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.

Cyclic Reconfigurable Flow Shop under Different Configurations Modeling and Optimization Based on Timed Event Graph

Based on the idea that modules are independent of machines, different combinations of modules and machines result in different configurations and the system performances differ under different configurations, a kind of cyclic reconfigurable flow shops are proposed for the new manufacturing paradigm-reconfigurable manufacturing system. The cyclic reconfigurable flow shop is modeled as a timed event graph. The optimal configuration is defined as the one under which the cyclic reconfigurable flow shop functions with the minimum cycle time and the minimum number of pallets. The optimal configuration, the minimum cycle time and the minimum number of pallets can be obtained in two steps.

Enterprise resource planning implementation decision & optimization models

To study the uncertain optimization problems on implementation schedule, time-cost trade-off and quality in enterprise resource planning （ERP） implementation, combined with program evaluation and review technique （PERT）, some optimization models are proposed, which include the implementation schedule model, the timecost trade-off model, the quality model, and the implementation time-cost-quality synthetic optimization model. A PERT-embedded genetic algorithm （GA） based on stochastic simulation technique is introduced to the optimization models solution. Finally, an example is presented to show that the models and algorithm are reasonable and effective, which can offer a reliable quantitative decision method for ERP implementation.

Optimization method of refracturing timing for old shale gas wells

Based on the elastic theory of porous media,embedded discrete fracture model and finite volume method,and considering the micro-seepage mechanism of shale gas,a fully coupled seepage-geomechanical model suitable for fractured shale gas reservoirs is established,the optimization method of refracturing timing is proposed,and the influencing factors of refracturing timing are analyzed based on the data from shale gas well in Fuling of Sichuan Basin.The results show that due to the depletion of formation pressure,the percentage of the maximum horizontal principal stress reversal area in the total area increases and then decreases with time.The closer the area is to the hydraulic fracture,the shorter the time for the peak of the stress reversal area percentage curve to appear,and the shorter the time for the final zero return(to the initial state).The optimum time of refracturing is affected by matrix permeability,initial stress difference and natural fracture approach angle.The larger the matrix permeability and initial stress difference is,the shorter the time for stress reversal area percentage curve to reach peak and return to the initial state,and the earlier the time to take refracturing measures.The larger the natural fracture approach angle is,the more difficult it is for stress reversal to occur near the fracture,and the earlier the optimum refracturing time is.The more likely the stress reversal occurs at the far end of the artificial fracture,the later the optimal time of refracturing is.Reservoirs with low matrix permeability have a rapid decrease in single well productivity.To ensure economic efficiency,measures such as shut-in or gas injection can be taken to restore the stress,and refracturing can be implemented in advance.

A time-optimal aircraft-following model based on Pontryagin's minimum principle

A time-optimal aircraft-following model is introduced to address air traffic flow interference by velocity reduction. The objective function is set up as minimizing the recovery time during which the separation minima are not infringed and the separation of the air traffic flow returns to the initial separation at the terminal time. Pontryagin＇s minimum principle is used to solve the optimum aircraft-following velocity control law. An analytical minimum safe following separation is also provided under the time-optimal control law. The simulation results show that the precision first-order tracking accuracy is achieved without losing the separation.

Research on Optimize Prediction Model and Algorithm about Chaotic Time Series

We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means. Key words chaotic time series - parameter identification - optimal prediction model - improved change ruler method CLC number TP 273 Foundation item: Supported by the National Natural Science Foundation of China (60373062)Biography: JIANG Wei-jin (1964-), male, Professor, research direction: intelligent compute and the theory methods of distributed data processing in complex system, and the theory of software.

A Stochastic Model for Optimizing the Patching Time of Software Bugs

New bugs and vulnerabilities are discovered and reported from time to time even after software products are released. One of the common ways to handle these bugs is to patch the software. In this paper, the authors propose a stochastic model for optimizing the patching time for software bugs and vulnerabilities. The optimal patching time can be computed in the patching script development and operational costs in fix. The authors present two case studies using the Nimda worm vulnerability in Microsoft Internet Information Services web server and the bug report of the Debian project. These studies indicate that the patch applications are later than their optimal fix time.

DELAY-TIME MODEL BASED ON IMPERFECT INSPECTION OF AIRCRAFT STRUCTURE WITHIN FINITE TIME SPAN

According to the failure characteristics of aircraft structure, a delay-time model is an effective method to optimize maintenance for aircraft structure. To imitate the practical situation as much as possible, imperfect inspections, thresholds and repeated intervals are concerned in delay-time models. Since the suggestion by the existing delay-time models that the inspections are implemented in an infinite time span lacks practical value, a de- lay-time model with imperfect inspection within a finite time span is proposed. In the model, the nonhomogenous Poisson process is adopted to obtain the renewal probabilities between two different successive inspections on de- fects or failures. An algorithm is applied based on the Nelder-Mead downhill simplex method to solve the model. Finally, a numerical example proves the validity and effectiveness of the model.

Network-based structure optimization method of the anti-aircraft system

The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities.Firstly, the thought of combat network model(CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength(CAST) logic and influence network(IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network(TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed.Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-Ⅱ(NSGA2) is used to solve the multiobjective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-Ⅲ(NSGA3)and strength Pareto evolutionary algorithm-Ⅱ(SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

Grey series time-delay predicting model in state estimation for power distribution networks

A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.

Genetic programming-based chaotic time series modeling

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

Active suspension with optimal control based on a full vehicle model

The 7-DOF model of a full vehicle with an active suspension is developed in this paper.The model is written into the state equation style.Actuator forces are treated as inputs in the state equations.Based on the basic optimal control theory,the optimal gains for the control system are figured out.So an optimal controller is developed and implemented using Matlab/Simulink,where the Riccati equation with coupling terms is deduced using the Hamilton equation.The all state feedback is chosen for the controller.The gains for all vehicle variables are traded off so that majority of indexes were up to optimal.The active suspension with optimal control is simulated in frequency domain and time domain separately,and compared with a passive suspension.Throughout all the simulation results,the optimal controller developed in this paper works well in the majority of instances.In all,the comfort and ride performance of the vehicle are improved under the active suspension with optimal control.

Reliability Growth Modeling and Optimal Release Policy Under Fuzzy Environment of an N-version Programming System Incorporating the Effect of Fault Removal Efficiency

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery （recovery block scheme）, fault masking （N-version programming （NVP）） or a combination of both （Hybrid scheme）. These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model （SRGM） is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

Optimal Model of Continuous Knowledge Transfer in the Big Data Environment

With market competition becoming fiercer,enterprises must update their products by constantly assimilating new big data knowledge and private knowledge to maintain their market shares at different time points in the big data environment.Typically,there is mutual influence between each knowledge transfer if the time interval is not too long.It is necessary to study the problem of continuous knowledge transfer in the big data environment.Based on research on one-time knowledge transfer,a model of continuous knowledge transfer is presented,which can consider the interaction between knowledge transfer and determine the optimal knowledge transfer time at different time points in the big data environment.Simulation experiments were performed by adjusting several parameters.The experimental results verified the model’s validity and facilitated conclusions regarding their practical application values.The experimental results can provide more effective decisions for enterprises that must carry out continuous knowledge transfer in the big data environment.

Melting Time Prediction Model for Induction Furnace Melting Using Specific Thermal Consumption from Material Charge Approach

A system-level evaluation was used to analyze the induction furnace operation and process system in this study. This paper presents an investigation into the relationship between the instantaneous chemical composition of a molten bath and its energy consumption in steelmaking. This was evaluated using numerical modelling to solve for the estimated melting time prediction for the induction furnace operation. This work provides an insight into the lowering of energy consumption and estimated production time in steelmaking using material charge balancing approach. Enthalpy computation was implemented to develop an energy consumption model for the molten metal using a specific charge composition approach. Computational simulation program engine (CastMELT) was also developed in Java programming language with a MySQL database server for seamless specific charge composition analysis and testing. The model performance was established using real-time production data from a cast iron-based foundry with a 1 and 2-ton induction furnace capacity and a medium carbon-based foundry with a 10- and 15-ton induction furnace capacity. Using parameter fitting techniques on the measured operational data of the induction furnaces at different periods of melting, the results from the model predictions and real-time melting showed good correlation between 81% - 95%. A further analysis that compared the relationship between the mass composition of a current molten bath and melting, time showed that energy consumption can be reduced with effective material balancing and controlled charge. Melting time was obtained as a function of the elemental charge composition of the molten bath in relation to the overall scrap material charge. This validates the approach taken by this research using material charge and thermodynamic of melting to optimize and better control melting operation in foundry and reduce traditional waste during iron and steel making.

Optimal Stopping Time for Holding an Asset

In this paper, we consider the problem to determine the optimal time to sell an asset that its price conforms to the Black-Schole model but its drift is a discrete random variable taking one of two given values and this probability distribution behavior changes chronologically. The result of finding the optimal strategy to sell the asset is the first time asset price falling into deterministic time-dependent boundary. Moreover, the boundary is represented by an increasing and continuous monotone function satisfying a nonlinear integral equation. We also conduct to find the empirical optimization boundary and simulate the asset price process.

A Study of Discrete-Time Optimum Current Controller for the Virtual Synchronous Generator under Constraints

In recent years, power generation using renewable energy sources has been increasing as a solution to the global warning problem. Wind power generation can generate electricity day and night, and it is relatively more efficient among the renewable energy sources. The penetration level of variable-speed wind turbines continues to increase. The interconnected wind turbines, however, have no inertia and no synchronous power. Such devices can have a serious impact on the transient stability of the power grid system. One solution to stabilize such grid with renewable energy sources is to provide emulated inertia and synchronizing power. We have proposed an optimal design method of current control for virtual synchronous generators. This paper proposes an optimal control method that can follow the virtual generator model under constrains. As a result, it is shown that the proposed system can suppress the peak of the output of semiconductor device under instantaneous output voltage drop.

Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.

基于flushing time的春季水库优化调度可行性研究

针对三峡水库支流库湾春季水华问题,通过建立三维水动力模型,探讨了利用调峰调度和潮汐式调度促进干支流水体交换的可行性。研究表明,当4月水库实施调峰调度时,整个库湾水体的flushing time较常规调度有所缩短,尤其是库湾上游至中上游区域;当5月水库开始实施潮汐式调度时,水体体积较大的库湾下游区域的flushing time较常规调度方案有所缩短。可见,两种水库优化调度方案对增大干支流水体交换均具有可行性。当水库实施调峰调度和潮汐式调度联合调度时,整个库湾水体的平均flushing time较常规调度方案有不同程度的缩短,表明该联合调度方案更能促进干支流水体的交换。研究成果可为支流库湾水环境问题的防治提供借鉴。

基于互信息和Just-in-Time优化的回声状态网络

为了提高回声状态网络(ESN)的适应性,提出基于互信息(MI)和Just-in-Time(JIT)的优化方法,对ESN的输入伸缩参数以及输出层进行优化,所得网络称为MI-JIT-ESN.ESN的优化方法分为两部分:一是基于网络输入与输出之间的互信息,对网络的多个输入伸缩参数进行调整;二是基于JIT优化的局部输出层,对ESN的隐层输出数据进行局部重新建模,从而提升ESN输出层的回归拟合精度.将MI-JIT-ESN应用于青霉素补料分批发酵过程建模.结果显示,MI-JIT优化方法能提高模型的适应性,并优于其他比较方法.