Resumo:
The construction industry together with the mining sector, especially the areas responsible for the extraction of metallic substances, are currently directly responsible for a significant portion of the environmental impact due to the high consumption of raw materials. With the need to rationalize natural resources, new proposals for the use of recyclable waste have been gaining scale with the development of dosages to improve the construction process, with special emphasis on the civil construction sector. Millions of tons of iron ore tailings (IOT), a by-product of iron ore processing, are discarded annually. Given this scenario, this work aimed to propose a new use for IOT in partial replacement of natural aggregates for coating mortars, simultaneously varying the water/cement ratio and additive content of the composition. The proper specifications of flexural tensile strength (Rt), compressive strength (Rc) and tensile bond strength (Ra) were evaluated in comparison to the traditional system. The strategy of this work is to combine Operations Research techniques for multiple response optimization, evaluating the interference of each isolated factor and proposing the best configuration for mortar dosage. The Experimental Design, based on the Central Composite Design (CCD) considered 3 control variables: replacement content of iron ore tailings (IOT), additive content (ADT) and water/cement ratio (W/C). Principal Component Analysis (PCA) and Desirability function were also used in the search for the ideal answer. The mortar dosage optimization indicated that the controllable parameters should assume the following configuration: IOT = 27.63%, ADT = 1.08% and W/C = 0.80, with mortar resistance values in the hardened state equal to Rt=11.38 MPa, Rc = 21.67 MPa and Ra = 0.55 MPa. The values obtained after the optimization are in accordance with the norms that define the mechanical and rheological requirements for mortars and compatible with the resistance of standard coating mortars. Confirmation experiments carried out validated the applied modeling, showing that the real optimal results are close to the optimized results.
