Tri-objective central composite design optimization of co-gasification of eucalyptus biomass and polypropylene waste

Mojaver, Parisa; Hasanzadeh, Rezgar; Chitsaz, Ata; Azdast, Taher; Mojaver, Mehran

doi:10.1007/s13399-022-02597-9

Cited by 14 publications

(4 citation statements)

References 47 publications

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“…Also,

x_{i}

and

x_{j}

are the independent variables and

ε

is the experimental error. Based on the analysis of variance (ANOVA), the efficiency of the regression model is evaluated using

R^{2}

as Equation (3) 34 :

R^{2} goodbreak= 1 goodbreak- \frac{{ss}_{r}}{{ss}_{T}},

where

{ss}_{r}

is the residual sum of squares and

{ss}_{T}

is the total sum of squares.…”

Section: Methodsmentioning

confidence: 99%

Process‐property relationship in polylactic acid composites reinforced by iron microparticles and 3D printed by fused filament fabrication

Hasanzadeh,

Mihankhah,

Azdast

et al. 2023

Polymer Engineering & Sci

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Polylactic acid (PLA) is the most widely used material in the fused filament fabrication (FFF) technique, which is a biocompatible thermoplastic. However, PLA's usefulness is limited by its narrow processing window and relatively low mechanical properties. Therefore, PLA composites have been developed to enhance its properties for FFF printing. A key challenge in producing composite parts via this method is to find the correlation between the mechanical properties of the parts and the process parameters. This knowledge is essential for optimizing the printing process to achieve the desired mechanical properties for composite parts industries such as aerospace, automotive, and medical, where high‐performance composite materials are crucial. The ability to control and predict the mechanical properties of FFF‐printed composite parts is critical for their successful integration into these industries. In this study, the effect of nozzle temperature (NT), printing speed (PS), and nominal porosity (POR) on the impact strength and specific impact strength of PLA/iron composites was examined using FFF. Response surface methodology (RSM) was used to optimize the experimental design. The results revealed that POR had the most significant effect on the impact resistance data, while NT had the least effect. Reducing the POR led to improved impact resistance in the samples. Multi‐objective optimization results showed that the lowest NT (190°C), the lowest POR (30%), and a PS of 50 mm/s were the optimal conditions for multiple objectives. RSM was also utilized to develop mathematical models of impact properties, focusing on varying NT, POR, and PS, which can be used to predict desired impact properties.Highlights Nominal porosity has the most influence on the impact strength of PLA/iron composites. Optimum values were temperature of 190°C, nominal porosity of 30%, and speed of 50 mm/s. RSM was effective in enhancing the mechanical properties of composite materials. RSM models provide a predictive tool for future FFF‐printed composite parts. Maximum impact strength of 4.44 kJ/m2 was achieved.

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“…Also,

x_{i}

and

x_{j}

are the independent variables and

ε

is the experimental error. Based on the analysis of variance (ANOVA), the efficiency of the regression model is evaluated using

R^{2}

as Equation (3) 34 :

R^{2} goodbreak= 1 goodbreak- \frac{{ss}_{r}}{{ss}_{T}},

where

{ss}_{r}

is the residual sum of squares and

{ss}_{T}

is the total sum of squares.…”

Section: Methodsmentioning

confidence: 99%

Process‐property relationship in polylactic acid composites reinforced by iron microparticles and 3D printed by fused filament fabrication

Hasanzadeh,

Mihankhah,

Azdast

et al. 2023

Polymer Engineering & Sci

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“…The present review article only discusses biomass–plastic co-gasification research studies. Biomass–plastic co-gasification has been proposed as a possible strategy to circumvent problems that often arise during plastic mono-gasification, such as feeding issues, plastic agglomeration, and the development of contaminants . Co-gasification offers the potential to reduce plastic waste in landfills by promoting environmentally responsible plastic waste management and the generation of clean energy …”

Section: Co-gasification Of Lignocellulosic Biomass (Lcb) and Plasticsmentioning

confidence: 99%

“…Biomass−plastic co- gasification has been proposed as a possible strategy to circumvent problems that often arise during plastic monogasification, such as feeding issues, plastic agglomeration, and the development of contaminants. 75 Co-gasification offers the potential to reduce plastic waste in landfills by promoting environmentally responsible plastic waste management and the generation of clean energy. 76 The intrinsic properties of waste plastics, such as spectacular tar formation, adhesiveness, high volatile content, and low heat conductivity, impede their valorization in existing gasification technologies and pose a formidable barrier to the deployment of the mono-gasification of plastic.…”

Section: Co-gasification Of Lignocellulosic Biomass (Lcb) and Plasticsmentioning

confidence: 99%

A Critical Outlook on Lignocellulosic Biomass and Plastics Co-Gasification: A Mini-Review

2022

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The generation of solid waste and lignocellulosic biomass (LCB) residues and their environmentally responsible disposal have emerged as major global challenges. A major part (around 12%) of solid waste is plastic, and the increased accumulation has become a serious public health and environmental hazard. For the combined management of residual LCB biomass and plastic waste, thermochemical conversion, particularly gasification, appears to be a potential alternative. Gasification is a process that turns high carbon-containing solid feedstocks into a combustible gas mixture known as syngas or producer gas based on the gasifying agent. Co-gasification (the gasification of two distinct feedstocks) has the potential to facilitate eco-friendly plastic waste disposal while also contributing to the production of green energy. The literature on the co-gasification of LCB and plastic waste is reviewed in the current study. These are mostly experimental studies on biomass and plastics carried out in various reactors with multiple gasifying mediums. The synergistic effects of various feedstock compositions and attributes are examined during co-gasification using experimental studies and reactor-level modeling. The impacts of operating parameters on the gas yield, tar formation, and gasifier performance are also analyzed. Finally, the challenges, research gaps, and the way toward commercialization in the context of co-gasification of various combinations of LCB and plastics are presented.

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“…Response surface methodology (RSM) is one of the most applicable designs of experimental methods [18]. RSM helps to present a regression model to predict the response variable based on the considered input parameters [19,20]. In addition to RSM, genetic algorithms (GA) can be used to optimize any process or system.…”

Section: Introductionmentioning

confidence: 99%

Process Integration of Hydrogen Production Using Steam Gasification and Water-Gas Shift Reactions: A Case of Response Surface Method and Machine Learning Techniques

Okoji,

Taiwo,

Adeoye

et al. 2024

International Journal of Energy Research

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An equilibrium-based steady-state simulator model that predicts and optimizes hydrogen production from steam gasification of biomass is developed using ASPEN Plus software and artificial intelligence techniques. Corn cob’s chemical composition was characterized to ensure the biomass used as a gasifier and with potential for production of hydrogen. Artificial intelligence is used to examine the effects of the significant input variables on response variables, such as hydrogen mole fraction and hydrogen energy content. Optimizing the steam-gasification process using response surface methodology (RSM) considering a variety of biomass-steam ratios was carried out to achieve the best results. Hydrogen yield and the impact of main operating parameters were considered. A maximum hydrogen concentration is found in the gasifier and water-gas shift (WGS) reactor at the highest steam-to-biomass (S/B) ratio and the lowest WGS reaction temperature, while the gasification temperature has an optimum value. ANFIS was used to predict hydrogen of mole fraction, 0.5045 with the input parameters of S/B ratio of 2.449 and reactor pressure and temperature of 1 bar and 848°C, respectively. With the steam-gasification model operating at temperature (850°C), pressure (1 bar), and S/B ratio of 2.0, an ASPEN simulator achieved a maximum of 0.5862 mole fraction of hydrogen, while RSM gave an increase of 19.0% optimum hydrogen produced over the ANFIS prediction with the input parameters of S/B ratio of 1.053 and reactor pressure and temperature of 1 bar and 850°C, respectively. Varying the gasifier temperature and S/B ratio have, on the other hand, a crucial effect on the gasification process with artificial intelligence as a unique tool for process evaluation, prediction, and optimization to increase a significant impact on the products especially hydrogen.

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Tri-objective central composite design optimization of co-gasification of eucalyptus biomass and polypropylene waste

Cited by 14 publications

References 47 publications

Process‐property relationship in polylactic acid composites reinforced by iron microparticles and 3D printed by fused filament fabrication

Process‐property relationship in polylactic acid composites reinforced by iron microparticles and 3D printed by fused filament fabrication

A Critical Outlook on Lignocellulosic Biomass and Plastics Co-Gasification: A Mini-Review

Process Integration of Hydrogen Production Using Steam Gasification and Water-Gas Shift Reactions: A Case of Response Surface Method and Machine Learning Techniques

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