Multi-objective optimization of a novel combined cooling, dehumidification and power system using improved M-PSO algorithm

Chang, Jinwei; Li, Zhi; Huang, Yan; Yu, Xiaonan; Jiang, Ruicheng; Huang, Rui; Yu, Xiaoli

doi:10.1016/j.energy.2021.122487

Cited by 37 publications

(12 citation statements)

References 17 publications

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“…It can be seen that the complex global search process of particle swarm algorithm is composed of many interacting local searches. With this strategy, the particle swarm algorithm is able to solve high-dimensional, constrained complex problems, but the local search capability is too prominent, causing problems such as premature convergence and easy to fall into local extremes (Chang et al, 2021).…”

Section: Solution Of the Lower Modelmentioning

confidence: 99%

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

Zhang

et al. 2022

Front. Energy Res.

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The power-to-gas (P2G) technology transforms the unidirectional coupling of power network and natural gas network into bidirectional coupling, and its operational characteristics provide an effective way for wind and solar energy accommodation. The paper proposes a bi-level optimal dispatch model for the integrated energy system with carbon capture system and P2G facility. The upper model is an optimal allocation model for coal-fired units, and the lower model is an economic dispatch model for the integrated energy system. Moreover, the upper model is solved by transforming the model into a mixed-integer linear programming problem and calling CPLEX, and the lower model is a multi-objective planning problem, which is solved by improving the small-habitat particle swarm algorithm. Finally, the simulation is validated by the MATLAB platform, and the results show that the simultaneous consideration of carbon capture system and P2G facility improves the economics of the integrated energy system and the capacity of wind and solar energy accommodation.

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Section: Solution Of the Lower Modelmentioning

confidence: 99%

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

Zhang

et al. 2022

Front. Energy Res.

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“…The results showed that the DRORC system had the highest thermal and exergy efficiency, followed by the SRORC system, and the BORC system had the lowest. Chang et al [ 43 ] proposed a cooling and dehumidification power combination system based on an internal combustion engine in a HT and high‐humidity environment. The power demand can be met using batteries and ORC units.…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of a Waste Heat Recovery Organic Rankine Cycle System with a Steam–Water Dual Heat Source

Wang

Yuan

2023

Energy Tech

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The organic Rankine cycle (ORC) system in plants, powered by dual steam–water heat sources, has significant power generation potential and practical research value. Herein, the conditions of 700 kPa saturated steam and 650 kPa, 90 °C water heat source are considered. Four configurations of steam–water dual heat source waste heat recovery ORC systems are proposed. The independent parameters affecting the net output power of the system are obtained by developing a mathematical model and optimizing it using the particle swarm optimization method. The results show that the location of the pinch‐point temperature difference in various ORC loops and the allowable working pressure of the heat exchanger are determinants of independent parameters. The net output powers of the conventional dual‐loop ORC (CD‐ORC), single‐loop ORC (S‐ORC), split‐flow dual‐loop ORC (SFD‐ORC), and split‐flow triple‐loop ORC SFT‐ORC systems under the optimal design parameters are 2415.73, 2168.6, 2599.62, and 2716.75 kW, respectively. In addition, S‐ORC has the highest exergy efficiency of 55.17%. SFD‐ORC and SFT‐ORC have ≈48% exergy efficiency, and CD‐ORC has the lowest exergy efficiency of 45.33%.

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“…Conventional ANN models tend to fall into local extremum with large datasets, while the strength of rubber concrete is influenced by a variety of nonlinear factors, so the use of intelligent algorithms is appropriate. Particle swarm optimization (PSO) algorithms were widely used in model optimization due to their fast convergence of iterations and simplicity of operation [ 18 , 19 , 20 ]. However, due to the fixed value of the inertia factor of the PSO algorithm, this makes the search space of the particles small and causes the algorithm to easily fall into local extremes late in the iteration.…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Prediction of Rubber Concrete Based on Artificial Neural Network Model with Hybrid Particle Swarm Optimization Algorithm

Huang

Wang

et al. 2022

Materials

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Conventional neural networks tend to fall into local extremum on large datasets, while the research on the strength of rubber concrete using intelligent algorithms to optimize artificial neural networks is limited. Therefore, to improve the prediction accuracy of rubber concrete strength, an artificial neural network model with hybrid algorithm optimization was developed in this study. The main strategy is to mix the simulated annealing (SA) algorithm with the particle swarm optimization (PSO) algorithm, using the SA algorithm to compensate for the weak global search capability of the PSO algorithm at a later stage while changing the inertia factor of the PSO algorithm to an adaptive state. For this purpose, data were first collected from the published literature to create a database. Next, ANN and PSO-ANN models are also built for comparison while four evaluation metrics, MSE, RMSE, MAE, and R2, were used to assess the model performance. Finally, compared with empirical formulations and other neural network models, the result shows that the proposed optimized artificial neural network model successfully improves the accuracy of predicting the strength of rubber concrete. This provides a new option for predicting the strength of rubber concrete.

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Multi-objective optimization of a novel combined cooling, dehumidification and power system using improved M-PSO algorithm

Cited by 37 publications

References 17 publications

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

Design and Optimization of a Waste Heat Recovery Organic Rankine Cycle System with a Steam–Water Dual Heat Source

Compressive Strength Prediction of Rubber Concrete Based on Artificial Neural Network Model with Hybrid Particle Swarm Optimization Algorithm

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