Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Improved Fuzzification Method for Multi-Objective Decision-Making and Its Application in Evaluation of Highway Planning

A new fuzzification method for multi-objective decision-making and selective sorting is proposed on the basis of the fuzzy consistent relation, and the specific algorithm is presented. The method is applied to the evaluation of highway planning of Zhanjiang city. To decrease the subjectivity in the process of decision-making, the LOWA operator is introduced, and a discussion on how to select appropriate weights involved in multi-objective sorting is made. It is concluded that it is feasible to apply the fuzzy consistent relation to multi-objective decision-making analysis, and the improved fuzzication method is workable.

Optimal setting and placement of FACTS devices using strength Pareto multi-objective evolutionary algorithm

This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.

Application of Grey Situation Decision-Making Theory in Site Selection of a Waste Sanitary Landfill

An application of an unequal-weighted multi-objective decision making method in site selection of a waste sanitary landfill is discussed. The eight factors, which affected possible options, were: size and capacity of the landfill, permeability of the stratum, the average difference in elevation between the groundwater level and the bottom of the landfill pit, quality and source of clay, the quality grade of the landfill site, the effect of landfill engineering on nearby residents, distance to the water supply and the water source as well as the cost of construction and waste transport. These are determined, given the conditions of the geological environment, the need for environmental protection and landfill site construction and transportation related to the design and operation of a sanitary landfill. The weights of the eight factors were further investigated based on the difference in their relevance. Combined with practical experience from Xuzhou city (Jiangsu province, China), the objectives, effects and weights of grey decision-making were deter- mined and the process and outcome of the landfill site selection are stated in detail. The decision-making results have been proven to be acceptable and correct. As we show, unequal-weighted multi-objective grey situation decision-mak- ing is characterized by easy calculations and good maneuverability when used in landfill site selection. The number of factors (objectives) affecting the outcome and the quantitative method of qualitative indices can be adjusted on the basis of concrete conditions in landfill site selection. Therefore, unequal-weighted multi-objective grey situation decision making is a feasible method in selecting landfill sites which offers a reference method for landfill site selection else- where. It is a useful, rational and scientific exploration in the choice of`a landfill site.

Multi-Objective Structural Optimization of Wind Turbine Tower Using Nondominated Sorting Genetic Algorithm

A multi-objective optimization process for wind turbine steel towers is described in present work.The objective functions are tower top deformation and mass.The tower's height,radius and thickness are considered as design variables.The mathematical relationships between objective functions and variables were predicted by adopting a response surface methodology(RSM).Furthermore,the multi-objective non-dominated sorting genetic algorithm-II(NSGA-II)is adopted to optimize the tower structure to achieve accurate results with the minimum top deformation and total mass.A case study on a 2MW wind turbine tower optimization is given,which computes the desired tower structure parameters.The results are compared with the original tower:a reduction of tower top deformation reduction by about 16.5%and a reduction of a mass by about 1.5%could be achieved for such an optimization process.

Static Optimization of a Compliant Vertical Access Riser

The compliant vertical access riser (CVAR) is a new riser concept with good compliance;it can significantly reduce operating costs by eliminating the need for additional machines to operate wells directly on the platform.In this study,we determined the optimal riser parameters in terms of the stress and riser weight by optimizing the CVAR,and we compared the optimization resuits.A two-dimensional nonlinear static CVAR model was deduced according to the principles of virtual work and variation,and the model was verified using MATLAB.Design of experiments and Kriging method were used to reduce the number of sample calculations and improve the modeling accuracy.An appropriate selection of the multi-objective optimization problem (MOP) and the non-dominated sorting genetic algorithm helped to optimize the CVAR design.The non-dominated sorting genetic algorithm Ⅱ was used to solve the Pareto frontier of the optimization model in order to provide decision makers with more choices for the optimization results.After optimizing the riser parameters,the geometry of the riser was smoother,and the stress and stress differences were greatly reduced;the maximum equivalent stresses at the top and bottom were reduced by 36.6% and 44%,respectively.In addition,the stress difference in the buoyancy block area was reduced by 20.9%,and the weight of the riser was increased significantly by 28.1%.

Multi-objective optimization for deepwater dynamic umbilical installation analysis

We suggest a method of multi-objective optimization based on approximation model for dynamic umbilical installation. The optimization aims to find out the most cost effective size, quantity and location of buoyancy modules for umbilical installation while maintaining structural safety. The approximation model is constructed by the design of experiment (DOE) sampling and is utilized to solve the problem of time-consuming analyses. The non-linear dynamic analyses considering environmental loadings are executed on these sample points from DOE. Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the Pareto solution set through an evolutionary optimization process. Intuitionist fuzzy set theory is applied for selecting the best compromise solution from Pareto set. The optimization results indicate this optimization strategy with approximation model and multiple attribute decision-making method is valid, and provide the optimal deployment method for deepwater dynamic umbilical buoyancy modules.

Novel Models and Algorithms of Load Balancing for Variable-structured Collaborative Simulation under HLA/RTI

High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.

Research on the Infuence of Multiple Parameters on the Responses of a B-type Subway Train

To obtain improved comprehensive crashworthiness criteria for a B-type subway train,the infuence laws of the vehicle design collision weight M and empty stroke D on the train’s collision responses were investigated,and multiobjective optimization and decision-making were performed to minimize TS(total compression displacement along the moving train)and TAMA(the overall mean acceleration along the moving train).Firstly,a one-dimensional train collision dynamics model was established and verifed by comparing with the results of the fnite element model.Secondly,based on the dynamics model,the infuence laws of M and D on the collision responses,such as the energy-absorbing devices’displacements and absorbed energy,vehicles’velocity and acceleration,TS,TAMA and the coupling correlation efect were investigated.Then,surrogate models for TS and TAMA were developed using the optimal Latin hypercube method(OLHD)and response surface method(RSM),and multi-objective optimization was conducted using the particle swarm optimization algorithm method(MPOSO).Finally,the entropy method was used to obtain the weight coefcients for TS and TAMA,and multi-objective decision-making was performed.The results indicate that D and M signifcantly afect the compression displacements and energy absorption of the frst three collision interfaces,but have limited impact on the last three collision interfaces.The velocity versus time curves of vehicle M1 and M2 are shifted and parallel with diferent D.However,the velocity versus time curves of all the vehicles are shifted but gradually divergent with diferent M.The maximum collision instantaneous accelerations of the vehicles are directly determined by M,but are only slightly afected by D.Under the coupling efect,all concerned collision responses are strongly correlated with M;however,the responses are weakly correlated with D except for the compression displacement at the M2-M3 collision interface and the maximum collision instantaneous acceleration of vehicle M2.The comprehensive crashworthiness criteria of the B-type subway train were signifcantly improved after multi-objective optimization and decision-making.The research provides more theoretical and engineering application references for the subway train crashworthiness design.

Closed-loop supply chain network equilibrium strategy model with environmental protection objectives

With the increasing popularity of ecological civilization and sustainable development,enterprises should consider environmental protection measures in their operations in addition to pursue their economic interests.This paper establsihes a closed-loop supply chain network model composed of multiple suppliers,manufacturers,retailers,recyclers,and demand markets—regarding their dual goals of the profit maximization and the minimization of carbon emissions.The conditions necessary for establishing overall equilibrium and an equilibrium model of the entire closed-loop supply chain network are determined by applying variational inequality and dual theory.A modified projection contraction algorithm is used to design a model-solving program.Finally,using numerical examples,the paper conducts a comparative static analysis on important parameters such as the weight coefficients of environmental protection objectives and consumers'awareness of low-carbon environmental protection and attains some beneficial enlightenment on management.The results indicate that when the environmental protection objectives of a certain type of enterprise increases,both the economic benefits and environmental protection performance will improve;when the environmental protection objectives of all enterprises increases simultaneously,environmental protection performance improves significantly,but the changes in economic benefits of different enterprises are inconsistent and profit coordination is more complex.Although consumers’awareness of low-carbon preference could improve environmental performance,it reduces the overall profits of network members and the entire closed-loop supply chain network as a whole.The above conclusions can be used as a reference for the government in designing low-carbon environmental protection policy and in closed-loop supply chain research.

Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer

Paths planning of Unmanned Aerial Vehicles(UAVs)in a dynamic environment is considered a challenging task in autonomous flight control design.In this work,an efficient method based on a Multi-Objective MultiVerse Optimization(MOMVO)algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving obstacles.Such a path planning task is formulated as a multicriteria optimization problem under operational constraints.The proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving obstacles.The vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the goal.To choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives,the modified Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)is investigated.A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm(MSSA),Multi-Objective Grey Wolf Optimizer(MOGWO),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-Dominated Genetic Algorithm II(NSGAII)is used as a basis for the performance comparison.Demonstrative results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning method.The obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.

Conjunctive Use of Engineering and Optimization in Water Distribution System Design

Water Distribution Systems (WDSs) design and operation are usually done on a case-by-case basis. Numerous models have been proposed in the literature to solve specific problems in this field. The implementation of these models to any real-world WDS optimization problem is left to the discretion of designers who lack the necessary tools that will guide them in the decision-making process for a given WDS design project. Practitioners are not always very familiar with optimization applied to water network design. This results in a quasi-exclusive use of engineering judgment when dealing with this issue. In order to support a decision process in this field, the present article suggests a step-by-step approach to solve the multi-objective design problem by using both engineering and optimization. A genetic algorithm is proposed as the optimization tool and the targeted objectives are: 1) to minimize the total cost (capital and operation), 2) to minimize the residence time of the water within the system and 3) to maximize a network reliability metric. The results of the case study show that preliminary analysis can significantly reduce decision variables and computational burden. Therefore, the approach will help network design practitioners to reduce optimization problems to a more manageable size.

Mixed Least Square Method for Priority of Complementary Judgement Matrix and Its Algorithm

Based on the concept of multiplicative fuzzy consistent complementary judgement matrix, the mixed least square method （MLSM） for priority of complementary judgement matrix is proposed and proved. Then, the corresponding convergent iterative algorithm is given and its convergence is proved. Finally, some main properties of the developed priority method, such as rank preservation under strong condition, etc., ate introduced. The theoretical analyses show that the MLSM can sufficiently reflect the preference information of the decision maker, and is easy to realize on a computer.

Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm

Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.

Long-term sustainability and resilience enhancement of building portfolios

The role of community building portfolios in socioeconomic development and the growth of the built environ-ment cannot be overstated.Damage to these structures can have far-reaching consequences on socioeconomic and environmental aspects,requiring a long-term perspective for recovery.As communities aim to enhance their resilience and sustainability,there is a cost burden that needs to be considered.To address this issue,this pa-per proposes a community-level performance enhancement approach that focuses on optimizing the long-term resilience and sustainability of community building portfolios,taking into account recurrent seismic hazards.A Gaussian process surrogate-based multi-objective optimization framework is utilized to optimize the cost objec-tive while considering performance indicators for resilience and sustainability.The proposed framework involves using performance-based assessment methods to evaluate the socioeconomic and environmental consequences under stochastic and recurrent seismic hazard scenarios.These evaluated indicators are then used to efficiently optimize the community resilience and sustainability,taking into account the retrofit costs.Finally,approximate Pareto-optimal solutions are extracted and utilized for decision-making.In summary,this paper presents a novel approach for optimizing the long-term resilience and sustainability of community building portfolios by consid-ering recurrent seismic hazards.The proposed framework incorporates performance-based assessment methods and multi-objective optimization techniques to achieve an optimal balance between cost,resilience,and sustain-ability,with the ultimate goal of enhancing community well-being and decision-making in the face of seismic hazards.

Security region based security-constrained unit commitment

As the issues of security and stability of power systems are becoming increasingly significant,it is necessary to consider the constraints of the static voltage stability and transient stability,which are closely related to the active power dispatch of power systems,in the daily power dispatch,i.e.the unit commitment.However,due to the complexity of these constraints and limitation of the existing analysis methods,there has been no unit commitment model reported so far that can deal with these security constraints.On the other hand,as lack of effective measures to evaluate the security margin of dispatch schemes,it is difficult for power system operators to integrate both the security and economy of power systems in unit commitment.To resolve the above-mentioned issues,a security region based security-constrained unit commitment model is presented in the paper,which gives consideration to both the security and economy of power systems.For the first time,the active power flow constraint,the static voltage stability constraint and the transient stability constraint are taken into account in unit commitment at the same time.The model presented in the paper takes the operating cost,the branch transmission capacity margin,the static voltage stability margin and the transient stability margin as sub-objectives.By adjusting the weighting factors of sub-objectives,it is convenient to adjust the preference on the security and economy of power systems and reach a balance.The IEEE RTS-24 test system is adopted to validate the correctness and the efficiency of the proposed model.

Optimal microgrid planning for enhancing ancillary service provision

Microgrids have presented themselves as an effective concept to guarantee a reliable,efficient and sustainable electricity delivery during the current transition era from passive to active distribution networks.Moreover,microgrids could offer effective ancillary services(AS)to the power utility,although this will not be possible before the traditional planning and operation methodologies are updated.Hence,a probabilistic multi-objective microgrid planning(POMMP)methodology is proposed in this paper to contemplate the large number of variables,multiple objectives,and different constraints and uncertainties involved in the microgrid planning.The planning methodology is based on the optimal size and location of energy distributed resources with the goal of minimizing the mismatch power in islanded mode,while the residual power for AS provision and the investment and operation costs of the microgrid in grid-connected mode are maximized and minimized,respectively.For that purpose,probabilistic models and a true multi-objective optimization problem are implemented in the methodology.The methodology is tested in an adapted PG&E 69-bus distribution system and the non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ)optimization method and an analytic hierarchy process for decision-making are used to solve the optimization problem.

Multi-verse optimizer based parameters decision with considering tool life in dry hobbing process

Dry hobbing has received extensive attention for its environmentally friendly processing pattern.Due to the absence of lubricants,hobbing process is highly dependent on process parameters combination since using unreasonable parameters tends to affect the machining performance.Besides,the consideration of tool life is frequently ignored in gear hobbing.Thus,to settle the above issues,a multiobjective parameters decision approach considering tool life is developed.Firstly,detailed quantitative analysis between process parameters and hobbing performance,i.e.,machining time,production cost and tool life is introduced.Secondly,a multi-objective parameters decision-making model is constructed in search for optimum cutting parameters(cutting velocity v,axial feed rate f_(a))and hob parameters(hob diameter d_(0),threads z_(0)).Thirdly,a novel algorithm named multi-objective multi-verse optimizer(MOMVO)is utilized to solve the presented model.A case study is exhibited to show the feasibility and reliability of the proposed approach.The results reveal that(i)a balance can be achieved among machining time,production cost and tool life via appropriate process parameters determination;(ii)optimizing cutting parameters and hob parameters simultaneously contributes to optimal objectives;(iii)considering tool life provides usage precautions support and process parameters guidance for practical machining.

Interactive DE for solving combined security environmental economic dispatch considering FACTS technology

This paper presents an efficient interactive differential evolution ODE） to solve the multi-objective security environmental/economic dispatch （SEED） pro- blem considering multi shunt flexible AC transmission system （FACTS） devices. Two sub problems are proposed. The first one is related to the active power planning to minimize the combined total fuel cost and emissions, while the second is a reactive power planning （RPP） using multi shunt FACTS device based static VAR compensator （SVC） installed at specified buses to make fine corrections to the voltage deviation, voltage phase profiles and reactive power violation. The migration operation inspired from biogeography-based optimization （BBO） algorithm is newly introduced in the proposed approach, thereby effectively exploring and exploiting promising regions in a space search by creating dynamically new efficient partitions. This new mechanism based migration between individuals from different subsystems makes the initial partitions to react more by changing experiences. To validate the robustness of the proposed approach, the proposed algorithm is tested on the Algerian 59-bus electrical network and on a large system, 40 generating units considering valve-point loading effect. Comparison of the results with recent global optimization methods show the superiority of the proposed IDE approach and confirm its potential for solving practical optimal power flow in terms of solution quality and convergence characteristics.