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Mechanism on minimization of excess sludge in oxic-settling-anaerobic (OSA) process

WANG Jianfang, ZHAO Qingliang, JIN Wenbiao, LIN Jikan

Frontiers of Environmental Science & Engineering 2008, Volume 2, Issue 1,   Pages 36-43 doi: 10.1007/s11783-008-0001-4

Abstract: It has been concluded that multiple causes resulted in the minimization of excess sludge in the OSA system
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Optimization of cold-end system of thermal power plants based on entropy generation minimization

Frontiers in Energy 2022, Volume 16, Issue 6,   Pages 956-972 doi: 10.1007/s11708-021-0785-5

Abstract: Cold-end systems are heat sinks of thermal power cycles, which have an essential effect on the overall performance of thermal power plants. To enhance the efficiency of thermal power plants, multi-pressure condensers have been applied in some large-capacity thermal power plants. However, little attention has been paid to the optimization of the cold-end system with multi-pressure condensers which have multiple parameters to be identified. Therefore, the design optimization methods of cold-end systems with single- and multi-pressure condensers are developed based on the entropy generation rate, and the genetic algorithm (GA) is used to optimize multiple parameters. Multiple parameters, including heat transfer area of multi-pressure condensers, steam distribution in condensers, and cooling water mass flow rate, are optimized while considering detailed entropy generation rate of the cold-end systems. The results show that the entropy generation rate of the multi-pressure cold-end system is less than that of the single-pressure cold-end system when the total condenser area is constant. Moreover, the economic performance can be improved with the adoption of the multi-pressure cold-end system. When compared with the single-pressure cold-end system, the excess revenues gained by using dual- and quadruple-pressure cold-end systems are 575 and 580 k$/a, respectively.

Keywords： cold-end system     entropy generation minimization     optimization     economic analysis     genetic algorithm    

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Power system reconfiguration and loss minimization for a distribution systems using “Catfish PSO” algorithm

K Sathish KUMAR,S NAVEEN

Frontiers in Energy 2014, Volume 8, Issue 4,   Pages 434-442 doi: 10.1007/s11708-014-0313-y

Abstract: reconfiguration (DFR) problem for a 33-bus and a 16-bus sample network, which effectively ensures the loss minimization

Keywords： distribution system reconfiguration (DFR)     power loss reduction     catfish particle swarm optimization (catfish PSO)     radial structure    

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Robust design approach to the minimization of functional performance variations of products and systems

J. ZHANG, H. DU, D. XUE, P. GU

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 379-392 doi: 10.1007/s11465-020-0607-1

Abstract: Functional performance variations of products and systems are often used to measure the qualities of products and systems considering the changes in the design parameter values caused by uncertainties. A robust design approach has been developed in this research to minimize the functional performance variations considering the design parameter uncertainties by identifying the boundaries of the functional performance variations through optimization. In this work, a mathematical model is developed to describe the relationships among functional performance, design configurations and parameters, and design parameter uncertainties. A multi-level optimization model is established to identify: (1) The optimal design configuration, (2) the optimal values of design parameters, and (3) the boundaries of functional performance variations. Sensitivity analysis considering the impact of parameter uncertainties on functional performance variation boundaries has also been conducted. A case study on the design of a truss system has been conducted. Case study results show that the sensitivities of functional performance variation boundaries to the design parameter uncertainties can be reduced significantly using the new robust design approach.

Keywords： product design     robust design     design optimization     uncertainties    

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Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and

Homayoun EBRAHIMIAN,Bahman TAHERI,Nasser YOUSEFI

Frontiers in Energy 2015, Volume 9, Issue 4,   Pages 426-432 doi: 10.1007/s11708-015-0376-4

Abstract: The aim of this paper is simultaneous minimization of hydrothermal units to reach the best solution by

Keywords： practical constraints of units     pollution function     inlet steam valve     up-ramp rate of units     improved ABC algorithm    

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Forward osmosis coupled with lime-soda ash softening for volume minimization of reverse osmosis

Jiandong Lu, Shijie You, Xiuheng Wang

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 1, doi: 10.1007/s11783-020-1301-6

Abstract: Abstract • Forward osmosis (FO) coupled with chemical softening for CCI ROC minimization • Effectivethis study, we focused on coupling forward osmosis (FO) with chemical softening (FO-CS) for the volume minimizationThis study provides a proof-of-concept demonstration of the FO-CS coupling process for ROC volume minimization

Keywords： Coal chemical industry     Forward osmosis     Chemical softening     Reverse osmosis concentrate    

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Training time minimization for federated edge learning with optimized gradient quantization and bandwidth Research Article

Peixi LIU, Jiamo JIANG, Guangxu ZHU, Lei CHENG, Wei JIANG, Wu LUO, Ying DU, Zhiqin WANG,jiangjiamo@caict.ac.cn,gxzhu@sribd.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1247-1263 doi: 10.1631/FITEE.2100538

Abstract: Training a machine learning model with (FEEL) is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge networks. In this study, the problem is investigated in a quantized FEEL system, where heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication rounds. The training time is modeled by taking into account the communication time, computation time, and the number of communication rounds. Based on the proposed training time model, the intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Furthermore, a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap, based on which the closed-form expressions for the number of communication rounds and the total training time are obtained. Constrained by the total bandwidth, the problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of through successive convex approximation and the subproblem of bandwidth allocation by bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.

Keywords： Federated edge learning     Quantization optimization     Bandwith allocation     Training time minimization    

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Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch

Syamasree BISWAS(RAHA), Kamal Krishna MANDAL, Niladri CHAKRABORTY

Frontiers in Energy 2013, Volume 7, Issue 2,   Pages 174-181 doi: 10.1007/s11708-013-0246-x

Abstract: The reactive power dispatch (RPD) problem is a very critical optimization problem of power system which minimizes the real power loss of the transmission system. While solving the said problem, generator bus voltages and transformer tap settings are kept within a stable operating limit. In connection with the RPD problem, solving reactive power is compensated by incorporating shunt capacitors. The particle swarm optimization (PSO) technique is a swarm intelligence based fast working optimization method which is chosen in this paper as an optimization tool. Additionally, the constriction factor is included with the conventional PSO technique to accelerate the convergence property of the applied optimization tool. In this paper, the RPD problem is solved in the case of the two higher bus systems, i.e., the IEEE 57-bus system and the IEEE 118-bus system. Furthermore, the result of the present paper is compared with a few optimization technique based results to substantiate the effectiveness of the proposed study.

Keywords： real power loss minimization     voltage stability     constriction factor     particle swarm optimization (PSO)    

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Comparison between inhibitor and uncoupler for minimizing excess sludge production of an activated sludge process

CHEN Guowei, XI Pengge, XU Deqian, YU Hanqing

Frontiers of Environmental Science & Engineering 2007, Volume 1, Issue 1,   Pages 63-66 doi: 10.1007/s11783-007-0012-6

Abstract: In order to study the minimization of excess sludge production, the reduction in the excess sludge production

Keywords： uncoupling coefficient     Cu     minimization     presence     uncoupling    

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Development of a hydrodynamic model and the corresponding virtual software for dual-loop circulating fluidized beds

Shanwei Hu, Xinhua Liu

Frontiers of Chemical Science and Engineering 2021, Volume 15, Issue 3,   Pages 579-590 doi: 10.1007/s11705-020-1953-6

Abstract: This article proposed a general method for modeling multi-loop CFB systems by utilizing the energy minimization

Keywords： multi-loop circulating fluidized bed     mathematical modeling     energy minimization multiscale     virtual fluidization    

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Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

Frontiers in Energy 2012, Volume 6, Issue 4,   Pages 394-402 doi: 10.1007/s11708-012-0211-0

Abstract: on statistical learning theory, achieves good generalization ability by adopting a structural risk minimizationinduction principle aimed at minimizing a bound on the generalization error of a model rather than the minimization

Keywords： support vector machines (SVM)     structural risk minimization (SRM)     equivalent capacity margin (ECM)     restoration    

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Stochastic extra-gradient based alternating direction methods for graph-guided regularizedminimization None

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 755-762 doi: 10.1631/FITEE.1601771

Abstract: In this study, we propose and compare stochastic variants of the extra-gradient alternating direction method, named the stochastic extra-gradient alternating direction method with Lagrangian function (SEGL) and the stochastic extra-gradient alternating direction method with augmented Lagrangian function (SEGAL), to minimize the graph-guided optimization problems, which are composited with two convex objective functions in large scale. A number of important applications in machine learning follow the graph-guided optimization formulation, such as linear regression, logistic regression, Lasso, structured extensions of Lasso, and structured regularized logistic regression. We conduct experiments on fused logistic regression and graph-guided regularized regression. Experimental results on several genres of datasets demonstrate that the proposed algorithm outperforms other competing algorithms, and SEGAL has better performance than SEGL in practical use.

Keywords： Stochastic optimization     Graph-guided minimization     Extra-gradient method     Fused logistic regression     Graph-guided    

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Minimization of total energy consumption in an m-machine flow shop with an exponential time-dependent

Lingxuan LIU, Zhongshun SHI, Leyuan SHI

Frontiers of Engineering Management 2018, Volume 5, Issue 4,   Pages 487-498 doi: 10.15302/J-FEM-2018042

Abstract:

This study investigates an energy-aware flow shop scheduling problem with a time-dependent learning effect. The relationship between the traditional and the proposed scheduling problem is shown and objective is to determine a job sequence in which the total energy consumption is minimized. To provide an efficient solution framework, composite lower bounds are proposed to be used in a solution approach with the name of Bounds-based Nested Partition (BBNP). A worst-case analysis on shortest process time heuristic is conducted for theoretical measurement. Computational experiments are performed on randomly generated test instances to evaluate the proposed algorithms. Results show that BBNP has better performance than conventional heuristics and provides considerable computational advantage.

Keywords： flow shop     energy-aware scheduling     learning effect     nested partition     worst-case error bound    

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Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy Article

Yong DING, Tuo HU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 2001-2008 doi: 10.1631/FITEE.1700287

Abstract: promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation minimization

Keywords： Low-dose computed tomography (CT)     CT imaging     Total variation     Sparse dictionary learning    

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Title Author Date Type Operation

Mechanism on minimization of excess sludge in oxic-settling-anaerobic (OSA) process

WANG Jianfang, ZHAO Qingliang, JIN Wenbiao, LIN Jikan

Journal Article

Optimization of cold-end system of thermal power plants based on entropy generation minimization

Journal Article

Power system reconfiguration and loss minimization for a distribution systems using “Catfish PSO” algorithm

K Sathish KUMAR,S NAVEEN

Journal Article

Robust design approach to the minimization of functional performance variations of products and systems

J. ZHANG, H. DU, D. XUE, P. GU

Journal Article

Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and

Homayoun EBRAHIMIAN,Bahman TAHERI,Nasser YOUSEFI

Journal Article

Forward osmosis coupled with lime-soda ash softening for volume minimization of reverse osmosis

Jiandong Lu, Shijie You, Xiuheng Wang

Journal Article

Training time minimization for federated edge learning with optimized gradient quantization and bandwidth

Peixi LIU, Jiamo JIANG, Guangxu ZHU, Lei CHENG, Wei JIANG, Wu LUO, Ying DU, Zhiqin WANG,jiangjiamo@caict.ac.cn,gxzhu@sribd.cn

Journal Article

Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch

Syamasree BISWAS(RAHA), Kamal Krishna MANDAL, Niladri CHAKRABORTY

Journal Article

Comparison between inhibitor and uncoupler for minimizing excess sludge production of an activated sludge process

CHEN Guowei, XI Pengge, XU Deqian, YU Hanqing

Journal Article

Development of a hydrodynamic model and the corresponding virtual software for dual-loop circulating fluidized beds

Shanwei Hu, Xinhua Liu

Journal Article

Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

Journal Article

Stochastic extra-gradient based alternating direction methods for graph-guided regularizedminimization

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

Journal Article

Minimization of total energy consumption in an m-machine flow shop with an exponential time-dependent

Lingxuan LIU, Zhongshun SHI, Leyuan SHI

Journal Article

Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy

Yong DING, Tuo HU

Journal Article