Chinese Journal of Chemical Engineering最新文献

{"title":"Enhanced CO2 separation performance of Pebax®2533 mixed matrix membrane incorporated by synthesized mixed-ligand UiO-67","authors":"Mohammad Ali Kavianpour,&nbsp;Reza Abedini","doi":"10.1016/j.cjche.2025.05.002","DOIUrl":"10.1016/j.cjche.2025.05.002","url":null,"abstract":"<div><div>In this study, Pebax®2533 polymer was used as the continuous phase and UiO-67 was employed as the filler to prepare mixed matrix membranes. UiO-67 is usually synthesized using two ligands: biphenyl-4,4′-dicarboxylate (bpdc) and 2,2′-bipyridine-5,5′-dicarboxylic acid (bpy). In this research, UiO-67 was synthesized not only with these two ligands but also using a mixed ligand approach (50% bpdc and 50% bpy). The synthesized UiOs were incorporated into the polymer matrix at mass percentages ranging from 0% to 2% to form the mixed matrix membranes (MMMs). Membranes containing UiO-67 with mixed ligands exhibited a greater affinity for CO₂ compared to other membranes. Various analytical techniques, including X-ray diffraction, thermogravimetric analyzer, Fourier transform infrared spectroscope (FTIR), field emission scanning electron microscope (FESEM), and differential scanning calorimetry, were used to analyze the properties of the prepared membranes. The FTIR spectrum confirmed all desired bands of Pebax®2533 and UiO-67 in the MMMs. The FESEM images showed that the pure Pebax membrane has a uniform structure, and the developed membranes are uniformly incorporated with the synthesized UiO-67 nanoparticles. Gas permeation measurements indicated that CO₂ permeability and CO₂/CH₄ selectivity increased from 402.7 Barrer (1 Barrer = 1.33 × 10⁻¹⁴ m³(STP)·m·m⁻²·s⁻¹·kPa⁻¹) and 9.32 for the pure Pebax membrane at 1.0 MPa to 770.1 Barrer and 16.96 in the modified membrane. Additionally, the gas permeation test results demonstrated that adding functionalized porous nanofillers increases the CO₂ separation performance. Permeability tests at different temperatures revealed that as temperature was raised, at constant pressure, CO₂ permeability for the membrane containing the mixed ligand increased from 682.2 Barrer to 733.5 Barrer, While CO₂/CH₄ selectivity decreased from 15.46 to 13.43.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"85 ","pages":"Pages 76-94"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment of normal operating zone models by boundary points for CSTR-DC-recycle chemical processes","authors":"Poku Gyasi,&nbsp;Jiandong Wang,&nbsp;Mengyao Wei,&nbsp;Hao Jing","doi":"10.1016/j.cjche.2025.03.012","DOIUrl":"10.1016/j.cjche.2025.03.012","url":null,"abstract":"<div><div>Integrated continuous stirred-tank reactors and distillation columns with recycle (CSTR-DC-recycle) are essential components in chemical processes. This paper proposes a method to establish a normal operating zone (NOZ) model to represent allowable variations of the CSTR-DC-recycle chemical processes. The NOZ is a geometric space containing all safe operating points of the CSTR-DC-recycle chemical processes, so that it is an effective model for process monitoring. The novelty of the proposed method is to establish the NOZ model based on boundary points. The boundary points make it possible to capture the actual geometric space irrespective of the space shape. In contrast, existing methods represent the NOZ of processes by fixed mathematical models such as ellipsoidal and convex-hull models; they are not suitable for the CSTR-DC-recycle chemical processes whose NOZs cannot be exactly defined by fixed mathematical structures. Simulated case studies based on Aspen Hysys software are given to illustrate the proposed method.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"85 ","pages":"Pages 140-157"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lower size limit of raw coal for efficient beneficiation in air-fluidized bed with magnetite particles","authors":"Yalong Cao,&nbsp;Donghui Liu,&nbsp;Quanhong Zhu","doi":"10.1016/j.cjche.2025.05.009","DOIUrl":"10.1016/j.cjche.2025.05.009","url":null,"abstract":"<div><div>A feasible criterion was established to determine the lower size limit of raw coal (<em>d</em>_pRm) for efficient beneficiation in the air-fluidized bed with magnetite particles. The feasibility of using small magnetite particles to accommodate the fine raw coal was demonstrated from the experimental perspective. The minimum size for the magnetite particles to be fluidized smoothly was clarified as 47.1 μm, which corresponded to the border between Geldart-B and -A groups. Since the gangue and coal components in the raw coal were crushed into the same size, <em>d</em>_pRm depended on the greater one between <em>d</em>_pGm (minimum size required for the gangue particles to sink towards the bottom) and <em>d</em>_pCm (minimum size required for the coal particles to float towards the top). <em>d</em>_pGm was determined as 259 μm by supposing that provided the gangue particles accumulated in the lower half bed, they could be potentially extracted from the bottom. On the other hand, it was observed that the coal particles could always accumulate in the upper half bed. Under such circumstances, <em>d</em>_pCm was revealed as 9.8 μm since finer coal particles would be blown out by air before the 47.1 μm sized magnetite particles became fluidized. Eventually, <em>d</em>_pRm was clarified as 259 μm, agreeing with the common view that raw coal coarser than 6 mm could be effectively beneficiated in the air-fluidized bed with magnetite particles. Additionally, the difficulty in beneficiating the fine raw coal was revealed to arise more from the remixing of sorted gangue particles than that of separated coal particles.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"85 ","pages":"Pages 158-166"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preparation and evaluation of palladium/kieselguhr composites for hydrogen isotope separation","authors":"Yuting Liu,&nbsp;Manquan Fang,&nbsp;Wenqing Wu,&nbsp;Guanghui Zhang,&nbsp;Guikai Zhang","doi":"10.1016/j.cjche.2025.04.003","DOIUrl":"10.1016/j.cjche.2025.04.003","url":null,"abstract":"<div><div>The palladium/kieselguhr composites (Pd/K) were prepared by the PdCl₂ dipping-reducing method. The effects of the preparation conditions on the Pd/K were studied, such as the heat treatment, dipping time, palladium concentration in solution and number of loading cycles. The pore structure and palladium content of the Pd/K were measured by the Brunauer–Emmett–Teller method and an inductively coupled plasma mass spectrometry. The appearance and palladium element distribution were measured by a scanning electron microscope. It is found that the palladium element is more densely distributed in the irregular and porous parts of the kieselguhr particles, so the kieselguhr is superior to Al₂O₃ as the carrier material. The heat treatment can improve the pore permeability and increase the palladium content for the Pd/K. Increasing the dipping time, palladium concentration in solution and number of loading cycles is beneficial to increase the palladium content of the Pd/K, but more loading cycles may lead to the pore collapse, which obstructs the interaction with the hydrogen isotope gases. A kind of Pd/K was prepared under a set of optimized conditions and was packed in a separation column. This Pd/K was proved to be of high performance and durable by some hydrogen–deuterium separation experiments.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"85 ","pages":"Pages 95-104"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven predictive model for solubility: A case study of the NaCl-Na2SO4-H2O system","authors":"Yuan Wang ,&nbsp;Mengyue Chen ,&nbsp;Jingwei Tian ,&nbsp;Weidong Zhang ,&nbsp;Dahuan Liu","doi":"10.1016/j.cjche.2025.05.019","DOIUrl":"10.1016/j.cjche.2025.05.019","url":null,"abstract":"<div><div>Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential. It provides theoretical support for salt lake resource development and wastewater treatment technologies. This study proposes an innovative solubility prediction approach. It addresses the limitations of traditional thermodynamic models. This is particularly important when experimental data from various sources contain inconsistencies. Our approach combines the Weighted Local Outlier Factor technique for anomaly detection with a Deep Ensemble Neural Network architecture. This methodology effectively removes local outliers while preserving data distribution integrity, and integrates multiple neural network sub-models to comprehensively capture system features while minimizing individual model biases. Experimental validation demonstrates exceptional prediction performance across temperatures from −20 °C to 150 °C, achieving a coefficient of determination of 0.989 after Bayesian hyperparameter optimization. This data-driven approach provides more accurate and universally applicable solubility predictions than conventional thermodynamic models, offering theoretical guidance for industrial applications in salt lake resource utilization, separation process optimization, and environmental salt management systems.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"84 ","pages":"Pages 254-265"},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pure component property estimation framework using explainable machine learning methods","authors":"Jianfeng Jiao ,&nbsp;Xi Gao ,&nbsp;Jie Li","doi":"10.1016/j.cjche.2025.05.011","DOIUrl":"10.1016/j.cjche.2025.05.011","url":null,"abstract":"<div><div>Accurate prediction of pure component physiochemical properties is crucial for process integration, multiscale modelling, and optimization. In this work, an enhanced framework for pure component property prediction by using explainable machine learning methods is proposed. In this framework, the molecular representation method based on the connectivity matrix effectively considers atomic bonding relationships to automatically generate features. The supervised machine learning model random forest is applied for feature ranking and pooling. The adjusted <em>R</em>² is introduced to penalize the inclusion of additional features, providing an assessment of the true contribution of features. The prediction results for normal boiling point (<em>T</em>_<em>b</em>), liquid molar volume (<em>L</em>_<em>mv</em>), critical temperature (<em>T</em>_<em>c</em>) and critical pressure (<em>P</em>_<em>c</em>) obtained using Artificial Neural Network and Gaussian Process Regression models confirm the accuracy of the molecular representation method. Comparison with GC based models shows that the root-mean-square error on the test set can be reduced by up to 83.8%. To enhance the interpretability of the model, a feature analysis method based on Shapley values is employed to determine the contribution of each feature to the property predictions. The results indicate that using the feature pooling method reduces the number of features from 13316 to 100 without compromising model accuracy. The feature analysis results for <em>T</em>_<em>b</em>, <em>L</em>_<em>mv</em>, <em>T</em>_<em>c</em>, and <em>P</em>_<em>c</em> confirms that different molecular properties are influenced by different structural features, aligning with mechanistic interpretations. In conclusion, the proposed framework is demonstrated to be feasible and provides a solid foundation for mixture component reconstruction and process integration modelling.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"84 ","pages":"Pages 158-178"},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent chemical synthesis based on microchemical engineering technology","authors":"Yongqi Pan ,&nbsp;Yazi Yu ,&nbsp;Lijie Wang ,&nbsp;Guogang Hu ,&nbsp;Yujun Wang ,&nbsp;Guangsheng Luo","doi":"10.1016/j.cjche.2025.05.010","DOIUrl":"10.1016/j.cjche.2025.05.010","url":null,"abstract":"<div><div>Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions; however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"84 ","pages":"Pages 274-288"},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SmdaNet: A hierarchical hard sample mining and domain adaptation neural network for fault diagnosis in industrial process","authors":"Zhenhua Yu,&nbsp;Zongyu Yao,&nbsp;Weijun Wang,&nbsp;Qingchao Jiang,&nbsp;Zhixing Cao","doi":"10.1016/j.cjche.2025.05.003","DOIUrl":"10.1016/j.cjche.2025.05.003","url":null,"abstract":"<div><div>Fault diagnosis in industrial process is essential for ensuring production safety and efficiency. However, existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains, resulting in suboptimal performance and robustness. Therefore, this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive (SmdaNet). First, the method uses deep belief networks (DBN) to build a diagnostic model. Hard samples are mined based on the loss values, dividing the data set into hard and easy samples. Second, elastic weight consolidation (EWC) is used to train the model on hard samples, effectively preventing information forgetting. Finally, the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions. Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy, robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"84 ","pages":"Pages 146-157"},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian optimization of operational and geometric parameters of microchannels for targeted droplet generation","authors":"Zifeng Li ,&nbsp;Xiaoping Guan ,&nbsp;Jingchang Zhang ,&nbsp;Qiang Guo ,&nbsp;Qiushi Xu ,&nbsp;Ning Yang","doi":"10.1016/j.cjche.2025.05.015","DOIUrl":"10.1016/j.cjche.2025.05.015","url":null,"abstract":"<div><div>Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"84 ","pages":"Pages 244-253"},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of ionic liquid toxicity by interpretable machine learning","authors":"Haijun Feng ,&nbsp;Li Jiajia ,&nbsp;Zhou Jian","doi":"10.1016/j.cjche.2025.04.018","DOIUrl":"10.1016/j.cjche.2025.04.018","url":null,"abstract":"<div><div>The potential toxicity of ionic liquids (ILs) affects their applications; how to control the toxicity is one of the key issues in their applications. To understand its toxicity structure relationship and promote its greener application, six different machine learning algorithms, including Bagging, Adaptive Boosting (AdaBoost), Gradient Boosting (GBoost), Stacking, Voting and Categorical Boosting (CatBoost), are established to model the toxicity of ILs on four distinct datasets including Leukemia rat cell line IPC-81 (IPC-81), Acetylcholinesterase (AChE), <em>Escherichia coli</em> (<em>E.coli</em>) and <em>Vibrio fischeri</em>. Molecular descriptors obtained from the simplified molecular input line entry system (SMILES) are used to characterize ILs. All models are assessed by the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (<em>R</em>²). Additionally, an interpretation model based on SHapley Additive exPlanations (SHAP) is built to determine the positive and negative effects of each molecular feature on toxicity. With additional parameters and complexity, the Catboost model outperforms the other models, making it a more reliable model for ILs' toxicity prediction. The results of the model's interpretation indicate that the most significant positive features, SMR_VSA5, PEOE_VSA8, Kappa2, PEOE_VSA6, SMR_VSA5, PEOE_VSA6 and EState_VSA1, can increase the toxicity of ILs as their levels rise, while the most significant negative features, VSA_EState7, EState_VSA8, PEOE_VSA9 and FpDensityMorgan1, can decrease the toxicity as their levels rise. Also, an IL's toxicity will grow as its average molecular weight and number of pyridine rings increase, whereas its toxicity will decrease as its hydrogen bond acceptors increase. This finding offers a theoretical foundation for rapid screening and synthesis of environmentally-benign ILs.</div></div>","PeriodicalId":9966,"journal":{"name":"Chinese Journal of Chemical Engineering","volume":"84 ","pages":"Pages 201-210"},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}