Towards Production and Energy Coupling System Modeling and Simulation for Energy Optimization in the Process Industry

The production and energy coupling system is used to mainly present energy flow, material flow, information flow, and their coupling interaction. Through the modeling and simulation of this system, the performance of energy flow can be analyzed and optimized in the process industry. In order to study this system, the component based hybrid Petri net methodology (CpnHPN) is proposed, synthesizing a number of extended Petri net methods and using the concept of energy place, material place, and information place. Through the interface place in CpnHPN, the component based encapsulation is established, which enables the production and energy coupling system to be built, analyzed, and optimized on the multi-level framework. Considering the block and brief simulation for hybrid system, the CpnHPN model is simulated with Simulink/Stateflow. To illustrate the use of the proposed methodology, the application of CpnHPN in the energy optimization of chlorine balance system is provided.

Modeling, Simulation and Optimization of a Whole Industrial Catalytic Naphtha Reforming Process on Aspen Plus Platform

A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat-form. The technique utilizes the strong databases, complete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the proposed kinetic model. The calculated results are in fair agreement with the actual operating data. Based on the model of the whole reforming process, the process is opti-mized and the optimization results are tested in the actual industrial unit for about two months. The test shows that the process profit increases about 1000yuan·h-1 averagely, which is close to the calculated result.

Computational Simulations of Bone Remodeling under Natural Mechanical Loading or Muscle Malfunction Using Evolutionary Structural Optimization Method

Live bone inherently responds to applied mechanical stimulus by altering its internal tissue composition and ultimately biomechanical properties, structure and function. The final formation may structurally appear inferior by design but complete by function. To understand the loading response, this paper numerically investigated structural remodeling of mature sheep femur using evolutionary structural optimization method (ESO). Femur images from Computed Tomography scanner were used to determine the elastic modulus variation and subsequently construct finite element model of the femur with stiffest elasticity measured. Major muscle forces on dominant phases of healthy sheep gait were imposed on the femur under static mode. ESO was applied to progressively alter the remodeling of numerically simulated femur from its initial to final design by iteratively removing elements with low strain energy density (SED). The computations were repeated with two different mesh sizes to test the convergence. The elements within the medullary canal had low SEDs and therefore were removed during the optimization. The SEDs in the remaining elements varied with angle around the circumference of the shaft. Those elements with low SED were inefficient in supporting the load and thus fundamentally explained how bone remodels itself with less stiff inferior tissue to meet load demand. This was in line with the Wolff’s law of transformation of bone. Tissue growth and remodeling process was found to shape the sheep femur to a mechanically optimized structure and this was initiated by SED in macro-scale according to traditional principle of Wolff’s law.

Battlefield target intelligence system architecture modeling and system optimization

To address the current problems of poor generality,low real-time,and imperfect information transmission of the battlefield target intelligence system,this paper studies the battlefield target intelligence system from the top-level perspective of multi-service joint warfare.First,an overall planning and analysis method of architecture modeling is proposed with the idea of a bionic analogy for battlefield target intelligence system architecture modeling,which reduces the difficulty of the planning and design process.The method introduces the Department of Defense architecture framework(DoDAF)modeling method,the multi-living agent(MLA)theory modeling method,and other combinations for planning and modeling.A set of rapid planning methods that can be applied to model the architecture of various types of complex systems is formed.Further,the liveness analysis of the battlefield target intelligence system is carried out,and the problems of the existing system are presented from several aspects.And the technical prediction of the development and construction is given,which provides directional ideas for the subsequent research and development of the battlefield target intelligence system.In the end,the proposed architecture model of the battlefield target intelligence system is simulated and verified by applying the colored Petri nets(CPN)simulation software.The analysis demonstrates the reasonable integrity of its logic.

A Novel Modeling, Simulation and Optimization Approach of Crude Oil Cold Stripping Process

Cold stripping is the most common process for crude oil sweetening in oilfields particularly at offshore installations because of its low price and relatively easy operating conditions in comparison with other sweetening processes. In this paper the cold stripping process in tray column has been modeled mathematically in static and dynamic modes, and solved with the MATLAB software. This process has been used in the existing treatment facilities of an offshore oil production complex for verifying the model results. With the help of HYSYS software the effective parameters on the process have been discussed and the optimized conditions finalized after some plant modifications for improving the performance of stripper columns have been proposed.

Application of Modelica/MWorks on modeling,simulation and optimization for electro-hydraulic servo valve system

Electro-hydraulic servo valve is a typical complicated multi-domain system constituted by mechanical, electric, hydraulic and magnetic components, which is widely used in electro-hydraulic servo systems such as construction machinery, heavy equipment, weapon and so forth. The traditional method of modeling and simulation of servo valve is based on block diagram or signal flow, which cannot describe the servo valve system from components level nor be used in modeling and simulation of overall servo systems. In the procedure of traditional method, computational causality must be involved in modeling of servo valve, which is inconvenient to execute modification on components or parameters. Modelica is an object-oriented modeling language which is suited for large, complex, heterogeneous and multi-domain systems. The key features of Modelica are multi-domain, object-oriented and non-causal, which are suitable for modeling of servo valve and make the model readable, reusable, and easy to modify. The simulation results show similar curves with traditional method. This new servo valve modeling and simulation method can provide the engineers a more efficient way to design and optimize a servo valve and an overall servo system.

Modeling and Optimization of Ethane Steam Cracking Process in An Industrial Tubular Reactor with Improved Reaction Scheme

Ethane steam cracking process in an industrial reactor was investigated.An one-demsional(1D)steady-state model was developed firstly by using an improved molecular reaction scheme and was then simulated in Aspen Plus.A comparison of model results with industrial data and previously reported results showed that the model can predict the process kinetics more accurately.In addition,the validated model was used to study the effects of different process variables,including coil outlet temperature(COT),steam-to-ethane ratio and residence time on ethane conversion,ethylene selectivity,products yields,and coking rate.Finally,steady-state optimization was conducted to the operation of industrial reactor.The COT and steam-to-ethane ratio were taken as decision variables to maximize the annual operational profit.

Molecular-Based Simulation of Feedstock Properties for Complex Reaction System

The kinetics of complex reaction systems were studied on molecular level with the combined method of Monte Carlo simulation and Structural Oriented Lumping by focusing on deep catalytic cracking （DCC） process, the model parameters were optimized by means of routine analytic data of a DCC unit. A model was established to transform the feedstock of the complex reaction systems such as DCC to 1000-10000 pseudo-molecules with the Monte Carlo simulation and every molecule was expressed by 19 attributes. The results of model simulation showed that these pseudo-molecules reflected the characteristics of feedstock very well and their average properties gave a good agreement with the plant data.

Application of Assembly Modeling and Simulation Technology to a Large Size Wind-drive Generator

Challenges still remain in carrying out assembly modeling efficiently in virtual assembly （VA） fields. One of the root causes is the apparent weakness in effective description of assembly knowledge and information. The assembly modeling, disassembly modeling, assembly interference inspection, assembly sequence planning and optimization, and assembly simulation display for key techniques is studied theoretically in this paper. An example of product assembly modeling is provided to illustrate the effectiveness of the proposed approach. On the basis of re- search, using assembly simulation techniques and multimedia techniques to finish structure design in linkage design of a large size wind-drive generator. The application of the modeling method has shortened the lead time dramatically.

Intelligent modeling and optimization on time-sharing power dispatching system for electrolytic zinc process

Based on real time price counting of electric power, an optimization model of time sharing power for electrolytic zinc process(EZP) was established by means of an incremental fuzzy neural network(FNN), which is adopted to approximate the relationship of current efficiency, current density and acidity. Penalty function introduced and optimal objective function reconstructed, a single loop simulated annealing algorithm(SAA) by using mutation and extending searching spaces was used to obtain optimal time sharing power scheme. Industrial practical results show that the whole system can greatly decrease the power consumption of EZP and increase the time sharing profits.

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.

Numerical simulation and combination optimization of aluminum holding furnace linings based on simulated annealing

To reduce heat loss and save cost, a combination decision model of reverb aluminum holding furnace linings in aluminum casting industry was established based on economic thickness method, and was resolved using simulated annealing. Meanwhile, a three-dimensional mathematical model of aluminum holding furnace linings was developed and integrated with user-defined heat load distribution regime model. The optimal combination was as follows: side wall with 80 mm alumino-silicate fiber felts, 232 mm diatomite brick and 116 mm chamotte brick; top wall with 50 mm clay castables, 110 mm alumino-silicate fiber felts and 200 mm refractory concrete;and bottom wall with 232 mm high-alumina brick, 60 mm clay castables and 68 mm diatomite brick. Lining temperature from high to low was successively bottom wall, side wall, and top wall. Lining temperature gradient in increasing order of magnitude was refractory layer and insulation layer. It was indicated that the results of combination optimization of aluminum holding furnace linings were valid and feasible, and its thermo-physical mechanism and cost characteristics were reasonably revealed.

Body Structure Design, Simulation and Parameter Optimization of a Crawler Type Mobile Flat Bedplate Frame Composite Car

The report mainly studied the crawler frame motion platform to reduce weight and increase the intensity. Report described the structural design process which using CAD/CAE technology for solid modeling, simulation analysis, parameter optimization. And it also explained the outstanding advantages of CAD/CAE technology in mechanical design as well as simulation analysis.

Numerical Modeling and Simulation of CIGS-Based Solar Cells with ZnS Buffer Layer

Usually a buffer layer of cadmium sulphide is used in high efficiency solar cells based on Cu(In,Ga)Se2(CIGS). Because of cadmium toxicity, many in-vestigations have been conducted to use Cd-free buffer layers. Our work focuses on this type of CIGS-based solar cells where CdS is replaced by a ZnS buffer layer. In this contribution, AFORS-HET software is used to simulate n-ZnO: Al/i-ZnO/n-ZnS/p-CIGS/Mo polycrystalline thin-film solar cell where the key parts are p-CIGS absorber layer and n-ZnS buffer layer. The characteristics of these key parts: thickness and Ga-content of the absorber layer, thickness of the buffer layer and doping concentrations of absorber and buffer layers have been investigated to optimize the conversion efficiency. We find a maximum conversion efficiency of 26% with a short-circuit current of 36.9 mA/cm2, an open circuit voltage of 824 mV, and a fill factor of 85.5%.

Modeling Simulation Technology Research for Distribution Network Planning

This paper proposes to use the power system simulation software CYME to plan, model and simulate for an actual distribution network for improving the reliability and efficiency, enhancing the efficiency and capacity, simulating the abnormal condition of distribution network, and presenting operation program of safe, reliable and having simulation record statements. The modeling simulation results show that the software module has lots of advantages including high accuracy, ideal reliability, powerful practicality in simulation and analysis of distribution network, it only need to create once model, the model can sufficiently satisfy multifarious types of simulation analysis required for the distribution network planning.

Methodology for Optimization-OrientedComputer Simulation and Animation

The methodology for optimization-oriented computer simulation and animationis proposed. The paper considere how to implement optimization-orientedsimulation throush the simulation activities in an attempt to intesrate animatedsimulation with optimization idea. It also deals with the application of multiple-objective optimisation in simulation study. Furthermore, the principles forevaluating and obtaining the optimum design and factor level combination of asimulated system are presented with respect to the underlying rationale.Inaddition,the approach for optimication objectives driven simulation analysis isexamined .

Thermodynamic performance modeling,optimization and numerical simulation of RBCC ejector mode

Ejector mode is a unique and critical phase of wide-range rocket-based combined cycle(RBCC)engine.In this paper,a quasi-one-dimensional thermodynamic performance modeling method,with more detailed model treatments for the inlet-diffuser system,pri-mary/secondaryflow interaction,and pressure feedback matching,was developed for operating characteristics studies and multi-objective optimization analysis of the ejector mode of an actual RBCC engine.A series of three-dimensional simulations of separate inlet and fullflowpath was completed to validate the modeling study and provide further insight into the operating charac-teristics.The primary/secondary equilibrium pressure ratio functions a significant effect on ejector mode performance,a higher performance augmentation can be obtained by lower rocket pressure ratio,larger mixing section area ratio,smaller throttling throat and higher equivalence ratio,within an appropriate range.The positive performance augmentation can be realized at lowflight Mach conditions,the coordination and trade-off relationships between specific im-pulse,performance augmentation ratio and thrust-to-area ratio during ejector mode are present by the Pareto-front from MOP analysis.It is further verified by CFD simulation that,the operating back-pressure at the exit of inlet-diffuser system functions a decisive influence on the airbreathing characteristics,the pressure feedback and matching should be well-controlled for secondaryflowrate and performance augmentation.The thermodynamic modeling analysis re-sults are basically consistent with those of numerical simulation,to validate the rationality and effectiveness of the modeling method.

Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning

Public sector decision-making typically involves complex problems that are riddled with competing performance objecttives and possess design requirements which are difficult to capture at the time that supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable stochastic uncertainty and frequently numerous stakeholders exist that hold completely incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the problem formulation, which can greatly impact the acceptability of any proposed solutions. While a mathematically optimal solution might provide the best solution to a modelled problem, normally this will not be the best solution to the underlying real problem. Therefore, in public environmental policy formulation, it is generally preferable to be able to create several quantifiably good alternatives that provide very different approaches and perspectives to the problem. This study shows how a computationally efficient simulation-driven optimization approach that com- bines evolutionary optimization with simulation can be used to generate multiple policy alternatives that satisfy required system criteria and are maximally different in decision space. The efficacy of this modelling-to-generate-alternatives method is specifically demonstrated on a municipal solid waste management facility expansion case.

An Overview of Recently Developed Coupled Simulation Optimization Approaches for Reliability Based Minimum Cost Design of Water Retaining Structures

This paper reviews several recently-developed techniques for the minimum-cost optimal design of water-retaining structures (WRSs), which integrate the effects of seepage. These include the incorporation of uncertainty in heterogeneous soil parameter estimates and quantification of reliability. This review is limited to methods based on coupled simulation-optimization (S-O) models. In this context, the design of WRSs is mainly affected by hydraulic design variables such as seepage quantities, which are difficult to determine from closed-form solutions or approximation theories. An S-O model is built by integrating numerical seepage modeling responses to an optimization algorithm based on efficient surrogate models. The surrogate models (meta-models) are trained on simulated data obtained from finite element numerical code solutions. The proposed methodology is applied using several machine learning techniques and optimization solvers to optimize the design of WRS by incorporating different design variables and boundary conditions. Additionally, the effects of several scenarios of flow domain hydraulic conductivity are integrated into the S-O model. Also, reliability based optimum design concepts are incorporated in the S-O model to quantify uncertainty in seepage quantities due to uncertainty in hydraulic conductivity estimates. We can conclude that the S-O model can efficiently optimize WRS designs. The ANN, SVM, and GPR machine learning technique-based surrogate models are efficiently and expeditiously incorporated into the S-O models to imitate the numerical responses of simulations of various problems.

Reaction network design and hybrid modeling of S Zorb

At present,many countries are becoming more and more stringent in terms of sulfur content in fuel oil.S Zorb is a kind of desulfurization technology with advantages of exceptional desulfurization efficiency and small impact on octane number.To meet the needs of environmental requirements and the trend of digitalization in the petrochemical industry,a first-principle model of S Zorb was established based on industry data.In order to describe the desulfurization and the other side reactions,a reaction network was designed and the kinetic parameters were estimated by the particle swarm optimization algorithm.Two hybrid models based on the first-principle model and support vector regression method were established to correct the mass fraction of sulfur and predict the research octane number of the refined gasoline respectively.The results indicate that the hybrid models can predict the mass fraction of PIONA,sulfur content and research octane number of the refined gasoline accurately,of which the mean absolute percentage errors are less than 6%.Hybrid models were then applied to optimize the decision variables to minimize the research octane number loss.Optimization results show that the average reduction of the loss of research octane number is 21.8%,which suggests that the models developed hold promise for guiding practical production.