State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO client.Although a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development only.Several frameworks have been developed focusing on guiding software systemmanagers concerning offshore software outsourcing.However,none of these studies delivered comprehensive guidelines for managing the whole process of OSMO.There is a considerable lack of research working on managing OSMO from a vendor’s perspective.Therefore,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s perspective.This study validated the preliminary OSMO process model via a case study research approach.The results showed that the OSMO process model is applicable in an industrial setting with few changes.The industrial data collected during the case study enabled this paper to extend the preliminary OSMO process model.The refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

A Process Oriented Integration Model for Smart Health Services

Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its residents.Smart integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing urbanization.Currently,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efficient collection and sharing of data,along with inadequate collaboration of software and hardware.Aiming to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling technique.Using this integration modeling solution,atfirst,the service participants,processes and tasks of smart services are identified and then standard illustrations are developed for the better understand-ing of the integrated service group environment.Business process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart home.The research is concluded with the integration process model application for the required data sharing among different service groups.The outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.

Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.

Hybrid Neural Network Model for RH Vacuum Refining Process Control

A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.

Event-driven process execution model for process virtual machine

Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.

Process Modeling and Dynamic Simulation for EAST Helium Refrigerator

In this paper,the process modeling and dynamic simulation for the EAST helium refrigerator has been completed.The cryogenic process model is described and the main components are customized in detail.The process model is controlled by the PLC simulator,and the realtime communication between the process model and the controllers is achieved by a customized interface.Validation of the process model has been confirmed based on EAST experimental data during the cool down process of 300-80 K.Simulation results indicate that this process simulator is able to reproduce dynamic behaviors of the EAST helium refrigerator very well for the operation of long pulsed plasma discharge.The cryogenic process simulator based on control architecture is available for operation optimization and control design of EAST cryogenic systems to cope with the long pulsed heat loads in the future.

On Treatment Patterns for Modeling Medical Treatment Processes in Clinical Practice Guidelines

Clinical practice guidelines(CPGs)contain evidence-based and economically reasonable medical treatment processes.Executable medical treatment processes in healthcare information systems can assist the treatment processes.To this end,business process modeling technologies have been exploited to model medical treatment processes.However,medical treatment processes are usually flexible and knowledge-intensive.To reduce the effort in modeling,we summarize several treatment patterns(i.e.,frequent behaviors in medical treatment processes in CPGs),and represent them by three process modeling languages(i.e.,BPMN,DMN,and CMMN).Based on the summarized treatment patterns,we propose a pattern-based integrated framework for modeling medical treatment processes.A modeling platform is implemented to support the use of treatment patterns,by which the feasibility of our approach is validated.An empirical analysis is discussed based on the coverage rates of treatment patterns.Feedback from interviewed physicians in a Chinese hospital shows that executable medical treatment processes of CPGs provide a convenient way to obtain guidance,thus assisting daily work for medical workers.

Improving reservoir volumetric estimations in petroleum resource assessment using discovery process models

The reservoir volumetric approach represents a widely accepted, but flawed method of petroleum play resource calculation. In this paper, we propose a combination of techniques that can improve the applicability and quality of the resource estimation. These techniques include： 1） the use of the Multivariate Discovery Process model （MDP） to derive unbiased distribution parameters of reservoir volumetric variables and to reveal correlations among the variables; 2） the use of the Geo-anchored method to estimate simultaneously the number of oil and gas pools in the same play; and 3） the crossvalidation of assessment results from different methods. These techniques are illustrated by using an example of crude oil and natural gas resource assessment of the Sverdrup Basin, Canadian Archipelago. The example shows that when direct volumetric measurements of the untested prospects are not available, the MDP model can help derive unbiased estimates of the distribution parameters by using information from the discovered oil and gas accumulations. It also shows that an estimation of the number of oil and gas accumulations and associated size ranges from a discovery process model can provide an alternative and efficient approach when inadequate geological data hinder the estimation. Cross-examination of assessment results derived using different methods allows one to focus on and analyze the causes for the major differences, thus providing a more reliable assessment outcome.

Formalization and Verification of Business Process Modeling Based on UML and Petri Nets

In order to provide a quantitative analysis and verification method for activity diagrams based business process modeling, a formal definition of activity diagrams is introduced. And the basic requirements for activity diagrams based business process models are proposed. Furthermore, the standardized transformation technique between business process models and basic Petri nets is presented and the analysis method for the soundness and well-structured properties of business processes is introduced.

Model-based optimal design of phase change ionic liquids for efficient thermal energy storage

The selection of phase change material(PCM)plays an important role in developing high-efficient thermal energy storage(TES)processes.Ionic liquids(ILs)or organic salts are thermally stable,non-volatile,and non-flammable.Importantly,researchers have proved that some ILs possess higher latent heat of fusion than conventional PCMs.Despite these attractive characteristics,yet surprisingly,little research has been performed to the systematic selection or structural design of ILs for TES.Besides,most of the existing work is only focused on the latent heat when selecting PCMs.However,one should note that other properties such as heat capacity and thermal conductivity could affect the TES performance as well.In this work,we propose a computer-aided molecular design(CAMD)based method to systematically design IL PCMs for a practical TES process.The effects of different IL properties are simultaneously captured in the IL property models and TES process models.Optimal ILs holding a best compromise of all the properties are identified through the solution of a formulated CAMD problem where the TES performance of the process is maximized.[MPyEtOH][TfO]is found to be the best material and excitingly,the identified top nine ILs all show a higher TES performance than the traditional PCM paraffin wax at 10 h thermal charging time.

Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.

Approximate-model Based Estimation Method for Dynamic Response of Forging Processes

Many high-quality forging productions require the large-sized hydraulic press machine（HPM） to have a desirable dynamic response. Since the forging process is complex under the low velocity, its response is difficult to estimate. And this often causes the desirable low-velocity forging condition difficult to obtain. So far little work has been found to estimate the dynamic response of the forging process under low velocity. In this paper, an approximate-model based estimation method is proposed to estimate the dynamic response of the forging process under low velocity. First, an approximate model is developed to represent the forging process of this complex HPM around the low-velocity working point. Under guaranteeing the modeling performance, the model may greatly ease the complexity of the subsequent estimation of the dynamic response because it has a good linear structure. On this basis, the dynamic response is estimated and the conditions for stability, vibration, and creep are derived according to the solution of the velocity. All these analytical results are further verified by both simulations and experiment. In the simulation verification for modeling, the original movement model and the derived approximate model always have the same dynamic responses with very small approximate error. The simulations and experiment finally demonstrate and test the effectiveness of the derived conditions for stability, vibration, and creep, and these conditions will benefit both the prediction of the dynamic response of the forging process and the design of the controller for the high-quality forging. The proposed method is an effective solution to achieve the desirable low-velocity forging condition.

Simulation-Based Construction of Three-Dimensional Process Model for Punching Cartridge Cases

A method of constructing three-dimensional process model for the punching cartridge cases is presented based on DEFORM simulation analysis. Using DEFORM software,the finite element simulation models for the punching and forming process of cartridge cases are established,and the corresponding simulation result model of each intermediate procedure is obtained by continuously performing the forming process simulation. The simulation model cannot annotate size and process information due to poor interface between DEFORM software and CAD software. Thus,a 3D annotation module is developed with secondary development technology of UG NX software. Consequently,the final process model with dimension and process information is obtained. Then,with the current 3D process management system,the 3D punching and forming process design of cartridge cases can be completed further. An example is also provided to illustrate that the relative error between the simulation process model and the physical model is less than 2%,which proves the validity and reliability of the proposed method in this study.

Finite element modeling of consolidation of composite laminates

Advanced fiber reinforced polymer composites have been increasingly applied to various structural components. One of the important processes to fabricate high performance laminated composites is an autoclave assisted prepreg lay-up. Since the quality of laminated composites is largely affected by the cure cycle, selection of an appropriate cure cycle for each application is important and must be optimized. Thus, some fundamental model of the consolidation and cure processes is necessary for selecting suitable parameters for a specific application. This article is concerned with the ＂flow-compaction＂ model during the autoclave processing of composite materials. By using a weighted residual method, two-dimensional finite element formulation for the consolidation process of thick thermosetting composites is presented and the corresponding finite element code is developed. Numerical examples, including comparison of the present numerical results with one-dimensional and twodimensional analytical solutions, are given to illustrate the accuracy and effectiveness of the proposed finite element formulation. In addition, a consolidation simulation of AS4/3501-6 graphite/epoxy laminate is carded out and compared with the experimental results available in the literature.

PROTOTYPE SURFACE MICRO- PRECISION IN FUSED DEPOSITION MODELING PROCESS

To aim at prototype parts fabricated with fused deposition modeling （FDM） process, the problems how to improve and enhance their surface micro-precision are studied. The producing mechanism of surface roughness is explained with three aspects concretely including the principle error of rapid prototyping （RP） process, the inherent characteristics of FDM process, and some mi- cro-scratches on the surface of the extruded fiber. Based on the micro-characters of section shape of the FDM prototype, a physical model reflecting the outer shape characters is abstracted. With the physical simplified and deduced, the evaluating equations of surface roughness are acquired. According to the FDM sample parts with special design for experimental measurement, the real surface roughness values of different inclined planes are obtained. And the measuring values of surface roughness are compared with the calculation values. Furthermore, the causes of surface roughness deviation between measuring values and calculation values are respectively analyzed and studied. With the references of analytic conclusions, the measuring values of the experimental part surface are revised, and the revised values nearly accord with the calculation values. Based on the influencing principles of FDM process parameters and special post processing of FDM prototype parts, some concrete measures are proposed to reduce the surface roughness of FDM parts, and the applying effects are better.

COLLISION AVOIDANCE DECISION-MAKING MODEL OF MULTI-AGENTS IN VIRTUAL DRIVING ENVIRONMENT WITH ANALYTIC HIERARCHY PROCESS

Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.

GENERALIZED SIMULATION MODEL FOR MILLED SURFACE TOPOGRAPHY-APPLICATION TO PERIPHERAL MILLING

Based on analyzing various factors influencing milled surface topography, firstly, a generalized model for milled surface topography is proposed. Secondly, using the principles of transformation matrix and vector operation, the trajectory equation of cutting edge relative to workpiece is derived. Then, a three dimensional topography simulation algorithm is constructed through dividing the workpiece into regular grids. Finally, taking the peripheral milling process as an example, the generalized model is simplified, and the corresponding simulation examples are given. The results indicate that it is very efficient for the generalized model to be used to analyze and simulate the peripherally milled surface topography.

A numerical model of coastline deformation for sandy beach at downstream of a jetty

A reformed numerical model based on the 'one-line theory' for beach deformation is presented. In this model, the change of beach slope during coastline procession is considered.A wave numerical model combined with wave refraction, diffraction and reflection is used to simulate wave climate to increase numerical accuracy.The results show that the numerical model has a good precision based on the adequate field data. The results can be applied to practical engineering.