作者：
Zhu, YonghuaDrake, SamLue, HaishenXia, JunHohai Univ
State Key Lab Hydrol Water Resources & Hydraul En Coll Water Resources & Environm Naijing 210098 Peoples R China Univ Arizona
Arizona Remote Sensing Ctr Off Arid Lands Studies Tucson AZ 85719 USA Hohai Univ
Dept Appl Math Naijing 210098 Peoples R China Chinese Acad Sci
Inst Geog Sci & Nat Resources Res Key Lab Water Cycle Relat Land Surface Proc Beijing 100101 Peoples R China
With overly-rapid socio-economic development and population increases, water abstraction for agricultural, industrial and municipal use increases rapidly, while the water left for ecological maintenance decreases grea...
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With overly-rapid socio-economic development and population increases, water abstraction for agricultural, industrial and municipal use increases rapidly, while the water left for ecological maintenance decreases greatly. At the same time, large amounts of polluted water are discharged into rivers because purification plants are inadequate or not built in time, causing serious eco-environmental problems in the Haihe river basins which make regional development unsustainable. Estimating eco-environmental carrying capacity related to water is a key to curbing overuse of water and resolving eco-environmental problems. Because of different trends in water resources development and resultant eco-environmental problems in different sub-basins of the Haihe river, there are different water-related eco-environmental carrying capacities (EECCs) in these sub-basins. Time-series and multi-objective optimization methods are used to determine the EECC in various eco-environmental regions of the Haihe river basins, China. The results show that the entirety of the Haihe river basins will not reach a stable, sustainable state until about 2033, through gradual amelioration of eco-environmental problems. The various eco-regions of the sub-basins will need different lengths of time to reach their own stable states because of different available water resources, eco-environmental problems and social and economic development.
An optimizationmethod of chromogenic agent was mainly studied to detect the concentration of metal ions in zinc metallurgy wastewater based on ultraviolet-visible spectrometry. Firstly, a chromogenic agent was select...
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An optimizationmethod of chromogenic agent was mainly studied to detect the concentration of metal ions in zinc metallurgy wastewater based on ultraviolet-visible spectrometry. Firstly, a chromogenic agent was selected to reduce the sensitivity of Zn(II) that solved the problem of spectral masking caused by high concentration of Zn(II). However, the range of noiseless wavelength, linearity of Zn(II) and additivity of absorbance were serious influenced by the dosage of chromogenic agent. So, a multi-objective optimization method with the target of maximum range of noiseless wavelength, maximum linearly dependent coefficient of Zn(II) and the optimum additivity of absorbance was proposed to optimize the dosage of chromogenic agent. Finally, the optimization result are verified by feature interval association-partial least squares regression. The average relative errors of Zn(II), Cu(II), Co(II) and Ni(II) were 5.43%, 6.65%, 6.24% and 6.87%, respectively. It indicates that the detection method using optimal chromogenic agent exhibit high-precision and low-error for analyzing the concentration of Zn(II), Cu(II), Co(II) and Ni(II). (C) 2018 IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
In this paper, a dual stator brushless hybrid excitation machine (DSBHEM) is designed and analyzed, with high flux regulation, high torque output and low torque ripple. There are many design parameters of the DSBHEM, ...
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ISBN:
(纸本)9781728151359
In this paper, a dual stator brushless hybrid excitation machine (DSBHEM) is designed and analyzed, with high flux regulation, high torque output and low torque ripple. There are many design parameters of the DSBHEM, which lead to the contradiction of speed range, torque output and torque ripple. Therefore, a framework of multi-objective optimization method (MOOM) is proposed to improve the overall performance of the DSBHEM. Firstly, the sensitivity of the design variables is analyzed, and the critical variables are extracted, and the response surface model (RSM) is constructed. The Pareto solution set is obtained by non-dominated sorting genetic algorithms 2 (NSGA2), and the optimal design is selected. The performance comparison between the initial and the optimal design is carried out. Finally, a prototype machine is manufactured to verify its performance.
Advancement of Distributed Generation (DG) technologies and market regulations have made DGs a clean and cost effective source of energy. Installation of DGs and their impacts on the performance of power systems can b...
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ISBN:
(纸本)9781538661598
Advancement of Distributed Generation (DG) technologies and market regulations have made DGs a clean and cost effective source of energy. Installation of DGs and their impacts on the performance of power systems can be realized by investigating proper location, size and type of DGs used. This paper considers, not only the reduction of real power loss and the voltage stability index (VSI) improvement as optimizationobjectives but also economic aspects of DGs. multi-objective Bat Algorithm (MOBA) is proposed to find the Pareto optimal set for multi-objective (MO) functions by varying weights. Two cases are considered based on the types of distributed generation used. DG generating only real power is considered in Case I where as DG capable of generating both real and reactive power is investigated in Case II. Both cases are carried out on IEEE 69 bus distribution system with multiple DG units. A comparative analysis is performed to analyze the quality of non-dominated solution with different scenarios for both the cases. The best compromise solution for optimal location and capacity of DG is determined using a fuzzy decision making procedure. Simulation results confirm the efficacy of the multi-objectivemethod presented in the paper.
With the development of high-throughput techniques, systems biology has been pushing researchers to focus on how to optimize the steering of biomolecular networks from their actual state to a desired state. This pheno...
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With the development of high-throughput techniques, systems biology has been pushing researchers to focus on how to optimize the steering of biomolecular networks from their actual state to a desired state. This phenomenon known as the "transittability" means that complex biomolecular networks can be steered from an unexpected state to a desired state. This paper investigates the optimization of the transittability of complex biomolecular networks taking into account different objective functions. To solve this problem, we propose a multi-objectiveoptimization approach which consists of two steps, the search and decision making step. The search step is based on a powerful multi-objective genetic algorithm, the non-dominated sorting genetic algrorithm (NSGA-II), to solve our problem and obtain a Pareto-optimal set. As regards the decision making step is based on the use of a multi-criteria decision making method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), for providing the best compromise solution according to the user preferences. The proposed approach was tested and applied to solve the steering of the p53 Signaling network. Experimental results illustrate the effectiveness of this approach. (C) 2018 The Authors. Published by Elsevier B.V.
The bolt-flange structure is most one of joint mode, and stress and mass are its major performance parameters. The multi-object optimization of a bolt-flange structure can be performed by using Finite element method a...
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ISBN:
(纸本)9783037851197
The bolt-flange structure is most one of joint mode, and stress and mass are its major performance parameters. The multi-object optimization of a bolt-flange structure can be performed by using Finite element method and optimizationmethod unitedly. The response surface design method was employed to determine the combination of geometrical parameters to be designed of the bolt-flange structure. The stress of the bolt-flange structure which has the different geometrical parameters was numerically simulated and analyzed by using the software ANSYS. The response surface model is obtained. The optimized geometrical parameters of the bolt-flange structure were obtained by using MATLAB multi-objective optimization method. The results showed that the maximum equivalent stress in the optimized bolt-flange structure decreased 13.4% than that in the original one and the mass of the optimized bolt-flange structure was lower 14.3% than that of the original one.
Finite element method and optimizationmethod are two major ones in engineering analysis. The optimization of a wedged-ring joint structure can be performed by using these two methods unitedly. The response surface de...
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ISBN:
(纸本)9780878492022
Finite element method and optimizationmethod are two major ones in engineering analysis. The optimization of a wedged-ring joint structure can be performed by using these two methods unitedly. The response surface design method was employed to determine the combination of geometrical parameters to be designed of the wedged-ring joint structure. The stress of the wedged-ring joint structure which has the different geometrical parameters was numerically simulated and analyzed by using engineering software ANSYS. The optimized geometrical parameters of the wedged-ring structure were obtained by using MATLAB multi-objective optimization method. The results show that the maximum stress and the mass of the optimized wedged-ring structure decrease 13.6% and 12.5%, respectively.
In this paper, voltage and reactive power control (VQC) of a system with load changes using a multi-objective optimization method is proposed. The objective is to minimize the transmission loss, voltage deviation, and...
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ISBN:
(纸本)9781467327299
In this paper, voltage and reactive power control (VQC) of a system with load changes using a multi-objective optimization method is proposed. The objective is to minimize the transmission loss, voltage deviation, and manipulated variable using a multiple objectiveoptimization Genetic Algorithm (GA). In this research, the simulation was carried out by using an excellent multi-objectiveoptimization GA known as Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II).
Oil production and polymer injection are two performance indicators of polymer flooding and are usually conflicting objectives. In order to obtain optimal trade-off solutions, this paper proposes a multi-objective glo...
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Oil production and polymer injection are two performance indicators of polymer flooding and are usually conflicting objectives. In order to obtain optimal trade-off solutions, this paper proposes a multi-objective global and local surrogate-assisted particle swarm optimization (MO-GLSPSO) method, which consists of alternative steps: global population prescreen and local population *** global steps use generalized regression neural network (GRNN) to prescreen a better population, and the local steps use radial basis function (RBF) as proxy to search for the next generation. The global steps aim to reduce the chance of generations being trapped in local minima, and the local steps obtain the optimal solutions with a fast convergence rate. The rates (liquid production rate and water injection rate) and polymer injection concentration of wells are tuned to obtain a Pareto-front that maximizes cumulative oil production and mini- mizes cumulative polymer injection. The MO-GLSPSO method is tested using both synthetic and Brugge benchmark cases. The iterations generally improve the oil production or reduce polymer injection and are stabilized at a Pareto-front of the two objectives. Improved sweep efficiency and polymer utility are also observed in the optimal results. The proposed method is also compared with other two methods, multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), to examine the pros and cons. The results indicate that MO-GLSPSO has a better pareto-front than others.
The performance of the high-pressure (HP) compressor is very important for the two-stage turbocharging system. However, the performance of HP compressor on the engine most of the time is poor at low speed and low mass...
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The performance of the high-pressure (HP) compressor is very important for the two-stage turbocharging system. However, the performance of HP compressor on the engine most of the time is poor at low speed and low mass flow conditions. These will lead to poor engine performance at low engine speed. The purpose of this paper is to improve the performance of the HP compressor at low speed and low mass flow conditions. The Latin hypercube design of the experiment method is used to establish the Kriging model and global optimization by multi-objective genetic algorithm NSGA-II to optimal HP compressor. The simulation results indicated that the flow field within the compressor was improved and the high entropy generation area was reduced. The new design delayed the mixing between the tip clearance leakage vortex flow and main flow. The low-speed performance of the HP compressor was improved. The turbocharger gas stand tests and engine bench tests were carried out. The results showed that the efficiency and pressure ratio of the optimized design is increased by 2.1% and 3%, respectively. The engine achieved better performance in low-speed conditions. The pumping means effective pressure (PMEP) and intake airflow increased by 7% and 4.98%, respectively, while brake specific fuel consumption (BSFC) and soot emissions decreased by 0.56% and 32.8%, respectively.
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