Mechanical and thermal properties of CNT-reinforced quartzite nano-composite for furnace lining

• Main Author: Tijjani, Yusuf
• Format: Thesis
• Language: English
• Published: 2018
• Subjects: Carbon nanotubes; Nanotubes - Research; Nanostructured materials - Mechanical properties
• Online Access: http://psasir.upm.edu.my/id/eprint/77281/1/FK%202018%20179%20ir.pdf; http://psasir.upm.edu.my/id/eprint/77281/

Nigerian local foundries employ the used-imported bricks from iron and steel industries to line the foundry cupola furnace. On the event of the closure of these industries, the alternative left was to identify the locally available refractory material that would be used for most effective lining of the cupola. Thus, after several trials of different locally available clays, natural crystalline quartzite refractory ceramic material; crystalline silica (SiO2) was eventually resorted to. The main problem associated with ceramic lining material is lack of good thermal shock resistance. This is more pronounced with silica/silica-based refractory linings; for instance, lining material used in a typical local foundry furnace in Kano State of Nigeria and vicinity that is prepared from local quartzite/crystalline silica, which develops unduly cracks on its surface during cooling after melting. This has been attributed to poor thermal conductivity by the previous researchers. To substantially obviate this setback, the present research aimed to incorporate carbon nanotubes (CNTs) within the matrix of quartzite brick. This entails manufacturing and characterization of CNT reinforced quartzite nano-composite. To obtain a stable suspension of CNTs that would be used in the preparation of homogeneously dispersed nanotubes in the quartzite matrix, the as-received pristine CNTs were functionalized with 6M H2SO4 acid by ultrasonication using a water bath sonicator. The pristine/functionalized CNTs were then comprehensively characterized by Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, Thermogravimetric analysis (TGA), Differential scanning calorimetry (DSC), and Scanning electron microscopy coupled with Energy dispersive X-ray spectroscopy (SEM/EDX). Also, the as-mined quartzite and clay were purified by soaking/washing with water and subsequently dried. The as-dried raw material were then subjected to crushing, grinding, pulverizing and grading using Jaw and Cone crushers, ball mill, pulverizer, and size distribution particle analyzer respectively. This is followed by structural and morphological characterization of the powdered quartzite and clay by Energy dispersive X-ray fluorescence spectrometer (ED-XRF), X-ray diffraction (XRD), and SEM/EDX. The durable refractory material for lining foundry cupola is developed by modified conventional powder processing (wet mixing method); involving preparation, cold compaction (by manual hydraulic press) and pressureless sintering (using tube furnace) at 1450oC, for 2 hours dwelling time, under argon atmosphere. Physical, mechanical, morphological and thermal characterizations were determined for the sintered 0, 0.01, 1, and 4 wt. % CNT-quartzite nano-composite blends. The compareable bulk density range obtained for the blends; 1.722 - 1.760g/cm3 is an evidance of a complete densification and good homogeneous dispersion of CNTs in teh green matrices, thus, good thermo-mechanical properties of the nano-composites. High proportion of tridymite, low percenttage of residual quartz (1.1%), low reversible thermal expansion, modest tensile (2.49 MPa), and compressive strenghts (17.39MPa), moderate Young's modulus; tensile (190MPa), high fracture strain in tension (0.013027), high (on-duty)thermal shock cycles (7 cycles), high thermal diffusivity (>8 x 10-7 m2/s at 117'C) and remarkable thermal conductivity >0.2 k/Wm-1 at 117'C of 1wt. % CNT-quartzite nano-composite signifies the possibility of potential application of CNTs as suntering aid (stabilizer, mineralizer, etc.) and toughening filler in conventional quartzite/silica refractory mixture for high durability. Finally, the Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP ANN) models were developed to predict the physical, mechanicals and thermal properties of the CNT-quartzite nano-composites having formulations within the range of those employed in the experimental process through dataset training, validation and testing. Additionally, the Graphical User Interface (GUI) was created in the study in order to have a user friendly interface for easy characterization of the CNT-quartzite nano-composites.