Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing addit...Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.展开更多
In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment techni...In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.展开更多
Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in ...Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains,resulting in suboptimal performance and robustness.Therefore,this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive(SmdaNet).First,the method uses deep belief networks(DBN)to build a diagnostic model.Hard samples are mined based on the loss values,dividing the data set into hard and easy samples.Second,elastic weight consolidation(EWC)is used to train the model on hard samples,effectively preventing information forgetting.Finally,the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions.Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy,robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.展开更多
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.展开更多
Conceptual process design (CPD) research focuses on finding design alternatives that address various design problems. It has a long history of well-established methodologies to answer these complex questions, such as ...Conceptual process design (CPD) research focuses on finding design alternatives that address various design problems. It has a long history of well-established methodologies to answer these complex questions, such as heuristics, mathematical programming, and pinch analysis. Nonetheless, progress continues from different formulations of design problems using bottom-up approaches, to the utilization of new tools such as artificial intelligence (AI). It was not until recently that AI methods were involved again in assisting the decision-making steps for chemical engineers. This has led to a gap in understanding AI's capabilities and limitations within the field of CPD research. Thus, this article aims to provide an overview of conventional methods for process synthesis, integration, and intensification approaches and survey emerging AI-assisted process design applications to bridge the gap. A review of all AI-assisted methods is highlighted, where AI is used as a key component within a design framework, to explain the utility of AI with comparative examples. The studies were categorized into supervised and reinforcement learning based on the machine learning training principles they used to enhance the understanding of requirements, benefits, and challenges that come with it. Furthermore, we provide challenges and prospects that can facilitate or hinder the progress of AI-assisted approaches in the future.展开更多
The big-tapered profiled ring disk is a key component of engines for rockets and missiles.A new forming technology,as called spinning-rolling process,has been proposed previously for the high performance,high efficien...The big-tapered profiled ring disk is a key component of engines for rockets and missiles.A new forming technology,as called spinning-rolling process,has been proposed previously for the high performance,high efficiency and low-cost manufacturing of the component.Blank design is the key part of plastic forming process design.For spinning-rolling process,the shape and size of the blank play a crucial role in process stability,deformation behavior and dimensional accuracy.So this work proposes a blank design method to determine the geometry structure and sizes of the blank.The mathematical model for calculating the blank size has been deduced based on volume conservation and neutral layer length invariance principle.The FE simulation and corresponding trial production of an actual big-tapered profiled ring disk show that the proposed blank design method is applicative.In order to obtain a preferred blank,the influence rules of blank size determined by different deformation degrees(rolling ratio k)on the spinning-rolling process are revealed by comprehensive FE simulations.Overall considering the process stability,circularity of the deformed ring disk and forming forces,a reasonable range of deformation degree(rolling ratio k)is recommended for the blank design of the new spinning-rolling process.展开更多
With the rapid growth of the global population and the increasing demand for healthier diets,improving the nutrient utilization efficiency of staple food crops has become a critical scientific and industrial chal-leng...With the rapid growth of the global population and the increasing demand for healthier diets,improving the nutrient utilization efficiency of staple food crops has become a critical scientific and industrial chal-lenge,prompting innovation in food processing technologies.This review introduces first the common nutritional challenges in the processing of staple food crops,followed by the comprehensive examination of research aiming to enhance the nutritional quality of staple food crop-based foods through innovative processing technologies,including microwave(MW),pulsed electric field(PEF),ultrasound,modern fer-mentation technology,and enzyme technology.Additionally,soybean processing is used as an example to underscore the importance of integrating innovative processing technologies for optimizing nutrient utilization in staple food crops.Although these innovative processing technologies have demonstrated a significant potential to improve nutrient utilization efficiency and enhance the overall nutritional pro-file of staple food crop-based food products,their current limitations must be acknowledged and addressed in future research.Fortunately,advancements in science and technology will facilitate pro-gress in food processing,enabling both the improvement of existing techniques as well as the develop-ment of entirely novel methodologies.This work aims to enhance the understanding of food practitioners on the way processing technologies may optimize nutrient utilization,thereby fostering innovation in food processing research and synergistic multi-technological strategies,ultimately providing valuable references to address global food security challenges.展开更多
Bone repair remains an important target in tissue engineering,making the development of bioactive scaffolds for effective bone defect repair a critical objective.In this study,β-tricalcium phosphate(β-TCP)scaffolds ...Bone repair remains an important target in tissue engineering,making the development of bioactive scaffolds for effective bone defect repair a critical objective.In this study,β-tricalcium phosphate(β-TCP)scaffolds incorporated with processed pyritum decoction(PPD)were fabricated using three-dimensional(3D)printing-assisted freeze-casting.The produced composite scaffolds were evaluated for their mechanical strength,physicochemical properties,biocompatibility,in vitro proangiogenic activity,and in vivo efficacy in repairing rabbit femoral defects.They not only demonstrated excellent physicochemical properties,enhanced mechanical strength,and good biosafety but also significantly promoted the proliferation,migration,and aggregation of pro-angiogenic human umbilical vein endothelial cells(HUVECs).In vivo studies revealed that all scaffold groups facilitated osteogenesis at the bone defect site,with theβ-TCP scaffolds loaded with PPD markedly enhancing the expression of neurogenic locus Notch homolog protein 1(Notch1),vascular endothelial growth factor(VEGF),bone morphogenetic protein-2(BMP-2),and osteopontin(OPN).Overall,the scaffolds developed in this study exhibited strong angiogenic and osteogenic capabilities both in vitro and in vivo.The incorporation of PPD notably promoted the angiogenic-osteogenic coupling,thereby accelerating bone repair,which suggests that PPD is a promising material for bone repair and that the PPD/β-TCP scaffolds hold great potential as a bone graft alternative.展开更多
The grain boundary diffusion process(GBDP)has proven to be an effective method for enhancing the coercivity of sintered Nd-Fe-B magnets.However,the limited diffusion depth and thicker shell struc-ture have impeded the...The grain boundary diffusion process(GBDP)has proven to be an effective method for enhancing the coercivity of sintered Nd-Fe-B magnets.However,the limited diffusion depth and thicker shell struc-ture have impeded the further development of magnetic properties.Currently,the primary debates re-garding the mechanism of GBDP with Tb revolve around the dissolution-solidification mechanism and the atomic substitution mechanism.To clarify this mechanism,the microstructure evolution of sintered Nd-Fe-B magnets during the heating process of GBDP has been systematically studied by quenching at different tem peratures.In this study,it was found that the formation of TbFe_(2) phase is related to the dis-solution of _(2)Fe_(14)B grains during GBDP with Tb.The theory of mixing heat and phase separation further confirms that the Nd_(2)Fe_(14)B phase dissolves to form a mixed phase of Nd and TbFe_(2),which then solidifies into the(Nd,Tb)_(2)Fe_(14)B phase.Based on the discovery of the TbFe_(2) phase,the dissolution-solidification mechanism is considered the primary mechanism for GBDP.This is supported by the elemental content of the two typical core-shell structures observed.展开更多
Process manufacturing is going through a critical transformation in response to escalating demands for efficiency,sustainability,and intelligent innovation.With process manufacturing being characterized by complex wor...Process manufacturing is going through a critical transformation in response to escalating demands for efficiency,sustainability,and intelligent innovation.With process manufacturing being characterized by complex workflows and high resource consumption,the process manufacturing industry is under mounting pressure to optimize resource utilization,enhance intelligent design,reduce carbon emissions,and address emerging challenges in quality assurance,safety,and information integration.展开更多
The so-called close-coupled gas atomization process involves melting a metal and using a high-pressure gas jet positioned close to the melt stream to rapidly break it into fine,spherical powder particles.This techniqu...The so-called close-coupled gas atomization process involves melting a metal and using a high-pressure gas jet positioned close to the melt stream to rapidly break it into fine,spherical powder particles.This technique,adapted for blast furnace slag granulation using a circular seam nozzle,typically aims to produce solid slag particles sized 30–140μm,thereby allowing the utilization of slag as a resource.This study explores the atomization dynamics of liquid blast furnace slag,focusing on the effects of atomization pressure.Primary atomization is simulated using a combination of the Volume of Fluid(VOF)method and the Shear Stress Transport k-ωturbulence model,while secondary atomization is analyzed through the Discrete Phase Model(DPM).The results reveal that primary atomization progresses in three stages:the slag column transforms into an umbrella-shaped liquid film,whose leading edge fragments into particles while forming a cavity-like structure,which is eventually torn into ligaments.This primary deformation is driven by the interplay of airflow velocity in the recirculation zone and the guide tube outlet pressure(Fp).Increasing the atomization pressure amplifies airflow velocity,recirculation zone size,expansion and shock waves,though the guide tube outlet pressure variations remain irregular.Notably,at 4.5 MPa,the primary deformation is most pronounced.Secondary atomization yields finer slag particles as a result of more vigorous primary atomization.For this pressure,the smallest average particle size and the highest yield of particles within the target range(30–140μm)are achieved.展开更多
In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversa...In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversarial networks for smallsample data has achieved a wide range of applications. However, the current generative adversarial networks applied in industrial processes do not impose realistic physical constraints on the generation of data, resulting in the generation of data that do not have realistic physical consistency. To address this problem, this paper proposes a physical consistency-based WGAN, designs a loss function containing physical constraints for industrial processes, and validates the effectiveness of the method using a common dataset in the field of industrial process fault diagnosis. The experimental results show that the proposed method not only makes the generated data consistent with the physical constraints of the industrial process, but also has better fault diagnosis performance than the existing GAN-based methods.展开更多
To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much at...To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing.However,the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed.In this perspective paper,we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing,with a focus on chemical product design,process synthesis and design,process control,and process safety and hazards.展开更多
The low-cost industrial application of perovskite solar cells requires an environmentally friendly and scalable fabrication process.However,achieving high-quality perovskite layers under these requirements is challeng...The low-cost industrial application of perovskite solar cells requires an environmentally friendly and scalable fabrication process.However,achieving high-quality perovskite layers under these requirements is challenging because the multi-step optimization with multiple intercorrelated experimental variables typically requires the development of a new deposition process.To address this,we propose a two-step machine learning approach for creating a new method for perovskite deposition in ambient air and antisolvent-free processing with a low-toxicity solvent triethyl phosphate(TEP).The two-step machine learning approach integrates a precursor solubility prediction model and a device-efficiency prediction model within a Bayesian optimization framework.This framework enables the information of solubility to be passed as a constraint function when optimizing the efficiency of perovskite solar cells,facilitating a quick optimization of a TEP-based,vacuum-quenching-assisted deposition in ambient air.Furthermore,the optimal precursor solution is subsequently applied to FAPbI_(3) perovskite devices,achieving a device power conversion efficiency of 24.26%under ambient conditions(23℃and~50%relative humidity).This work demonstrates the promising potential of machine learning to expedite new fabrication processes to fulfill industrial needs.展开更多
Constrained Friction Processing(CFP),a novel friction-based technique,has been developed to efficiently process fine-grained magnesium(Mg)rods,expanding the potential applications of biodegradable Mg alloys in medical...Constrained Friction Processing(CFP),a novel friction-based technique,has been developed to efficiently process fine-grained magnesium(Mg)rods,expanding the potential applications of biodegradable Mg alloys in medical implants.This study investigates the enhancement of mechanical properties through the implementation of multiple pass CFP(MP-CFP)in comparison to the conventional single pass CFP.The results reveal a substantial improvement in compressive yield strength(CYS),ultimate compressive strength,and failure plastic strain by 11%,28%,and 66%,respectively.A comprehensive analysis of material evolution during processing and the effects of the final microstructure on mechanical properties was conducted.The intricate material flow behavior during the final plunge stage of MP-CFP results in a reduced intensity of local basal texture and macrotexture.The diminished intensity of basal texture,combined with a low geometrical compatibility factor at the top of the rod after MP-CFP,effectively impedes slip transfer across grain boundaries.This leads to a local strain gradient along the compression direction,ultimately contributing to the observed enhancement in mechanical properties.The Mg-0.5Zn0.3Ca(wt.%)alloy,after texture modification by MP-CFP,exhibits a competitive CYS compared with other traditional methods,highlighting the promising application potential of MP-CFP.展开更多
Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industria...Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industrial process,this paper introduces the concept of intelligent ODM in industrial process,shapes its future directions,and highlights key technical challenges.By the tight conjoining of and coordination between industrial artificial intelligence(AI)with industrial control and optimization technologies,as well as the Industrial Internet with industrial computer management and control systems,an intelligent operational optimization decision-making methodology is proposed for complex industrial process.The intelligent ODM methodology and its successful application demonstrate that the tight conjoining of and coordination between next-generation information technologies with industrial control and optimization technologies will promote the development of industrial intelligent ODM.Finally,main research directions and ideas are outlined for realizing intelligent ODM in industrial process.展开更多
Xylonic acid is a versatile and valuable chemical with a range of industrial and potential health applications.The high demand for renewable energy and sustainable products has increased interest in xylonic acid as a ...Xylonic acid is a versatile and valuable chemical with a range of industrial and potential health applications.The high demand for renewable energy and sustainable products has increased interest in xylonic acid as a starting material for the production of variety of high value chemicals and biofuels.The production of xylonic acid from biomass have an added advantage that it provides an opportunity to utilize bio-waste materials that would otherwise be discarded,reducing environmental hazards and reducing waste disposal costs.The aim of this research was to establish a bioprocess for producing xylonic acid from acid pre-treated sawdust liquor via fermentation,utilizing metabolically engineered strain of Corynebacterium glutamicum ATCC 31831.A maximum titre of 48.5±0.2 g/L xylonic acid from 60 g/L xylose derived from biomass was obtained with a yield of 0.89 g/g xylose which corresponds to a conversion of 62%.30.8±0.4 g/L of xylonic acid crystals of 95.5%purity were successfully recovered from 1L sawdust APL(Acid Pre-treated Liquor)via solvent precipitation and crystallization.Purified xylonic acid crystals were checked for its antibacterial effect on Salmonella enterica MTCC 3224,Escherichia coli MTCC 443 and Staphylococcus aureus MTCC 96.展开更多
In the context of intelligent manufacturing,the modern hot strip mill process(HSMP)shows characteristics such as diversification of products,multi-specification batch production,and demand-oriented customization.These...In the context of intelligent manufacturing,the modern hot strip mill process(HSMP)shows characteristics such as diversification of products,multi-specification batch production,and demand-oriented customization.These characteristics pose significant challenges to ensuring process stability and consistency of product performance.Therefore,exploring the potential relationship between product performance and the production process,and developing a comprehensive performance evaluation method adapted to modern HSMP have become an urgent issue.A comprehensive performance evaluation method for HSMP by integrating multi-task learning and stacked performance-related autoencoder is proposed to solve the problems such as incomplete performance indicators(PIs)data,insufficient real-time acquisition requirements,and coupling of multiple PIs.First,according to the existing Chinese standards,a comprehensive performance evaluation grade strategy for strip steel is designed.The random forest model is established to predict and complete the parts of PIs data that could not be obtained in real-time.Second,a stacked performance-related autoencoder(SPAE)model is proposed to extract the deep features closely related to the product performance.Then,considering the correlation between PIs,the multi-task learning framework is introduced to output the subitem ratings and comprehensive product performance rating results of the strip steel online in real-time,where each task represents a subitem of comprehensive performance.Finally,the effectiveness of the method is verified on a real HSMP dataset,and the results show that the accuracy of the proposed method is as high as 94.8%,which is superior to the other comparative methods.展开更多
Accurate fault root cause diagnosis is essential for ensuring stable industrial production. Traditional methods, which typically rely on the entire time series and overlook critical local features, can lead to biased ...Accurate fault root cause diagnosis is essential for ensuring stable industrial production. Traditional methods, which typically rely on the entire time series and overlook critical local features, can lead to biased inferences about causal relationships, thus hindering the accurate identification of root cause variables. This study proposed a shapelet-based state evolution graph for fault root cause diagnosis (SEG-RCD), which enables causal inference through the analysis of the important local features. First, the regularized autoencoder and fault contribution plot are used to identify the fault onset time and candidate root cause variables, respectively. Then, the most representative shapelets were extracted to construct a state evolution graph. Finally, the propagation path was extracted based on fault unit shapelets to pinpoint the fault root cause variable. The SEG-RCD can reduce the interference of noncausal information, enhancing the accuracy and interpretability of fault root cause diagnosis. The superiority of the proposed SEG-RCD was verified through experiments on a simulated penicillin fermentation process and an actual one.展开更多
基金National Key Research and Development Program of China(2022YFB4600902)Shandong Provincial Science Foundation for Outstanding Young Scholars(ZR2024YQ020)。
文摘Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.
基金supported by the Major Science and Technology Project of Zhongshan City(No.2022AJ004)the Key Basic and Applied Research Program of Guangdong Province(Nos.2019B030302010 and 2022B1515120082)Guangdong Science and Technology Innovation Project(No.2021TX06C111).
文摘In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.
基金support from the following foundations:the National Natural Science Foundation of China(62322309,62433004)Shanghai Science and Technology Innovation Action Plan(23S41900500)Shanghai Pilot Program for Basic Research(22TQ1400100-16).
文摘Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains,resulting in suboptimal performance and robustness.Therefore,this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive(SmdaNet).First,the method uses deep belief networks(DBN)to build a diagnostic model.Hard samples are mined based on the loss values,dividing the data set into hard and easy samples.Second,elastic weight consolidation(EWC)is used to train the model on hard samples,effectively preventing information forgetting.Finally,the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions.Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy,robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.
基金supported in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
基金financial support from The University of Manchester
文摘Conceptual process design (CPD) research focuses on finding design alternatives that address various design problems. It has a long history of well-established methodologies to answer these complex questions, such as heuristics, mathematical programming, and pinch analysis. Nonetheless, progress continues from different formulations of design problems using bottom-up approaches, to the utilization of new tools such as artificial intelligence (AI). It was not until recently that AI methods were involved again in assisting the decision-making steps for chemical engineers. This has led to a gap in understanding AI's capabilities and limitations within the field of CPD research. Thus, this article aims to provide an overview of conventional methods for process synthesis, integration, and intensification approaches and survey emerging AI-assisted process design applications to bridge the gap. A review of all AI-assisted methods is highlighted, where AI is used as a key component within a design framework, to explain the utility of AI with comparative examples. The studies were categorized into supervised and reinforcement learning based on the machine learning training principles they used to enhance the understanding of requirements, benefits, and challenges that come with it. Furthermore, we provide challenges and prospects that can facilitate or hinder the progress of AI-assisted approaches in the future.
基金the National Natural Science Foundation of China(No.52275378)the National Key Laboratory for Precision Hot Processing of Metals(6142909200208)。
文摘The big-tapered profiled ring disk is a key component of engines for rockets and missiles.A new forming technology,as called spinning-rolling process,has been proposed previously for the high performance,high efficiency and low-cost manufacturing of the component.Blank design is the key part of plastic forming process design.For spinning-rolling process,the shape and size of the blank play a crucial role in process stability,deformation behavior and dimensional accuracy.So this work proposes a blank design method to determine the geometry structure and sizes of the blank.The mathematical model for calculating the blank size has been deduced based on volume conservation and neutral layer length invariance principle.The FE simulation and corresponding trial production of an actual big-tapered profiled ring disk show that the proposed blank design method is applicative.In order to obtain a preferred blank,the influence rules of blank size determined by different deformation degrees(rolling ratio k)on the spinning-rolling process are revealed by comprehensive FE simulations.Overall considering the process stability,circularity of the deformed ring disk and forming forces,a reasonable range of deformation degree(rolling ratio k)is recommended for the blank design of the new spinning-rolling process.
基金supported by the National Key Research and Development Program of China(2023YFD2100205)the Fujian Province Science and Technology Plan Project,China(2023N3008).
文摘With the rapid growth of the global population and the increasing demand for healthier diets,improving the nutrient utilization efficiency of staple food crops has become a critical scientific and industrial chal-lenge,prompting innovation in food processing technologies.This review introduces first the common nutritional challenges in the processing of staple food crops,followed by the comprehensive examination of research aiming to enhance the nutritional quality of staple food crop-based foods through innovative processing technologies,including microwave(MW),pulsed electric field(PEF),ultrasound,modern fer-mentation technology,and enzyme technology.Additionally,soybean processing is used as an example to underscore the importance of integrating innovative processing technologies for optimizing nutrient utilization in staple food crops.Although these innovative processing technologies have demonstrated a significant potential to improve nutrient utilization efficiency and enhance the overall nutritional pro-file of staple food crop-based food products,their current limitations must be acknowledged and addressed in future research.Fortunately,advancements in science and technology will facilitate pro-gress in food processing,enabling both the improvement of existing techniques as well as the develop-ment of entirely novel methodologies.This work aims to enhance the understanding of food practitioners on the way processing technologies may optimize nutrient utilization,thereby fostering innovation in food processing research and synergistic multi-technological strategies,ultimately providing valuable references to address global food security challenges.
基金supported by the National Science Foundation of China(Nos.81373970,81773902,81973484,and 32171402)the National College Students Innovation and Entrepreneurship Training Program(No.201810315019)+4 种基金the Postgraduate Research and Practice Innovation Program of Jiangsu Province(Nos.SJCX21_0712 and KYCX23_2052)the Scientific Research Project of Jiangsu Provincial Association of Traditional Chinese Medicine(No.XYLD2024013)the Youth Scientific Research Project of Jiangyin Municipal Health Commission(No.Q202402)the Natural Science Foundation Project of Nanjing University of Chinese Medicine(No.XZR2024173)the Jiangyin Science and Technology Innovation Special Fund Project(No.JY0603A011014230032PB),China.
文摘Bone repair remains an important target in tissue engineering,making the development of bioactive scaffolds for effective bone defect repair a critical objective.In this study,β-tricalcium phosphate(β-TCP)scaffolds incorporated with processed pyritum decoction(PPD)were fabricated using three-dimensional(3D)printing-assisted freeze-casting.The produced composite scaffolds were evaluated for their mechanical strength,physicochemical properties,biocompatibility,in vitro proangiogenic activity,and in vivo efficacy in repairing rabbit femoral defects.They not only demonstrated excellent physicochemical properties,enhanced mechanical strength,and good biosafety but also significantly promoted the proliferation,migration,and aggregation of pro-angiogenic human umbilical vein endothelial cells(HUVECs).In vivo studies revealed that all scaffold groups facilitated osteogenesis at the bone defect site,with theβ-TCP scaffolds loaded with PPD markedly enhancing the expression of neurogenic locus Notch homolog protein 1(Notch1),vascular endothelial growth factor(VEGF),bone morphogenetic protein-2(BMP-2),and osteopontin(OPN).Overall,the scaffolds developed in this study exhibited strong angiogenic and osteogenic capabilities both in vitro and in vivo.The incorporation of PPD notably promoted the angiogenic-osteogenic coupling,thereby accelerating bone repair,which suggests that PPD is a promising material for bone repair and that the PPD/β-TCP scaffolds hold great potential as a bone graft alternative.
基金supported by the National Key Research and Development Program of China(2022YFB3505503)the National Natural Science Foundation of China(52201230)+2 种基金the Key R&D Program of Shandong Province(2022CXGC020307)the China Postdoctoral Science Foundation(2022M71204)the Beijing NOVA Program(Z211100002121092).
文摘The grain boundary diffusion process(GBDP)has proven to be an effective method for enhancing the coercivity of sintered Nd-Fe-B magnets.However,the limited diffusion depth and thicker shell struc-ture have impeded the further development of magnetic properties.Currently,the primary debates re-garding the mechanism of GBDP with Tb revolve around the dissolution-solidification mechanism and the atomic substitution mechanism.To clarify this mechanism,the microstructure evolution of sintered Nd-Fe-B magnets during the heating process of GBDP has been systematically studied by quenching at different tem peratures.In this study,it was found that the formation of TbFe_(2) phase is related to the dis-solution of _(2)Fe_(14)B grains during GBDP with Tb.The theory of mixing heat and phase separation further confirms that the Nd_(2)Fe_(14)B phase dissolves to form a mixed phase of Nd and TbFe_(2),which then solidifies into the(Nd,Tb)_(2)Fe_(14)B phase.Based on the discovery of the TbFe_(2) phase,the dissolution-solidification mechanism is considered the primary mechanism for GBDP.This is supported by the elemental content of the two typical core-shell structures observed.
文摘Process manufacturing is going through a critical transformation in response to escalating demands for efficiency,sustainability,and intelligent innovation.With process manufacturing being characterized by complex workflows and high resource consumption,the process manufacturing industry is under mounting pressure to optimize resource utilization,enhance intelligent design,reduce carbon emissions,and address emerging challenges in quality assurance,safety,and information integration.
基金the Tangshan University Doctor Innovation Fund(Project Number:1402306).
文摘The so-called close-coupled gas atomization process involves melting a metal and using a high-pressure gas jet positioned close to the melt stream to rapidly break it into fine,spherical powder particles.This technique,adapted for blast furnace slag granulation using a circular seam nozzle,typically aims to produce solid slag particles sized 30–140μm,thereby allowing the utilization of slag as a resource.This study explores the atomization dynamics of liquid blast furnace slag,focusing on the effects of atomization pressure.Primary atomization is simulated using a combination of the Volume of Fluid(VOF)method and the Shear Stress Transport k-ωturbulence model,while secondary atomization is analyzed through the Discrete Phase Model(DPM).The results reveal that primary atomization progresses in three stages:the slag column transforms into an umbrella-shaped liquid film,whose leading edge fragments into particles while forming a cavity-like structure,which is eventually torn into ligaments.This primary deformation is driven by the interplay of airflow velocity in the recirculation zone and the guide tube outlet pressure(Fp).Increasing the atomization pressure amplifies airflow velocity,recirculation zone size,expansion and shock waves,though the guide tube outlet pressure variations remain irregular.Notably,at 4.5 MPa,the primary deformation is most pronounced.Secondary atomization yields finer slag particles as a result of more vigorous primary atomization.For this pressure,the smallest average particle size and the highest yield of particles within the target range(30–140μm)are achieved.
文摘In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversarial networks for smallsample data has achieved a wide range of applications. However, the current generative adversarial networks applied in industrial processes do not impose realistic physical constraints on the generation of data, resulting in the generation of data that do not have realistic physical consistency. To address this problem, this paper proposes a physical consistency-based WGAN, designs a loss function containing physical constraints for industrial processes, and validates the effectiveness of the method using a common dataset in the field of industrial process fault diagnosis. The experimental results show that the proposed method not only makes the generated data consistent with the physical constraints of the industrial process, but also has better fault diagnosis performance than the existing GAN-based methods.
基金funding support from the National Natural Science Foundation of China(62394343)the Program of Introducing Talents of Discipline to Universities(the 111 Project)(B17017).
文摘To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing.However,the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed.In this perspective paper,we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing,with a focus on chemical product design,process synthesis and design,process control,and process safety and hazards.
基金funding support from the Ministry of Science and Technology of P.R.China under Award No.2024YFE0213600the National Natural Science Foundation of China under Award No.52433013,52103286+1 种基金the Shaanxi Bureau of Science and Technology under Award No.2022KWZ-07the"Shccig-Qinling"Program under the contract No.SMYJY202300321C。
文摘The low-cost industrial application of perovskite solar cells requires an environmentally friendly and scalable fabrication process.However,achieving high-quality perovskite layers under these requirements is challenging because the multi-step optimization with multiple intercorrelated experimental variables typically requires the development of a new deposition process.To address this,we propose a two-step machine learning approach for creating a new method for perovskite deposition in ambient air and antisolvent-free processing with a low-toxicity solvent triethyl phosphate(TEP).The two-step machine learning approach integrates a precursor solubility prediction model and a device-efficiency prediction model within a Bayesian optimization framework.This framework enables the information of solubility to be passed as a constraint function when optimizing the efficiency of perovskite solar cells,facilitating a quick optimization of a TEP-based,vacuum-quenching-assisted deposition in ambient air.Furthermore,the optimal precursor solution is subsequently applied to FAPbI_(3) perovskite devices,achieving a device power conversion efficiency of 24.26%under ambient conditions(23℃and~50%relative humidity).This work demonstrates the promising potential of machine learning to expedite new fabrication processes to fulfill industrial needs.
基金Ting Chen thanks the China Scholarship Council for the Award of a Fellowship(No.202006230137)Benjamin Klusemann ac-knowledges funding by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-project number 544306307+1 种基金Banglong Fu acknowledge the financial support of the National Natural Science Foundation of China(Grant No.52405386)State Key Laboratory of Precision Welding&Joining of Materials and Structures(Grant No.MSWJ-24M13).
文摘Constrained Friction Processing(CFP),a novel friction-based technique,has been developed to efficiently process fine-grained magnesium(Mg)rods,expanding the potential applications of biodegradable Mg alloys in medical implants.This study investigates the enhancement of mechanical properties through the implementation of multiple pass CFP(MP-CFP)in comparison to the conventional single pass CFP.The results reveal a substantial improvement in compressive yield strength(CYS),ultimate compressive strength,and failure plastic strain by 11%,28%,and 66%,respectively.A comprehensive analysis of material evolution during processing and the effects of the final microstructure on mechanical properties was conducted.The intricate material flow behavior during the final plunge stage of MP-CFP results in a reduced intensity of local basal texture and macrotexture.The diminished intensity of basal texture,combined with a low geometrical compatibility factor at the top of the rod after MP-CFP,effectively impedes slip transfer across grain boundaries.This leads to a local strain gradient along the compression direction,ultimately contributing to the observed enhancement in mechanical properties.The Mg-0.5Zn0.3Ca(wt.%)alloy,after texture modification by MP-CFP,exhibits a competitive CYS compared with other traditional methods,highlighting the promising application potential of MP-CFP.
基金supported by the Research Program of the Liaoning Liaohe Laboratory(LLL23ZZ-05-012)China Academy of Engineering Institute of Land Cooperation Consulting Project(2023-DFZD-60-02)+3 种基金the Key Research and Development Program of Liaoning Province(2023JH26/10200011)the National Natural Science Foundation of China(61991404)the National Key Research and Development Program of China(2024YFB3309700)the Science and Technology Major Project 2024 of Liaoning Province(2024JH1/11700048).
文摘Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industrial process,this paper introduces the concept of intelligent ODM in industrial process,shapes its future directions,and highlights key technical challenges.By the tight conjoining of and coordination between industrial artificial intelligence(AI)with industrial control and optimization technologies,as well as the Industrial Internet with industrial computer management and control systems,an intelligent operational optimization decision-making methodology is proposed for complex industrial process.The intelligent ODM methodology and its successful application demonstrate that the tight conjoining of and coordination between next-generation information technologies with industrial control and optimization technologies will promote the development of industrial intelligent ODM.Finally,main research directions and ideas are outlined for realizing intelligent ODM in industrial process.
基金The funding has been received from CSIR New Delhi.
文摘Xylonic acid is a versatile and valuable chemical with a range of industrial and potential health applications.The high demand for renewable energy and sustainable products has increased interest in xylonic acid as a starting material for the production of variety of high value chemicals and biofuels.The production of xylonic acid from biomass have an added advantage that it provides an opportunity to utilize bio-waste materials that would otherwise be discarded,reducing environmental hazards and reducing waste disposal costs.The aim of this research was to establish a bioprocess for producing xylonic acid from acid pre-treated sawdust liquor via fermentation,utilizing metabolically engineered strain of Corynebacterium glutamicum ATCC 31831.A maximum titre of 48.5±0.2 g/L xylonic acid from 60 g/L xylose derived from biomass was obtained with a yield of 0.89 g/g xylose which corresponds to a conversion of 62%.30.8±0.4 g/L of xylonic acid crystals of 95.5%purity were successfully recovered from 1L sawdust APL(Acid Pre-treated Liquor)via solvent precipitation and crystallization.Purified xylonic acid crystals were checked for its antibacterial effect on Salmonella enterica MTCC 3224,Escherichia coli MTCC 443 and Staphylococcus aureus MTCC 96.
基金supported by the National Natural Science Foundation of China(NSFC)under Grants(Nos.U21A20483,62373040 and 62273031).
文摘In the context of intelligent manufacturing,the modern hot strip mill process(HSMP)shows characteristics such as diversification of products,multi-specification batch production,and demand-oriented customization.These characteristics pose significant challenges to ensuring process stability and consistency of product performance.Therefore,exploring the potential relationship between product performance and the production process,and developing a comprehensive performance evaluation method adapted to modern HSMP have become an urgent issue.A comprehensive performance evaluation method for HSMP by integrating multi-task learning and stacked performance-related autoencoder is proposed to solve the problems such as incomplete performance indicators(PIs)data,insufficient real-time acquisition requirements,and coupling of multiple PIs.First,according to the existing Chinese standards,a comprehensive performance evaluation grade strategy for strip steel is designed.The random forest model is established to predict and complete the parts of PIs data that could not be obtained in real-time.Second,a stacked performance-related autoencoder(SPAE)model is proposed to extract the deep features closely related to the product performance.Then,considering the correlation between PIs,the multi-task learning framework is introduced to output the subitem ratings and comprehensive product performance rating results of the strip steel online in real-time,where each task represents a subitem of comprehensive performance.Finally,the effectiveness of the method is verified on a real HSMP dataset,and the results show that the accuracy of the proposed method is as high as 94.8%,which is superior to the other comparative methods.
基金support from the following foundations:the National Natural Science Foundation of China(62322309,62433004)Shanghai Science and Technology Innovation Action Plan(23S41900500)Shanghai Pilot Program for Basic Research(22TQ1400100-16).
文摘Accurate fault root cause diagnosis is essential for ensuring stable industrial production. Traditional methods, which typically rely on the entire time series and overlook critical local features, can lead to biased inferences about causal relationships, thus hindering the accurate identification of root cause variables. This study proposed a shapelet-based state evolution graph for fault root cause diagnosis (SEG-RCD), which enables causal inference through the analysis of the important local features. First, the regularized autoencoder and fault contribution plot are used to identify the fault onset time and candidate root cause variables, respectively. Then, the most representative shapelets were extracted to construct a state evolution graph. Finally, the propagation path was extracted based on fault unit shapelets to pinpoint the fault root cause variable. The SEG-RCD can reduce the interference of noncausal information, enhancing the accuracy and interpretability of fault root cause diagnosis. The superiority of the proposed SEG-RCD was verified through experiments on a simulated penicillin fermentation process and an actual one.
