Modeling and analysis of air combustion and steam regeneration in methanol to olefins processes

Light olefins is the incredibly important materials in chemical industry.Methanol to olefins(MTO),which provides a non-oil route for light olefins production,received considerable attention in the past decades.However,the catalyst deactivation is an inevitable feature in MTO processes,and regeneration,therefore,is one of the key steps in industrial MTO processes.Traditionally the MTO catalyst is regenerated by removing the deposited coke via air combustion,which unavoidably transforms coke into carbon dioxide and reduces the carbon utilization efficiency.Recent study shows that the coke species over MTO catalyst can be regenerated via steam,which can promote the light olefins yield as the deactivated coke species can be essentially transferred to industrially useful synthesis gas,is a promising pathway for further MTO processes development.In this work,we modelled and analyzed these two MTO regeneration methods in terms of carbon utilization efficiency and technology economics.As shown,the steam regeneration could achieve a carbon utilization efficiency of 84.31%,compared to 74.74%for air combustion regeneration.The MTO processes using steam regeneration can essentially achieve the near-zero carbon emission.In addition,light olefins production of the MTO processes using steam regeneration is 12.81%higher than that using air combustion regeneration.In this regard,steam regeneration could be considered as a potential yet promising regeneration method for further MTO processes,showing not only great environmental benefits but also competitive economic performance.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Coupled bio-chemo-hydro-mechanical modeling of microbially induced calcite precipitation process considering biomass encapsulation using a micro-scale relationship

Geomaterials with inferior hydraulic and strength characteristics often need improvement to enhance their engineering behaviors.Traditional ground improvement techniques require enormous mechanical effort or synthetic chemicals.Sustainable stabilization technique such as microbially induced calcite precipitation(MICP)utilizes bacterial metabolic processes to precipitate cementitious calcium carbonate.The reactive transport of biochemical species in the soil mass initiates the precipitation of biocement during the MICP process.The precipitated biocement alters the hydro-mechanical performance of the soil mass.Usually,the flow,deformation,and transport phenomena regulate the biocementation technique via coupled bio-chemo-hydro-mechanical(BCHM)processes.Among all,one crucial phenomenon controlling the precipitation mechanism is the encapsulation of biomass by calcium carbonate.Biomass encapsulation can potentially reduce the biochemical reaction rate and decelerate biocementation.Laboratory examination of the encapsulation process demands a thorough analysis of associated coupled effects.Despite this,a numerical model can assist in capturing the coupled processes influencing encapsulation during the MICP treatment.However,most numerical models did not consider biochemical reaction rate kinetics accounting for the influence of bacterial encapsulation.Given this,the current study developed a coupled BCHM model to evaluate the effect of encapsulation on the precipitated calcite content using a micro-scale semiempirical relationship.Firstly,the developed BCHM model was verified and validated using numerical and experimental observations of soil column tests.Later,the encapsulation phenomenon was investigated in the soil columns of variable maximum calcite crystal sizes.The results depict altered reaction rates due to the encapsulation phenomenon and an observable change in the precipitated calcite content for each maximum crystal size.Furthermore,the permeability and deformation of the soil mass were affected by the simultaneous precipitation of calcium carbonate.Overall,the present study comprehended the influence of the encapsulation of bacteria on cement morphology-induced permeability,biocement-induced stresses and displacements.

HV Process Model of Software Development

Software Development Life Cycle (SDLC) is one of the major ingredients for the development of efficient software systems within a time frame and low-cost involvement. From the literature, it is evident that there are various kinds of process models that are used by the software industries for the development of small, medium and long-term software projects, but many of them do not cover risk management. It is quite obvious that the improper selection of the software development process model leads to failure of the software products as it is time bound activity. In the present work, a new software development process model is proposed which covers the risks at any stage of the development of the software product. The model is named a Hemant-Vipin (HV) process model and may be helpful for the software industries for development of the efficient software products and timely delivery at the end of the client. The efficiency of the HV process model is observed by considering various kinds of factors like requirement clarity, user feedback, change agility, predictability, risk identification, practical implementation, customer satisfaction, incremental development, use of ready-made components, quick design, resource organization and many more and found through a case study that the presented approach covers many of parameters in comparison of the existing process models. .

A Study on the Work Process-Based Practical Training Model for Basic Nursing Skills in Vocational Colleges

Objective:To explore and analyze the work process-based practical training teaching model for basic nursing skills in vocational colleges and its implementation effects.Methods:A total of 82 nursing students from our school were selected for the study,which was conducted from April 2023 to April 2024.Using a random number table method,the students were divided into an observation group and a control group,each with 41 students.The control group received conventional practical training teaching,while the observation group followed the work process-based practical training model for basic nursing skills.The assessment scores and teaching satisfaction of the two groups were compared.Results:The comparison of assessment scores showed that the observation group performed significantly better than the control group(P<0.05).The comparison of teaching satisfaction also indicated that the observation group had significantly higher satisfaction than the control group(P<0.05).Conclusion:The work process-based practical training teaching model for basic nursing skills in vocational colleges can improve students’assessment scores and enhance teaching satisfaction,demonstrating its value for wider application.

Loose-stratification model in separation process for vanadium pre-concentration from stone coal

A technology of one-stage roughing and one-stage scavenging vanadium pre-concentration with shaking table was investigated for improving vanadium grade and decreasing acid consumption minerals content based on the quantitative evaluation of minerals by scanning electronic microscopy （QEMSCAN）. In order to visually illustrate how the vanadium-bearing minerals were separated from system, a loose-stratification model was established with Bagnold shear loose theory and Kelly stratification hypothesis. Through the model, it was inferred that fine fraction and coarse fraction of vanadium-bearing muscovite particles easily became the concentrate in roughing and scavenging stages, respectively. The type of the dominant effect on the loose-stratification was confirmed. In the roughing stage, gravity sedimentation played a leading role in the loose-stratification process. However, in the scavenging stage, shearing dispersion pressure caused by asymmetric motion of table deck took an important part in the loose-stratification process. Finally, the correction of the loose-stratification model was validated by the practical experiment.

Process model of IT impacts on firm competitiveness based on application capability of information technology

To investigate the process of information technology （IT） impacts on firm competitiveness, an integrated process model of IT impacts on firm competitiveness is brought forward based on the process-oriented view, the resource-based view and the complementary resource view, which is comprised of an IT conversion process, an information system （IS） adoption process, an IS use process and a competition process. The application capability of IT plays the critical role, which determines the efficiency and effectiveness of the aforementioned four processes. The process model of IT impacts on firm competitiveness can also be used to explain why, under what situations and how IT can generate positive organizational outcomes, as well as theoretical bases for further empirical study.

Research on Petri Net Based Modeling and Analyzing Methods for Workflow Process

Workflow management is an important aspect in CSCW at present. The elementary knowledge of workflow process is introduced, the Petri nets based process modeling methodology and basic definitions are provided, and the analysis and verification of structural and behavioral correctness of workflow process are discussed. Finally, the algorithm of verification of process definitions is proposed.

Dynamic business process modeling and verification for inter-organizational collaboration

To achieve an on-demand and dynamic composition model of inter-organizational business processes, a new approach for business process modeling and verification is introduced by using the pi-calculus theory. A new business process model which is multi-role, multi-dimensional, integrated and dynamic is proposed relying on inter-organizational collaboration. Compatible with the traditional linear sequence model, the new model is an M x N multi-dimensional mesh, and provides horizontal and vertical formal descriptions for the collaboration business process model. Finally, the pi-calculus theory is utilized to verify the deadlocks, livelocks and synchronization of the example models. The result shows that the proposed approach is efficient and applicable in inter-organizational business process modeling.

DECISION-MAKING OF SLICING SCHEME IN FUSED DEPOSITION MODELING PROCESS BASED ON ANALYTICAL HIERARCHICAL PROCESS

Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluation is introduced to make decision in slicing schemes for a processing part. The application in determining the slicing scheme for a computer mouse during prototyping shows that the method increases the rationality during decision- making and improves quality and efficiency for the prototyping part.

An Effective Approach to Verify the Correctness of Workflow Process Models Based on Petri Net

Along with the extensive use of workflow, analysis methods to verify the correctness of the workflow are becoming more and more important. In the paper, we exploit the verification method based on Petri net for workflow process models which deals with the verification of workflow and finds the potential errors in the process design. Additionally, an efficient verification algorithm is given.

Construction of Value Models of Consumer Creation for Agricultural Products Based on the Analytic Hierarchy Process(AHP)

With the increased competition of modern economy and globalization,consumer creation which based on the analysis of consumer behavior was more and more attentioned and respected by business.Based on the meaning and characteristics of agricultural product consumer creation,index system of value model of agricultural product consumer creation was put forward through analytical hierarchy process（AHP）.The weights of the indicators and related indicators of impact on the value were analyzed,and value models of agricultural product consumer creation were constructed to provide ideas for development of agricultural product consumer market and research of consumer value.Consumer creation was constructed to provide ideas for development of agricultural product consumer market and research of consumer value.

Study on the Yield Prediction Model of Processing Tomato Based on the Grey System Theory

[Objective] The research aimed to study the yield prediction model of processing tomato based on the grey system theory.[Method] The variation trend of processing tomato yield was studied by using the grey system theory,and GM（1,1）grey model of processing tomato yield prediction was established.The processing tomato yield in Xinjiang during 2001-2009 was as the example to carry out the instance analysis.[Result] The model had the high forecast accuracy and strong generalization ability,and was reliable for the prediction of recent processing tomato yield.[Conclusion] The research provided the reference for the macro-control of tomato industry,the processing and storage of tomato in Xinjiang.

Quality prediction of batch process using the global-local discriminant analysis based Gaussian process regression model

The conventional single model strategy may be ill- suited due to the multiplicity of operation phases and system uncertainty. A novel global-local discriminant analysis （GLDA） based Gaussian process regression （GPR） approach is developed for the quality prediction of nonlinear and multiphase batch processes. After the collected data is preprocessed through batchwise unfolding, the hidden Markov model （HMM） is applied to identify different operation phases. A GLDA algorithm is also presented to extract the appropriate process variables highly correlated with the quality variables, decreasing the complexity of modeling. Besides, the multiple local GPR models are built in the reduced- dimensional space for all the identified operation phases. Furthermore, the HMM-based state estimation is used to classify each measurement sample of a test batch into a corresponding phase with the maximal likelihood estimation. Therefore, the local GPR model with respect to specific phase is selected for online prediction. The effectiveness of the proposed prediction approach is demonstrated through the multiphase penicillin fermentation process. The comparison results show that the proposed GLDA-GPR approach is superior to the regular GPR model and the GPR based on HMM （HMM-GPR） model.

Modeling texture evolution during rolling process of AZ31 magnesium alloy with elasto-plastic self consistent model

To gain a better understanding about texture evolution during rolling process of AZ31 alloy, polycrystalline plasticity model was implemented into the explicit FE package, ABAQUS/Explicit by writing a user subroutine VUMAT. For each individual grain in the polycrystalline aggregate, the rate dependent model was adopted to calculate the plastic shear strain increment in combination with the Voce hardening law to describe the hardening response, the lattice reorientation caused by slip and twinning were calculated separately due to their different mechanisms. The elasto-plastic self consistent （EPSC） model was employed to relate the response of individual grain to the response of the polycrystalline aggregate. Rolling processes of AZ31 sheet and as-cast AZ31 alloy were simulated respectively. The predicted texture distributions are in aualitative a^reement with experimental results.

Ore-blending optimization model for sintering process based on characteristics of iron ores

An ore-blending optimization model for the sintering process is an intelligent system that includes iron ore characteristics, expert knowledge and material balance. In the present work, 14 indices are proposed to represent chemical composition, granulating properties and high temperature properties of iron ores. After the relationships between iron ore characteristics and sintering performance are established, the ＂two-step＂ method and the simplex method are introduced to build the model by distinguishing the calculation of optimized blending proportion of iron ores from that of other sintering materials in order to improve calculation efficiency. The ore-blending optimization model, programmed by Access and Visual Basic, is applied to practical production in steel mills and the results prove that the present model can take advantage of the available iron ore resource with stable sinter yield and quality performance but at a lower cost.

Computer Modeling: an Important Tool in Materials Processing

The development of techniques to control and use materials, especially metallic materials continues to play a key role in the technological advancement of modem day society. A variety of materials processes have been developed overtime, and are applied to design and fabricate products. Heat treatment of metallic materials has been the method of choice applied to improve mechanical properties such as strength, hardness, and toughness, as well as to improve the homogeneity of properties in materials such as corrosion resistance. The molt significant success stories in the application of materials processes involve the heat-treatment of steel. The reasons for this largely lie in the ease with which steel microstructures can be altered by controlled heating and cooling. The objective of computer modeling of materials processes is to maximize on the beneficial factors while keeping to a minimum the negating effects such as components distortion and residual stresses. Computer modeling is a powerful tool in achieving this objective in the sense that with it, the effect of parameters associated with materials processes can be analyzed systematically in order to optimize them before actual production begins.

Fault monitoring based on mutual information feature engineering modeling in chemical process

A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

Application of Radar-Measured Rain Data in Hydrological Processes Modeling during the Intensified Observation Period of HUBEX

On the basis of Digital Elevation Model data, the raster flow vectors, watershed delineation, and spatial topological relationship are generated by the Martz and Garbrecht method for the upper area of Huangnizhuang station in the Shihe Catchment with 805 km2 of area, an intensified observation field for the HUBEX/GAME Project. Then, the Xin’anjiang Model is applied for runoff production in each grid element where rain data measured by radar at Fuyang station is utilized as the input of the hydrological model. The elements are connected by flow vectors to the outlet of the drainage catchment where runoff is routed by the Muskingum method from each grid element to the outlet according to the length between each grid and the outlet. The Nash-Sutcliffe model efficiency coefficient is 92.41% from 31 May to 3 August 1998, and 85.64%, 86.62%, 92.57%, and 83.91%, respectively for the 1st, 2nd, 3rd, and 4th flood events during the whole computational period. As compared with the case where rain-gauge data are used in simulating the hourly hydrograph at Huangnizhuang station in the Shihe Catchment, the index of model efficiency improvement is positive, ranging from 27.56% to 69.39%. This justifies the claim that radar-measured data are superior to rain-gauge data as inputs to hydrological modeling. As a result, the grid-based hydrological model provides a good platform for runoff computation when radar-measured rain data with highly spatiotemporal resolution are taken as the input of the hydrological model.

Efficient Deviation Detection Between a Process Model and Event Logs

Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.