In this work, several HZSM-5 catalysts with different Si/Al ratios treated with acids are selected as catalysts and used for hydration of cyclohexene to cyclohexanol. The results indicated that HZSM-5 (Si/Al = 38) modified with 4 mol·L^-1 nitric acid was selected as an efficient catalyst for improving the hydration efficiency of cyclohexene. Furthermore, the microstructures and properties of fresh, used and regenerated acid-modified catalysts have been characterized by X-ray diffraction, scanning electron microscopy, nitrogen adsorption/desorption isotherm, Fourier transform infrared, thermal gravimetric analyzer, ammonia temperature programmed desorption and pyridine adsorbs Fourier transform infrared. The characterization results indicated that the total surface areas and pore volume of HZSM-5 zeolite increased after nitric acid treatment due to the formation of mesoporous structure. This benefits the diffusion rate of reactants and products, which improves the hydration efficiency and stability of the catalyst. Under the catalysis of HZSM-5, the conversion of cyclohexene was found to be 9.0%. However, treatment of HZSM-5 with nitric acid enhanced the conversion of cyclohexene to 12.2%, achieving a selectivity of 99.7% for cyclohexanol under optimal reaction conditions. This work affords a mild and efficient approach for improving the hydration efficiency and has potential industrial application value.

Iron tailings are a common solid waste resource, posing serious environmental and spatial challenges. This study proposed a novel hydrogen-based reduction roasting (HRR) technology for the processing of iron tailings using a combined beneficiation and metallurgy approach. Pilot-cale experiment results indicated that under the gas composition of CO:H₂ = 1:3, and optimal roasting conditions at a reduction temperature of 520 ℃, the majority of weakly magnetic hematite transforms into strongly magnetic magnetite during the reduction process. Combining roasting products with a magnetic separation–grinding–magnetic selection process yields a final iron concentrate with a grade of 56.68% iron and a recovery rate of 86.54%. Theoretical calculations suggested the annual production value can reach 29.7 million USD and a reduction of 20.79 tons of CO₂ emissions per year. This highlights that the use of HRR in conjunction with traditional beneficiation processes can effectively achieve comprehensive utilization of iron tailings, thereby reducing environmental impact.

This study investigates the corrosion inhibition potential of Datura stramonium seed extracts on mild steel in 1.0 mol·L^-1 HCl and 0.5 mol·L^-1 H₂SO₄, utilizing both ethanolic and aqueous extracts as eco-friendly inhibitors. Electrochemical techniques, thermodynamic studies, and quantum chemical calculations were employed to evaluate the adsorption mechanism and inhibitory action at the metal/electrolyte interface. Maximum inhibition efficiencies of 93.1% in HCl and 97.7% in H₂SO₄ were achieved with the ethanolic extract at a concentration of 0.2 g·L^-1, while the aqueous extract demonstrated 93.8% inhibition in HCl and 96.6% in H₂SO₄. Polarization curves indicated mixed-type inhibition with a slight anodic bias. The thermodynamic analysis of two extracts in both environments indicated that the K_ads increased and that the ΔG_ads were close to -40 kJ·mol^-1, suggesting that the adsorption followed the Langmuir isotherm, indicating a combination of physical and chemical adsorption. SEM/EDX analysis confirmed the formation of a protective layer, while quantum chemical studies further validated strong adsorption, evidenced by a low ΔE of 2.396 eV and an adsorption energy of -878 kcal·mol^-1 (1 kcal·mol^-1 = 4.18 kJ·mol^-1). These results demonstrate that Datura stramonium extracts are promising inhibitors, particularly in sulfuric acid, for industrial applications. Reason: Improved clarity, vocabulary, and technical accuracy while maintaining the original meaning.

The exosomes hold significant potential in disease diagnosis and therapeutic interventions. The objective of this study was to investigate the potential of aqueous two-phase systems (ATPSs) for the separation of bovine milk exosomes. The milk exosome partition behaviors and bovine milk separation were investigated, and the ATPSs and bovine milk whey addition was optimized. The optimal separation conditions were identified as 16% (mass) polyethylene glycol 4000, 10% (mass) dipotassium phosphate, and 1% (mass) enzymatic hydrolysis bovine milk whey. During the separation process, bovine milk exosomes were predominantly enriched in the interphase, while protein impurities were primarily found in the bottom phase. The process yielded bovine milk exosomes of 2.0 × 10¹¹ particles per ml whey with high purity (staining rate>90%, 7.01 × 10¹⁰ particles per mg protein) and high uniformity (polydispersity index <0.03). The isolated exosomes were characterized and identified by transmission electron microscopy, zeta potential and size distribution. The results demonstrated aqueous two-phase extraction possesses a robust capability for the enrichment and separation of exosomes directly from bovine milk whey, presenting a novel approach for the large-scale isolation of exosomes.

Catalysis has made great contributions to the productivity of human society. Therefore, the pursuit of new catalysts and research on catalytic processes has never stopped. Continuous and in-depth catalysis research significantly increases the complexity of dynamic systems and multivariate optimization, thus posing higher challenges to research methodologies. Recently, the significant advancement of generative artificial intelligence (AI) provides new opportunities for catalysis research. Different from traditional discriminative AI, this state-of-the-art technique generates new samples based on existing data and accumulated knowledge, which endows it with attractive potential for catalysis research — a field featuring a vast exploration space, diverse data types and complex mapping relationships. Generative AI can greatly enhance both the efficiency and innovation capacity of catalysis research, subsequently fostering new scientific paradigms. This perspective covers the basic introduction, unique advantages of this powerful tool, and presents cases of generative AI implemented in various catalysis researches, including catalyst design and optimization, characterization technique enhancement and guidance for new research paradigms. These examples highlight its exceptional efficiency and general applicability. We further discuss the practical challenges in implementation and future development perspectives, ultimately aiming to promote better applications of generative AI in catalysis.

Carbon-supported mercury catalysts are extensively employed in calcium carbide-based polyvinyl chloride (PVC) industries, but the usage of mercury-based catalysts can pose an environmental threat due to the release of mercury into the surrounding area during the operation period. In this study, a highly active and stable mercury-based catalyst was developed, utilizing the nitrogen atom of the support as the anchor site to enhance the interaction between active sites (HgCl₂) and the carbon support (N-AC). Thermal loss rate testing and thermogravimetric analysis results demonstrate that, compared to commercial activated carbon, N-doped carbon can effectively increase the heat stability of HgCl₂. The obtained mercury-based catalysts (HgCl₂/N-AC) exhibit significant catalytic performance, achieving 2.5 times the C₂H₂ conversion of conventional HgCl₂/AC catalysts. Experimental analysis combined with theoretical calculations reveals that, contrary to the Eley-Rideal (ER) mechanism of HgCl₂/AC, the HgCl₂/N-AC catalyst follows the Langmuir-Hinshelwood (LH) adsorption mechanism. The nitrogen sites and HgCl₂ on the catalyst enhance the adsorption capabilities of the HCl and C₂H₂, thereby improving the catalytic performance. Based on the modification of the active center by these solid ligands, the loading amount of HgCl₂ on the catalyst can be further reduced from the current 6.5% to 3%. Considering the absence of successful industrial applications for mercury-free catalysts, and based on the current annual consumption of commercial mercury chloride catalysts in the PVC industry, the widespread adoption of this technology could annually reduce the usage of chlorine mercury by 500 tons, making a notable contribution to mercury compliance, reduction, and emissions control in China. It also serves as a bridge between mercury-free and low-mercury catalysts. Moreover, this solid ligand technology can assist in the application research of mercury-free catalysts.

In the current era of renewable energy prominence, the wide operational capacity of coal-fired boilers has emerged as crucial for ensuring the sustainability of power plants. However, attaining ultra-low nitrogen oxides (NO_x) emissions during periods of low-load operations presents a significant and persistent challenge for coal power enterprises. While techniques such as biomass re-burning and advanced re-burning have shown promise in enhancing NO reduction efficiency above 800 ℃, their elevated levels of chlorine (Cl) and alkali metals pose potential risks to boiler equipment integrity. Therefore, this study proposes the utilization of biomass char derived from pyrolysis as a dual-purpose solution to enhance NO reduction efficiency while safeguarding boiler integrity during low-load operations. Findings indicate that pyrolysis treatment effectively reduces the Cl and alkali metal content of biomass. Specifically, it was determined that biomass char produced through deeply pyrolysis at 300 ℃ achieves the highest NO reduction efficiency while minimizing the presence of harmful components. At a reduction temperature of 700 ℃, both re-burning and advanced re-burning techniques exhibit NO reduction efficiencies of 55.90% and 62.22%, which is already an ideal deficiency at low temperatures. The addition of water vapor at 700-800 ℃ obviously avoids the oxidation of ammonia to NO in advanced re-burning. Upon further analysis, denitrification efficiency in biomass char re-burning and advanced re-burning is influenced not only by volatile content but also by physicochemical properties such as porosity and surface functional group distribution under certain reaction conditions. This study provides a theoretical framework for the industrial implementation of biomass char for NO control in coal-fired power plants, offering insights into optimizing NO reduction efficiency while mitigating potential risks to boiler equipment.

Microemulsions are usually used to prepare nanomaterials. The formation behavior of microemulsions is crucial to the preparation of nanomaterials. Water in the internal phase is usually replaced by electrolyte solutions to prepare nanomaterials. Knowing the effects of electrolyte solution on the phase behavior of microemulsion is significant to the nanomaterial preparation. Microemulsion systems were studied by a conductivity method with cyclohexane as oil, Triton X-100 as surfactant, hexanol as cosurfactant, and deionized water or the electrolyte solutions of Cu(Ac)₂ and Zn(Ac)₂ as aqueous phases. The results showed that the replacement of water with electrolyte solution had a strong effect on the phase behavior of microemulsion system. The O/W microemulsion zone in water system was not observed in the studied electrolyte system. The shape and area of the corresponding phase zone in electrolyte system were different from that in water system. The microemulsion regions of electrolyte solution systems were always larger than that of water system. Zn(Ac)₂ showed a larger microemulsion region than Cu(Ac)₂ at 0.1 mol·L^-1. The microemulsion phase region formed by 0.1 mol·L^-1 Zn(Ac)₂ + 0.1 mol·L^-1 Cu(Ac)₂ was smaller than that formed by 0.1 mol·L^-1 Zn(Ac)₂ or 0.1 mol·L^-1 Cu(Ac)₂ lonely. With the increase of electrolyte concentration in the electrolyte solution and the rise of temperature, the microemulsion region shrank gradually. The changes of interactions between different components in the system should be responsible to the variation of phase behavior. The results provide important information for the microemulsion system with electrolyte solution as aqueous phase.

Morphology and growth rate of carbon dioxide hydrate on the interface between liquid carbon dioxide and humic acid solutions were studied in this work. It was found that after the growth of the hydrate film at the interface, further growth of hydrate due to the suction of water in the capillary system formed between the wall of the cuvette and the end boundary of the hydrate layer occurs. Most probably, substantial effects on the formation of this capillary system may be caused by variations in reactor wall properties, for example, hydrophobic-hydrophilic balance, roughness, etc. We found, that the rate of CO₂ hydrate film growth on the surface of the humic acid aqueous solution is 4-fold to lower in comparison with the growth rate on the surface of pure water. We suppose that this is caused by the adsorption of humic acid associates on the surface of hydrate particles and, as a consequence, by the deceleration of the diffusion of dissolved carbon dioxide to the growing hydrate particle.

Molecular dynamics simulations were performed to study the microscopic working mechanism of fast hydrate formation from active ice. We successfully simulated the cyclic process of ice melt-hydrate formation-ice melt. The simulation results showed that active ice could significantly accelerate the formation of hydrates and exhibit high gas storage capacity. The oxygen atoms of the sulfate group in SDS formed hydrogen bonds with the hydrogen atoms of water molecules in the ice, destroying the orderly arranged structures of the ice surface. SDS also acted as a promoter to accelerate the mass transfer of guests in the liquid phase, thereby promoting the nucleation and growth of hydrates. The ordered structures of liquid phase formed by ice melting and the formation of cage-like structures facilitated by ice surface defects were beneficial to the nucleation and growth of hydrates. The formation of the hydrate shell accelerated the migration of the guests from the gas phase to the liquid phase. As the ice continued to melt, sufficient guests and water molecules ensured the stable growth of hydrates.

The direct oxidation of methane to methanol (DOMM) has been recognized as a significant technology for efficiently utilizing low-concentration coalbed methane (LCMM) and supplying liquid fuel. Herein, the noble metals (Pt, Pd and Ru) modified Cu/alkalized sepiolite (CuX/SEPA) catalysts were prepared and used for the DOMM in a gas-phase system at low temperatures. The CuRu/SEPA exhibited the highest methanol production of 53 μmol·g^-1·h^-1 and methanol selectivity of 90% under the optimal reaction conditions. Various characterizations demonstrated that the addition of Ru promoted the formation of Cu²⁺ and the contraction of Cu—Si/Al bonds to reduce the distance between framework Al atoms of SEPA to further generate more Al pairs, which facilitated the formation of reactive dicopper species ([Cu₂O]²⁺ or [Cu₂O₂]²⁺). Investigation of the reaction mechanism revealed that [Cu₂O]²⁺ or [Cu₂O₂]²⁺ species could adsorb and activate methane to form CH₃O* species and ultimately generated methanol with the assistance of water.

HKUST-1 is being considered as a promising storage medium for adsorbed natural gas (ANG), but the practical application still calls for the improvement on the adsorption capacity for methane, hydro-stability and apparent thermal conductivity. Here, incorporation and carbonization were employed to ameliorate the performances of HKUST-1, and the effect of mixing graphene oxide (GO) and graphite intercalation compounds (GIC) as well as equipping honeycomb heat exchanging device (HHED) on mitigating the thermal effect was also evaluated. Researches were conducted in terms of adsorption equilibrium of methane on the samples and the dynamic characteristic of a storage vessel during a typical flow rate of charge/discharge which is in correspondence with the typical consumption rate of the fuel required by the power unit. Results show that, in comparing with those of the sample (GOH-5) prepared by incorporating with 5% (mass) GO, the sample (EH-2) incorporated with the same mass of composite formed by mixing GO and GIC in a mass ratio 2:1 had 2.0%, 4.4%, 1.2% and 28.4% increment in specific surface area, specific microporous volume, mean pore width and thermal conductivity. Results also reveal that, within the temperature-pressure range 273–323 K and 0.3–3.5 MPa, the mean useable capacity (UC) of methane on EH-2 and GOH-5 samples consolidated under pressure 2 MPa is nearly equal, and the average useable capacity ratio (UCR) on the storage system obtained the largest value while HHED + GOH-P (formed by GOH-5 under pressure 2 MPa) was filled into the system. It suggests that incorporating HKUST-1 with composite contained certain amount of GIC is conducive to improving the thermal conductivity, but equipping HHED within the storage system is more effective in improving the performance of the ANG system.

Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases, naphtha, diesel, and other products through cracking reactions. Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield, or to increase product yield while reducing energy consumption. This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm, which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm. The reactor model used in this article is simulated based on a twenty-five lumped kinetic model. Through model and test function verification, the proposed optimization scheme exhibits significant advantages in the multi-objective optimization process of hydrocracking.

Noise is inevitable in electrical capacitance tomography (ECT) measurements. This paper describes the influence of noise on ECT performance for measuring gas–solids fluidized bed characteristics. The noise distribution is approximated by the Gaussian distribution and added to experimental capacitance data with various intensities. The equivalent signal strength () that equals the signal-to-noise ratio of packed beds is used to evaluate noise levels. Results show that the Pearson correlation coefficient, which indicates the similarity of solids fraction distributions over pixels, increases with, and reconstructed images are more deteriorated at lower . Nevertheless, relative errors for average solids fraction and bubble size in each frame are less sensitive to noise, attributed to noise compromise caused by the process of pixel values. These findings provide useful guidance for assessing the accuracy of ECT measurements of multiphase flows.

Continuous-flow upgrading of pentaerythritol synthesis technology via base-catalyzed aldol and Cannizzaro reactions of formaldehyde and acetaldehyde faces the challenge of effectively controlling the critical side reaction of hydroxymethyl acetaldehyde (HA) to the acrolein intermediate. Here, we first identified the forms of industrial formaldehyde as methane diol that easily converts to the alkaline formaldehyde under alkaline (NaOH) environment. The carbonyl group of alkaline formaldehyde induces deprotonation of acetaldehyde instead of the recognized base-hydroxyl group-induced deprotonation, and it needs to overcome only 18.31 kcal·mol^-1 (1 kcal = 4.186 kJ) energy barrier to form key intermediates of HA. The sodium solvation cage formed by NaOH hexa-coordinated formaldehyde effectively inhibits the alkalinity, thus contributing to a high energy barrier (46.21 kcal·mol^-1) to unwanted acrolein formation. In addition, the solvation cage gradually opens to increase the alkalinity with the consumption of formaldehyde, thus facilitating the subsequent Cannizzaro reaction (to overcome 11.77 kcal·mol^-1). In comparison, strong alkalinity promotes the formation of acrolein (36.65 kcal·mol^-1) to initiate the acetal side reaction, while weak alkalinity reduces the possibility of the Cannizzaro reaction (to overcome 20.44 kcal·mol^-1). This theoretically reveals the importance of the segmented feeding of weak and strong bases to successively control the aldol reaction and Cannizzaro reaction, and the combination of Na₂CO₃ or HCOONa with NaOH improves the pentaerythritol yield by 7% to 13% compared to that of NaOH alone (70% yield) within 1 min at a throughput of 155.7 ml·min^-1.

Recovering waste heat is essential for primary energy savings and carbon emission reduction. To provide direct and reliable suggestions for factories to recover waste heat, energetic, economic and exergoeconomic comparison between direct heat exchange (DHE) and open-cycle mechanical heat pump (MHP) under various operating conditions is carried out in this work. The price ratios R_ES (electricity to steam) and R_HS (hot water to steam) are introduced to quantify regional impacts and conduct quantitative analysis. A semi-empirical formula is obtained to explore the exergoeconomic performance of the two systems. For waste heat within 373.15-423.15 K, the exergy efficiency of the DHE with a temperature difference of 10-90 K is always lower than that of the MHP with a temperature lift of 10-50 K. The economic performance of the two systems has a break-even point, depending on the operating parameters and relative prices of electricity, steam, and hot water. Under the average R_ES (3.8) in China, if R_HS is higher than 0.748, the annual revenue of the DHE is always higher, whereas the MHP is more economical when R_HS is lower than 0.110. In regions where R_ES is higher than 4.353, the annual revenue of the MHP will be negative in some cases.

The distillation process is an important chemical process, and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling, thus improving the efficiency of process optimization or monitoring studies. However, the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals, which brings challenges to accurate data-driven modelling of distillation processes. This paper proposes a systematic data-driven modelling framework to solve these problems. Firstly, data segment variance was introduced into the K-means algorithm to form K-means data interval (KMDI) clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction. Secondly, maximal information coefficient (MIC) was employed to calculate the nonlinear correlation between variables for removing redundant features. Finally, extreme gradient boosting (XGBoost) was integrated as the basic learner into adaptive boosting (AdaBoost) with the error threshold (ET) set to improve weights update strategy to construct the new integrated learning algorithm, XGBoost-AdaBoost-ET. The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.

Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty. Due to the outstanding ability for high-level feature extraction, stacked autoencoder (SAE) has been widely used to improve the model accuracy of soft sensors. However, with the increase of network layers, SAE may encounter serious information loss issues, which affect the modeling performance of soft sensors. Besides, there are typically very few labeled samples in the data set, which brings challenges to traditional neural networks to solve. In this paper, a multi-scale feature fused stacked autoencoder (MFF-SAE) is suggested for feature representation related to hierarchical output, where stacked autoencoder, mutual information (MI) and multi-scale feature fusion (MFF) strategies are integrated. Based on correlation analysis between output and input variables, critical hidden variables are extracted from the original variables in each autoencoder's input layer, which are correspondingly given varying weights. Besides, an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers. Then, the MFF-SAE method is designed and stacked to form deep networks. Two practical industrial processes are utilized to evaluate the performance of MFF-SAE. Results from simulations indicate that in comparison to other cutting-edge techniques, the proposed method may considerably enhance the accuracy of soft sensor modeling, where the suggested method reduces the root mean square error (RMSE) by 71.8%, 17.1% and 64.7%, 15.1%, respectively.

Unconventional natural gas has become an important supplement to conventional energy sources, and the process of enrichment and purification of methane from low concentration coalbed methane is crucial. To this end, we report a copper-based metal–organic framework (MOF), ZJNU-119Cu, featuring two methane traps constructed with uncoordinated carboxylic acid oxygens and open metal sites. ZJNU-119Cu exhibits a high methane adsorption capacity (58.2 cm³·g^-1) at 298 K and 0.1 MPa and excellent CH₄/N₂ separation performance under dynamic conditions. Density-functional theory calculations combined with grand canonical Monte Carlo simulation theory reveal the interaction mechanism for the uncoordinated carboxylic acid oxygen atoms and open metal sites in ZJNU-119Cu with CH₄. The gas adsorption isotherms, heat of adsorption calculations, and breakthrough separation experiments indicate that this MOF is a very promising adsorbent for CH₄/N₂ separation.

In current spent nuclear fuel reprocessing, the predominant method involves chemical extraction, leveraging the differing distribution ratios of elements to achieve separation and purification. Effective separation of uranium (U), plutonium (Pu), and neptunium (Np) typically relies on redox processes that alter their oxidation states during extraction. Therefore, reductants play a critical role in reprocessing processes. An important shift in the advanced reprocessing process is the use of salt-free reagents in the actinide separation process. In addition, the salt content in the reprocessing stream is often indicative of the overall technological sophistication of the process, and it is critical to reform the reductants used in the main process stream. Salt-free reductants have attracted much attention in recent years for basic and applied research in reprocessing processes because of their advantages such as being easily destroyed, not introducing salts, reacting quickly, simplifying the process, and reducing the amount of waste. This study summarizes emerging salt-free reagents with potential applications in reprocessing, and outlines their kinetic and chemical reaction mechanism properties in reducing Pu(IV) and Np(VI). The conclusion discusses the future potential of salt-free reagents in reprocessing. This study summarizes the currently well-studied salt-free reductants and offers recommendations and future research directions in salt-free alternatives.