BACKGROUND During emergency endoscopic retrograde cholangiopancreatography(ERCP),the safety and feasibility of performing one-stage endoscopic treatment for patients with acute cholangitis(AC)due to choledocholithiasi...BACKGROUND During emergency endoscopic retrograde cholangiopancreatography(ERCP),the safety and feasibility of performing one-stage endoscopic treatment for patients with acute cholangitis(AC)due to choledocholithiasis are unclear.AIM To investigate the safety and feasibility of one-stage endoscopic treatment for moderate to severe AC.METHODS We enrolled all patients diagnosed with moderate to severe cholangitis due to common bile duct stones from January 2019 to July 2023.The outcomes were compared in this study between patients who underwent ERCP within 24 h and those who underwent ERCP 24 h later,employing a propensity score(PS)frame-work.Our primary outcomes were intensive care unit(ICU)admission rates,ICU length of stay,and duration of antibiotic use.RESULTS In total,we included 254 patients and categorized them into two groups based on the time elapsed between admission and intervention:The urgent group(≤24 h,n=102)and the elective group(>24 h,n=152).Ninety-three pairs of patients with similar characteristics were selected by PS matching.The urgent ERCP group had more ICU admissions(34.4%vs 21.5%,P=0.05),shorter ICU stays(3 d vs 9 d,P<0.001),fewer antibiotic use(6 d vs 9 d,P<0.001),and shorter hospital stays(9 d vs 18.5 d,P<0.001).There were no significant differences observed in adverse events,in-hospital mortality,recurrent cholangitis occurrence,30-d readmission rate or 30-d mortality.CONCLUSION Urgent one-stage ERCP provides the advantages of a shorter ICU stay,a shorter duration of antibiotic use,and a shorter hospital stay.展开更多
This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control ...This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.展开更多
We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed proces...We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.展开更多
Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have b...Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.展开更多
Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation indust...Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.展开更多
Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and oper...Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.展开更多
In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technologica...In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technological stages of the wine. The consecutive, parallel and spontaneous development of several redox processes and their impact on the quality, microbiological and crystalline stability of white wines were examined. The reduction of additive and subtractive technological interventions, of the amounts of adjuvants (sulphurous anhydride) is essential for the production of organic wines.展开更多
In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the in...In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.展开更多
Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches...Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.展开更多
Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized poi...Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized pointwise maxima of those processes are attracted by the Brown-Resnick process.展开更多
The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classificatio...The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.展开更多
The sensory perception of food is a dynamic process,which is closely related to the release of flavor substances during oral processing.It’s not only affected by the food material,but also subjected to the individual...The sensory perception of food is a dynamic process,which is closely related to the release of flavor substances during oral processing.It’s not only affected by the food material,but also subjected to the individual oral environment.To explore the oral processing characteristics of soft-boiled chicken,the sensory properties,texture,particle size,viscosity,characteristic values of electronic nose and tongue of different chicken samples were investigated.The correlation analysis showed that the physical characteristics especially the cohesiveness,springiness,resilience of the sample determined oral processing behavior.The addition of chicken skin played a role in lubrication during oral processing.The particle size of the bolus was heightened at the early stage,and the fluidity was enhanced in the end,which reduced the chewing time to the swallowing point and raised the aromatic compounds signal of electronic nose.But the effect of chicken skin on chicken thigh with relatively high fat content,was opposite in electronic nose,which had a certain masking effect on the perception of umami and sweet taste.In conclusion,fat played a critical role in chicken oral processing and chicken thigh had obvious advantages in comprehensive evaluation of soft-boiled chicken,which was more popular among people.展开更多
Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics.Machine learning provides an innovative approach to exploring complex physical problems.In this study,the nuc...Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics.Machine learning provides an innovative approach to exploring complex physical problems.In this study,the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process.First,the binding energies for 2238 nuclei with Z>20 and N>20 are calculated using the Gaussian process in a physically motivated feature space,yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV.The results show the good learning ability of the Gaussian process in the studies of binding energies.Then,the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020.The theoretical results are in good agreement with the experimental data,reflecting the good predictive power of the Gaussian process.Moreover,the-decay energies for 1169 nuclei with 50≤Z≤110 are derived from the theoretical binding energies calculated using the Gaussian process.The average deviation and the standard deviation are,respectively,0.047 MeV and 0.070 MeV.Noticeably,the calculated-decay energies for the two new isotopes 204 Ac(Huang et al.Phys Lett B 834,137484(2022))and 207 Th(Yang et al.Phys Rev C 105,L051302(2022))agree well with the latest experimental data.These results demonstrate that the Gaussian pro-cess is reliable for the calculations of nuclear binding energies.Finally,the-decay properties of some unknown actinide nuclei are predicted using the Gaussian process.The predicted results can be useful guides for future research on binding energies and-decay properties.展开更多
Light olefins is the incredibly important materials in chemical industry.Methanol to olefins(MTO),which provides a non-oil route for light olefins production,received considerable attention in the past decades.However...Light olefins is the incredibly important materials in chemical industry.Methanol to olefins(MTO),which provides a non-oil route for light olefins production,received considerable attention in the past decades.However,the catalyst deactivation is an inevitable feature in MTO processes,and regeneration,therefore,is one of the key steps in industrial MTO processes.Traditionally the MTO catalyst is regenerated by removing the deposited coke via air combustion,which unavoidably transforms coke into carbon dioxide and reduces the carbon utilization efficiency.Recent study shows that the coke species over MTO catalyst can be regenerated via steam,which can promote the light olefins yield as the deactivated coke species can be essentially transferred to industrially useful synthesis gas,is a promising pathway for further MTO processes development.In this work,we modelled and analyzed these two MTO regeneration methods in terms of carbon utilization efficiency and technology economics.As shown,the steam regeneration could achieve a carbon utilization efficiency of 84.31%,compared to 74.74%for air combustion regeneration.The MTO processes using steam regeneration can essentially achieve the near-zero carbon emission.In addition,light olefins production of the MTO processes using steam regeneration is 12.81%higher than that using air combustion regeneration.In this regard,steam regeneration could be considered as a potential yet promising regeneration method for further MTO processes,showing not only great environmental benefits but also competitive economic performance.展开更多
[Objectives]To compare the effects of traditional processing and fresh processing on the quality of Polygonatum odoratum decoction piece.[Methods]The effects of fresh processing and traditional processing on the quali...[Objectives]To compare the effects of traditional processing and fresh processing on the quality of Polygonatum odoratum decoction piece.[Methods]The effects of fresh processing and traditional processing on the quality of P.odoratum decoction piece were compared and analyzed with appearance characteristics,total ash content,extract content,total polysaccharides content,and total flavonoids content as the evaluation indexes.[Results]Fresh processing method in different production areas has different effects on P.odoratum decoction piece.P.odoratum was dried in oven of 50℃.When moisture content was 41.44%-59.67%,it was cut.After complete drying at 50℃,the moisture content of dried P.odoratum was 8.94%-9.60%,and ethanol-soluble extract content was 77.29%-78.20%,and water-soluble extract was 77.7%-78.14%.At this time,the appearance characteristics of section of P.odoratum decoction piece were better than that of traditional processing,which was yellowish white.The total polysaccharide content was higher than that of traditional processing,and the content of total flavonoids was statistically significant different from that of traditional processing.[Conclusions]The quality of P.odoratum decoction piece by fresh processing is better than that of the traditional processing,and it is feasible to use fresh processing.展开更多
In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge pr...In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.展开更多
To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean d...To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.展开更多
The coal-to-ethanol process,as the clean coal utilization,faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol.Referring to at least eight columns,the synth...The coal-to-ethanol process,as the clean coal utilization,faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol.Referring to at least eight columns,the synthesis of the ethanol distillation system is impracticable for exhaustive comparison and difficult for conventional superstructure-based optimization as rigorous models are used.This work adopts a superstructure-based framework,which combines the strategy that adaptively selects branches of the state-equipment network and the parallel stochastic algorithm for process synthesis.High-performance computing significantly reduces time consumption,and the adaptive strategy substantially lowers the complexity of the superstructure model.Moreover,parallel computing,elite search,population redistribution,and retention strategies for irrelevant parameters are used to improve the optimization efficiency further.The optimization terminates after 3000 generations,providing a flowsheet solution that applies two non-sharp splitting options in its distillation sequence.As a result,the 59-dimension superstructure-based optimization was solved efficiently via a differential evolution algorithm,and a high-quality solution with a 28.34%lower total annual cost than the benchmark was obtained.Meanwhile,the solution of the superstructure-based optimization is comparable to that obtained by optimizing a single specific configuration one by one.It indicates that the superstructure-based optimization that combines the adaptive strategy can be a promising approach to handling the process synthesis of large-scale and complex chemical processes.展开更多
Hydroconversion of coal tar to produce aromatic hydrocarbons(BTX)represents a crucial strategy for the highvalue hierarchical utilization of coal.This study focused on the hydrocracking of hydrorefined products derive...Hydroconversion of coal tar to produce aromatic hydrocarbons(BTX)represents a crucial strategy for the highvalue hierarchical utilization of coal.This study focused on the hydrocracking of hydrorefined products derived from coal tar to enhance the production of benzene,toluene,and xylene(BTX).Various reaction conditions,including reaction temperature,hydrogen pressure,space velocity,and hydrogen-to-oil volume ratio,were systematically explored to optimize BTX yields while also considering the process’s economic feasibility.The results indicate that increasing the reaction temperature from 360℃ to 390℃ significantly favors the production of BTX,with yields increasing from 21.42%to 41.14%.Similarly,an increase in hydrogen pressure from 4 MPa to 6 MPa boosts BTX production,with yields rising from 36.31%to 41.14%.Reducing the space velocity from 2 h^(-1) to 0.5 h^(-1) also favors the BTX production process,with yields increasing from 37.96%to 45.13%.Furthermore,raising the hydrogen-to-oil volume ratio from 750 to 1500 improves BTX yields from 41.61%to 45.44%.Through economic analysis,the optimal conditions for BTX production were identified as a reaction temperature of 390℃,hydrogen pressure of 5-6 MPa,space velocity of 1 h^(-1),and hydrogen-to-oil volume ratio of 1000,achieving a BTX yield of 43.73%.This investigation highlights the importance of a holistic evaluation of hydrocracking conditions to optimize BTX production.Furthermore,the findings offer valuable insights for the design and operation of industrial hydrocracking processes aimed at efficiently converting coal tar-derived hydrorefined feedstock into BTX.展开更多
基金The study was approved by the Ethics Committee(2019ZDSYLL094-P01).
文摘BACKGROUND During emergency endoscopic retrograde cholangiopancreatography(ERCP),the safety and feasibility of performing one-stage endoscopic treatment for patients with acute cholangitis(AC)due to choledocholithiasis are unclear.AIM To investigate the safety and feasibility of one-stage endoscopic treatment for moderate to severe AC.METHODS We enrolled all patients diagnosed with moderate to severe cholangitis due to common bile duct stones from January 2019 to July 2023.The outcomes were compared in this study between patients who underwent ERCP within 24 h and those who underwent ERCP 24 h later,employing a propensity score(PS)frame-work.Our primary outcomes were intensive care unit(ICU)admission rates,ICU length of stay,and duration of antibiotic use.RESULTS In total,we included 254 patients and categorized them into two groups based on the time elapsed between admission and intervention:The urgent group(≤24 h,n=102)and the elective group(>24 h,n=152).Ninety-three pairs of patients with similar characteristics were selected by PS matching.The urgent ERCP group had more ICU admissions(34.4%vs 21.5%,P=0.05),shorter ICU stays(3 d vs 9 d,P<0.001),fewer antibiotic use(6 d vs 9 d,P<0.001),and shorter hospital stays(9 d vs 18.5 d,P<0.001).There were no significant differences observed in adverse events,in-hospital mortality,recurrent cholangitis occurrence,30-d readmission rate or 30-d mortality.CONCLUSION Urgent one-stage ERCP provides the advantages of a shorter ICU stay,a shorter duration of antibiotic use,and a shorter hospital stay.
基金supported in part by the Natural Sciences Engineering Research Council of Canada (NSERC)。
文摘This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.
基金partially supported by the National Natural Science Foundation of China(11871244)the Fundamental Research Funds for the Central Universities,JLU。
文摘We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.
基金the National Natural Science Foundation of China(62003298,62163036)the Major Project of Science and Technology of Yunnan Province(202202AD080005,202202AH080009)the Yunnan University Professional Degree Graduate Practice Innovation Fund Project(ZC-22222770)。
文摘Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.
基金supported in part by the National Key Research and Development Program of China(2021YFC2902703)the National Natural Science Foundation of China(62173078,61773105,61533007,61873049,61873053,61703085,61374147)。
文摘Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.
基金supported by the National Key Research and Development Program of China (2020YFB1713800)the National Natural Science Foundation of China (92267205)+1 种基金the Hunan Provincial Innovation Foundation for Postgraduate (CX2022 0267)the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0181)。
文摘Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.
文摘In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technological stages of the wine. The consecutive, parallel and spontaneous development of several redox processes and their impact on the quality, microbiological and crystalline stability of white wines were examined. The reduction of additive and subtractive technological interventions, of the amounts of adjuvants (sulphurous anhydride) is essential for the production of organic wines.
基金Supported by Heilongjiang Provincial Fruit Tree Modernization Agro-industrial Technology Collaborative Innovation and Promotion System Project(2019-13)。
文摘In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.
基金support of the National Key Research and Development Program of China(2021YFB4000505).
文摘Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(LY18A010020)the Innovation of Jiaxing City:A Program to Support the Talented Persons.
文摘Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized pointwise maxima of those processes are attracted by the Brown-Resnick process.
基金funded by the Informatization Plan of Chinese Academy of Sciences(Grant No.CASWX2021SF-0102)the National Key R&D Program of China(Grant Nos.2022YFA1603903,2022YFA1403800,and 2021YFA0718700)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925408,11921004,and 12188101)the Chinese Academy of Sciences(Grant No.XDB33000000)。
文摘The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.
基金supported by China Agriculture Research System of MOF and MARA(CARS-41)Wens Fifth Five R&D Major Project(WENS-2020-1-ZDZX-007)。
文摘The sensory perception of food is a dynamic process,which is closely related to the release of flavor substances during oral processing.It’s not only affected by the food material,but also subjected to the individual oral environment.To explore the oral processing characteristics of soft-boiled chicken,the sensory properties,texture,particle size,viscosity,characteristic values of electronic nose and tongue of different chicken samples were investigated.The correlation analysis showed that the physical characteristics especially the cohesiveness,springiness,resilience of the sample determined oral processing behavior.The addition of chicken skin played a role in lubrication during oral processing.The particle size of the bolus was heightened at the early stage,and the fluidity was enhanced in the end,which reduced the chewing time to the swallowing point and raised the aromatic compounds signal of electronic nose.But the effect of chicken skin on chicken thigh with relatively high fat content,was opposite in electronic nose,which had a certain masking effect on the perception of umami and sweet taste.In conclusion,fat played a critical role in chicken oral processing and chicken thigh had obvious advantages in comprehensive evaluation of soft-boiled chicken,which was more popular among people.
基金the National Key R&D Program of China(No.2023YFA1606503)the National Natural Science Foundation of China(Nos.12035011,11975167,11947211,11905103,11881240623,and 11961141003).
文摘Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics.Machine learning provides an innovative approach to exploring complex physical problems.In this study,the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process.First,the binding energies for 2238 nuclei with Z>20 and N>20 are calculated using the Gaussian process in a physically motivated feature space,yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV.The results show the good learning ability of the Gaussian process in the studies of binding energies.Then,the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020.The theoretical results are in good agreement with the experimental data,reflecting the good predictive power of the Gaussian process.Moreover,the-decay energies for 1169 nuclei with 50≤Z≤110 are derived from the theoretical binding energies calculated using the Gaussian process.The average deviation and the standard deviation are,respectively,0.047 MeV and 0.070 MeV.Noticeably,the calculated-decay energies for the two new isotopes 204 Ac(Huang et al.Phys Lett B 834,137484(2022))and 207 Th(Yang et al.Phys Rev C 105,L051302(2022))agree well with the latest experimental data.These results demonstrate that the Gaussian pro-cess is reliable for the calculations of nuclear binding energies.Finally,the-decay properties of some unknown actinide nuclei are predicted using the Gaussian process.The predicted results can be useful guides for future research on binding energies and-decay properties.
基金the financial support from the Strategic Priority Research Program of Chinese Academy of Sciences(XDA21010100)。
文摘Light olefins is the incredibly important materials in chemical industry.Methanol to olefins(MTO),which provides a non-oil route for light olefins production,received considerable attention in the past decades.However,the catalyst deactivation is an inevitable feature in MTO processes,and regeneration,therefore,is one of the key steps in industrial MTO processes.Traditionally the MTO catalyst is regenerated by removing the deposited coke via air combustion,which unavoidably transforms coke into carbon dioxide and reduces the carbon utilization efficiency.Recent study shows that the coke species over MTO catalyst can be regenerated via steam,which can promote the light olefins yield as the deactivated coke species can be essentially transferred to industrially useful synthesis gas,is a promising pathway for further MTO processes development.In this work,we modelled and analyzed these two MTO regeneration methods in terms of carbon utilization efficiency and technology economics.As shown,the steam regeneration could achieve a carbon utilization efficiency of 84.31%,compared to 74.74%for air combustion regeneration.The MTO processes using steam regeneration can essentially achieve the near-zero carbon emission.In addition,light olefins production of the MTO processes using steam regeneration is 12.81%higher than that using air combustion regeneration.In this regard,steam regeneration could be considered as a potential yet promising regeneration method for further MTO processes,showing not only great environmental benefits but also competitive economic performance.
基金Supported by Guangxi Science and Technology Major Project(GUIKE AA22096020)Central Guidance for Local Scientific and Technological Development Funds(ZY20230102)+2 种基金Guilin City Science Research and Technology Development Plan Project(20220104-4,20210202-1,2020011203-1,2020011203-2)Open Project of Guangxi Key Laboratory of Tumor Immunology and Microenvironment Regulation(2022KF005)College Students Innovative Entrepreneurial Training Plan Program(202210601015).
文摘[Objectives]To compare the effects of traditional processing and fresh processing on the quality of Polygonatum odoratum decoction piece.[Methods]The effects of fresh processing and traditional processing on the quality of P.odoratum decoction piece were compared and analyzed with appearance characteristics,total ash content,extract content,total polysaccharides content,and total flavonoids content as the evaluation indexes.[Results]Fresh processing method in different production areas has different effects on P.odoratum decoction piece.P.odoratum was dried in oven of 50℃.When moisture content was 41.44%-59.67%,it was cut.After complete drying at 50℃,the moisture content of dried P.odoratum was 8.94%-9.60%,and ethanol-soluble extract content was 77.29%-78.20%,and water-soluble extract was 77.7%-78.14%.At this time,the appearance characteristics of section of P.odoratum decoction piece were better than that of traditional processing,which was yellowish white.The total polysaccharide content was higher than that of traditional processing,and the content of total flavonoids was statistically significant different from that of traditional processing.[Conclusions]The quality of P.odoratum decoction piece by fresh processing is better than that of the traditional processing,and it is feasible to use fresh processing.
文摘In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.
基金the Sichuan Science and Technology Program(Nos.23ZHCG0049,2023YFG0078,23ZHCG0030,2021ZDZX0007)SCU-SUINING Project(2022CDSN-14).
文摘To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.
文摘The coal-to-ethanol process,as the clean coal utilization,faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol.Referring to at least eight columns,the synthesis of the ethanol distillation system is impracticable for exhaustive comparison and difficult for conventional superstructure-based optimization as rigorous models are used.This work adopts a superstructure-based framework,which combines the strategy that adaptively selects branches of the state-equipment network and the parallel stochastic algorithm for process synthesis.High-performance computing significantly reduces time consumption,and the adaptive strategy substantially lowers the complexity of the superstructure model.Moreover,parallel computing,elite search,population redistribution,and retention strategies for irrelevant parameters are used to improve the optimization efficiency further.The optimization terminates after 3000 generations,providing a flowsheet solution that applies two non-sharp splitting options in its distillation sequence.As a result,the 59-dimension superstructure-based optimization was solved efficiently via a differential evolution algorithm,and a high-quality solution with a 28.34%lower total annual cost than the benchmark was obtained.Meanwhile,the solution of the superstructure-based optimization is comparable to that obtained by optimizing a single specific configuration one by one.It indicates that the superstructure-based optimization that combines the adaptive strategy can be a promising approach to handling the process synthesis of large-scale and complex chemical processes.
文摘Hydroconversion of coal tar to produce aromatic hydrocarbons(BTX)represents a crucial strategy for the highvalue hierarchical utilization of coal.This study focused on the hydrocracking of hydrorefined products derived from coal tar to enhance the production of benzene,toluene,and xylene(BTX).Various reaction conditions,including reaction temperature,hydrogen pressure,space velocity,and hydrogen-to-oil volume ratio,were systematically explored to optimize BTX yields while also considering the process’s economic feasibility.The results indicate that increasing the reaction temperature from 360℃ to 390℃ significantly favors the production of BTX,with yields increasing from 21.42%to 41.14%.Similarly,an increase in hydrogen pressure from 4 MPa to 6 MPa boosts BTX production,with yields rising from 36.31%to 41.14%.Reducing the space velocity from 2 h^(-1) to 0.5 h^(-1) also favors the BTX production process,with yields increasing from 37.96%to 45.13%.Furthermore,raising the hydrogen-to-oil volume ratio from 750 to 1500 improves BTX yields from 41.61%to 45.44%.Through economic analysis,the optimal conditions for BTX production were identified as a reaction temperature of 390℃,hydrogen pressure of 5-6 MPa,space velocity of 1 h^(-1),and hydrogen-to-oil volume ratio of 1000,achieving a BTX yield of 43.73%.This investigation highlights the importance of a holistic evaluation of hydrocracking conditions to optimize BTX production.Furthermore,the findings offer valuable insights for the design and operation of industrial hydrocracking processes aimed at efficiently converting coal tar-derived hydrorefined feedstock into BTX.