Improved green strength of ceramic bodies through extrusion using hydroxypropyl methylcellulose as binder

Jiang, Guangpeng; Tao, Dongliang

doi:10.1111/ijac.13482

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…The anionic surfactant and wetting agent sodium dodecylbenzene sulfonate (SDBS) [ 34 ] were used to alter the adsorbent’s wettability and surface activity, while also increasing its adsorption capacity. Combining the reinforcing agent (A2C + HPMC + Na 2 SiO 4 + KH-602) enhanced the adsorbent’s strength and immersion loss rate [ 35 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

Formulation Optimization and Performance Prediction of Red Mud Particle Adsorbents Based on Neural Networks

Li,

Wang,

Wang

2024

Molecules

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Red mud (RM), a bauxite residue, contains hazardous radioactive wastes and alkaline material and poses severe surface water and groundwater contamination risks, necessitating recycling. Pretreated RM can be used to make adsorbents for water treatment. However, its performance is affected by many factors, resulting in a nonlinear correlation and coupling relationship. This study aimed to identify the best formula for an RM adsorbent using a mathematical model that examines the relationship between 11 formulation types (e.g., pore-assisting agent, component modifier, and external binder) and 9 properties (e.g., specific surface area, wetting angle, and Zeta potential). This model was built using a back-propagation neural network (BP) based on single-factor experimental data and orthogonal experimental data. The model trained and predicted the established network structure to obtain the optimal adsorbent formula. The RM particle adsorbents had a pH of 10.16, specific surface area (BET) of 48.92 m2·g−1, pore volume of 2.10 cm3·g−1, compressive strength (ST) of 1.12 KPa, and 24 h immersion pulverization rate (ηm) of 3.72%. In the removal of total phosphorus in flotation tailings backwater, it exhibited a good adsorption capacity (Q) and total phosphorous removal rate (η) of 48.63 mg·g−1 and 95.13%, respectively.

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Section: Methodsmentioning

confidence: 99%

Formulation Optimization and Performance Prediction of Red Mud Particle Adsorbents Based on Neural Networks

Li,

Wang,

Wang

2024

Molecules

View full text Add to dashboard Cite

show abstract

“…The anionic surfactant and wetting agent sodium dodecylbenzene sulfonate (SDBS) [26] were used to alter the adsorbent's wettability and surface activity, while also increasing its adsorption capacity. Combining the reinforcing agent (A2C+HPMC + Na2SiO4 + KH-602 enhanced the adsorbent's strength and immersion loss rate [27,28].…”

Section: Test Materialsmentioning

confidence: 99%

Formulation Optimization and Performance Prediction of Red Mud Particle Adsorbents Based on Neural Networks

Li,

Yang,

Wang

et al. 2024

Preprint

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Red mud (RM), a bauxite residue, contains hazardous radioactive wastes and alkaline material and poses severe surface water and groundwater contamination risks, necessitating recycling. Pretreated RM can be used to make adsorbents for water treatment. However, its performance is affected by many factors, resulting in a nonlinear correlation and coupling relationship. This study aimed to identify the best formula for an RM adsorbent using a mathematical model that examines the relationship between 11 formulation types (e.g., pore-assisting agent, component modifier, and external binder) and nine properties (e.g., specific surface area, wetting angle, and Zeta potential, among others). This model was built using a back-propagation neural networks(BP) based on single-factor experimental data and orthogonal test data. The model trained and predicted the established network structure to obtain the optimal adsorbent formula. The RM particle adsorbents had a pH of 10.16, specific surface area(BET) of 48.92 m2·g−1, pore volume of 2.10 cm3·g−1, compressive strength(ST)) of 1.12 KPa, and 24-h immersion pulverization rate(η_m) of 3.72%. In the removal of total phosphorus in flotation tailings backwater, it exhibited a good adsorption capacity (Q)and total phosphorous removal rate(η) of 48.63 mg·g−1 and 95.13%, respectively.

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“…During dry compaction of ceramic powder, binders are often used to improve the strength of the ceramic compact for handling and further processing. The mechanical strength of the green compact must be high enough for smooth ejection from the dies and subsequent green machining 7–9 . Several literature reports address the effect of the binder during dry pressing on the mechanical strength of green compacts 10–13 …”

Section: Introductionmentioning

confidence: 99%

“…The mechanical strength of the green compact must be high enough for smooth ejection from the dies and subsequent green machining. [7][8][9] Several literature reports address the effect of the binder during dry pressing on the mechanical strength of green compacts. [10][11][12][13] A wide variety of organic and inorganic binders have been used for providing green strength in the traditional ceramic compact.…”

Section: Introductionmentioning

confidence: 99%

Fabrication and characterization of silica ceramic compact prepared using Aloe‐Vera mucilage as a binder

Kumari

Tiwari

Mohanta

et al. 2022

Int J Applied Ceramic Tech

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In this study, silica compacts were fabricated through a powder processing route at different compaction pressure, using Aloe-Vera (AV) mucilage as a binder. The silica compacts were prepared at 90, 100, and 110 MPa compaction pressure using 0%-16 wt% of AV binder. The optimum amount of AV binder was 14 wt% for both 90 and 100 MPa and 12 wt% for 110 MPa. The maximum achieved green density and green strength of silica compacts at the optimum binder amount were 62.3% and 4 MPa, respectively at 110 MPa compaction pressure. The green silica compacts prepared at 110 MPa compaction pressure exhibited a minimum porosity of 21% and maximum flexural strength of 15 MPa after sintering at 1400 • C. The green silica compacts with the optimum amount of binder were strong enough for machining. The Fourier transform infrared spectroscopy analysis revealed the functional groups present in AV mucilage. The binder burnout characteristic of AV mucilage in the silica compact was determined by thermogravimetric analysis and differential thermal analysis. Additionally, AV gel acted as a binder and solvent simultaneously for ceramic compaction.

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Improved green strength of ceramic bodies through extrusion using hydroxypropyl methylcellulose as binder

Cited by 3 publications

References 19 publications

Formulation Optimization and Performance Prediction of Red Mud Particle Adsorbents Based on Neural Networks

Formulation Optimization and Performance Prediction of Red Mud Particle Adsorbents Based on Neural Networks

Formulation Optimization and Performance Prediction of Red Mud Particle Adsorbents Based on Neural Networks

Fabrication and characterization of silica ceramic compact prepared using Aloe‐Vera mucilage as a binder

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