Abstract
Cement global demand shows continued growth and a significant increase in the production volume, which may negatively impact the non-renewable natural resources and the environment, which is incompatible with sustainability goals. Cement kiln dust (CKD) is a primary concern associated with clinker manufacturing as a waste byproduct. Similarly, the mining industry produces copper tailing as unwanted material while beneficiating the ore, creating environmental problems due to difficulty in managing worldwide generated quantities that reach billions of metric tons. This study investigated the beneficial utilization of cement kiln dust and copper tailing as undesirable wastes in industrial applications through underground mines' cemented paste backfill (CPB). Sixty different mixtures were prepared with three types of CKD collected from various cement manufacturers and were accordingly used with a proportion of 5, 10, and 15% to partially replace ordinary Portland cement (OPC) and pozzolan Portland cement (PPC) binders, represented in hundreds of CPB samples. The hardened specimens were subjected to density, uniaxial compressive strength (UCS), and axial deformation measurements to evaluate the physical and mechanical properties at curing up to 90 days. Meanwhile, X-ray powder diffraction (XRD) was extensively applied to chemically investigate the hydration products of CPB-hardened mixtures. Moreover, we developed a UCS predictive model applying two techniques: multiple variables regression analysis and artificial neural network (ANN). The results showed that the tricalcium silicate (Alite) and dicalcium silicate (Belite) phases form C-S-H upon hydrations and provide high strength in the binary mixtures. Meanwhile, the CKD's lime saturation factor (LSF) governed the strength value in the ternary mixtures that utilized copper tailings. That makes CKD practical in the CPB mixture when partially replacing the OPC and PPC binders, with a proportion of up to 15%. In addition, the ANN technique's predictive model exhibited a significant positive correlation with excellent statistical parameters that achieved 0.995, 0.065, and 0.911 for R2, RMSE, and MAE, respectively.