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.

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.

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.

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.

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.

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.

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.

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.

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.

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.

Distributed Control of Chemical Process Networks

In this paper,we present a review of the current literature on distributed(or partially decentralized) control of chemical process networks.In particular,we focus on recent developments in distributed model predictive control,in the context of the specific challenges faced in the control of chemical process networks.The paper is concluded with some open problems and some possible future research directions in the area.

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.

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.

Use of bio-based products towards more sustainable road paving binders: A state-of-the-art review

Many industrial sectors exploit fossil sources to develop useful and necessary materials for our needs,such as bituminous paving materials.Bitumen,a key component of asphalt mixtures,is derived from oil refining and its properties are influenced by the crude oil source and refining process,resulting in a significant carbon footprint.With growing awareness of resource depletion and environmental concerns,pavement researchers are exploring sustainable alternatives to reduce dependence on fossil sources.This includes a rising trend in using renewable materials like biomasses to produce bio-based binders as substitutes for bitumen,aiming for a more sustainable approach.Biomasses,including vegetal and animal wastes,and waste cooking oils,as substitutes for crude oil in the production of bio-binders.Through thermochemical conversion(TCC),such as pyrolysis,biomasses can be converted into bio-char and bio-oils,which can replace fossil-based components in binders.Researchers have utilized these bio-products to reduce the dependency on fossil fuels in binders.However,there are no set minimum requirements for bio-components in bio-based binders.As the percentage of replaced bitumen increases,various types of binders are produced,including modified bitumen,extended bitumen,and alternative binders,where the fossil replacement is gradual.Overall rheological tests on bio-binders,reveal that those containing biochar exhibit increased viscosity,stiffness,rutting resistance,and sometimes antioxidant properties.Conversely,bio-binders with bio-oils as bitumen substitutes show poorer performance at high temperatures but improved behavior at low temperatures.These results suggest that bio-binders could provide versatile solutions for various climatic and loading conditions in road construction.However,the development of pavement mixtures based on bio-binders has not been studied in depth and requires further attention to unlock its full potential.As sustainability considerations,including life cycle assessments(LCA)and life cycle cost analyses(LCC),are crucial aspects for future studies.It is essential not only to collect data on the performance characteristics of bio-binders but also to understand their environmental impact and recyclability.In-depth evaluations using methods such as LCA and LCC will provide valuable insights into the overall sustainability and long-term viability of these products.

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.

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety.Small or incipient irregularities may lead to severe degradation in complex chemical processes,and the conventional process monitoring techniques cannot detect these irregularities.In this study to improve the performance of monitoring,an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis(MSPCA) with cumulative sum(CUSUM) and exponentially weighted moving average(EWMA) control charts.The new Hotelling's T~2 and square prediction error(SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data.The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods.The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults.Moreover,a comparative study shows that the SPEEWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.