solubility in ILs over a wider range of temperatures and pressures, providing additional chemical insights into IL-NH<sub>3</sub> system that cations played a more important role for NH<sub>3</sub> solubility. These results highlighted the developed IFC-GBR model offered valuable insights for helping guide the process design of absorbing NH<sub>3</sub> through IL-based technology.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 223-232"},"PeriodicalIF":9.1,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data","authors":"Zhiqiang Wu , Yunquan Chen , Bingjian Zhang , Jingzheng Ren , Qinglin Chen , Huan Wang , Chang He","doi":"10.1016/j.gce.2024.08.004","DOIUrl":"10.1016/j.gce.2024.08.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 233-248"},"PeriodicalIF":9.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Deep learning-based prediction of velocity and temperature distributions in metal foam with hierarchical pore structure","authors":"Yixiong Lin , Zhengqi Wu , Shiqi You , Chen Yang , Qinglian Wang , Wang Yin , Ting Qiu","doi":"10.1016/j.gce.2024.08.003","DOIUrl":"10.1016/j.gce.2024.08.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 209-222"},"PeriodicalIF":9.1,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Establishing a generalized model for accurate prediction of higher heating values of substances with large ash fractions","authors":"Peng Jiang , Lin Li , Han Lin , Tuo Ji , Liwen Mu , Yuanhui Ji , Xiaohua Lu , Jiahua Zhu","doi":"10.1016/j.gce.2024.08.002","DOIUrl":"10.1016/j.gce.2024.08.002","url":null,"abstract":"<div><div>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 (<em>D</em><sub>R</sub>) and ash content (<em>C</em><sub>ash</sub>). First, ultimate analysis of biomass was applied to establish the calculation method of <em>D</em><sub>R</sub>; then, the correlation between <em>D</em><sub>R</sub>, <em>C</em><sub>ash</sub>, and HHV was analyzed using the Pearson Correlation Coefficient; subsequently, the HHV = <em>f</em> (<em>D</em><sub>R</sub><em>, C</em><sub>ash</sub>) model was developed using regression analysis. Furthermore, the accuracy was compared to previous literature in terms of correlation coefficient (<em>R</em><sup>2</sup>), root mean square error (RMSE), and mean absolute error (MAE). Results revealed that this model provided attractive accuracy with <em>R</em><sup>2</sup> = 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 <em>R</em><sup>2</sup> of 0.961, 0.989, and 0.939, respectively. Conclusively, this work proposed the use of <em>D</em><sub>R</sub> for HHV estimation, which was not only a simple and accurate approach but also widely applicable to various fuels.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 372-379"},"PeriodicalIF":9.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Yuting Ruan , Yongtao Wang , Jia Yao , Haoran Li

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

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

{"title":"Screening HFC/HFO and ionic liquid for absorption refrigeration at the atomic scale by the prediction model of machine learning","authors":"Jianchun Chu , Maogang He , Georgios M. Kontogeorgis , Xiangyang Liu , Xiaodong Liang","doi":"10.1016/j.gce.2024.07.004","DOIUrl":"10.1016/j.gce.2024.07.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 357-364"},"PeriodicalIF":9.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms","authors":"Ao Yang , Shirui Sun , Lu Qi , Zong Yang Kong , Jaka Sunarso , Weifeng Shen","doi":"10.1016/j.gce.2024.07.003","DOIUrl":"10.1016/j.gce.2024.07.003","url":null,"abstract":"<div><div>This study aims to significantly improve existing quantitative structure-property relationship (QSPR) models for predicting the octanol-water partition coefficient (<em>K</em><sub>OW</sub>). This is because accurate predictions of <em>K</em><sub>OW</sub> are crucial for assessing the environmental behavior and bioaccumulation potential of chemicals. Previous models have reported determination coefficient (R<sup>2</sup>) values between 0.9451 and 0.9681, and this research seeks to exceed these benchmarks. Three machine learning (ML) models are explored, <em>i.e.</em>, 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 R<sup>2</sup>, 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 R<sup>2</sup> 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 <em>K</em><sub>OW</sub> prediction accuracy but also enhances the interpretability of QSPR models.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 193-199"},"PeriodicalIF":9.1,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Multi-criteria computational screening of [BMIM][DCA]@MOF composites for CO2 capture","authors":"Mengjia Sheng, Xiang Zhang, Hongye Cheng, Zhen Song, Zhiwen Qi","doi":"10.1016/j.gce.2024.07.002","DOIUrl":"10.1016/j.gce.2024.07.002","url":null,"abstract":"<div><div>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 CO<sub>2</sub> capture. First, the IL [BMIM][DCA] with a large CO<sub>2</sub> 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 CO<sub>2</sub>/N<sub>2</sub> selectivity and CO<sub>2</sub> working capacity of 700 representative IL@MOFs were assessed <em>via</em> 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 CO<sub>2</sub>/N<sub>2</sub> selectivity and CO<sub>2</sub> 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.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 200-208"},"PeriodicalIF":9.1,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Outside Back Cover 封底外侧

IF 9.1

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

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

引用次数: 0