Multi-objective bat optimization for a biomass gasifier integrated energy system based on 4E analyses

Cao, Yan; Dhahad, Hayder A.; Farouk, Naeim; Xia, Weifeng; Rad, Hima Nikafshan; Ghasemi, Amir; Kamranfar, Saeed; Sani, Mostafa Mostafavi; Shayesteh, Ali Akbar

doi:10.1016/j.applthermaleng.2021.117339

Cited by 72 publications

(8 citation statements)

References 69 publications

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“…A multi-objective optimization model was established by Wei et al [30] to maximize the energy saving rate and minimize the energy expenditure, and the NSGA-II algorithm was used to determine a series of optimal resource scheduling strategies. Several excellent multi-objective optimization algorithms have also been proposed in recent years to address the schedule problems, for example, Cao et al [31] focused on a 4E analyses of a biomass gasifier integrated energy system, using multi-objective bat optimization. There are also studies on load prediction and parameter optimization using the evolutionary algorithm and neural network [32] [33].…”

Section: Literature Surveymentioning

confidence: 99%

A BCS-GDE multi-objective optimization algorithm for combined cooling, heating and power model with decision strategies

Sun

Deng

et al. 2022

Applied Thermal Engineering

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Section: Literature Surveymentioning

confidence: 99%

A BCS-GDE multi-objective optimization algorithm for combined cooling, heating and power model with decision strategies

Sun

Deng

et al. 2022

Applied Thermal Engineering

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“…Another SI-based algorithm to solve the multi-objective optimization problem of CHP systems was presented by Cao et al [181], in 2021. This multi-objective method was the Bat optimization algorithm that was used to optimize an innovative biomass gasifier system for combined heat and power production.…”

Section: • Other Si-based Algorithmsmentioning

confidence: 99%

Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview

Alsagri

Alrobaian

2022

Energies

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Combined heat and power (CHP) plants are known as efficient technologies to reduce environmental emissions, balance energy costs, and increase total energy efficiency. To obtain a more efficient system, various optimization methods have been employed, based on numerical, experimental, parametric, and algorithmic optimization routes. Due to the significance of algorithmic optimization, as a systematic method for optimizing energy systems, this novel review paper is focused on the meta-heuristic optimization algorithms, implemented in CHP energy systems. By considering the applied objective functions, the main sections are divided into single-objective and multi-objective algorithms. In each case, the units’ combination is briefly detailed, the objective functions are introduced, and analyses are conducted. The main aim of this paper is to gather a database for the optimization of CHPs, demonstrate the effect of the applied optimization methods on the objective functions, and finally, introduce the most efficient methods. The most significant feature of this paper is that it covers all types of CHP optimization issues including scheduling, sizing, and designing problems, finding the extent of each optimization issue in the relevant papers in the last decade. Based on the findings, in the single-objective problems the combined heat and power economic dispatch (CHPED) issue as a subcategory of the scheduling problems is introduced as the most paid topic; the designing issue is known as the lowest paid topic. In the multi-objective problems, working on various types of CHP optimization problems has been conducted with an almost similar share. The combined heat and power economic emission dispatch (CHPEED) problem with the most share, and the sizing issue with the lowest share. The CHP designing and sizing optimization issues could be introduced as topics to work on more in the future. Additionally, the numerical results of CHPED and CHPEED problems solved by various algorithms are presented and compared. In this regard, specified test systems are considered.

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“…Trusted on the exoreversible thermodynamic model in the TEG the input and output exergies to/of the system are the thermal exergy and the electrical power, respectively. Therefore, matching to the thermodynamic laws, exergetic balance in TEG may be obtained as below [98,99]:…”

Section: Exergy Investigation Of Tegmentioning

confidence: 99%

A Comprehensive Thermoeconomic Evaluation and Multi-Criteria Optimization of a Combined MCFC/TEG System

et al. 2021

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In this study, an integrated molten carbonate fuel cell (MCFC), thermoelectric generator (TEG), and regenerator energy system has been introduced and evaluated. MCFC generates power and heating load. The exit fuel gases of the MCFC is separated into three sections: the first section is transferred to the TEG to generate more electricity, the next chunk is conducted to a regenerator to boost the productivity of the suggested plant and compensate for the regenerative destructions, and the last section enters the surrounding. Computational simulation and thermodynamic evaluation of the hybrid plant are carried out utilizing MATLAB and HYSYS software, respectively. Furthermore, a thermoeconomic analysis is performed to estimate the total cost of the product and the system cost rate. The offered system is also optimized using multi-criteria genetic algorithm optimization to enhance the exergetic efficiency while reducing the total cost of the product. The power generated by MCFC and TEG is 1247.3 W and 8.37 W, respectively. The result explicates that the provided electricity and provided efficiency of the suggested plant is 1255.67 W and 38%, respectively. Exergy inquiry outcomes betokened that, exergy destruction of the MCFC and TEG is 13,945.9 kW and 262.75 kW, respectively. Furthermore, their exergy efficiency is 68.22% and 97.31%, respectively. The impacts of other parameters like working temperature and pressure, thermal conductance, the configuration of the advantage of the materials, etc., on the thermal and exergetic performance of the suggested system are also evaluated. The optimization outcomes reveal that in the final optimum solution point, the exergetic efficiency and total cost of the product s determined at 70% and 30 USD/GJ.

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Multi-objective bat optimization for a biomass gasifier integrated energy system based on 4E analyses

Cited by 72 publications

References 69 publications

A BCS-GDE multi-objective optimization algorithm for combined cooling, heating and power model with decision strategies

A BCS-GDE multi-objective optimization algorithm for combined cooling, heating and power model with decision strategies

Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview

A Comprehensive Thermoeconomic Evaluation and Multi-Criteria Optimization of a Combined MCFC/TEG System

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