Modeling and multi-objective optimization of variable air gasification performance parameters using Syzygium cumini biomass by integrating ASPEN Plus with Response surface methodology (RSM)

Singh, Deepak Kumar; Tirkey, Jeewan Vachan

doi:10.1016/j.ijhydene.2021.03.054

Cited by 63 publications

(19 citation statements)

References 53 publications

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“…The Y Syngas , LHV Syngas , CGE, and CCE were estimated using eqs – , normalY syngas = normalf normallow rate of syngas 0.25em ( Nm 3 / h ) f low rate of coal feed 0.25em ( kg / h ) LHV syngas 0.25em ( MJ / Nm 3 ) = 35.81 x CH 4 + 12.63 x CO + 10.79 x normalH 2 where, x i denotes the volume fractions of each component of the syngas on a dry basis. CGE 0.25em ( % ) = normalY syngas × LHV syngas LHV coal × 100 …”

Section: Methodsmentioning

confidence: 99%

“…After the cleaning step, this N 2 -rich syngas is used in a catalytic reactor to produce cleaner liquid fuels. 24 For instance, Yan et al 13 reported the successful pilot-scale demonstration of such a continuous process in their study to convert biosyngas derived from wood chips having a composition of 47% N 2 , 21% CO, 18% H 2 , 12% CO 2 , and 2% CH 4 to aviation turbine range liquid fuel in a catalytic reactor. Lu et al 21 synthesized gasoline range hydrocarbons with improved octane quality using N 2rich cleaned biosyngas with a H 2 /CO molar ratio of 1 produced from a downdraft fixed-bed gasifier using ambient air as a gasification medium.…”

Section: Introductionmentioning

confidence: 99%

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High-Ash Low-Rank Coal Gasification: Process Modeling and Multiobjective Optimization

2022

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The diversification of coal for its sustainable utilization in producing liquid transportation fuel is inevitable in countries with huge coal reserves. Gasification has been contemplated as one of the most promising thermochemical routes to convert coal into high-quality syngas, which can be utilized to produce liquid hydrocarbons through catalytic Fischer−Tropsch (F-T) synthesis. Liquid transportation fuel production through coal gasification could help deal with environmental challenges and renewable energy development. The present study aims to develop an equilibrium model of a downdraft fixed-bed gasifier using Aspen Plus simulator to predict the syngas compositions obtained from the gasification of high-ash low-rank coal at different operating conditions. Air is used as a gasifying agent in the present study. The model validation is done using published experimental and simulation results from previous investigations. The sensitivity analysis is done to observe the influence of the major operating parameters, such as equivalence ratio (ER), gasification temperature, and moisture content (MC), on the performance of the CL-RMC concerning syngas generation. The gasification performance of CL-RMC is analyzed by defining various performance parameters such as syngas composition, hydrogento-carbon monoxide (H 2 /CO), molar ratio, syngas yield (Y Syngas ), the lower heating value of syngas (LHV Syngas ), cold gas efficiency (CGE), and carbon conversion efficiency (CCE). The combined effects of the major operating parameters are studied through the response surface methodology (RSM) using the design of experiments. The optimized condition of the major operational parameters is determined for a target value of a H 2 /CO molar ratio of 1 and the maximum CGE and CCE using the multiobjective optimization approach. The high-degree accurate regression model equations were generated for the H 2 /CO molar ratio, CGE, and CCE using the variance analysis (ANOVA) tool. The optimal conditions of the major operating parameters, i.e., ER, gasification temperature, MC for the H 2 /CO molar ratio of 1, and the maximum CGE and CCE, are found to be 0.5, 655 °C, and 16.36 wt %, respectively. The corresponding optimal values of CGE and CCE are obtained as 22 and 16.36%, respectively, with a cumulative composite desirability value of 0.7348. The findings of the present investigation can be decisive for future developmental projects in countries concerning the utilization of high-ash low-rank coal in liquid fuel production through the gasification route.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Ash Low-Rank Coal Gasification: Process Modeling and Multiobjective Optimization

2022

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“…The gasification conditions were 800°C and 1 atm and air was preferred as gasifiying agent. Softwood pellets was used as feedstock with 31.6 kg/h flow rate, equivalent ratio (ER) was determined 0.2 and air flow rate was 35.244 Nm 3 /h (Singh & Tirkey, 2021). The syngas composition obtained by the model developed in this study under the same conditions with the experimental and simulation studies and their comparison results were given in Figure 2.…”

Section: Model Validationmentioning

confidence: 98%

Investigation of Oak Wood Biochar Gasification in Downdraft Gasifier Using Aspen Plus Simulation

Sezer¹,

Fuçucu²,

Özveren³

2021

bes

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In response to increasing energy demands and the inevitable release of greenhouse gases into the environment, researchers have turned their attention to biomass energy production. Biomass offers a lot of benefits; however, it has poor calorific value due to its higher moisture and volatile content. Carbonization can be used to overcome this disadvantage and produce biomass with a higher carbon content. One of the most promising thermochemical conversion techniques is gasification. In this study, biochar from oak wood was gasified in a steam environment using Aspen Plus® simulation. The downdraft gasifier model was developed under steady-state equilibrium parameters based on the minimization of Gibbs free energy. As consistent with the literature, H2 was produced higher amount in the steam gasification (55.65%) compared with the air gasification (17.36%). Parametric analyzes were performed to investigate the influence of the operating parameters on the composition of the syngas. Results depicted that the increasing temperature and steam/biochar ratio raised the H2 concentration from 53.21% to 55.72% and 54.50% to 56.84% in syngas respectively. Temperature also showed positive influence on the LHV of syngas that changed between 19620-19708 kJ/kg. The steam/biochar ratio affected the LHV of syngas negatively thus it decreased from 20257 to 18777 kJ/kg.

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“…To do so, response surface methodology (RSM) along with modelling and simulation in Aspen Plus ® can be used as a tool to define proper conditions along the process. RSM is a tool that determines the optimal response by coupling mathematical and statistical models while decreasing the number of experimental runs [23]. Different studies have combined the use of Aspen Plus ® and RSM to optimize different processes such as gasification of Sysigium cumini [23], methanol steam reforming to produce H 2 for HT-PEMFC [24], extraction of γ-Oryzanol from defatted rice bran [25], ESR to produce H 2 for low temperature PEMFC (LT-PEMFC) [20], biomass steam-gasification [26], dividing wall distillation columns [27], and industrial CO 2 capture [28].…”

Section: Introductionmentioning

confidence: 99%

“…RSM is a tool that determines the optimal response by coupling mathematical and statistical models while decreasing the number of experimental runs [23]. Different studies have combined the use of Aspen Plus ® and RSM to optimize different processes such as gasification of Sysigium cumini [23], methanol steam reforming to produce H 2 for HT-PEMFC [24], extraction of γ-Oryzanol from defatted rice bran [25], ESR to produce H 2 for low temperature PEMFC (LT-PEMFC) [20], biomass steam-gasification [26], dividing wall distillation columns [27], and industrial CO 2 capture [28]. The main purpose of Aspen Plus ® is to model and simulate the complete process, whereas the aim of RSM is to determine the optimal conditions based on an experimental design that includes factors that might affect the performance of the overall process.…”

Section: Introductionmentioning

confidence: 99%

Biomass Potential for Producing Power via Green Hydrogen

et al. 2021

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Hydrogen (H2) has become an important energy vector for mitigating the effects of climate change since it can be obtained from renewable sources and can be fed to fuel cells for producing power. Bioethanol can become a green H2 source via Ethanol Steam Reforming (ESR) but several variables influence the power production in the fuel cell. Herein, we explored and optimized the main variables that affect this power production. The process includes biomass fermentation, bioethanol purification, H2 production via ESR, syngas cleaning by a CO-removal reactor, and power production in a high temperature proton exchange membrane fuel cell (HT-PEMFC). Among the explored variables, the steam-to-ethanol molar ratio (S/E) employed in the ESR has the strongest influence on power production, process efficiency, and energy consumption. This effect is followed by other variables such as the inlet ethanol concentration and the ESR temperature. Although the CO-removal reactor did not show a significant effect on power production, it is key to increase the voltage on the fuel cell and consequently the power production. Optimization was carried out by the response surface methodology (RSM) and showed a maximum power of 0.07 kWh kg−1 of bioethanol with an efficiency of 17%, when ESR temperature is 700 °C. These values can be reached from different bioethanol sources as the S/E and CO-removal temperature are changed accordingly with the inlet ethanol concentration. Because there is a linear correlation between S/E and ethanol concentration, it is possible to select a proper S/E and CO-removal temperature to maximize the power generation in the HT-PEMFC via ESR. This study serves as a starting point to diversify the sources for producing H2 and moving towards a H2-economy.

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Modeling and multi-objective optimization of variable air gasification performance parameters using Syzygium cumini biomass by integrating ASPEN Plus with Response surface methodology (RSM)

Cited by 63 publications

References 53 publications

High-Ash Low-Rank Coal Gasification: Process Modeling and Multiobjective Optimization

High-Ash Low-Rank Coal Gasification: Process Modeling and Multiobjective Optimization

Investigation of Oak Wood Biochar Gasification in Downdraft Gasifier Using Aspen Plus Simulation

Biomass Potential for Producing Power via Green Hydrogen

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