Optimal performance of a combined heat-power system with a proton exchange membrane fuel cell using a developed marine predators algorithm

Sun, Xianke; Wang, Gaoliang; Xu, Linzhi; Yuan, Huang; Yousefi, Nasser

doi:10.1016/j.jclepro.2020.124776

Cited by 26 publications

(7 citation statements)

References 27 publications

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“…Similar to PEMFC-CHP systems, many studies have been reported in the literature regarding the optimal design of PEMFC-CCHP system [145]. Sun et al [146] used Marine Predators Optimization Algorithm (MPOA) to improve and optimize the environment, economics, and thermodynamic performance of PEMFC-CCHP system composed of 5 kW PEMFC, AC, humidifier, and gas compressor. The electricity generated by the PEMFC is used to run the compressor.…”

Section: Açıkkalp and Ahmadimentioning

confidence: 99%

PEMFC Poly-Generation Systems: Developments, Merits, and Challenges

et al. 2021

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Significant research efforts are directed towards finding new ways to reduce the cost, increase efficiency, and decrease the environmental impact of power-generation systems. The poly-generation concept is a promising strategy that enables the development of a sustainable power system. Over the past few years, the Proton Exchange Membrane Fuel Cell-based Poly-Generation Systems (PEMFC-PGSs) have received accelerated developments due to the low-temperature operation, high efficiency, and low environmental impact. This paper provides a comprehensive review of the main PEMFC-PGSs, including Combined Heat and Power (CHP) co-generation systems, Combined Cooling and Power (CCP) co-generation systems, Combined Cooling, Heat, and Power (CCHP) tri-generation systems, and Combined Water and Power (CWP) co-generation systems. First, the main technologies used in PEMFC-PGSs, such as those related to hydrogen production, energy storage, and Waste Heat Recovery (WHR), etc., are detailed. Then, the research progresses on the economic, energy, and environmental performance of the different PEMFC-PGSs are presented. Also, the recent commercialization activities on these systems are highlighted focusing on the leading countries in this field. Furthermore, the remaining economic and technical obstacles of these systems along with the future research directions to mitigate them are discussed. The review reveals the potential of the PEMFC-PGS in securing a sustainable future of the power systems. However, many economic and technical issues, particularly those related to high cost and degradation rate, still need to be addressed before unlocking the full benefits of such systems.

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Section: Açıkkalp and Ahmadimentioning

confidence: 99%

PEMFC Poly-Generation Systems: Developments, Merits, and Challenges

et al. 2021

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“…Its prominence derives primarily from its simplicity, flexibility, and issue independence. According to myriads of recent researches, MH algorithms outperform traditional algorithms for a variety of issues, including energy conversion and management, 31 image-based learning, 32 healthcare, 33 recommender system, 34 fault diagnosis, 35 power system, 36 sentiment analysis, 37 fuzzy systems, 38 text mining, 39 web page classification, 40 and so forth. Due to their widespread popularity in different areas, two major flaws identified in the applications of MH algorithms are stasis at a local maximum and premature convergence.…”

Section: Related Workmentioning

confidence: 99%

Excogitating marine predators algorithm based on random opposition‐based learning for feature selection

Balakrishnan

Dhanalakshmi

Khaire

2021

Concurrency and Computation

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Obtaining precise information from a high-dimensional dataset is one of the most difficult tasks as datasets contain more features and fewer samples. The high-dimensionality of the dataset reduces predictive capability and increases the computational complexity of the analytical model. The widespread employment of meta-heuristic methods to handle the challenge of high-dimensional datasets has been exceptional in recent years. The marine predators algorithm (MPA) is a recently developed meta-heuristic algorithm based on the "survival-of-the-fittest" notion. This research critique overcomes the drawbacks of the existing MPA and proposes a feature selection model using random opposition-based learning (ROBL). The searching for the optimum solution in a single direction of the traditional MPA reduces its performance. The incorporation of ROBL in the MPA enhances its ability to reconnoiter bigger search space. The proposed algorithm generates a new population based on the initial and random opposite population. The performance of ROBL-MPA is inspected on six high-dimensional microarray datasets. The results of the proposed ROBL-MPA are compared to traditional MPA and opposition based MPA (OBL-MPA). The proposed ROBL-MPA outperforms traditional MPA based on several benchmark performance analysis tests.

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“…Feature selection (FS) is an initial procedure that can be used to reduce data dimensions whilst increasing prediction accuracy. FS is widely used in pattern recognition (Gunal & Edizkan, 2008), sentiment analysis (Madasu & Elango, 2020), power systems (Sun et al, 2021), text classification (Kou, 2020), signal processing (Chen et al, 2021) and so forth. FS helps to improve generalisation by minimising over‐fitting (Khaire & Dhanalakshmi, 2020), in timing calculations and memory, and improve classification accuracy by developing accurate predictive models.…”

Section: Introductionmentioning

confidence: 99%

S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

2022

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The African vulture optimization algorithm (AVOA) is a recently developed metaheuristic algorithm that imitates the eating and movement patterns of authentic African vultures. AVOA is developed to address the continuous optimization problem. However, AVOA is unable to solve the discrete search space, this inspires us to develop the binary AVOA for feature selection problems in classification tasks. The suggested BAVOA incorporates an eight-transfer function (S-shaped and V-shaped) for transforming a continuous variable to a binary one. Using 14 benchmark data sets, the proposed technique is compared against 15 conventional binary metaheuristics algorithms in terms of classification accuracy, fitness function, number of selected features and converging ability. Furthermore, the results are statistically analysed using Wilcoxon test. The comparative findings of S-Shaped and V-shaped transfer functions indicate the superior performance of BAVOA methods, particularly S2-BAVOA, in contrast to other transfer function. Based on results, it turns out that the suggested technique converges to the global minimum in several iterations based on the selection of optimal characteristics, fitness values and higher classification accuracy as compared to the classical binary metaheuristic algorithms.

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Optimal performance of a combined heat-power system with a proton exchange membrane fuel cell using a developed marine predators algorithm

Cited by 26 publications

References 27 publications

PEMFC Poly-Generation Systems: Developments, Merits, and Challenges

PEMFC Poly-Generation Systems: Developments, Merits, and Challenges

Excogitating marine predators algorithm based on random opposition‐based learning for feature selection

S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

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