Performances of syngas production and deposited coke regulation during co-gasification of biomass and plastic wastes over Ni/γ-Al2O3 catalyst: Role of biomass to plastic ratio in feedstock

Xu, Didi; Xiong, Ya; Ye, Jiandong; Su, Youfeng; Dong, Qing; Zhang, Shuping

doi:10.1016/j.cej.2019.123728

Cited by 114 publications

(40 citation statements)

References 54 publications

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“…The intensity of the peaks in the spectra correlate with the TGA results presented in Table 5. The two bands are usually assigned to D (1345 cm −1 ) and G (1580 cm −1 ) vibration modes of carbonaceous species 62,63 . The D band is usually assigned to disordered carbon filaments, carbon nanoparticles, or amorphous carbon, while the G band is assigned to the sp 2 CC vibrations of ordered graphitic carbon.…”

Section: Resultsmentioning

confidence: 99%

“…The two bands are usually assigned to D (1345 cm −1 ) and G (1580 cm −1 ) vibration modes of carbonaceous species. 62,63 The D band is usually assigned to disordered carbon filaments, carbon nanoparticles, or amorphous carbon, while the G band is assigned to the sp 2 C C vibrations of ordered graphitic carbon. It was also noticed that the weak G band of Ni/γ-AlOOH-950, where NiAl 2 O 4 was the sole Ni species before reduction, appeared at a higher wavenumber value of 1600 cm −1 .…”

Section: Catalytic Activity Testsmentioning

confidence: 99%

“…This shift in the band could be referred to some unusual structural disorder as was suggested in similar literature reports where a peak around 1610 cm −1 was assigned to disordered aromatic structures. 62 It has been accepted that the graphitized carbon has a higher negative impact on the catalytic activity, which correlate with its lower tendency to be gasified compared with disordered carbon and carbon nanostructures. 63 All tested catalysts presented in Figure 12 showed both types of carbon with small variation in the G/D band intensity ratio, I G /I D .…”

Section: Catalytic Activity Testsmentioning

confidence: 99%

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Enhanced selectivity of syngas in partial oxidation of methane: A new route for promising Ni‐alumina catalysts derived from Ni/ γ‐AlOOH with modified Ni dispersion

et al. 2020

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Partial oxidation of methane was studied over new Ni nanocatalysts prepared from Ni-impregnated γ-AlOOH, which were compared with their counterparts derived from impregnated γ-Al 2 O 3 and α-Al 2 O 3. The prepared catalysts were characterized by powder X-ray diffraction (XRD), N 2-sorption, H 2-temperatue programmed reduction, scanning electron microscopy, transmission electron microscopy, thermal gravimetric analysis, CO 2-and NH 3-temperatureprogrammed desorption, Raman spectroscopy, CO chemisorption, and diffuse reflectance infrared Fourier transform spectroscopy of adsorbed CO. Employing γ-AlOOH as the support precursor resulted in significantly improved textural properties and catalytic activity. The structure of the support precursor and its surface properties showed a strong influence on the type of Ni active species and their interactions with the support. γ-AlOOH-derived catalysts showed NiO as the dominant Ni species when calcined at 500 C to 650 C, while NiAl 2 O 4 became the sole phase at higher temperatures. On the other hand, mixtures of NiO and NiAl 2 O 4 formed over γ-Al 2 O 3 , calcined before impregnation, regardless of the precalcination temperature. All γ-AlOOHderived catalysts showed higher specific surface areas compared to their counterparts derived from impregnated γ-Al 2 O 3. Upon calcination at moderate temperatures, γ-AlOOH-derived catalysts also showed modified textural and morphological properties of the Ni active particles, including higher Ni dispersion, larger metal surface area, and smaller Ni crystallites. The support precursor and the pretreatment conditions showed a strong influence on the catalytic performance, which was referred to their significant effect on the type of the Ni species and interactions with the support. All catalysts showed higher catalytic activity than the α-Al 2 O 3-derived catalyst, with CH 4 conversion between 86% and 88.5% at 700 C. While γ-AlOOHand γ-Al 2 O 3-derived catalysts calcined at 650 C showed a stable CH 4 conversion around 88%, and higher syngas

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Section: Resultsmentioning

confidence: 99%

Section: Catalytic Activity Testsmentioning

confidence: 99%

Section: Catalytic Activity Testsmentioning

confidence: 99%

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Enhanced selectivity of syngas in partial oxidation of methane: A new route for promising Ni‐alumina catalysts derived from Ni/ γ‐AlOOH with modified Ni dispersion

et al. 2020

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“…Burra and Gupta [38] also report that the overlap of thermal decomposition temperature regimes is necessary for the synergistic interaction between biomass and plastics. It has been suggested that interaction between volatiles and fixed carbon involves radical donor from the biomass degradation initiating and enhancing plastic polymer chain scission [17,38,42]. Lopez et al [13] found a synergistic effect between biomass and high-density polyethylene, where the feedstock was co-fed into a fluidised spouted bed reactor followed by catalytic steam reforming in a separate fluidised bed reactor.…”

Section: Oxygenated Hydrocarbons Dry (Co 2 ) Reformingmentioning

confidence: 99%

“…This was attributed to the reduced formation of amorphous type carbon which is detrimental to catalytic activity and higher formation of filamentous type carbon which has a lower impact on activity [25]. Xu et al [42] also used a two-stage reactor system with fixed bed fast pyrolysis of a biomass/plastic mixture (rice husks/polyethylene) followed by fixed bed catalytic reactor with a Ni/Al 2 O 3 catalyst. Pyrolysis-catalytic steam reforming experiments showed an optimum biomass/polyethylene ratio of 1:1 which resulted in maximum H 2 and CO yield attributed to steam reforming and water gas shift reactions.…”

Section: Oxygenated Hydrocarbons Dry (Co 2 ) Reformingmentioning

confidence: 99%

Co-pyrolysis–catalytic steam reforming of cellulose/lignin with polyethylene/polystyrene for the production of hydrogen

Akubo

Nahil

Williams

2020

Waste Dispos. Sustain. Energy

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Co-pyrolysis of biomass biopolymers (lignin and cellulose) with plastic wastes (polyethylene and polystyrene) coupled with downstream catalytic steam reforming of the pyrolysis gases for the production of a hydrogen-rich syngas is reported. The catalyst used was 10 wt.% nickel supported on MCM-41. The influence of the process parameters of temperature and the steam flow rate was examined to optimize hydrogen and syngas production. The cellulose/plastic mixtures produced higher hydrogen yields compared with the lignin/plastic mixtures. However, the impact of raising the catalytic steam reforming temperature from 750 to 850 °C was more marked for lignin addition. For example, the hydrogen yield for cellulose/polyethylene at a catalyst temperature of 750 °C was 50.3 mmol g−1 and increased to 60.0 mmol g−1 at a catalyst temperature of 850 °C. However, for the lignin/polyethylene mixture, the hydrogen yield increased from 25.0 to 50.0 mmol g−1 representing a twofold increase in hydrogen yield. The greater influence on hydrogen and yield for the lignin/plastic mixtures compared to the cellulose/plastic mixtures is suggested to be due to the overlapping thermal degradation profiles of lignin and the polyethylene and polystyrene. The input of steam to the catalyst reactor produced catalytic steam reforming conditions and a marked increase in hydrogen yield. The influence of increased steam input to the process was greater for the lignin/plastic mixtures compared to the cellulose/plastic mixtures, again linked to the overlapping thermal degradation profiles of the lignin and the plastics. A comparison of the Ni/MCM-41 catalyst with Ni/Al2O3 and Ni/Y-zeolite-supported catalysts showed that the Ni/Al2O3 catalyst gave higher yields of hydrogen and syngas. Graphic abstract

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Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Bin Abu Sofian,

Lim,

Manickam

et al. 2023

ChemBioEng Reviews

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The growing potential of sustainable materials such as polyhydroxyalkanoates (PHAs), polylactic acid (PLA), alginate, carrageenan, and ulvan for bioplastics production presents an opportunity to promote a sustainable circular economy. This review investigates their properties, applications, and challenges. Bioplastics derived from algae offer an environmentally friendly alternative to petroleum‐based plastics, a shift of paramount importance to society due to the escalating environmental concerns associated with traditional plastics. The role of the internet‐of‐things (IoT) and machine learning in refining these bioplastics' production and development processes is emphasized. IoT monitors cultivation conditions, data collection, and process control for more sustainable production. Machine learning can enhance algae cultivation, increasing the supply of raw materials for algal bioplastics and improving their efficiency and output. The study results indicate the promise of algae‐based bioplastics, IoT, and machine learning in fostering a more environmentally sustainable future. By harnessing these advanced technologies, optimization of bioplastic production is possible, potentially revolutionizing the materials industry and addressing existing challenges toward achieving a sustainable circular economy.

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Performances of syngas production and deposited coke regulation during co-gasification of biomass and plastic wastes over Ni/γ-Al2O3 catalyst: Role of biomass to plastic ratio in feedstock

Cited by 114 publications

References 54 publications

Enhanced selectivity of syngas in partial oxidation of methane: A new route for promising Ni‐alumina catalysts derived from Ni/ γ‐AlOOH with modified Ni dispersion

Enhanced selectivity of syngas in partial oxidation of methane: A new route for promising Ni‐alumina catalysts derived from Ni/ γ‐AlOOH with modified Ni dispersion

Co-pyrolysis–catalytic steam reforming of cellulose/lignin with polyethylene/polystyrene for the production of hydrogen

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

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