Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing rapidly,the competition is becoming increasingly fierce,and the digital transformation of the production line is imminent.As one of themost important components of heavy vehicles,the transmission front andmiddle case assembly lines have a high degree of automation,which can be used as a pilot for the digital transformation of production.To ensure the visualization of digital twins(DT),consistent control logic,and real-time data interaction,this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line.Firstly,theDT-based systemarchitecture is designed,and theDT model is created by constructing the visualization model,logic model,and data model of the assembly line.Then,a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line.Eventually,some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment.

Manufacturing Resource Planning Technology Based on Genetic Programming Simulationnn

Network-based manufacturing is a kind of distributed system, which enables manufacturers to finish production tasks as well as to grasp the opportunities in the market, even if manufacturing resources are insufficient. One of the main problems in network-based manufacturing is the allocation of resources and the assignment of tasks rationally, according to flexible resource distribution. The mapping rules and relations between production techniques and resources are proposed, followed by the definition of the resource unit. Ultimately, the genetic programming method for the optimization of the manufacturing system is put forward. A set of software for the optimization system of simulation process using genetic programming techniques has been developed, and the problems of manufacturing resource planning in network-based manufacturing are solved with the simulation of optimizing methods by genetic programming. The optimum proposal of hardware planning, selection of company and scheduling will be obtained in theory to help company managers in scientific decision-making.

Availability-based simulation and optimization modeling framework for open-pit mine truck allocation under dynamic constraints

We present a novel system productivity simulation and optimization modeling framework in which equipment availability is a variable in the expected productivity function of the system. The framework is used for allocating trucks by route according to their operating performances in a truck-shovel system of an open-pit mine, so as to maximize the overall productivity of the fleet. We implement the framework in an originally designed and specifically developed simulator-optimizer software tool. We make an application on a real open-pit mine case study taking into account the stochasticity of the equipment behavior and environment. The total system production values obtained with and without considering the equipment reliability, availability and maintainability (RAM) characteristics are compared. We show that by taking into account the truck and shovel RAM aspects, we can maximize the total production of the system and obtain specific information on the production availability and productivity of its components.

Visual Dynamic Simulation and Optimization of Zhangjiuhe Diversion Project

With the aim of visualizing the real-time simulation calculation of water delivery system (WDS), a structural drawing-oriented (SDO) simulation technique was presented, and applied to Zhangjiuhe Diversion Project, which is a long-distance water delivery system constructed for draw- ing water from the Zhangjiuhe River to Kunming city. Taking SIMULINK software as simulating plat-form, the technique established a visual dynamic simulation model for the system. The simulation procedure of the system was simplified,and the efficiency of modeling was also enhanced according to the modularization and reutilization of the simulation program. Furthermore, a self-optimization model was presented. Based on the digital simulation models, the on line controlled optimization link was added, and the input data can be continually optimized according to the feedback information of simulating output. The system was thus optimized automatically. Built upon MATLAB software, simulation optimization of the Zhangjiuhe Diversion Project was achieved, which provides a new way for the research of optimal operation of WDS.

Preliminary Hydrogeologic Modeling and Optimal Monitoring Network Design for a Contaminated Abandoned Mine Site Area: Application of Developed Monitoring Network Design Software

In abandoned mine sites, i.e., mine sites where mining operations have ended, wide spread contaminations are often evident, but the potential sources and pathways of contamination especially through the subsurface, are difficult to identify due to inadequate and sparse geochemical measurements available. Therefore, it is essential to design and implement a planned monitoring net-work to obtain essential information required for establishing the potential contamination source locations, i.e., waste dumps, tailing dams, pits and possible pathways through the subsurface, and to design a remediation strategy for rehabilitation. This study presents an illustrative application of modeling the flow and transport processes and monitoring network design in a study area hydrogeologically resembling an abandoned mine site in Queensland, Australia. In this preliminary study, the contaminant transport process modeled does not incorporate the reactive geochemistry of the contaminants. The transport process is modeled considering a generic conservative contaminant for the illustrative purpose of showing the potential application of an optimal monitoring design methodology. This study aims to design optimal monitoring network to: 1) minimize the contaminant solute mass estimation error;2) locate the plume boundary;3) select the monitoring locations with (potentially) high concentrations. A linked simulation optimization based methodology is utilized for optimal monitoring network design. The methodology is applied utilizing a recently developed software package CARE-GWMND, developed at James Cook University for optimal monitoring network design. Given the complexity of the groundwater systems and the sparsity of pollutant concentration observation data from the field, this software is capable of simulating the groundwater flow and solute transport with spatial interpolation of data from a sparse set of available data, and it utilizes the optimization algorithm to determine optimum locations for implementing monitoring wells.

Efficiency analysis of numerical integrations for finite element substructure in real-time hybrid simulation

Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.

Simulation optimization for train movement on a single-track railway

Optimizing train movement has a great significance for railway traffic. In this paper, based on the optimal velocity car-following model, we propose a new simulation model for optimizing train movement in railway traffic. Here a kind of single-track railway is considered. Our aim is to reduce the energy consumption of train movement and ensure the train being on time by controlling the velocity curve of train movement. The simulation results indicate that the proposed model is effective for optimizing train movement. In addition, some major characteristics of train movement can be well captured. This method provides a new way to optimize train movement in railway traffic.

Optimized Simulation Design of Double Glass Curtain Wall Shading System in Hot Summer and Cold Winter Zone

The glass curtain wall is widely favored by the owners for its good appearance modeling efthct. In using process, however, excessive energy consumption, low level indoor eomtort and other problems of glass curtain wall are often exposed. Aiming at office buildings in hot Summer and cold Winter zone, taking the optimization of thermal comfort of double glass curtain wall in the summer and the reduetion of building energy consumption throughout the year as the breakthrough point, using the method of energy simulation analysis, through changing the size of internal shading component in the simulated room, this paper analyzes and summarizes the variation law of its energy consumption value, to explore the relatively reasonable design plan of shading systems of the building with glass curtain wall.

Probability Simulation Optimization Approach Using Orthogonal Genetic Algorithm

In order to solve the complex optimization problem dealing with uncertain phenomenon effectively, this paper presents a probability simulation optimization approach using orthogonal genetic algorithm. This approach synthesizes the computer simulation technology, orthogonal genetic algorithm and statistical test method faultlessly, which can solve complex optimization problem effectively. In this paper, the author gives the correlative conception of probability simulation optimization and describes the probability simulation optimization approach using orthogonal genetic algorithm in detail. Theoretically speaking, it has a strong rationality and maneuverability that can apply probability method in solving the complex optimization problems with uncertain phenomenon. In demonstration, the optimization performance of this method is better than other traditional methods. Simulation resuh suggests that the approach referred to this paper is feasible, correct and valid.

Multiphase Flow Simulation of New Vapor Distributor in Dividing Wall Column and Control Mechanism

A new vapor distributor based on the Coanda effect is added to the Dividing Wall column(DWC),and the multiphase flow simulation is performed using ANSYS Fluent by this model.The results show that with the addition of the liquid phase,the new vapor distributor still follows the Coanda effect.Hereby,the vapor is ejected from the slits of the distributor to take away the surrounding vapor,and a negative pressure is formed under the distributor,so as to achieve the purpose of regulating Rv.Analogously to the working principle of vapor distributor,a certain amount of vapor is drawn out from a position of prefractionator,which is equivalent to the vapor ejected by the distributor.The same amount of vapor is fed into the main column,which corresponds to the gas phase at the inlet of the distributor.The Rv is adjusted by changing the speed of the input or output vapor.Simulation results show that adding this control mechanism on the basis of temperature or concentration control structure can better achieve the effect of vapor distribution.

On-Line Tuning Scheme for the Generalized Predictive Controller via Simulation Optimization

Predictive control has recently received much attention from researchers. However a challenging problem to be solved is how to tune the parameters of the predictive controller. So far, only few guidelines related to tuning of the parameters of predictive controllers have been provided in literature. In practice, these parameters are generally off-line determined by the designers' experience. From the point of view of process control, it is difficult to find out the optimal parameters for the control system based on a single quadratic performance index, which is used in the standard predictive control algorithm. The fuzzy decision-making function is investigated in this paper. Firstly, M control actions are achieved by unconstrained predictive control algorithm, and fuzzy goals and fuzzy constraints are then calculated and the global satisfaction degree is obtained by fuzzy inference. Moreover, the weighting coefficient λ in the cost function is tuned using simulation optimization according to the fuzzy criteria.

Simulation and Optimized Design of Grid Concentration Transducer for Two-Phase Flow

A new concentration transducer for the measurement of gas/solid two-phase flow in the profile of pipeline was studied. The design of grid electrode was employed, and hence the distribute of electric field in the space was homogeneous effectively. The variance of measurement value for the influence of flow regime variations has greatly reduced, and the precision of measurement was improved. Meanwhile, optimization of axial property in the grid electrode increases the ability of tracing and measuring of the concentration signal in real-time.

Numerical simulation and optimization of the cylinder head water jacket based on the two-phase flow boiling heat transfer

Boiling heat transfer and the controllability of the thermal load of the cylinder head were studied.The thermodynamic phase change characteristics of the cylinder head coolant were considered and the mass,momentumand energy transfers between two phases were calculated with the interface transfer submodels by using the computational fluid dynamics software CFX. Results showed that compared with the single-phase flow without considering the boiling heat transfer,the sub-cooled boiling heat transfer of the cylinder head was greatly increased. According to the results of the numerical simulation,an optimized structure of the water jacket was proposed. Finally,temperature and velocity of coolant,diameter of flow passage and mean bubble diameter that influences sub-cooled boiling were studied using the orthogonal experiment method.

Experimental Design of Measuring Soil-Water Characteristic Curve of Unsaturated Soil Using Bayesian Approach

Soil-water characteristic curve (SWCC) is significant to estimate the site-specific unsaturated soil properties (such as unsaturated shear strength and coefficient of permeability) for geotechnical analyses involving unsaturated soils. Determining SWCC can be achieved by fitting data points obtained according to the prescribed experimental scheme, which is specified by the number of measuring points and their corresponding values of the control variable. The number of measuring points is limited since direct measurement of SWCC is often costly and time-consuming. Based on the limited number of measuring points, the estimated SWCC is unavoidably associated with uncertainties, which depends on measurement data obtained from the prescribed experimental scheme. Therefore, it is essential to plan the experimental scheme so as to reduce the uncertainty in the estimated SWCC. This study presented a Bayesian approach, called OBEDO, for probabilistic experimental design optimization of measuring SWCC based on the prior knowledge and information of testing apparatus. The uncertainty in estimated SWCC is quantified and the optimal experimental scheme with the maximum expected utility is determined by Subset Simulation optimization (SSO) in candidate experimental scheme space. The proposed approach is illustrated using an experimental design example given prior knowledge and the information of testing apparatus and is verified based on a set of real loess SWCC data, which were used to generate random experimental schemes to mimic the arbitrary arrangement of measuring points during SWCC testing in practice. Results show that the arbitrary arrangement of measuring points of SWCC testing is hardly superior to the optimal scheme obtained from OBEDO in terms of the expected utility. The proposed OBEDO approach provides a rational tool to optimize the arrangement of measuring points of SWCC test so as to obtain SWCC measurement data with relatively high expected utility for uncertainty reduction.

Optimization of the Number and Location of Boreholes for Gassy Soil Site Investigation Considering the Statistical Uncertainty

The research addresses the prevalence of gassy soil, containing methane (CH4), within the soil particles of southeast coastal areas of China, such as the Quaternary deposit in the Hangzhou Bay area. This soil exhibits spatial variability in the distribution of gas pressure, posing a potential threat of engineering disasters, including fire outbreaks and blasting, during the construction of underground projects. Consequently, it is crucial to assess the risk state of gas pressure, involving accurate identification and reduction of associated uncertainty, through site investigation. This is indispensable prior to the commencement of underground projects. However, during the site investigation stage, the random field parameters that quantify the spatial variability distribution of gas pressure (e.g., mean value, standard deviations, and scale of fluctuation) are unknown, introducing corresponding statistical uncertainty. Therefore, the most significant consideration for planning site investigation from an engineering perspective involves determining the risk state of gas pressure while considering the statistical uncertainty of these random field parameters. This consideration heavily relies on the engineering experience gained from current site investigation practices. To address this challenge, the study introduces a probabilistic site investigation optimization method designed for planning the site investigation scheme for gassy soils, including determining the number and locations of boreholes. The method is based on the expected state-identification probability, representing the probability of identifying the risk state of gas pressure, and takes into account the statistical uncertainty of random field parameters. The proposed method aims to determine an optimal investigation scheme before conducting the site investigation, leveraging prior knowledge. This optimal scheme is identified using Subset Simulation Optimization (SSO) in the space of candidate site investigations, maximizing the value of the expected state-identification probability at the minimal value point. Finally, the paper illustrates the proposed approach through a case study.

Effect of Planting Density and Fertilizer Application on Fiber Yield of Ramie (Boehmeria nivea)

As the most important cultural practices for ramie （Boehmeria nivea） production, the single effects of plant density and nitrogen （N）, phosphorus （P）, potassium （K） fertilization on yield are well documented. To achieve the high yield and quality of ramie fiber, it is principal to quantify the fertilizer dosage for ramie sustainable production. A central composite design （CCD） was adopted with three replications, for a two-year field experiment （2008-2009） in the Yangtze River Valley of China. The aim was to evaluate both the individual and combined effects of plant density and N, P, and K fertilization on yield and fiber quality. The effects of the four factors on yield and quality of ramie cultivar Huazhu 4 were tested. Then mathematical models of the relationship among the four factors affecting ramie production and quality were established and analyzed to optimize the four factors, and used to establish optimum cultivation methods for the elite cultivar suitable to this area of the Yangtze River Valley. Supplements of N （X2）, P 0（3） and K 0（4）, and the density （X0 with N （X2） interaction significantly influenced strength in tests of mature ramie. The influence on fiber yield in the two year tests by the four factors ranked as follows： X3〉X4〉X~〉X2. We established the optimization technique attaining yield of 2 600 kg ha-~ in the tests as following： density of 28 350-31 650 plants ha-1, and supplements of N, P and K of 363-387, 98.58-105.48 and 280.20- 319.8 kg ha-1, respectively. It was concluded that nitrogen mostly improved plant growth and fiber yield while potassium had discernible effects on fiber quality.

Modeling and optimization of industrial Fischer–Tropsch synthesis with the slurry bubble column reactor and iron-based catalyst

To optimize industrial Fischer-Tropsch （IT） synthesis with the slurry bubble column reactor （SBCR） and iron- based catalyst, a comprehensive process model for IT synthesis that includes a detailed SBCR model, gas liquid separation model, simplified CO2 removal model and tail gas cycle model was developed. An effective iteration algorithm was proposed to solve this process model, and the model was validated by industrial demonstration experiments data （SBCR with 5.8 m diameter and 30 m height）, with a maximum relative error 〈 10% for predicting the SBCR performances. Subsequently, the proposed model was adopted to optimize the industrial SBCR performances simultaneously considering process and reactor parameters variations. The results show that C5＋yield increases as catalyst loading increases within 10-70 ton and syngas H2/CO value decreases within 1.3-1.6, but it doesn＇t increase obviously when the catalyst loading exceeds 45 ton （about 15 wt% concentration）. Higher catalyst loading will result in higher difficulty for wax/catalyst separation and higher catalyst cost. There- fore, the catalyst loading （45 ton） is recommended for the industrial demonstration SBCR operation at syngas H2/ CO = 1.3, and the C5 ＋ yield is about 402 ton＂ per day, which has an about 16% increase than the industrial dem- onstration run result.

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.

Decision Model of Knowledge Transfer in Big Data Environment

A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.

Investigation on and industrial application of degrading of methanol feed in methanol to propylene process

At present, methanol to propylene(MTP) technology developed by Lurgi Company is adopted for commercial plants and refined methanol with the purity ≥99.85 wt% is required as the feed of MTP unit in Lurgi's technology.Therefore, high energy cost for refined methanol production is one of the bottlenecks to improve the economy of MTP technology. Reducing the grade of feed refined methanol may be an effective method to save energy and reduce operation costs in MTP process. In this work, experiments and process simulation were carried out to investigate the influence and feasibility of degrading the methanol feed. Experiments were conducted to investigate the influence of crude methanol feed on conversion and selectivity of MTP reaction as well as the performance of ZSM-5 catalyst. The experimental results showed that degrading the methanol feed had no obvious influence on the conversion and selectivity of MTP reactions and the catalyst deactivation was caused by the carbon accumulation and metals deposition on the active sites. The process simulation results showed that the influence on the conversion and selectivity as well as the stream load of MTP process was negligible if 98 mol% methanol was used as feed. Finally, industrial experiments were conducted by adjusting the operation parameters to degrade of feed methanol of the commercial 500 kt·a^(-1) MTP unit of Ningmei Group in China. The results of industrial application illustrated that annually 180 kt fuel coal and 150 kt desalted water as well as 1770 MW·h^(-1) electricity would be saved when the water content increased from 0.01% to 0.4%. This work has identified the feasibility to improve MTP technology by degrading the methanol feed.