Optimization of mix proportions and mechanical properties of coal gangue-based multi-solid-waste geopolymer mortars
Source abstract
Abstract
To address the challenges of high production, large stockpiles, and low utilization of coal-based solid waste, a coal gangue-based geopolymer was developed using coal gangue powder, ground granulated blast furnace slag (GGBFS), and fly ash as cementitious precursors. A combination of sodium silicate solution and NaOH served as the alkali activator, while desulfurized gypsum was introduced as a performance-modifying additive. An orthogonal experimental design combined with gray relational analysis was employed to quantitatively evaluate the effects of coal gangue content, sodium silicate modulus, alkaline activator dosage, and desulfurization gypsum content on the mechanical properties of the geopolymer. A corresponding gray prediction model, GM(1,3), was established to forecast the mechanical performance of coal gangue-based geopolymers.The results indicated that the significance ranking of the factors affecting the mechanical properties of the geopolymer mortar was: sodium silicate modulus > desulfurization gypsum content > alkaline activator dosage > coal gangue content. The optimal mix design was determined to be 55% coal gangue content, 0.7 sodium silicate modulus, 8% alkaline activator dosage, and 7% desulfurization gypsum content. The geopolymer synthesized with this optimized mix exhibited a compressive strength of 23.3 MPa and a flexural strength of 3.7 MPa, respectively, compared to the unoptimized mix (50% coal gangue, sodium silicate modulus 0.7, 11% alkali activator, 8% desulfurized gypsum) These findings provide a theoretical basis for improving the utilization efficiency of coal gangue-based solid waste.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Innovative concrete reinforcement materials · Grouting, Rheology, and Soil Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (44)
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