Causal temporal graph attention network for fault diagnosis of chemical processes

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.

Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

Hydrochemical Characterisation and Assessment of the Level of Contamination of Groundwater Collected by Private Waterworks in the Town of Moundou in the South of Chad

Groundwater is the main source of drinking water for large cities in most African countries. In Moundou, for example, the conventional groundwater supply system is failing. To compensate for this state failure, the population is building boreholes and wells, most of which tap the surface water table, generally referred to as the “water table”. The aim of this study is to characterize these waters in order to assess their level of contamination and, by extension, the degree of pollution of the water table. Major elements such as: Chloride (Cl-), Sulfate (SO42-), Nitrate (NO3-), Calcium (Ca2+), magnesium (Mg2+), sodium (Na+) and potassium (K+) were analysed by Liquid Chromatography and the Bicarbonate ion (HCO3-) was determined by the titrimetric method. The methodology applied is based on a combination of hydrochemical techniques and statistical analysis (PCA and CHA). A sampling campaign was carried out during high-water periods. The results of the physico-chemical analyses show mineralization ranging from 7.29 to 3670 μS/cm, with an average of 487.44 μS/cm. The groundwater studied is generally acidic, with a pH ranging from 3.26 to 6.41. Based on their anions, they are classified into four main hydrochemical facies: chloride and sulphate facies, calcium and magnesium facies, sodium and potassium facies and bicarbonate facies. The various correlations between major ions and statistical analyses have enabled us to identify three hydrogeochemical processes involved in water mineralization. The dominant process is silicate hydrolysis, followed by cation exchange, then anthropogenic input, which influences mineralization by polluting the water.

Protective mechanism of Paeoniae Radix Alba against chemical liver injury based on network pharmacology,molecular docking,and in vitro experiments

Objective:To explore and validate the potential targets of Paeoniae Radix Alba(P.Radix,Bai Shao)in protecting against chemical liver injury through network pharmacology,molecular docking technology,and in vitro cell experiments.Methods:Network pharmacology was used to identify the common potential targets of P.Radix and chemical liver injury.Molecular docking was used to fit the components,which were subsequently verified in vitro.A cell model of hepatic fibrosis was established by activating hepatic stellate cell(HSC)-LX2 cells with 10 ng/mL transforming growth factor-β1.The cells were exposed to different concentrations of total glucosides of paeony(TGP),the active substance of P.Radix,and then evaluated using the cell counting kit-8 assay,enzyme-linked immunosorbent assay,and western blot.Results:Analysis through network pharmacology revealed 13 key compounds of P.Radix,and the potential targets for preventing chemical liver injury were IL-6,AKT serine/threonine kinase 1,jun protooncogene,heat shock protein 90 alpha family class A member 1(HSP90AA1),peroxisome proliferator activated receptor gamma(PPARG),PTGS2,and CASP3.Gene Ontology(GO)enrichment analysis indicated the involvement of response to drugs,membrane rafts,and peptide binding.Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis revealed that the main pathways involved lipid and atherosclerosis and chemical carcinogenesis-receptor activation.Paeoniflorin and albiflorin exhibited strong affinity for HSP90AA1,PTGS2,PPARG,and CASP3.Different concentrations of TGP can inhibit the expression of COL-I,COL-III,IL-6,TNF-a,IL-1β,HSP-90a,and PTGS2 while increasing the expression of PPAR-γand CASP3 in activated HSC-LX2 cells.Conclusion:P.Radix primarily can regulate targets such as HSP90AA1,PTGS2,PPARG,CASP3.TGP,the main active compound of P.Radix,protects against chemical liver injury by reducing the inflammatory response,activating apoptotic proteins,and promoting the apoptosis of activated HSCs.

The autophagy-lysosome pathway:a potential target in the chemical and gene therapeutic strategies for Parkinson’s disease

Parkinson’s disease is a common neurodegenerative disease with movement disorders associated with the intracytoplasmic deposition of aggregate proteins such asα-synuclein in neurons.As one of the major intracellular degradation pathways,the autophagy-lysosome pathway plays an important role in eliminating these proteins.Accumulating evidence has shown that upregulation of the autophagy-lysosome pathway may contribute to the clearance ofα-synuclein aggregates and protect against degeneration of dopaminergic neurons in Parkinson’s disease.Moreover,multiple genes associated with the pathogenesis of Parkinson’s disease are intimately linked to alterations in the autophagy-lysosome pathway.Thus,this pathway appears to be a promising therapeutic target for treatment of Parkinson’s disease.In this review,we briefly introduce the machinery of autophagy.Then,we provide a description of the effects of Parkinson’s disease–related genes on the autophagy-lysosome pathway.Finally,we highlight the potential chemical and genetic therapeutic strategies targeting the autophagy–lysosome pathway and their applications in Parkinson’s disease.

SIMPLIFIED FRAMEWORK FOR DESCRIPTION OF MIXING WITH CHEMICAL REACTIONS(Ⅱ)CHEMICAL REACTIONS IN THE DIFFERENT MIXING REGIONS

An integral description of chemical reactions taken place under mixing was formulated on the basis of the three-region model pictured in Part Ⅰ of this study. A mathematically simplified version of the model was developed. It was shown that the converted portion of the material in the dispersion region can be neglected provided that the initial concentration ratio of A and B was less than 0.1, and the effects of micro - and macromixing on the chemical reactions could be represented by two dimensionless numbers: Dα and Π. The model was applied to two reacting systems: competitive-consecutive reactions and parallel reactions. Agreement between model prediction and experimental results demonstrated that the present framework, though in simplified form, is capable of describing reactive mixing without loss of any significant features of the process.

An Overview of Metal-Organic Frameworks for Green Chemical Engineering

Given the current global energy and environmental issues resulting from the fast pace of industrialization,the discovery of new functional materials has become increasingly imperative in order to advance science and technology and address the associated challenges.The boom in metal–organic frameworks(MOFs)and MOF-derived materials in recent years has stimulated profound interest in exploring their structures and applications.The preparation,characterization,and processing of MOF materials are the basis of their full engagement in industrial implementation.With intensive research in these topics,it is time to promote the practical utilization of MOFs on an industrial scale,such as for green chemical engineering,by taking advantage of their superior functions.Many famous MOFs have already demonstrated superiority over traditional materials in solving real-world problems.This review starts with the basic concept of MOF chemistry and ends with a discussion of the industrial production and exploitation of MOFs in several fields.Its goal is to provide a general scope of application to inspire MOF researchers to convert their focus on academic research to one on practical applications.After the obstacles of cost,scale-up preparation,processability,and stability have been overcome,MOFs and MOF-based devices will gradually enter the factory,become a part of our daily lives,and help to create a future based on green production and green living.

Dietary, Anthropometric, Biochemical and Psychiatric Indices in Shift Work Nurses

Objectives: The aim of this study was to compare nutrients intake, anthropometric, biochemical and psychiatric indices between shift working and day-time nurses. Methods: A cross-sectional comparative study was conducted in which ninety eight female nurses (55 day-time workers and 43 shift workers) from six educational hospitals of Jondi-Shapour University of medical sciences, Ahvaz, Iran were participated. A questionnaire including dietary, anthropometric, disease history and lifestyle pattern questions was completed and 3-day 24-hour recalls, Beck Depression Inventory (BDI) and Hospital Anxiety and Depression Scale (HADS) were collected. Serum hs-CRP and 25(OH)D3 concentrations were measured by immunoturbidimetric and electrochemiluminescent immunoassay method, respectively. Anthropometric indices were measured according to World health organization standard protocol. Independent sample t and chi-square tests were used for statistical analysis. Results: There was a lower dietary intake of thiamin, riboflavin, niacin, folate, magnesium and iron in shift worker compared with day-time nurses (p < 0.05). No significant differences in serum hs-CRP concentrations, serum 25(OH)D3 levels, vitamin D deficiency percentage, hemoglobin and hematocrite concentrations, and also anthropometric and psychiatric variables were found between two groups. Duration of exposure to sunlight was significantly higher in shift workers than in day time nurses. Engagement time in weekly physical exercise was around 11 times greater in day-time nurses compared with the shift work nurses (p = 0.001). Conclusions: This study showed that shift working is associated with some nutritional deficiencies and sedentary lifestyle among female nurses.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

A Rational Design of Metal–Organic Framework Nanozyme with High‑Performance Copper Active Centers for Alleviating Chemical Corneal Burns

Metal–organic frameworks(MOFs)have attracted significant research interest in biomimetic catalysis.However,the modulation of the activity of MOFs by precisely tuning the coordination of metal nodes is still a significant challenge.Inspired by metalloenzymes with well-defined coordination structures,a series of MOFs containing halogen-coordinated copper nodes(Cu-X MOFs,X=Cl,Br,I)are employed to elucidate their structure–activity relationship.Intriguingly,experimental and theoretical results strongly support that precisely tuning the coordination of halogen atoms directly regulates the enzyme-like activities of Cu-X MOFs by influencing the spatial configuration and electronic structure of the Cu active center.The optimal Cu–Cl MOF exhibits excellent superoxide dismutase-like activity with a specific activity one order of magnitude higher than the reported Cu-based nanozymes.More importantly,by performing enzyme-mimicking catalysis,the Cu–Cl MOF nanozyme can significantly scavenge reactive oxygen species and alleviate oxidative stress,thus effectively relieving ocular chemical burns.Mechanistically,the antioxidant and antiapoptotic properties of Cu–Cl MOF are achieved by regulating the NRF2 and JNK or P38 MAPK pathways.Our work provides a novel way to refine MOF nanozymes by directly engineering the coordination microenvironment and,more significantly,demonstrating their potential therapeutic effect in ophthalmic disease.

Adaptive multiscale convolutional neural network model for chemical process fault diagnosis

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

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.

Valorization of Camellia oleifera oil processing byproducts to value-added chemicals and biobased materials: A critical review

The C.oleifera oil processing industry generates large amounts of solid wastes,including C.oleifera shell(COS)and C.oleifera cake(COC).Distinct from generally acknowledged lignocellulosic biomass(corn stover,bamboo,birch,etc.),Camellia wastes contain diverse bioactive substances in addition to the abundant lignocellulosic components,and thus,the biorefinery utilization of C.oleifera processing byproducts involves complicated processing technologies.This reviewfirst summarizes various technologies for extracting and converting the main components in C.oleifera oil processing byproducts into value-added chemicals and biobased materials,as well as their potential applications.Microwave,ultrasound,and Soxhlet extractions are compared for the extraction of functional bioactive components(tannin,flavonoid,saponin,etc.),while solvothermal conversion and pyrolysis are discussed for the conversion of lignocellulosic components into value-added chemicals.The application areas of these chemicals according to their properties are introduced in detail,including utilizing antioxidant and anti-in-flammatory properties of the bioactive substances for the specific application,as well as drop-in chemicals for the substitution of unrenewable fossil fuel-derived products.In addition to chemical production,biochar fabricated from COS and its applications in thefields of adsorption,supercapacitor,soil remediation and wood composites are comprehensively reviewed and discussed.Finally,based on the compositions and structural characteristics of C.oleifera byproducts,the development of full-component valorization strategies and the expansion of the appli-cationfields are proposed.

Ubiquitous Sensor Network for Chemical Sensors

Wireless sensor networks have been identified as one of the most important technologies for the 21 st century.Recent advances in micro sensor fabrication technology and wireless communication technology enable the practical deployment of large-scale,low-power,inexpensive sensor networks.Such an approach offers an advantage over traditional sensing methods in many ways:large-scale,dense deployment not only extends spatial coverage and achieves higher resolution,but also increases the system's fault-tolerance and robustness.Moreover,the ad-hoc nature of wireless sensor networks makes them even more attractive for military and other risk-associated applications,such as environmental observation and habitat monitoring.

Jet formation and penetration performance of a double-layer charge liner with chemically-deposited tungsten as the inner liner

This paper proposes a type of double-layer charge liner fabricated using chemical vapor deposition(CVD)that has tungsten as its inner liner.The feasibility of this design was evaluated through penetration tests.Double-layer charge liners were fabricated by using CVD to deposit tungsten layers on the inner surfaces of pure T2 copper liners.The microstructures of the tungsten layers were analyzed using a scanning electron microscope(SEM).The feasibility analysis was carried out by pulsed X-rays,slug-retrieval test and static penetration tests.The shaped charge jet forming and penetration law of inner tungsten-coated double-layer liner were studied by numerical simulation method.The results showed that the double-layer liners could form well-shaped jets.The errors between the X-ray test results and the numerical results were within 11.07%.A slug-retrieval test was found that the retrieved slug was similar to a numerically simulated slug.Compared with the traditional pure copper shaped charge jet,the penetration depth of the double-layer shaped charge liner increased by 11.4% and>10.8% respectively.In summary,the test results are good,and the numerical simulation is in good agreement with the test,which verified the feasibility of using the CVD method to fabricate double-layer charge liners with a high-density and high-strength refractory metal as the inner liner.

A Survey on Chemical Hazards and Liver Function in Rubber Industry Workers in Iran

Rubber industries workers are at risk of hepatic dysfunction from exposure to rubber dust and a mixture of different hydrocarbons. This study aimed to evaluate the relation between chemical hazards in workplace and serum hepatic transaminases in rubber industry workers in Iran. This historical cohort study was conducted in a rubber-manufacturing industry in Mashhad. It con-sisted of 91 workers with chemical substances exposure and 85 workers not exposed to chemicals. Hepatic transaminases including alanine transaminase (ALT) and aspartate transaminase (AST) were compared between these two groups. Required data were collected through questionnaire, liver enzymes evaluation, and environmental monitoring of chemical substances. The data were analyzed with SPSS 11.5. A total of 176 workers enrolled in this study included 113 (64.2%) male and 73 (35.8%) female. The mean of age and employment duration in exposed group were 29.22 ± 3.99 and 5.41 ± 2.77 and in non-exposed group were 30.33 ± 4.11 and 5.29 ± 2.77 respectively. The mean of ALT in exposed and non-exposed groups was 31.3 ± 8.04 and 23.8 ± 10.42 (p = 0.01) and the mean of AST was 29.6 ± 9.7 and 27.3 ± 8.2 (p = 0.1) respectively. A significant difference was shown between mean of ALT at the date of employment and at the time of the study in exposed group. According to our findings evaluation of serum transaminases is a useful screening method which might be beneficial in early detection of hepatocellular injuries in rubber industry workers.

Oxidative DNA damages by chemical exposures at work

Oxidative DNA damage is an inevitable consequence of cellular metabolism, with a propensity for increased levels following toxic insult. Of the molecules subject to oxidative modification, DNA has received the greatest attention, with biomarkers of exposure and effect closest to validation. There are many chemicals in workplaces that could cause oxidative DNA damages such as carcinogens. This review concentrated on studies published between the years 2000 and 2012 that used to detect 8-oxodG in humans (workers), laboratory animals and in cell lines. Given the recent toxicological results from oxidative stress, it is important to review these studies to improve the current understanding of the oxidative DNA damages by chemical exposures at work. It also suggests that biomarkers may be responsible for understanding the role of oxidative DNA damage in disease, highlighting the need for further studies.

UAV-assisted data collection for wireless sensor networks with dynamic working modes

Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.

Flow characteristics and hot workability of a typical low-alloy high-strength steel during multi-pass deformation

Heavy components of low-alloy high-strength(LAHS) steels are generally formed by multi-pass forging. It is necessary to explore the flow characteristics and hot workability of LAHS steels during the multi-pass forging process, which is beneficial to the formulation of actual processing parameters. In the study, the multi-pass hot compression experiments of a typical LAHS steel are carried out at a wide range of deformation temperatures and strain rates. It is found that the work hardening rate of the experimental material depends on deformation parameters and deformation passes, which is ascribed to the impacts of static and dynamic softening behaviors. A new model is established to describe the flow characteristics at various deformation passes. Compared to the classical Arrhenius model and modified Zerilli and Armstrong model, the newly proposed model shows higher prediction accuracy with a confidence level of 0.98565. Furthermore, the connection between power dissipation efficiency(PDE) and deformation parameters is revealed by analyzing the microstructures. The PDE cannot be utilized to reflect the efficiency of energy dissipation for microstructure evolution during the entire deformation process, but only to assess the efficiency of energy dissipation for microstructure evolution in a specific deformation parameter state.As a result, an integrated processing map is proposed to better study the hot workability of the LAHS steel, which considers the effects of instability factor(IF), PDE, and distribution and size of grains. The optimized processing parameters for the multi-pass deformation process are the deformation parameters of 1223–1318 K and 0.01–0.08 s^(-1). Complete dynamic recrystallization occurs within the optimized processing parameters with an average grain size of 18.36–42.3 μm. This study will guide the optimization of the forging process of heavy components.

THE USE OF DNA ADDUCTS AS MEASURE OF ENVIRONMENTAL CHEMICAL EXPOSURES IN THE WORK PLACE.

Workers in many occupational settings areexposed to toxic chemicals and/or chemicalmixtures.Many of these chemicals or theirmetabolites are electrophilic genotoxic agentsand potential carcinogens which can covalentlybind to cellular nucleophiles such as DNA toform DNA adducts.DNA adducts,therefore,