Genetic Algorithm Approach For Optimisation Of Silica Extraction For Micro-Crystalline Silicon Production


  • Department: Industrial And Production Engineering
  • Project ID: IPE0005
  • Access Fee: ₦5,000
  • Pages: 210 Pages
  • Reference: YES
  • Format: Microsoft Word
  • Views: 273
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Solar collectors mainly produced from poly-crystalline (P-Si) and micro-crystalline silicon (µ-Si) are in high demand in Nigeria due to persistent power challenges. The silicon are mostly imported and µ-Si is preferable due to its low cost. The µ-Si is extracted from Rice Husk (RH). However, the procedure of setting its optimal process variables (temperature, time and solvent volume) which are determinants of the reduction in cost is under reported in the literature. The aim of this study was to develop an approach for the optimisation of the extraction of µ-Si from rice husk using Genetic Algorithm (GA).

Ofada (F36) RH was collected from Erin-Oke town of Osun State in Nigeria. The RH was analysed for Volatile Matter (VM), Fixed Carbon (FC) and Ash constituents using standard method. The RH (1Kg) was run through two processes, one was treated with water to obtain Prewashed Rice Husk (PRH), while the other with 1 mole of oxalic acid in the range of 1050 mL to obtain Leached Rice Husk (LRH). Central Composite Design (CCD) was used to determine the experimental combinations of the predictor variables: temperature (400800ºC), time (2-6 h) and solvents (water and oxalic acid).Ten grams each of PRH and LRH were used to determine their silica yields (Y1 and Y2). Response equations were used as a fitness function for the GA optimisation. Optimal process variables levels predicted by the GA were validated by confirmatory experiments. The silica and silicon were characterised using X-Ray Diffraction and Scanning Electron Microscope. Optimal silicon was investigated using Raman spectroscopy. Data were analysed using t-test at 0.05 .

The VM, FC and ash content were 70.7, 11.3 and 18.0%, respectively, while the Y1 and Y2 for the 75 experimental combinations from CCD were 86.9-92.3% and 93.2-98.0%, respectively. Second order response surface equations gave the best fit for Y1 and Y2. The optimal values of the predictor variables from the GA, namely: temperature, time and solvent volume were 648.8ºC, 6 h, 40.0 mL and 657.9ºC, 3.6 h, 40.0 mL for PRH and LRH, respectively. There were no significant differences between the values obtained from GA and those of the validation. The characterisation for both PRH and LRH showed amorphous silica, which implies that it is not hazardous to human beings. However, silica from LRH revealed porosity of large grain size due to acid leach, which makes it better for solar collectors’ production than silica from PRH. The morphology of silicon produced from PRH and LRH revealed P-Si and µ-Si, respectively. Silicon yield of 2.3 g was observed in PRH and 4.2 g was observed in LRH respectively. Peak of 520.0 cm-1 was observed for silicon from LRH which was not significantly different from the peak for Si (521 cm-1).

Leached Rice Husk was best for the production of Micro-crystalline silicon, while Prewashed Rice Husk was best for Poly-crystalline silicon through Genetic Algorithm. This is an effective tool for the determination of optimal predictor variables levels for silica production. 

  • Department: Industrial And Production Engineering
  • Project ID: IPE0005
  • Access Fee: ₦5,000
  • Pages: 210 Pages
  • Reference: YES
  • Format: Microsoft Word
  • Views: 273
Get this Project Materials
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