Optimal design of Kaibel dividing wall columns based on improved particle swarm optimization methods

Qian, Xing; Jia, Shengkun; Huang, Kejin; Chen, Haisheng; Yuan, Yang; Zhang, Liang

doi:10.1016/j.jclepro.2020.123041

Cited by 39 publications

(8 citation statements)

References 34 publications

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“…In the above equation, 𝑤 and 𝑤 are the maximum and minimum values of inertia weight, which are selected near the initial value before the LDW strategy is applied, and then modified according to the simulation results. 𝑁 is the maximum value of iterations [10,11] . The following describes an improved PSO algorithm optimization process in detail.…”

Section: Ladrc System Based On Optimized Pso Algorithmmentioning

confidence: 99%

Research on active disturbance rejection control of the main steam temperature of supercritical thermal power unit based on improved particle swarm optimization

Hao

Jin

Wang

et al. 2023

J. Phys.: Conf. Ser.

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The stability of the boiler’s main steam temperature is very important for the safe operation of the unit. At present, the mainstream PID control scheme is simple to use and convenient to set, but it cannot accomplish a acceptable ascendancy effect in the face of the main steam temperature control structure; with a long time delay, large inertia. In view of this situation, first of all, this paper introduces the linear active disturbance rejection control (LADRC) technology in a steam temperature control structure. The main loop adopts the linear active disturbance rejection controller and the subloop adopts the P-controller. Then, the particle swarm optimization algorithm is improved and used to modify the parameter of the LADRC, so that the LADRC system adjusted according to the theory can better complete the control task. Finally, a simulation to control the temperature is given, and the results show that the LADRC control system can track quickly and stably; in addition, as against with this traditional PID control strategy, it can achieve strong anti-interference performance.

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Section: Ladrc System Based On Optimized Pso Algorithmmentioning

confidence: 99%

Research on active disturbance rejection control of the main steam temperature of supercritical thermal power unit based on improved particle swarm optimization

Hao

Jin

Wang

et al. 2023

J. Phys.: Conf. Ser.

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“…Over the last few decades, a wide range of studies has demonstrated the use of meta-heuristic algorithms, and these algorithms are becoming a significant measure of modern optimization. The genetic algorithm (GA) and PSO, which are defined as metaheuristic algorithms, are becoming increasingly popular [91,92]. Kennedy and Eberhart developed the PSO algorithm in 1995, this algorithm has been built based on swarm behavior in nature such as birds flocking and fish schooling behavior [88].…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

“…Energies 2021, 14, x FOR PEER REVIEW 18 of 29 optimization. The genetic algorithm (GA) and PSO, which are defined as meta-heuristic algorithms, are becoming increasingly popular [91,92]. Kennedy and Eberhart developed the PSO algorithm in 1995, this algorithm has been built based on swarm behavior in nature such as birdsflocking and fish schooling behavior [88].…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

Review of the Estimation Methods of Energy Consumption for Battery Electric Buses

Al-Ogaili¹,

Al-Shetwi

Al‐Masri

et al. 2021

Energies

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In the transportation sector, electric battery bus (EBB) deployment is considered to be a potential solution to reduce global warming because no greenhouse gas (GHG) emissions are directly produced by EBBs. In addition to the required charging infrastructure, estimating the energy consumption of buses has become a crucial precondition for the deployment and planning of electric bus fleets. Policy and decision-makers may not have the specific tools needed to estimate the energy consumption of a particular bus network. Therefore, many state-of-the-art studies have proposed models to determine the energy demand of electric buses. However, these studies have not critically reviewed, classified and discussed the challenges of the approaches that are applied to estimate EBBs’ energy demands. Thus, this manuscript provides a detailed review of the forecasting models used to estimate the energy consumption of EBBs. Furthermore, this work fills the gap by classifying the models for estimating EBBs’ energy consumption into small-town depot and big-city depot networks. In brief, this review explains and discusses the models and formulations of networks associated with well-to-wheel (WTW) assessment, which can determine the total energy demand of a bus network. This work also reviews a survey of the most recent optimization methods that could be applied to achieve the optimal pattern parameters of EBB fleet systems, such as the bus battery capacity, charger rated power and the total number of installed chargers in the charging station. This paper highlights the issues and challenges, such as the impact of external factors, replicating real-world data, big data analytics, validity index, and bus routes’ topography, with recommendations on each issue. Also, the paper proposes a generic framework based on optimization algorithms, namely, artificial neural network (ANN) and particle swarm optimization (PSO), which will be significant for future development in implementing new energy consumption estimation approaches. Finally, the main findings of this manuscript further our understanding of the determinants that contribute to managing the energy demand of EBBs networks.

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“…Choosing the right emergency resources in a short time is essentially a combinatorial optimization problem. Optimization algorithm is an important tool, which is usually used to select emergency resources in emergency operations [7,8]. Particle swarm optimization (PSO) is an evolutionary algorithm, which is widely used in many fields [9,10].…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling of emergency resources for marine green tide disaster based on improved PSO algorithm

Zhang

Zhou²

2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

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The Yellow Sea Green Tide, which occurs repeatedly every year, has a significant impact on China's ecological environment and economy. At present, resource scheduling in the process of emergency response to green tide disasters depends more on disposal experience. In order to improve the reliability and efficiency of decision-making, we propose a resource scheduling model, which is used to optimize the allocation of resources. First, determine the main influencing factors, including the details of salvage objects and available resources. Next, with the maximum salvage efficiency as the goal, the resource scheduling model of marine green tide disaster is established. An improved particle swarm optimization (SAPSO) is used to solve the model. Finally, a test case is taken as an example for experimental verification. The results show that, compared with PSO algorithms, the resource scheduling scheme obtained by SAPSO algorithm is better. The model proposed in this paper can help decision-makers to make effective resource scheduling schemes.

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Optimal design of Kaibel dividing wall columns based on improved particle swarm optimization methods

Cited by 39 publications

References 34 publications

Research on active disturbance rejection control of the main steam temperature of supercritical thermal power unit based on improved particle swarm optimization

Research on active disturbance rejection control of the main steam temperature of supercritical thermal power unit based on improved particle swarm optimization

Review of the Estimation Methods of Energy Consumption for Battery Electric Buses

Optimal scheduling of emergency resources for marine green tide disaster based on improved PSO algorithm

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