Green optimization of vapour-cured geopolymer mortars: Predicting cost, mechanical properties, and environmental impact with artificial neural networks

Serhat Çelikten , Bilal Baran , Mustafa Sarıdemir
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Abstract

In this study, prediction and optimization of Na concentration, Ms modulus (total ratio of SiO₂/Na₂O in alkali activators), fly ash (FA) and granulated blast furnace slag (GGBFS) contents of geopolymer mortars produced by vapour curing in a prefabricated element production plant were carried out focusing on compressive strength, cost and environmental impacts. After the compressive strengths of geopolymer mortars made with comprehensive mixture designs were obtained experimentally, an artificial neural network (ANN) model was developed to estimate the compressive strength from Na, Ms, FA and GGBFS input variables. Moreover, primary energy (PE), global warming potential (GWP, equivalent-CO₂ emission) and cost were optimized separately in different compressive strength ranges with ANN predictions. The efficiency optimizations were also performed to evaluate the cost and environmental impacts per unit compressive strength. As a result of the optimizations, the maximum compressive strength of geopolymer mortar coded with 0.41 FA/0.59 GGBFS (Na = 6.75, Ms = 0.83) was determined as 54.42 MPa. The most efficient geopolymer mortar mixtures within the optimization ranges were 0.29 FA/0.71 GGBFS (Na = 3, Ms = 0.25, 27.79 MPa) regarding GWP with 1.82 (kgCO₂/m³)/MPa, 0.34 FA/0.66 GGBFS (Na = 3, Ms = 0.55, 31.49 MPa) regarding PE with 27 (MJ/m³)/MPa, and 0.38 FA/0.62 GGBFS (Na = 6.56, Ms = 0.66, 52.50 MPa) considering cost efficiency with 2.29 (€/m³)/MPa. FA + GGBFS based geopolymer mortars stand out as the most sustainable options in terms of energy efficiency and carbon footprint with lower PE/MPa and GWP/MPa values compared to 100 %FA and 100 %GGBFS based geopolymer mortars. Optimized geopolymer mortars are 78 %, 35 %, and 42 % more efficient than traditional OPC based mortars in terms of CO₂ emissions, energy consumption, and cost, respectively.
气相固化地聚合物砂浆的绿色优化:用人工神经网络预测成本、机械性能和环境影响
本研究针对某预制构件生产装置蒸汽固化地聚合物砂浆的抗压强度、成本和环境影响,对其Na浓度、Ms模量(碱活化剂中SiO₂/Na₂O的总比)、粉煤灰(FA)和高炉渣粒(GGBFS)含量进行了预测和优化。在实验获得综合配合比设计的地聚合物砂浆抗压强度后,建立了基于Na、Ms、FA和GGBFS输入变量的人工神经网络(ANN)模型。利用人工神经网络分别优化了不同抗压强度范围内的一次能源(PE)、全球变暖潜能值(GWP)、当量二氧化碳排放量(co2)和成本。效率优化也进行了评估成本和环境影响单位抗压强度。结果表明,编码为0.41 FA/0.59 GGBFS (Na = 6.75, Ms = 0.83)的地聚合物砂浆的最大抗压强度为54.42 MPa。在优化范围内,最有效的地聚合物砂浆混合料在GWP为1.82 (kgCO₂/m³)/MPa时为0.29 FA/0.71 GGBFS (Na = 3, Ms = 0.25, 27.79 MPa), PE为27 (MJ/m³)/MPa时为0.34 FA/0.66 GGBFS (Na = 3, Ms = 0.55, 31.49 MPa),考虑成本效益时为2.29(€/m³)/MPa时为0.38 FA/0.62 GGBFS (Na = 6.56, Ms = 0.66, 52.50 MPa)。FA + GGBFS基地聚合物砂浆与100% %FA和100% %GGBFS基地聚合物砂浆相比,PE/MPa和GWP/MPa值更低,在能源效率和碳足迹方面,FA + GGBFS基地聚合物砂浆是最具可持续性的选择。与传统的OPC基砂浆相比,优化后的地聚合物砂浆在CO₂排放、能耗和成本方面的效率分别提高了78 %、35 %和42 %。
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CiteScore
2.60
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