Parametric optimization and performance comparison of organic Rankine cycle with simulated annealing algorithm

Taking the ratio of heat transfer area to net power and heat recovery efficiency into account, a multi-objective mathematical model was developed for organic Rankine cycle （ORC）. Working fluids considered were R123, R134a, R141b, R227ea and R245fa. Under the given conditions, the parameters including evaporating and condensing pressures, working fluid and cooling water velocities were optimized by simulated annealing algorithm. The results show that the optimal evaporating pressure increases with the heat source temperature increasing. Compared with other working fluids, R123 is the best choice for the temperature range of 100--180℃ and R141 b shows better performance when the temperature is higher than 180 ℃. Economic characteristic of system decreases rapidly with the decrease of heat source temperature. ORC system is uneconomical for the heat source temperature lower than 100℃.

Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes

Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(DKPCA)and the multiway dynamic kernel principal component analysis(MDKPCA)fault detection algorithms are proposed,which are used for continuous and batch process fault detections,respectively.In addition,(II)a fault variable identification model based on reconstructed-based contribution(RBC)model that paves the way for determining the cause of the fault are proposed.The proposed fault diagnosis model was applied to Tennessee Eastman(TE)process and penicillin fermentation process for fault diagnosis.And compare with other fault diagnosis methods.The results show that the proposed method has better detection effects than other methods.Finally,the reconstruction-based contribution(RBC)model method is used to accurately locate the root cause of the fault and determine the fault path.

Efficient Lubytransform encodingalgorithm based on short cycle elimination

An effective Luby transform （LT） encoding algorithm based on short cycle elimination is proposed to improve decoding probabilities of short length LT codes. By searching the generator ma- trix, some special encoded symbols are generated by the encoder to effectively break the short cycles that have negative effect on the performance of LT codes. Analysis and numerical results show that by employing the proposed algorithm, the encoding complexity decreases and the decoding probabili- ties improve both in binary erasure channels （BECs） and additive white gauss noise （AWGN） chan- nels.

Optimal Dynamic Voltage Restorer Using Water Cycle Optimization Algorithm

This paper proposes a low complexity control scheme for voltage control of a dynamic voltage restorer(DVR)in a three-phase system.The control scheme employs the fractional order,proportional-integral-derivative(FOPID)controller to improve on the DVR performance in order to enhance the power quality in terms of the response time,steady-state error and total harmonic distortion(THD).The result obtained was compared with fractional order,proportionalintegral(FOPI),proportional-integral-derivative(PID)and proportional-integral(PI)controllers in order to show the effectiveness of the proposed DVR control scheme.A water cycle optimization algorithm(WCA)was utilized to find the optimal set for all the controller gains.They were used to solve four power quality issues;balanced voltage sag,balanced voltage swell,unbalanced voltage sag,and unbalanced voltage swell.It showed that one set of controller gain obtained from the WCA could solve all the power quality issues while the others in the literature needed an individual set of optimal gain for each power quality problem.To prove the concept,the proposed DVR algorithm was simulated in the MATLAB/Simulink software and the results revealed that the four optimal controllers can compensate for all the power quality problems.A comparative analysis of the results in various aspects of their dynamic response and%THD was discussed and analyzed.It was found that PID controller yields the most rapid performance in terms of average response time while FOPID controller yields the best performance in term of average%steady-state error.FOPI controller was found to provide the lowest THD percentage in the average%THD.FOPID did not differ much in average response from the PID and average%THD from FOPI;however,FOPID provided the most outstanding average steady-state error.According to the CBMA curve,the dynamic responses of all controllers fall in the acceptable power quality area.The total harmonic distortion(THD)of the compensated load voltage from all the controllers were within the 8%limit in accordance to the IEEE std.519-2014.

基于冗余度和方差的P-Cycle圈构造算法的研究

铁路光传送网络是高速铁路地面基础设施的神经中枢,为避免网络故障给铁路运营带来巨大损失,重点研究光传送网P-Cycle(Pre-configured Cycle)保护技术,提出在圈扩展时以所有候选圈上未保护工作容量的方差、冗余度两个指标为比较标准的RVPA(Redundancy and Variance Based P-Cycle Construction Algorithm)算法。圈扩展的过程中,算法将选择方差与冗余度能同时满足条件的候选圈作为本轮扩展圈,有效限制了完成保护的P-Cycle圈个数;圈扩展停止条件中,当UPL与参数M、冗余度的大小关系满足条件时,则停止圈扩展,从而限制圈上节点数,使圈个数与圈长度得到有效均衡;在仿真过程中,利用泛欧网络拓扑COST239对RVPA算法进行仿真,并对比分析不同M值下的性能。仿真结果表明,在相同空闲资源与待保护工作容量设定下,参数M取0.5时效果最优,并且RVPA算法的保护容量效率、所需圈的个数、算法整体耗时、总冗余度均优于已有的POCA(P-Cycle Optimization Configuration Heuristic Algorithm)算法。

Bi-level programming model and algorithm for optimizing headway of public transit line

Due to the fact that headway is a key factor to be considered in bus scheduling, this paper proposes a bi-level programming model for optimizing bus headway in public transit lines. In this model, with the interests of bus companies and passengers in mind, the upper-level model＇s objective is to minimize the total cost, which is affected by frequency settings, both in time and economy in the transit system. The lower-level model is a transit assignment model used to describe the assignment of passengers＇ trips to the network based on the optimal bus headway. In order to solve the proposed model, a hybrid genetic algorithm, namely the genetic algorithm and the simulated annealing algorithm （GA-SA）, is designed. Finally, the model and the algorithm are tested against the transit data, by taking some of the bus lines of Changzhou city as an example. Results indicate that the proposed model allows supply and demand to be linked, which is reasonable, and the solving algorithm is effective.

Algorithm for solving the bi-level decision making problem with continuous variables in the upper level based on genetic algorithm

Based on genetic algorithms, a solution algorithm is presented for the bi-level decision making problem with continuous variables in the upper level in accordance with the bi-level decision making principle. The algorithm is compared with Monte Carlo simulated annealing algorithm, and its feasibility and effectiveness are verified with two calculating examples.

格网中p-cycles重配置的快速实现算法

提出了一种用于双链路故障恢复和为动态业务提供保护的p-cycles重配置启发式算法.该算法可以提供网络生存性设计和网络管理的高效性和简洁性,并能提供快速的恢复操作.仿真结果表明,这种算法用于双链路故障恢复的重配置容量冗余度可达74%,平均计算时间为48ms;用于增量重配置的双故障冗余度为118%,计算时间为81ms.基于该算法的业务恢复可以满足大多数业务的最低可中断时间要求.分析表明该算法亦适用于动态业务的p-cycles重配置.

Genetic-fuzzy HEV control strategy based on driving cycle recognition

A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller （FLC） was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm （GA） for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy （0.2%） or CYC _ WVUSUB optimized fuzzy （0.7%）. The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions.

Multi-objective Transmission Expansion Planning Considering Life Cycle Cost

为克服目前全寿命周期成本（life cycle cost,LCC）技术的应用局限于设备运行或维护阶段的不足,针对输电网整体建立一个3维LCC层级模型,包括时间维度、元件维度和费用维度。费用维度进一步分解为设备级、系统级、外部环境成本。研究了以可靠性为中心的维护手段的应用对维护成本的影响,利用电量不足期望值计算故障成本。在此基础上,对风电等4种不确定因素进行建模,建立以LCC成本最小和切负荷量最小为多目标的输电网机会约束规划模型。采用正态边界交点算法联合改进小生境遗传算法,对模型有效求解。最后,分别对18节点系统和77节点系统进行算例分析。研究结果给出了帕累托解集及推荐的最优规划方案;此外,LCC费用分解图表明了各费用的比重和影响,有利于指导未来资产管理。

Chinese Business Cycles Identified by Coincident Indexes

In reference to the method of the Conference Board,the coincident indexes for China are constructed from a sample period between January 1990 and May 2012 and by51 chosen component indicators.The resulting coincident indexes have higher correlations with gross domestic product(GDP) growth rates than the China Economic Monitoring and Analysis Center(CEMAC) coincident index over the sample period between February2005 and May 2012.The peaks and troughs of the growth rates in several indicators are identified.The total number of peaks and troughs in the resulting coincident index is the same with the CEMAC coincident index.Unfortunately,these troughs don't signify recessions in the Chinese economy because the financial system has not seen a negative growth rate over the specific sample periods.The impacts of the Southeast Asian Financial Crisis and subprime mortgage crisis on the business cycle could be dated via a smoother index from HP filtering to the coincident index.

Optimization of the bioconversion of glycerol to ethanol using Escherichia coli by implementing a bi-level programming framework for proposing gene transcription control strategies based on genetic algorithms

In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approaches that can be exploited in order to enhance yield of processes which are based on biological reactions. Here, we propose an evolutionary approach aiming to suggest different mutant for augmenting ethanol yield using glycerol as substrate in Escherichia coli. We found that this algorithm, even though is far from providing the global optimum, is able to uncover genes that a global optimizer would be incapable of. By over-expressing accB, eno, dapE, and accA mutants in ethanol production was augmented up to 2 fold compared to its counterpart E. coli BW25113.

Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

Optimal Location and Sizing ofMulti-Resource Distributed Generator Based onMulti-Objective Artificial Bee Colony Algorithm

Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.

Off-design condition optimization of organic Rankine cycle based on genetic algorithm

Organic Rankine cycle(ORC)has been considered as one of the most promising technologies in industrial waste heat utilization and power generation.During the actual operation of ORC system,due to the fluctuation of cooling and heat sources,the system operates under off-design conditions in most cases.In this paper,thermodynamic model,heat transfer process description and power equipment model are established to evaluate the operating parameters of ORC for the off-design conditions.Evaporation temperature and condensation temperature are taken as independent parameters for the operation of ORC system.Genetic algorithm is adopted to optimize the independent parameters under the maximum net output power.The results show that the effect of optimizing independent parameters is to make the working fluid at the outlet of the preheater as close as possible to a saturated liquid state,and the working fluid at the inlet of the screw expander should be in a saturated gas state.With the optimal power output increasing by 19.1%for every 5°C increase in hot water inlet temperature,9.2%for every 20 kg/s increase in hot water mass flow rate,and 3.9%for every 1°C decrease in cooling water temperature.The optimization method of off-design operating conditions has good system performance and good engineering application prospects.

Quasi-Cyclic LDPC环优化构造算法

本文研究了准循环LDPC码的一致校验矩阵与其对应Tanner图环结构之间的关系,将局部平均围长优化算法引入优化构造算法中,提出了一种基于PEG算法的准循环扩展LDPC码构造的改进算法。在改进算法中,首先利用PEG算法产生基矩阵,然后基于平均围长最大准则选择循环移位置换矩阵对基矩阵进行准循环扩展,通过围长分布的优化,从而改善了迭代译码的性能。仿真结果表明,在码长相同、码率和度分布近似情况下,与文献[7]中基于PEG算法的准循环扩展构造的码字比较,改进算法构造的码字能够获得更加优异的性能。

Optimization and Simulation of Plastic Injection Process using Genetic Algorithm and Moldflow

The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research projects on plastic molding processes. An important branch of such research is focused on mold cooling system. Conventional cooling systems are most widely used because they are easy to make by using conventional machining processes. However, the non-uniform cooling processes are considered as one of their weaknesses. Apart from the conven- tional systems, there are also conformal cooling systems that are designed for faster and more uniform plastic mold cooling. In this study, the conformal cooling system is applied for the production of bowl-shaped product made of PP AZ564. Optimization is conducted to initiate machine setup parameters, namely, the melting temperature, injection pressure, holding pressure and holding time. The genetic algorithm method and Moldflow were used to optimize the injection process parameters at a minimum cycle time. It is found that, an optimum injection molding processes could be obtained by setting the parameters to the following values： TM=180℃; Pinj = 20MPa; Phold= 16MPa and thold=8s, with a cycle time of 14.11 s. Experiments using the conformal cooling system yielded an average cycle time of 14.19 s. The studied conformal cooling system yielded a volumetric shrinkage of 5.61% and the wall shear stress was found at 0.17 MPa. The difference between the cycle time obtained through simulations and experiments using the conformal cooling system was insignificant （below 1%）. Thus, combining process parameters optimization and simulations by using genetic algorithm method with Moldflow can be considered as valid.

Hybrid algorithm for project scheduling with capacity constraint

Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-level scheduling and project decomposition technology, and the genetic algorithm and tabu search is combined. Topological reordering technology is used to improve the efficiency of evaluation. Simulation results show the proposed algorithm can obtain satisfied scheduling results in acceptable time.

Genetic Algorithm for Concurrent Balancing of Mixed-Model Assembly Lines with Original Task Times of Models

The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.

An unequal clustering routing protocal for wireless sensor networks based on genetic algorithm

The imbalance of energy consumption in wireless sensor networks(WSNs)easily results in the“hot spot”problem that the sensor nodes in a particular area die due to fast energy consumption.In order to solve the“hot spot”problem in WSNs,we propose an unequal clustering routing algorithm based on genetic algorithm(UCR-GA).In the cluster head election phase,the fitness function is constructed based on the residual energy,density and distance between nodes and base station,and the appropriate node is selected as the cluster head.In the data transmission phase,the cluster head selects single-hop or multi-hop communication mode according to the distance to the base station.After we comprehensively consider the residual energy of the cluster head and its communication energy consumption with the base station,an appropriate relay node is selected.The designed protocal is simulated under energy homogeneous and energy heterogeneity conditions,and the results show that the proposed routing protocal can effectively balance energy consumption,prolong the life cycle of network,and is appicable to heterogeneous networks.