The design of Co-Mn composite oxides catalysts derived from MOF is significant for catalytic combustion of toluene. Here, a series of M-Co_aMn_bO_x, with enhanced catalytic properties compared with that of M-Co₃O₄, were successfully prepared through pyrolysis of Mn-doped Co-MOF. The as-synthesized M-Co₁Mn₁O_x (Co:Mn = 1:1) exhibits an optimal catalytic activity with 90% toluene conversion reached at 227 ℃, which benefits from the increase of Co³⁺, O_ads and the synergistic effect between Mn and Co. According to the analysis of the in situ diffuse reflectance infrared Fourier transform spectroscopy, toluene could be degraded easier on M-Co₁Mn₁O_x with lower activation energy than M-Co₃O₄. The main intermediate products are benzaldehyde, benzoic acid, anhydride, and maleate species. Those findings reveal the value of Mn doping for improved activity of toluene oxidation on MOF derived Co₃O₄, which provide a feasible method for the construction of toluene-oxidation catalysts.

The crystallization has significant influence on fluidity of slag and slag discharge of entrained-flow-bed (EFB) gasifier. The crystallization characteristics and fluidity of five synthetic slags with different MgO/CaO ratios prepared on the basis of the range of oxide contents of Zhundong coal ash were investigated in this study. The results show that with the MgO/CaO ratio increase, the initial crystallization temperature increases, and the main temperature range of crystallization ratio growth moves to higher temperature range gradually which causes T_p25 (T_p25 is the temperature corresponding to the viscosity of 25 Pa·s) to increase. Mg-rich crystals are formed preferentially than Ca-rich crystals when adding the same amount of MgO and CaO during cooling. The effective slagging operating temperature range decrease from 217 ℃ for the slag with a 0:4 MgO/CaO ratio to 44 ℃ for the slag with a 4:0 MgO/CaO ratio with the MgO/CaO ratio increase. The slags with 2:2 and 1:3 MgO/CaO ratios show similar effective slagging operating temperature range, T_p25 and the temperature corresponding to the viscosity of 2 Pa·s. However, compared with the slag with a 1:3 MgO/CaO ratio, the crystallization ratio and rate of slag with a 2:2 MgO/CaO ratio are lower within lower temperature range (1300–1200 ℃), causing its lower critical viscosity temperature and wider actual operating temperature range. Of the five slags, the widest effective slagging operating temperature range and the lowest T_p25 of the slag with a 0:4 MgO/CaO ratio due to its low crystallization ratio, and wider actual operating temperature range of the slag with a 2:2 MgO/CaO ratio make the two slags suitable for slag discharge of EFB gasifier.

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deep-learning algorithm based on a fully convolutional neural network (FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory (LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

The separation of propylene and propane is an important but challenging process, primarily achieved through energy-intensive distillation technology in the petrochemical industry. Here, we reported two natural C₄ linkers based metal–organic frameworks (MIP-202 and MIP-203) for C₃H₆/C₃H₈ separation. Adsorption isotherms and selectivity calculations were performed to study the adsorption performance for C₃H₆/C₃H₈ separation. Results show that C₃H₆/C₃H₈ uptake ratios (298 K, 100 kPa) for MIP-202 and MIP-203 are 2.34 and 7.4, respectively. C₃H₆/C₃H₈ uptake ratio (303 K, 100 kPa) for MIP-203 is up to 50.0. The mechanism for enhanced separation performance of C₃H₆/C₃H₈ on MIP-203 at higher temperature (303 K) was revealed by the in situ PXRD characterization. The adsorption selectivities of C₃H₆/C₃H₈ on MIP-202 and MIP-203 (298 K, 100 kPa) are 8.8 and 551.4, respectively. The mechanism for the preferential adsorption of C₃H₆ over C₃H₈ in MIP-202 and MIP-203 was revealed by the Monte Carlo simulation. The cost of organic ligands for MIP-202 and MIP-203 was lower than that of organic ligands for those top-performance MOFs. Our work sets a new benchmark for C₃H₆ sorbents with high adsorption selectivities.

Catalytic pyrolysis of digestate to produce aromatic hydrocarbons can be combined with anaerobic fermentation to effectively transform and utilize all biomass components, which can achieve the meaningful purpose of transforming waste into high-value products. This study explored whether catalytic pyrolysis of digestate is feasible to prepare aromatic hydrocarbons by analyzing the thermogravimetric characteristics, pyrolysis characteristics, and catalytic pyrolysis characteristics of digestate. For digestate pyrolysis, an increase in temperature was found to elevate the CO, CH₄, and monocyclic aromatic hydrocarbon (benzene, toluene, and xylene; BTX) content, whereas it decreased the contents of phenols, acids, aldehydes, and other oxygenates. Furthermore, the catalytic pyrolysis process effectively inhibited the acids, phenols, and furans in the liquid, whereas the yield of BTX increased from 25.45% to 45.99%, and the selectivity of xylene was also increased from 10.32% to 28.72% after adding ZSM-5. ZSM-5 also inhibited the production of nitrogenous compounds.

Highly efficient and robust electrocatalysts have been in urgent demand for oxygen evolution reaction (OER). For this purpose, high-cost carbon materials, such as graphene and carbon nanotubes, have been used as supports to metal oxides to enhance their catalytic activity. We report here a new Co₃O₄-based catalyst with nitrogen-doped porous carbon material as the support, prepared by pyrolysis of porous polyurea (PU) with Co(NO₃)₂ immobilized on its surface. To this end, PU was first synthesized, without any additive, through a very simple one-step precipitation polymerization of toluene diisocyanate in a binary mixture of H₂O-acetone at room temperature. By immersing PU in an aqueous solution of Co(NO₃)₂ at room temperature, a cobalt coordinated polymer composite, Co(NO₃)₂/PU, was obtained, which was heated at 500 ℃ in air for 2 h to get a hybrid, Co₃O₄/NC, consisting of Co₃O₄ nanocrystals and sp²-hybridized N-doped carbon. Using this Co₃O₄/NC as a catalyst in OER, a current density of 10 mA·cm^-2 was readily achieved with a low overpotential of 293 mV with a Tafel slope of 87 mV·dec^-1, a high catalytic activity. This high performance was well retained after 1000 recycled uses, demonstrating its good durability. This work provides therefore a facile yet simple pathway to fabrication of a new transition metal oxides-based N-doped carbon catalyst for OER with high performance.

The equilibrium and kinetic of hydrate in sediments can be affected by the presence of external components like bentonite with a relatively large surface area. To investigate the hydrate formation and decomposition behaviors in bentonite clay, the experiments of methane hydrate formation and decomposition using the multi-step decomposition method in bentonite with different water contents of 20%, 40% and 60% (mass) were carried out. The contents of bound, capillary and gravity water in bentonite clay and their roles during hydrate formation and decomposition were analyzed. In bentonite with water content of 20% (mass), the hydrate formation rate keeps fast during the whole formation process, and the final gas consumption under different initial formation pressures is similar. In bentonite with the water contents of 40% and 60% (mass), the hydrate formation rate declines significantly at the later stage of the hydrate formation. The final gas consumption of bentonite with the water contents of 40% and 60% (mass) is significantly higher than that with the water content of 20% (mass). During the decomposition process, the stable pressure increases with the decrease of the water content. Hydrate mainly forms in free water in bentonite clay. In bentonite clay with the water contents of 20% and 40% (mass), the hydrate forms in capillary water. In bentonite clay with the water content of 60% (mass), the hydrate forms both in capillary water and gravity water. The bound water of dry bentonite clay is about 3.93% (mass) and the content of capillary water ranges from 42.37% to 48.21% (mass) of the dry bentonite clay.

A new type of rubidium ion-imprinted polymer has been synthesized by the surface-imprinting technique using methacrylic acid as the functional monomer, the rubidium ion as the template, methanol as the solvent, and silica as a carrier. Ethylene glycol dimethacrylate and 2,2-azobisisobutyronitrile were used as acrosslinker and an initiator, respectively. In addition, based on the macrocyclic effect of crown ethers, the 18-crown-6 ligand was introduced as a ligand to fix the template ions better. Scanning electron microscopy, zeta-potential analysis, Fourier transform infrared spectroscopy, thermogravimetric analysis, and X-ray photoelectron spectroscopy were performed to characterize the ion-imprinted polymer. The effects of the preparation and adsorption conditions on the adsorption performance of the rubidium ion-imprinted polymer were investigated. The results indicated that the rubidium ion-imprinted polymer has high selectivity and faster kinetics than other adsorbents, with an equilibrium adsorption capacity of 200.19 mg·g^-1 at 298 K within 25 min. The sorption isotherm was well described by the Freundlich isotherm model, while the adsorption kinetics fitted the pseudo-second-order kinetic model. Consecutive adsorption–desorption experiments showed that the ion-imprinted polymer had good chemical stability and reusability.

β-lactam antibiotics in aquatic environment have severely damaged ecological stability and caused a series of environmental pollution problems to be solved urgently. Herein, a novel composite photocatalyst prepared by loading carbon dots (CDs) onto rod-like CoFe₂O₄ (CFO), which can effectively degrade amoxicillin (AMX) by photocatalytic/peroxymonosulfate (PMS) activation under visible light irradiation. The degradation results exhibits that the optimal degradation efficiency with 97.5% within 80 min is achievd by the CDs-CFO-5 composite. Such enhanced activity is ascribed to the introduction of CDs that effectively improves the separation efficiency of photogenerated electron pairs and creates new active sites as electron bridges that improve the photocatalytic performance. More importantly, a strong synergistic between CDs and photo-induced electrons generated from CFO can further activiate PMS to provide more SO₄^-· and ·OH radicals for boosting the degradation ability towards AMX. The present study aims to elucidate positive role of CDs in photocatalytic/peroxymonosulfate activation during the degradation reaction.

Polymer reaction engineering studies the design, operation, and optimization of reactors for industrial scale polymerization, based on the theory of polymerization kinetics and transfer processes (e.g., flow, heat and mass transfer). Although the foundation and development of this discipline are less than 80 years, the global production of polymers has exceeded 400 million tons per annum. It demonstrates that polymer reaction engineering is of vital importance to the polymer industry. Along with the maturity of production processes and market saturation for bulk polymers, emerging industries such as information technology, modern transportation, biomedicine, and new energy have continued to develop. As a result, the research objective for polymer reaction engineering has gradually shifted from maximizing the efficiency of the polymerization process to the precise regulation of high-end product-oriented macromolecules and their aggregation structures, i.e., from polymer process engineering to polymer product engineering. In this review, the frontiers of polymer reaction engineering are introduced, including the precise regulation of polymer chain structure, the control of primary aggregation structure, and the rational design of polymer products. We narrow down the topic to the polymerization reaction engineering of vinyl monomers. Moreover, the future prospects are provided for the field of polymer reaction engineering.

γ-Aminobutyric acid (GABA), a natural non-protein amino acid, plays an irreplaceable role in regulating the life activities of organisms. Nowadays, the separation and purification of food-grade GABA from fermentation broth is still a great challenge. This research utilized monosodium glutamate as a substrate for the production of high-purity GABA via an integrated process incorporating fermentation, purification, and crystallization. Firstly, 147 g·L^-1 GABA with a yield of 99.8% was achieved through fed-batch fermentation by Lactobacillus brevis CE701. Secondly, three integrated purification methods by ethanol precipitation were compared, and crude GABA with a purity of 89.85% was obtained by the optimized method. Thirdly, GABA crystals with a purity of 98.69% and a yield of 60% were further obtained through a designed crystallization process. Furthermore, the GABA industrial production process model was established by Superproper Designer V10 software, and material balance and economic analysis were carried out. Ethanol used in the process was recovered with a recovery of 98.79% through Aspen simulated extractive distillation. Then the fixed investment (equipment purchase and installation costs) for an annual production of 80 t GABA will be about 833000 USD; the total annual production cost (raw material cost and utility cost) will be about 641000 USD. The annual sale of GABA may be at the range of 2400000-4000000 USD and the payback period will be about 1-2 year. This integrated process provides a potential way for the industrial-scale production of food-grade GABA.

Bioethanol, as a clean and renewable fuel, has gained increasing attention due to its major environmental benefits. Pervaporation (PV) is a promising and competitive technique for the recovery of ethanol from bioethanol fermentation systems due to the advantages of environmental friendliness, low energy consumption and easy coupling with fermentation process. The main challenge for the industrial application of ethanol perm-selective membranes is to break the trade-off effect between permeability and selectivity. As membrane is the heart of the pervaporation separation process, this article attempts to provide a comprehensive survey on the breakthroughs of ethanol perm-selective PV membranes from the perspectives of tailoring membrane materials to enhance PV separation performance. The research and development of polymeric and organic/inorganic hybrid membranes are reviewed to explore the fundamental structure–property-performance relationships. It is found that mixed matrix membranes with well-designed membrane structures offer the hope of better control overphysi-/chemical micro-environment and cavity/pore size as well as size distribution, which may provide both high permeability and membrane selectivity to break the trade-off effect. The tentative perspective on the possible future directions of ethanol perm-selective membranes is also briefly discussed, which may provide some insights in developing a new generation of high-performance PV membranes for ethanol recovery.

In this work, by establishing a three-dimensional physical model of a 1000-ton industrial multi-jet combustion reactor, a hexahedral structured grid was used to discretize the model. Combined with realizable k–ε model, eddy-dissipation-concept, discrete-ordinate radiation model, hydrogen 19-step detailed reaction mechanism, air age user-defined-function, velocity field, temperature field, concentration field and gas arrival time in the reactor were numerically simulated. The Euler–Lagrange method combined with the discrete-phase-model was used to reveal the flow characteristics of particles in the reactor, and based on this, the effects of the reactor aspect ratios, central jet gas velocity and particle size on the flow field characteristics and particle back-mixing degree in the reactor were investigated. The results show that with the decrease of aspect ratio in the combustion reactors, the velocity and temperature attenuation in the reactor are intensified, the vortex phenomenon is aggravated, and the residence time distribution of nanoparticles is more dispersed. With the increase in the central jet gas velocities in reactors, the vortex lengthens along the axis, the turbulence intensity increases, and the residence time of particles decreases. The back-mixing degree and residence time of particles in the reactor also decrease with the increase in particle size. The simulation results can provide reference for the structural regulation of nanoparticles and the structural design of combustion reactor in the process of gas combustion synthesis.

Intelligent fault recognition techniques are essential to ensure the long-term reliability of manufacturing. Due to the variations in material, equipment and environment, the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating modes. Despite much progress in statistical learning and deep learning for fault recognition, most models are constrained by abundant diagnostic expertise, inefficient multiscale feature extraction and unruly multimode condition. To overcome the above issues, a novel fault diagnosis model called adaptive multiscale convolutional neural network (AMCNN) is developed in this paper. A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data, embedding the adaptive attention module to adjust the selection of relevant fault pattern information. The triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode condition. The benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed method. Compared with other common models, AMCNN shows its outstanding fault diagnosis performance and great generalization ability.

Due to outstanding performance in cheminformatics, machine learning algorithms have been increasingly used to mine molecular properties and biomedical big data. The performance of machine learning models is known to critically depend on the selection of the hyper-parameter configuration. However, many studies either explored the optimal hyper-parameters per the grid searching method or employed arbitrarily selected hyper-parameters, which can easily lead to achieving a suboptimal hyper-parameter configuration. In this study, Hyperopt library embedding with the Bayesian optimization is employed to find optimal hyper-parameters for different machine learning algorithms. Six drug discovery datasets, including solubility, probe-likeness, hERG, Chagas disease, tuberculosis, and malaria, are used to compare different machine learning algorithms with ECFP6 fingerprints. This contribution aims to evaluate whether the Bernoulli Naïve Bayes, logistic linear regression, AdaBoost decision tree, random forest, support vector machine, and deep neural networks algorithms with optimized hyper-parameters can offer any improvement in testing as compared with the referenced models assessed by an array of metrics including AUC, F1-score, Cohen’s kappa, Matthews correlation coefficient, recall, precision, and accuracy. Based on the rank normalized score approach, the Hyperopt models achieve better or comparable performance on 33 out 36 models for different drug discovery datasets, showing significant improvement achieved by employing the Hyperopt library. The open-source code of all the 6 machine learning frameworks employed in the Hyperopt python package is provided to make this approach accessible to more scientists, who are not familiar with writing code.

To address the shortcomings of existing particulate matter trapping technology, especially the low separation efficiency of fine particles, herein, a novel gas cyclone–liquid jet separator was developed to research fine particle trapping. First, numerical simulation methods were used to investigate the flow field characteristics and dust removal efficiency of the separator under different working conditions, and to determined suitable experimental conditions for subsequent dust removal experiments. Afterward, the separation efficiency of the separator against five kinds of common particles, including g-C₃N₄, TiO₂, SiC, talc, and SiO₂, was experimentally studied. A maximum separation efficiency of 99.48% was achieved for particles larger than 13.1 μm, and 96.55% efficiency was achieved for particles larger than 2 μm. The best crushing atomization effect was achieved for the separator when u_G was 10 m·s^-1 and u_L was 3 m·s^-1, while the best separation effect was achieved when u_G was 10 m·s^-1 and u_L was 3.75 m·s^-1. Studies have shown that the gas cyclone–liquid jet separator has excellent applicability in the separation of fine particles.

Membrane technology features inspiring excellence from numerous separation technologies for CO₂ capture from post-combustion gas. Polyvinylamine (PVAm)-based facilitated transport membranes show significantly high separation performance, which has been proven promising for industrial scale-up. However, commercialized PVAm with low molecular weight and excessive crystallinity is not available to prepare high-performance membranes. Herein, the synthesis process of PVAm was optimized by regulating polymerization and acidic hydrolytic conditions. The membranes based on PVAm with a molecular weight of 154 kDa and crystallinity of 11.37% display high CO₂ permeance of 726 GPU and CO₂/N₂ selectivity of 55 at a feed gas pressure of 0.50 MPa. Furthermore, we established a PVAm synthesis reactor with an annual PVAm solution (1%(mass)) capacity of over 7000 kg and realized the scaled-up manufacture of both PVAm and composite membranes.

With excellent biocompatibility and unique physiochemical properties, nanocelluloses including cellulose nanocrystals (CNCs) and cellulose nanofibrils (CNFs) are promising candidates for preparing biomedical hydrogels. CNCs and CNFs are different in morphology and surface charges. Herein, CNCs and two CNFs (CNFs-C, Carboxylated CNFs; CNFs-P, Phosphorylated CNFs) were synthesized and applied to fabricate hydrogels through metal crosslinking. Aluminum crosslinking was found to be the best choice for enhancing the strength. This study systematically compared the morphologies, storage modulus, loss factor, continuous shear ramp, self-healing, swelling, in vitro degradation and injectable properties of the fabricated hydrogels. Further, a radar chart is summarized as guidelines to direct the rational selection to meet the specific requirements of further biomedical applications. At the same nanocellulose concentration and after Al³⁺ crosslinking, CNCs hydrogels had strong water holding capacity twice as much as that of CNFs hydrogels. While CNFs hydrogels showed higher hardness and stronger resistance to degradation than that of CNCs. These results provide detailed insights into nanocellulose hydrogels, making it possible to use these guidelines to select hydrogels for desired performance.

The adsorption of protein molecules to oil/water (O/W) interface is of critical importance for the product design in a wide range of technologies and industries such as biotechnology, food industry and pharmaceutical industry. In this work, with ovalbumin (OVA) as the model protein, the adsorption conformations at the O/W interface and the adsorption stability have been systematically studied via multiple simulation methods, including all-atom molecular dynamic (AAMD) simulations, coarse-grained molecular dynamic (CGMD) simulations and enhanced sampling methods. The computational results of AAMD and CGMD show that the hydrophobic tail of OVA tends to be folded under long time relaxation in aqueous phase, and multiple adsorption conformations can exist at the interface due to heterogeneous interactions raising from oil and water respectively. To further study the adsorption sites of the protein, the adsorption kinetics of OVA at the O/W interface is simulated using metadynamics method combined with CGMD simulations, and the result suggests the existence of multiple adsorption conformations of OVA at interface with the head-on conformation as the most stable one. In all, this work focuses on the adsorption behaviors of OVA at squalene/water interface, and provides a theoretical basis for further functionalization of the proteins in emulsion-based products and engineering.

Excellent mechanical properties are the prerequisite for the application of superhydrophobic polymer coatings. However, significantly improving the mechanical properties without affecting other properties such as hydrophobicity is a huge challenge. In this study, a superhydrophobic coating with excellent mechanical properties was prepared by spraying a mixture of polysiloxane resins based on three siloxane monomers, hexadecyltrimethoxysilane (HDTMS) modified nano-SiO₂ particles (SiO₂-HDTMS) and polystyrene-grafted halloysite nanotubes (HNTs-PS). SiO₂-HDTMS dispersed homogeneously in polysiloxane coatings and the water contact angle of corresponding coating exceeding 150°, achieving superhydrophobicity. The SiO₂-HDTMS/HNTs-PS/polysiloxane composite coatings showed excellent abrasion resistance with the water coating contact angle remaining above 150° after 90 abrasion cycles, indicating that HNTs-PS can significantly improve the mechanical properties of the coating without affecting the hydrophobic properties of the coating. The achieved coating also exhibited excellent antifouling and acid and alkali corrosion resistance. This work provides a convenient and ecologically friendly method to prepare superhydrophobic polysiloxane composite coating with excellent mechanical properties, which is promising in the application of anti-fouling, anti-corrosion, and oil–water separation etc.