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.

Microfluidic field strategy for enhancement and scale up of liquid-liquid homogeneous chemical processes by optimization of 3D spiral baffle structure

Due to the scale effect, the uniform distribution of reagents in continuous flow reactor becomes bad when the channel is enlarged to tens of millimeters. Microfluidic field strategy was proposed to produce high mixing efficiency in large-scale channel. A 3D spiral baffle structure(3SBS) was designed and optimized to form microfluidic field disturbed by continuous secondary flow in millimeter scale Y-shaped tube mixer(YSTM). Enhancement effect of the 3SBS in liquid-liquid homogeneous chemical processes was verified and evaluated through the combination of simulation and experiment. Compared with 1 mm YSTM, 10 mm YSTM with 3SBS increased the treatment capacity by 100 times, shortened the basic complete mixing time by 0.85 times, which proves the potential of microfluidic field strategy in enhancement and scale-up of liquid-liquid homogeneous chemical process.

Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes

Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.

Recent progress on equation-oriented optimization of complex chemical processes

Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient(PT)modeling method to address these difficulties,providing a fresh way forward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.

A New Optimal Control System Design for Chemical Processes

Based on frequency response and convex optimization,a novel optimal control system was developed for chemical processes.The feedforward control is designed to improve the tracking performance of closed loop chemical systems.The parametric model is not required because the system directly utilizes the frequency response of the loop transfer function,which can be measured accurately.In particular,the extremal values of magnitude and phase can be solved according to constrained quadratic programming optimizer and convex optimization.Simulation examples show the effectiveness of the method.The design method is simple and easily adopted in chemical industry.

Dynamic chemical processes on ZnO surfaces tuned by physisorption under ambient conditions

The catalytic properties of non-reducible metal oxides have intrigued continuous interest in the past decades.Often time,catalytic studies of bulk non-reducible oxides focused on their high-temperature applications owing to their weak interaction with small molecules.Hereby,combining ambient-pressure scanning tunneling microscopy(AP-STM),AP X-ray photoelectron spectroscopy(AP-XPS)and density functional theory(DFT)calculations,we studied the activation of CO and CO_(2)on ZnO,a typical nonreducible oxide and major catalytic material in the conversion of C1 molecules.By visualizing the chemical processes on ZnO surfaces at the atomic scale under AP conditions,we showed that new adsorbate structures induced by the enhanced physisorption and the concerted interaction of physisorbed molecules could facilitate the activation of CO and CO_(2)on ZnO.The reactivity of ZnO towards CO could be observed under AP conditions,where an ordered(2×1)–CO structure was observed on ZnO(1010).Meanwhile,chemisorption of CO_(2)on ZnO(1010)under AP conditions was also enhanced by physisorbed CO_(2),which minimizes the repulsion between surface dipoles and causes a(3×1)–CO_(2)structure.Our study has brought molecular insight into the fundamental chemistry and catalytic properties of ZnO surfaces under realistic reaction conditions.

A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.

Dynamic Multi-objective Optimization of Chemical Processes Using Modified BareBones MOPSO Algorithm

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.

Increasing significance of advanced physical/chemical processes in the development and application of sustainable wastewater treatment systems

The awareness of the problem of the scarcity of water of high quality has strongly changed the approach of wastewater treatment.Currently,there is an increasing need for the beneficial reuse of treated wastewater and to recover valuable products and energy from the wastewater.Because microbiological treatment methods are,only to a limited part,able to satisfy these needs,the role and significance of physical/chemical processes in wastewater treatment are gaining more and more interest.The specific future role and aim of the various physical/chemical treatment processes can be categorized in five groups:improvement of the performance of microbiological treatment processes,achievement of the high quality required for reuse of the effluent,recovery of valuable components and energy from the wastewater for beneficial reuse,desalination of brackish water and seawater,and treatment of concentrated liquid or solid waste residues produced in a wastewater treatment process.Development of more environmentally sustainable wastewater treatment chains in which physical/chemical processes play a crucial role,also requires application of process control and modeling strategies.This is briefly introduced by the elaboration of treatment scenarios for three specific wastewaters.

Development of Bulk Organic Chemical Processes—History,Status,and Opportunities for Academic Research

The production of bulk organic chemicals has a strong impact on our daily life.In this review,an overview of important industrial processes using homogeneous catalysts is given.Using specific carbonylation and hydrogenation case studies,we want to show how basic research can contribute to the development of such processes and what challenges exist in this area of academic research.

First-principles calculation of core-level binding energy shift in surface chemical processes

Combined with third generation synchrotron radiation light sources, X-ray photoelectron spectroscopy (XPS) with higher energy resolution, brilliance, enhanced surface sensitivity and photoemission cross section in real time found extensive applications in solid-gas interface chemistry. This paper reports the calculation of the core-level binding energy shifts (CLS) using the first-principles density functional theory. The interplay between the CLS calculations and XPS measurements to uncover the structures, adsorption sites and chemical reactions in complex surface chemical processes are highlight. Its application on clean low index (111) and vicinal transition metal surfaces, molecular adsorption in terms of sites and configuration, and reaction kinetics are domonstrated.

Green microfluidics in microchemical engineering for carbon neutrality

The concept of“carbon neutrality”poses a huge challenge for chemical engineering and brings great opportunities for boosting the development of novel technologies to realize carbon offsetting and reduce carbon emissions.Developing high-efficient,low-cost,energy-efficient and eco-friendly microfluidicbased microchemical engineering is of great significance.Such kind of“green microfluidics”can reduce carbon emissions from the source of raw materials and facilitate controllable and intensified microchemical engineering processes,which represents the new power for the transformation and upgrading of chemical engineering industry.Here,a brief review of green microfluidics for achieving carbon neutral microchemical engineering is presented,with specific discussions about the characteristics and feasibility of applying green microfluidics in realizing carbon neutrality.Development of green microfluidic systems are categorized and reviewed,including the construction of microfluidic devices by bio-based substrate materials and by low carbon fabrication methods,and the use of more biocompatible and nondestructive fluidic systems such as aqueous two-phase systems(ATPSs).Moreover,low carbon applications benefit from green microfluidics are summarized,ranging from separation and purification of biomolecules,high-throughput screening of chemicals and drugs,rapid and cost-effective detections,to synthesis of fine chemicals and novel materials.Finally,challenges and perspectives for further advancing green microfluidics in microchemical engineering for carbon neutrality are proposed and discussed.

Extraction and analysis of risk factors from Chinese chemical accident reports

Accidents in chemical production usually result in fatal injury,economic loss and negative social impact.Chemical accident reports which record past accident information,contain a large amount of expert knowledge.However,manually finding out the key factors causing accidents needs reading and analyzing of numerous accident reports,which is time-consuming and labor intensive.Herein,in this paper,a semiautomatic method based on natural language process(NLP)technology is developed to construct a knowledge graph of chemical accidents.Firstly,we build a named entity recognition(NER)model using SoftLexicon(simplify the usage of lexicon)+BERT-Transformer-CRF(conditional random field)to automatically extract the accident information and risk factors.The risk factors leading to accident in chemical accident reports are divided into five categories:human,machine,material,management,and environment.Through analysis of the extraction results of different chemical industries and different accident types,corresponding accident prevention suggestions are given.Secondly,based on the definition of classes and hierarchies of information in chemical accident reports,the seven-step method developed at Stanford University is used to construct the ontology-based chemical accident knowledge description model.Finally,the ontology knowledge description model is imported into the graph database Neo4j,and the knowledge graph is constructed to realize the structu red storage of chemical accident knowledge.In the case of information extraction from 290 Chinese chemical accident reports,SoftLexicon+BERT-Transformer-CRF shows the best extraction performance among nine experimental models.Demonstrating that the method developed in the current work can be a promising tool in obtaining the factors causing accidents,which contributes to intelligent accident analysis and auxiliary accident prevention.

Modeling and Optimization for Scheduling of Chemical Batch Processes

Chemical batch processes have become significant in chemical manufacturing. In these processes, large numbers of chemical products are produced to satisfy human demands in daily life. Recently, economy globalization has resulted, in growing worldwide competitions in tradi.tional chemical .process industry. In order to keep competitive in the global marketplace, each company must optimize its production management and set up a reactive system for market fluctuation. Scheduling is the core of production management in chemical processes. The goal of this paper is to review the recent developments in this challenging area. Classifications of batch scheduling problems and optimization methods are introduced. A comparison of six typical models is shown in a general benchmark example from the literature. Finally, challenges and applications in future research are discussed.

Study on trifluoromethanesulfonic acid-promoted synthesis of daidzein: Process optimization and reaction mechanism

Daidzein has been widely used in pharmaceuticals,nutraceuticals,cosmetics,feed additives,etc.Its preparation process and related reaction mechanism need to be further investigated.A cost-effective process for synthesizing daidzein was developed in this work.In this article,a two-step synthesis of daidzein(Friedel–Crafts acylation and[5+1]cyclization)was developed via the employment of trifluoromethanesulfonic acid(TfOH)as an effective promoting reagent.The effect of reaction conditions such as solvent,the amount of TfOH,reaction temperature,and reactant ratio on the conversion rate and the yield of the reaction,respectively,was systematically investigated,and daidzein was obtained in 74.0%isolated yield under optimal conditions.Due to the facilitating effect of TfOH,the Friedel–Crafts acylation was completed within 10 min at 90℃ and the[5+1]cyclization was completed within 180 min at 25℃.In addition,a possible reaction mechanism for this process was proposed.The results of the study may provide useful guidance for industrial production of daidzein on a large scale.

Emerging importance of shale gas to both the energy & chemicals landscape

This perspectives article is intended highlight the growing importance and emergence of shale gas as an energy resource and as a source of chemicals. Over the next decades huge amounts of newly discovered deposits of trapped gas are expected to be produced not only in the USA but elsewhere providing a wealth of methane and ethane not only used for energy production, but also for conversion to lower hydrocarbon chemicals. This manuscript seeks to focus on the potential of trapped natural gas around the world. The potential new volumes of trapped gas within shale or other mineral strata coming to the marketplace offer a tremendous opportunity if scientists can invent new, cost effective ways to convert this methane to higher value chemicals. Understanding how to selectively break a single C-H bond in methane while minimizing methane conversion to C02 is critical.

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.

Chemical reduction-induced fabrication of graphene hybrid fibers for energy-dense wire-shaped supercapacitors

The emerging one-dimensional wire-shaped supercapacitors(SCs)with structural advantages of low mass/volume structural advantages hold great interests in wearable electronic engineering.Although graphene fiber(GF)has full of vigor and tremendous potentiality as promising linear electrode for wire-shaped SCs,simultaneously achieving its facile fabrication process and satisfactory electrochemical performance still remains challenging to date.Herein,two novel types of graphene hybrid fibers,namely ferroferric oxide dots(FODs)@GF and N-doped carbon polyhedrons(NCPs)@GF,have been proposed via a simple and efficient chemical reduction-induced fabrication.Synergistically coupling the electroactive units(FODs and NCPs)with conductive graphene nanosheets endows the fiber-shaped architecture with boosted electrochemical activity,high flexibility and structural integrity.The resultant FODs@GF and NCPs@GF hybrid fibers as linear electrodes both exhibit excellent electrochemical behaviors,including large volumetric specific capacitance,good rate capability,as well as favorable electrochemical kinetics in ionic liquid electrolyte.Based on such two linear electrodes and ionogel electrolyte,a highperformance wire-shaped SC is effectively assembled with ultrahigh volumetric energy density(26.9 mW·cm^(-3)),volumetric power density(4900 mW·cm^(-3))and strong durability over 10,000 cycles under straight/bending states.Furthermore,the assembled wire-shaped SC with excellent flexibility and weavability acts as efficient energy storage device for the application in wearable electronics.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.