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.

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.

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.

Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.

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.

Chemical and biological flocculation process to treat municipal sewage and analysis of biological function

The pilot scale experimental apparatus and the procedure of the chemical and biological flocculation process to verify the feasibility in treating Shanghai municipal sewage were introduced in this paper. In addition, the biological function of the process was discussed. The results of optimal running showed that in the reaction tank, the concentration of mixed liquor suspended solid(MLSS) was 2 g/L, hydraulic retention time(HRT) was 35 min, dosage of liquid polyaluminium chloride(PAC) was 60 mg/L, and the concentration of polyacrylamide(PAM) was 0 5 mg/L. The effluent average concentrations of COD Cr , TP, SS and BOD 5 were 50 mg/L, 0 62 mg/L, 18 mg/L, and 17 mg/L, respectively. These were better than the designed demand. In addition, the existence of biological degradation in this system was proven by several methods. The removal efficiencies of the chemical and biological flocculation process were 20% higher than that of the chemical flocculation process above at the same coagulant dosage. The treatment process under different situations was evaluated on a pilot scale experiment, and the results provided magnificent parameters and optimal condition for future operation of the plant.

Microwave enhanced chemical reduction process for nitrite-containing wastewater treatment using sulfaminic acid

High-concentration nitrite-containing wastewater that presents extreme toxicity to human health and organisms is difficult to be treated using traditional biological process. In this study, a novel microwave-enhanced chemical reduction process （MECRP） using sulfarninic acid （SA） was proposed as a new manner to treat such type of wastewater. Based on lab-scale experiments, it was shown that 75%-80% nitrite （NO2-） could be removed within time as short as 4 min under 50 W microwave irradiation in pH range 5-10 when molar ratio of SA to nitrite （SA/NO2-） was 0.8. Pilot-scale investigations demonstrated that MECRP was able to achieve nitrite and chemical oxygen demand （COD） removal with efficiency up to 80% and 20%, respectively under operating conditions of SA concentration 80 kg/m3, SA/NO2- ratio 0.8, microwave power 3.4 kW, and stirring time 3 min. Five-day biological oxygen demand （BODs）/COD value of treated effluent after MECRP was increased from 0.05 to 0.36 （by 620%）, which clearly suggested a considerable improvement of biodegradability for subsequent biological treatment. This study provided a demonstration of using microwave irradiation to enhance reaction between SA and nitrite in a short time, in which nitrite in wastewater was completely converted into nitrogen gas without leaving any sludge and secondary pollutants.

Chemical looping gasification of maceral from low-rank coal: Products distribution and kinetic analysis on vitrinite

The product distribution and kinetic analysis of low-rank coal vitrinite were investigated during the chemical looping gasification(CLG)process.The acid washing method was used to treat low-rank coal,and the density gradient centrifugation method was adopted to obtain the coal macerals.By combining thermogravimetric analysis and online mass spectrometry,the influence of the heating rate and oxygen carrier(Fe2O3)blending ratio on product distribution was discussed.The macroscopic kinetic parameters were solved by the Kissinger-Akahira-Sunose(KAS)method,and the main gaseous product formation kinetic parameters were solved by the iso-conversion method.The results of vitrinite during slow heating chemical looping gasification showed that the main weight loss interval was 400–600℃,and the solid yield of sample vitrinite-Fe-10 at different heating rates was 64.30%–69.67%.When b=20℃·min^(-1),the maximum decomposition rate of vitrinite-Fe-10 was 0.312%min1.The addition of Fe2O_(3)reduced the maximum decomposition rate,but by comparing the chemical looping conversion characteristic index,it could be inferred that the chemical looping gasification of vitrinite might produce volatile substances higher than the pyrolysis process of vitrinite alone.The average activation energy of the reaction was significantly reduced during chemical looping gasification of vitrinite,which was lower than the average activation energy of 448.69 kJ·mol^(-1) during the pyrolysis process of vitrinite alone.The gaseous products were mainly CO and CO_(2).When the heating rate was 10℃·min^(-1),the highest activation energy for CH4 formation was 21.353 kJ·mol^(-1),and the lowest activation energy for CO formation was 9.7333 kJ·mol^(-1).This study provides basic data for exploring coal chemical looping gasification mechanism and reactor design by studying the chemical looping gasification process of coal macerals。

Surface Analysis of Chemical Stripping Titanium Alloy Oxide Films

The chemical stripping method of titanium alloy oxide films was studied. An environment friendly solution hydrogen peroxide and sodium hydroxide without hydrofluoric acid or fluoride were used to strip the oxide films. The morphologies of the surface and cross-section of the oxide films before and after the films stripping were characterized by using scanning electron microscopy （SEM）. The microstructure and chemical compositions of the oxide films before and after the films stripping were investigated by using Raman spectroscopy （Raman） and X-ray photoelectron spectroscopy （XPS）. It was shown that the thickness of the oxide film was in the range of 5-6 μm. The oxide films were stripped for 2 to 8 min in the solution. Moreover, the effect of the stripping time on the efficiency of the film stripping was investigated, and the optimum stripping time was between 6-8 min. When the stripping solution completely dissolved the whole film, the α/β microstructure of the titanium alloy Ti-10V-2Fe-3Al was partly revealed. The stripping mechanism was discussed in terms of the dissolution of film delamination. The hydrogen peroxide had a significant effect on the dissolution of the titanium alloy anodic oxide film. The feasibility of the dissolution reaction also was evaluated.

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.

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.

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.

Topology Chemical Process Research on the Condensing and Growing Phase Multi Micro Oil Droplets Based on Hydrodynamics

The hydrodynamic research about the droplet condensing of the multi phase liquid state on the surface of the coal glass and water discusses the deepening process of convex shape curve and the formation of S shape, and puts emphasis on describing the diagram formation method of the later. In the induction period the active diagram of the micro droplet is decided by pH value forming as convex shape diagram or S shape diagram. When pH value is above 4.0, the damage of convex shape diagram cannot be recovered, in that case produce S shape activity diagram. When pH value is equal to or above 12.0, the hard surface with alkali liquid state loses adhesion, so that the micro droplet condensing of the multi phase liquid state stops completely. The research result shows that the water cleaning conditions of getting rid of the oil micro droplets can be decided by the pH value.

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.

Opinion: Chemical--Mesoscopics..for the Mesoparticles Reactivity Explanation

The estimation of chemical particles reactivity and the determination of chemical reactions direction are the actual theme in new scientific trend-Chemical Mesoscopics.Paper includes the proposal about the using the theory of free energy linear dependence from physical organic chemistry and their applications for prognosis of reactions flowing.The semi-empiric constants is given according to mesoscopic physics definitions as well as the transformed Kolmogorov-Avrami equation is discussed.It is the development of Chemical Mesoscopics for organic reactivity estimation including nanostructures reactivity.

Influence of Chemical Precleaning on the Plasma Treatment Efficiency of Aluminum by RF Plasma Pencil

This paper is aimed to show the influence of initial chemical pretreatment prior to subsequent plasma activation of aluminum surfaces.The results of our study showed that the state of the topmost surface layer（i.e.the surface morphology and chemical groups）of plasma modified aluminum significantly depends on the chemical precleaning.Commonly used chemicals（isopropanol,trichlorethane,solution of Na OH in deionized water）were used as precleaning agents.The plasma treatments were done using a radio frequency driven atmospheric pressure plasma pencil developed at Masaryk University,which operates in Ar,Ar/O_2 gas mixtures.The effectiveness of the plasma treatment was estimated by the wettability measurements,showing high wettability improvement already after 0.3 s treatment.The effects of surface cleaning（hydrocarbon removal）,surface oxidation and activation（generation of OH groups）were estimated using infrared spectroscopy.The changes in the surface morphology were measured using scanning electron microscopy.Optical emission spectroscopy measurements in the near-to-surface region with temperature calculations showed that plasma itself depends on the sample precleaning procedure.

Significant Breakthrough in Industrial Test of the "Methanol to Olefins" Process Developed by Dalian Institute of Chemical Physics,Chinese Academy of Sciences

A process of ＂Methanol or Dimethylether to Olefins＂ developed by Dalian Institute of Chemical Physics （DICP）, designated as the DMTO process, has attained great success in industrial scaling up testing. DICP, by collaborating with the Xinxing Coal Chemical Co., Ltd. of Shaanxi Province and the Luoyang Petrochemical Engineering Co. of the SINOPEC Group, operated successfully a 50t（methanol）/d unit for the conversion of methanol to lower olefins, with a methanol conversion of close to 100%, and a selectivity to lower olefins（ethylene, propylene and butylenes） of higher than 90%. On 23rd August, the industrial test project has passed a state appraisal. The experts of the Appraisal Group, headed by Prof. YUAN Qingtang, academician of Chinese Academy of Engineering, drew the conclusions that the DMTO process, by utilizing a proprietary SAPO-34 catalyst system and a recycling fluidized bed reaction system for the production of lower olefins from methanol, is the first unit in the world having a capacity of producing nearly ten thousand tons lower olefins per year. The technological level of the industrial test is at a leading position internationally. This accomplishment will provide a sound base for the subsequent commercialization of the DMTO process.

Chemical Composition of Some Saline Lakes in the Tuva Region(Russia)

1 Introduction Salt lakes are very interesting natural objects which can be found in different places in the world.Russia is not an exception.Climate conditions are various in Russia,but the climate is not very hot.In Russia there are a lot of places where salt lakes are spread.One of them is the Tuva region where lakes with different compositions are

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.