Characterization of chemical composition of high viscosity heavy oils:Macroscopic properties, and semi-quantitative analysis of molecular composition using high-resolution mass spectrometry

Heavy oil is an important resource in current petroleum exploitation, and the chemical composition information of heavy oil is crucial for revealing its viscosity-inducing mechanism and solving practical exploitation issues. In this study, the techniques of high-temperature gas chromatography and high-resolution mass spectrometry equipped with an electrospray ionization source were applied to reveal the chemical composition of typical heavy oils from western, central, and eastern China. The results indicate that these heavy oils display significant variations in their bulk properties, with initial boiling points all above 200℃. Utilizing pre-treatment and ESI high-resolution mass spectrometry, an analysis of the molecular composition of saturated hydrocarbons, aromatic hydrocarbons, acidic oxygen compounds, sulfur compounds, basic nitrogen compounds, and neutral nitrogen compounds within the heavy oil was conducted. Ultimately, a semi-quantitative analysis of the molecular composition of the heavy oil was achieved by integrating the elemental content. The semi-quantitative analysis results of Shengli-J8 heavy oil and a conventional Shengli crude oil show that Shengli-J8 heavy oil lacks alkanes and low molecular weight aromatic hydrocarbons, which contributes to its high viscosity. Additionally,characteristic molecular sets for different heavy oils were identified based on the semi-quantitative analysis of molecular composition. The semi-quantitative analysis of molecular composition in heavy oils may provide valuable reference data for establishing theoretical models on the viscosity-inducing mechanism in heavy oils and designing viscosity-reducing agents for heavy oil exploitation.

Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm

Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.

Ensemble feature selection integrating elitist roles and quantum game model

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

Molecular investigation into the transformation of recalcitrant dissolved organic sulfur in refinery sour water during stripping process

Refinery sour water primarily originates from the tops of towers in various units and coker condensate,and cannot be discharged directly to a wastewater treatment plant due to high levels of chemical oxygen demand(COD)and organic sulfur contents.Even after the recovery of H_(2)S from the sour water by the stripping process,the effluent still contains a high concentration of dissolved organic sulfur(DOS),which can have a huge bad influence.While chemical composition of dissolved organic matter(DOM)in refinery wastewater has been extensively studied,the investigation of recalcitrant DOS from sour waters remains unclear.In the present study,chemical composition of sour water DOMs(especially DOS)was investigated using fluorescence spectroscopy(excitation-emission matrix,EEM)and mass spectrometry,including gas chromatography-mass spectrometry(GC-MS)and high-resolution Orbitrap MS.The GC-MS and EEM results showed that volatile and low-aromaticity compounds were effectively removed during the stripping process,while compounds with high hydrophilicity and humification degree were found to be more recalcitrant.The Orbitrap MS results showed that weak-polar oxygenated sulfur compounds were easier to be removed than oxygenated compounds.However,the effluent still contained significant amounts of sulfur-containing compounds with multiple sulfur atoms,particularly in the form of highly unsaturated and aromatic compounds.The Orbitrap MS/MS results of CHOS-containing compounds from the effluent indicate that the sulfur atoms may exist as sulfonates,disulfide bonds,thioethers.Understanding the composition and structure of sour water DOS is crucial for the development of effective treatment processes that can target polysulfide compounds and minimize their impact on the environment.

Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators

The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace(BF)system.In this work,a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed.The data on raw and fuel materials,process op-eration,smelting state,and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed.A novel method for the delay analysis of furnace heat indicators was established.The extracted delay variables were found to play an important role in modeling.The method that combined the genetic algorithm and stacking efficiently im-proved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model.The hit ratio for predicting the temperature of hot metal in the error range of±10℃ was 92.4%,and that for the chemical heat of hot metal in the error range of±0.1wt%was 93.3%.On the basis of the furnace heat prediction model and expert experience,a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels.These sugges-tions were highly accepted by BF operators.Finally,the comprehensive and dynamic model proposed in this work was successfully ap-plied in a practical BF system.It improved the BF temperature level remarkably,increasing the furnace temperature stability rate from 54.9%to 84.9%.This improvement achieved considerable economic benefits.

Revolutionizing stem cell research:unbiased insights through single-cell sequencing

Stem cells have shown great application potential in wound repair,tissue regeneration,and disease treatment.Therefore,a full understanding of stem cells and their related regulatory mechanisms in disease treatment is conducive to improving the therapeutic effect of stem cells.However,thus far,there are still many unsolved mysteries in thefield of stem cells due to technical limitations,which hinder the in-depth exploration of stem cells and their wide clinical application.Single-cell sequencing(SCS)has provided very powerful and unbiased insights into cell gene expression profiles at the single-cell level,bringing exciting results to the stem cellfield.At present,SCS has been widely applied in thefield of stem cells,covering various aspects,including lineage tracing the development of stem cells,identifying new stem cell types,exploring cellular heterogeneity,and identifying internal functional subpopulations.In this paper,we focus on the latest research progress and discuss the application of SCS technology in stem cells.

Mechanisms Underlying the Anti-Inflammatory, Anti-Pruritic, and Anti-Allergic Effects of the Fuyanjie Chinese Herbal Formula : Insights from Network Pharmacology

[Objectives] To examine the mechanisms underlying the anti-inflammatory, anti-pruritic, and anti-allergic effects of the Fuyanjie Chinese herbal formula, which comprises Radix Sophorae Flavescentis, Stemonae Radix, Fructus Cnidii, and Phellodendri Chinensis Cortex, on sensitive skin using network pharmacology.[Methods] The TCMSP database was employed to identify and extract the active ingredients and corresponding targets of the Fuyanjie formula. The collective targets were then intersected with the disease targets "pruritus", "dermatitis", and "skin allergy", which were obtained from the GeneCards database, in order to identify the core targets. The String database, Cytoscape software, and the cytoHubba plug-in were utilized to construct and analyze the protein-protein interaction (PPI) network, and the key hub genes were subsequently identified and quantified. A GO function analysis and KEGG enrichment analysis of core action targets were conducted utilizing the DAVID database.[Results] A total of 87 active ingredients were identified from the Fuyanjie formula through a screening process, which corresponded to a total of 254 targets of action. Through intersection analysis, 41 core targets of the Fuyanjie Chinese herbal formula were identified, which contribute to its anti-inflammatory, anti-pruritic, and anti-allergic effects. The findings indicated that quercetin, beta-sitosterol, stigmasterol, formononetin, luteolin, and other bioactive compounds present in the Fuyanjie Chinese herbal formula may interact with the targets IL-6, MMP-9, TNF, and IL-1β. These compounds were suggested to exert anti-inflammatory, anti-pruritic, and anti-allergic effects through pathways associated with the inflammatory response, including the IL-17 signaling pathway, TNF signaling pathway, NF-κB signaling pathway, etc.[Conclusions] Fuyanjie Chinese herbal formula may modulate skin conditions through a multifaceted mechanism of action that involves multiple components, targets, and pathways.

Research Progress of Antimicrobial Peptides and the Potential Applications of Bacillus subtilis

This paper presents a comprehensive account of antimicrobial peptides (AMPs) derived from various sources, including animal, plant, and microbial origins, along with an examination of their structural characteristics and biological activities. Specifically, the potential of Bacillus subtilis as a safe and effective host for the production of AMPs is discussed. B. subtilis exhibits a notable capacity for protein secretion and is also capable of efficiently producing AMPs without the presence of endotoxin contamination. The research indicates that the production efficiency of AMPs derived from B. subtilis can be significantly enhanced through the application of genetic engineering and synthetic biology techniques. This advancement holds considerable potential for applications in food preservation, agriculture, medicine, and various other fields. The paper additionally investigates the stability of AMPs under diverse conditions of temperature, pH, and enzymatic treatment, and highlights the necessity for further research to facilitate the advancement of these AMPs for practical applications.

Action Mechanism of Components from Gardenia jasminoides on Eye Skin Based on Network Pharmacology

[Objectives]To explore the pharmacological effects of Gardenia jasminoides and its potential benefits on eye skin.[Methods]TCMSP and SymMap databases were used to screen the active components and corresponding targets of G.jasminoides.Human eye skin-related targets were screened,and the active component-target network and protein-protein interaction(PPI)network were established.Gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were performed.[Results]Twenty-six active compounds were screened out from G.jasminoides,and 277 targets were obtained.From the Gencards database,26652 disease targets were retrieved and 205 related gene targets were screened.The active component-action target network of G.jasminoides constructed by Cytoscape software revealed the potential of G.jasminoides to play a role in multiple biological pathways.In addition,PPI-network analysis,GO function analysis and KEGG pathway enrichment analysis revealed that the active components of G.jasminoides mainly regulate the biological processes such as inflammatory response,oxidative stress and apoptosis,involving MAPK,NF-κB and other important signaling pathways.[Conclusions]This study provides a theoretical basis for the eye skin protection of G.jasminoides and an important clue for future drug development.

Insight into the Efficacy and Potential Mechanism of Fructus Aurantii on Skin Based on Network Pharmacology

[Objectives]This study was conducted to explore the mechanism and pharmacological activity of Fructus Aurantii on human skin through network pharmacology.[Methods]The active components and targets of Fructus Aurantii were screened byTCMSPand SymMap data-bases,and the targets were humanized after de-duplication by UniProt database.Relevant therapeutic targets were searched in Gencards data-base with"skin"as the key word,and those with higher weights were retained.An effective component-human skin action target network of Fructus Aurantii was established by Cytoscape software.Then,the topological analysis of protein-protein interaction(PPI)network was made by using String database and Cytoscape software.Gene Ontology(GO)funetional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were carried out by david database,and the analysis results were visualized by Hiplot soft-ware.[Results]Thirteen active compounds of Fructus Aurantii were obtained by screening,and 99 gene targets of Fructus Aurantii which might have pharmacological activity on human skin were obtained by intersection analysis.PTGS2,PTGS1,HSP90AB1,HSP90AAI,NCOA2 and PIK3CG might be core action targets of Fructus Aurantii on human skin according to the topological analysis of the active component-target network.The analysis of PPI network showed that IL-1β,INS,TNF,TP53 and ESR1 might be core action proteins of Fructus Auranti.GO enrichment analysis showed that Fructus Aurantii might balance the imbalance of skin microenvironment caused by various in-vitro stimuli by participating in biological processes such as response to xenobiotic stimulus and regulation of small molecule metabolic process.KEGG pathway enrichment analysis showed that the targets of Fructus Aurantii acting on human skin were enriched in pathways in cancer,Kaposi sarcoma-associated herpesvirus infection,pathways in measles,AGE-RAGE signaling pathway in diabetic complications,IL.-17 signaling pathway,etc.[Conclusions]Fructus Aurantii may act on human skin targets through a variety of active components,and play a regulatory role in skin-related diseases.

石墨烯基纤维储能器件的研究进展与展望

随着小型化、可穿戴等特征的智能电子以及物联网传感设备的发展,新型纤维状柔性化、小型化电化学储能器件已成为重要的研究方向。同时,对纤维材料和柔性储能器件的性能提出了更高的要求,如可任意弯折、可拉伸、可折叠、高储能密度等。石墨烯纤维具有独特的结构、优异的导电性、良好机械性能和电化学性质,已证明了是一种极具前景、高性能的新型纤维状柔性储能材料。目前,研究者已开发了多种石墨烯基纤维微观结构的调控策略来进一步改进其性能。本文首先系统总结了石墨烯基纤维的制备方法和其性能提升的策略,然后详细讨论其在柔性化纤维状超级电容器、金属离子电池、热电发电机、太阳能电池和相变材料等储能领域中的最新应用进展。最后,对石墨烯基纤维在能源存储和转换领域中存在的挑战和机会进行了展望。

A Thermoregulatory Flexible Phase Change Nonwoven for All‑Season High‑Efficiency Wearable Thermal Management

Phase change materials have a key role for wearable thermal management,but suffer from poor water vapor permeability,low enthalpy value and weak shape stability caused by liquid phase leakage and intrinsic rigidity of solid–liquid phase change materials.Herein,we report for the first time a versatile strategy for designed assembly of high-enthalpy flexible phase change nonwovens(GB-PCN)by wet-spinning hybrid grapheneboron nitride(GB)fiber and subsequent impregnating paraffins(e.g.,eicosane,octadecane).As a result,our GB-PCN exhibited an unprecedented enthalpy value of 206.0 J g^(−1),excellent thermal reliability and anti-leakage capacity,superb thermal cycling ability of 97.6%after 1000 cycles,and ultrahigh water vapor permeability(close to the cotton),outperforming the reported PCM films and fibers to date.Notably,the wearable thermal management systems based on GB-PCN for both clothing and face mask were demonstrated,which can maintain the human body at a comfortable temperature range for a significantly long time.Therefore,our results demonstrate huge potential of GB-PCN for human-wearable passive thermal management in real scenarios.

Coexistence of gastrointestinal stromal tumor, esophageal and gastric cardia carcinomas

Gastric gastrointestinal stromal tumor (GIST), esophageal squamous cell carcinoma and gastric cardia adenocarcinoma are distinct neoplasms originating from different cell layers; therefore, simultaneous development of such carcinomas is relatively rare. Auxiliary examinations revealed coexistence of esophageal and gastric cardia carcinoma with lymph node metastasis in a 77-year-old man. Intraoperatively, an extraluminal tumor (about 6.0 cm × 5.0 cm × 6.0 cm) at the posterior wall of the gastric body, a tumor (about 2.5 cm × 2.0 cm) in the lower esophagus, and an infiltrative and stenosing tumor (about 1.0 cm × 2.0 cm) in the gastric cardia were detected. Wedge resection for extraluminal gastric tumor, radical esophagectomy for lower esophageal tumor, and cardiac resection with gastroesophageal (supra-aortic arch anastomoses) were performed. Postoperative histological examination showed synchronous occurrence of gastric GIST, esophageal squamous cell carcinoma, and gastric cardia adenocarcinoma. Furthermore, immunohistochemistry indicated strong staining for c-Kit/CD117, Dog-1, Ki-67 and smooth muscle, while expression of S-100 and CD34 was negative.

Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.

Molecular transformation of dissolved organic matter in refinery wastewaters: Characterized by FT-ICR MS coupled with electrospray ionization and atmospheric pressure photoionization

Dissolved organic matter(DOM)in refinery wastewater is an extremely complex mixture of various organic compounds.Using mass spectrometry,it is impossible to characterize all of the DOM molecules with only one ionization source.In this study,negative-ion,electrospray ionization(ESI),positive-ion ESI,and positive-ion atmospheric pressure photoionization(APPI)were coupled with Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR MS)to analyze the molecular composition of DOM in a refinery wastewater stream during the treatment process.There were obvious differences in the heteroatom composition,number of DOM constituents,and chemical properties in refinery wastewater under the three ionization modes.Acidic CHO and CHOS compounds detected by(+)ESI,basic CHN and CHON compounds detected by(þ)ESI,and hydrocarbons detected by(+)APPI were analyzed to determine the molecular transformations that occurred during treatment.In an anaerobic biological treatment process,acidic CHO and CHOS compounds with a high oxygen content were preferentially removed,and acidic CHO and CHOS compounds with a low oxygen content were produced.In an aerobic biological process,acidic CHO and CHOS compounds with a low oxygen content were preferentially removed,and acidic CHO and CHOS compounds with a high oxygen content were produced.The whole biological treatment process has a poor removal efficiency for CHN and CHON compounds,and hydrocarbons.An activated carbon(AC)adsorption process removed different heteroatom compounds mainly with a low oxygen content for acidic and basic compounds.The transformation mechanism of CHO and CHOS compounds in the biological treatment process was analyzed by the Kendrick mass defect(KMD)theory and a mass difference network analysis.In the anaerobic process,large amounts of oxygenated CHO and CHOS compounds were degraded by decarboxylation,deoxydation,demethoxylation,and dehydration reactions,and converted to lower oxygen content compounds.In the aerobic processes,these low oxygen CHO and CHOS compounds mainly underwent carboxylation and oxidation reactions.This study determined the transformation characteristics and mechanisms of different types of organic compounds in refinery wastewater during the treatment process.The results provide guidance for the design and optimization of technologies for refinery wastewater treatment.

Hydrogen solubility prediction for diesel molecules based on a modified Henry equation

Diesel hydrotreatment removes heteroatoms and polycyclic aromatics in diesel in the presence of highpressure hydrogen gas.The hydrogen solubility is the basis for hydrogen consumption prediction and process design/optimization.In the presented study,we established a method to predict the hydrogen solubility of diesel molecules and mixture.A modified Henry equation was proposed to illustrate the hydrogen solubility variation among the temperature and pressure.The parameters of the modified Henry equation for typical molecules were regressed from literature data.Then we established an empirical correlation between the parameter and the structure and property of molecules.The method was then combined with a molecular-level compositional model to accurately predict the hydrogen solubility in diesel,illustrating the validity of the method.

Molecular composition of naphthenic acids in a Chinese heavy crude oil and their impacts on oil viscosity

Most heavy crude oils underwent biodegradation and generated a significant amount of naphthenic acids. Naphthenic acids are polar compounds with the carboxylic group and are considered as a major factor affecting the oil viscosity. However, the relationship between the molecular composition of naphthenic acids and oil viscosity is not well understood. This study examined a “clean” heavy oil with low contents of heteroatoms but had a high content of naphthenic acids. Naphthenic acids were fractionated by distillation and caustic extraction. The molecular composition was characterized by high-resolution Orbitrap mass spectrometry. It was found that the 2- and 3-ring naphthenic monoacids with 15–35 carbon atoms are dominant components of the acid fractions;the caustic extraction is capable of isolating naphthenic acids with less than 35 carbons, which is equivalent to the upper limit of the distillable components, but not those in the residue fraction;the total acid number of the heavy distillates is higher than that of the residue fraction;the viscosity of the distillation fraction increases exponentially with an increased boiling point of the distillates. Blending experiments indicates that there is a strong correlation between the oil viscosity and acids content, although the acid content is only a few percent of the total oil.

A method to determine the shell layout scheme for equipment battlefield damage tests under artillery fire

In this paper,a new method for determining the shell layout scheme is proposed,which can make the equipment damage data by the battlefield damage test resemble as close as possible the actual combat data.This method is based on the analysis of the impact point distribution and effective damage area of equipment.In order to obtain the position of the impact points,an impact point distribution model under artillery fire was established.Similarly,in order to obtain the effective damage area of equipment,the concepts of generalized damage area and task-based equipment functional damage probability were demonstrated,and the corresponding calculation model was established.Through case analysis,the shell layout scheme was effectively obtained,verifying the correctness of the proposed method.

材料、能源、机械工程中高效的电流变技术——从机理到应用

电流变(ER)技术是一种基于电流变效应的先进技术。电流变技术中最常见的材料是电流变液(ERF)。电流变液是一种软物质智能材料,其黏度可以通过施加电场来可逆地调节。电流变液的衍生物、一种新型的电响应软物质材料——电流变弹性体(ERE),由于其不沉降、易封装的优点也得到了越来越多的关注。电流变材料由于其可逆可调、快速响应、低能耗等特性在机械工程中有着广泛的应用。除了基础的电流变材料的合成和应用以外,电流变技术还应用在了能源材料制备、石油运输、储能等诸多领域。电流变技术在能源领域的应用为其在其他领域的潜在应用提供了一个很好的范例。本文结合最新的研究成果,从机理到应用,系统地综述了电流变技术在材料、能源和机械工程等领域的研究现状和未来发展前景。