Multiparameter-based product, energy and exergy optimizations for biomass gasification

Caglar, Basar; Tavsanci, Duygu; Bıyık, Emrah

doi:10.1016/j.fuel.2021.121208

Cited by 17 publications

(6 citation statements)

References 31 publications

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“…The lower heating value (LHV) has a relation with HHV and is shown in eq L H V .25em ( normalk normalJ / normalk normalg ) = H H V − 2443 × 8.936 × normalH where 2443 kJ/kg is the latent heat of vaporization of water and 8.936 kg/kg is the ratio of water to hydrogen.…”

Section: Biosolid Properties and Basic Thermochemical Datamentioning

confidence: 99%

“…Therefore, the total exergy of each stream, consisting of physical exergy (Ex ph ) and chemical exergy (Ex ch ), can be determined. The physical exergy refers to the amount of usable energy without chemical compositions and is expressed as normalE x i false( T , P false) normalp normalh = H i − H i , 0 − T 0 ( S i − S i , 0 ) …”

Section: Biosolid Properties and Basic Thermochemical Datamentioning

confidence: 99%

“…Chemical exergy is expressed as normalE x i false( T , P , x i false) normalc normalh = normalE x i ( T 0 , P 0 ) + R T 0 .25em ln ( x i ) where T 0 and P 0 are the temperature and pressure of the reference state, x i is the mole fraction of a pure chemical species i in the mixture, and R is the molar gas constant.…”

Section: Biosolid Properties and Basic Thermochemical Datamentioning

confidence: 99%

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Biosolid Gasification Performance Prediction Using a Stoichiometric Thermodynamic Model

Li,

Zhang,

2024

ACS Omega

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The gasification process can recover energy from biosolids produced in wastewater treatment. This paper developed a stoichiometric thermodynamic equilibrium model for biosolid gasification based on the biosolid properties, thermodynamic database, and equilibrium constants. If the calculation result showed that the quantity of char was negative, the quantity of char was put to zero, and the simulation was carried out again. The model was first verified by woody gasification under isothermal conditions, and the influence of a given temperature on biosolid gasification was simulated. The model further investigated the effects of different feedstock types, moisture contents, equivalence ratios, and reaction extensions on the adiabatic temperature, exergy efficiency, and syngas properties under autothermal conditions. The four factors were all the main factors for adiabatic temperature. The exergy efficiency depended more on the operation conditions than on the feedstock type. The H 2 concentration of the dry syngas in biosolid gasification exhibited a curve both against the given temperature under isothermal conditions and against the moisture content under autothermal conditions.

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Section: Biosolid Properties and Basic Thermochemical Datamentioning

confidence: 99%

Section: Biosolid Properties and Basic Thermochemical Datamentioning

confidence: 99%

Section: Biosolid Properties and Basic Thermochemical Datamentioning

confidence: 99%

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Biosolid Gasification Performance Prediction Using a Stoichiometric Thermodynamic Model

Li,

Zhang,

2024

ACS Omega

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“…where S Y and S Z are two solutions generated by the crossover strategy presented in Equation ( 16); A and B are two positions selected randomly over the population size; and → W 2 is the hungry weight of each solution, computed in the original HGS in Equation (7).…”

Section: Shortcomings Of the Original Hgsmentioning

confidence: 99%

“…These factors are land availability, policies development, and syngas effects. Caglar et al studied the use of three simultaneous gasifying agents, namely, air, H 2 O, and CO 2 , in the gasification process [7]. The performance was indicated by the product yields, cold gas efficiency, exergy efficiency, CO 2 emissions, and heat requirement of the gasifier.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Biomass Distributed Generators Using Modified Hunger Games Search to Reduce CO2 Emissions

Nassef

Houssein

Rezk

et al. 2023

JMSE

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Biomass is a renewable energy source because it is contained in organic material such as plants. This paper introduces a modified hunger games search for solving global optimization and biomass distributed generator problems. The hunger search algorithm is a very recent optimization algorithm. Despite its merits, it still needs some modifications. The proposed approach includes a new binary τ-based crossover strategy with satisfaction fulfillment step mechanisms. This new algorithm is designed to improve the original hunger games search algorithm by addressing some of its shortcomings, specifically, in solving problems related to global optimization such as finding the best possible solutions for biomass distributed generators. To assess the power of the new approach, its performance was evaluated on the IEEE CEC’2020 test suite against five recent and competitive algorithms. This comparison process included applying the Wilcoxon sign rank and Friedman statistical tests. Reducing the system losses and enhancing the network’s voltage profile are two main issues in the stability of radial distribution networks. Optimal allocation of biomass distributed generators in radial distribution networks can not only improve their stability but also guarantee good service to the customers. Consequently, this research work suggests an effective strategy based on the proposed approach to produce the optimal positions, sizes, and power factors of the biomass distributed generators in the network. Accordingly, the target is to mitigate the network’s active power loss such that the power flow and the bus voltage have to be maintained at their standard limits. Three distribution networks were considered for validating the superiority of the new proposed algorithm. These networks are the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus. The obtained results were compared with the gravitational search algorithm, whale optimization algorithm, grey wolf optimizer, Runge Kutta method, and the original hunger search algorithm. The new approach outperformed the other considered approaches in obtaining the optimal parameters, which mitigated the power loss to 11.6300, 5.2291, and 145.489 kW, with loss reduction of 94.49%, 97.68%, and 88.79% for the three networks, respectively.

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Upcycling of biomass using gasification process based on various biomass types and different gasifying agents: systematic multi-criteria decision and sensitivity analysis

Mojaver

Khalilarya

Chitsaz

et al. 2022

Biomass Conv. Bioref.

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Multiparameter-based product, energy and exergy optimizations for biomass gasification

Cited by 17 publications

References 31 publications

Biosolid Gasification Performance Prediction Using a Stoichiometric Thermodynamic Model

Biosolid Gasification Performance Prediction Using a Stoichiometric Thermodynamic Model

Optimal Allocation of Biomass Distributed Generators Using Modified Hunger Games Search to Reduce CO2 Emissions

Upcycling of biomass using gasification process based on various biomass types and different gasifying agents: systematic multi-criteria decision and sensitivity analysis

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