We propose to utilize artificial neural network (ANN) to optimize positions of a limited number of sensors for accurate monitoring, and demonstrate its effectiveness by a case study of four thermocouples in a directional solidification furnace. Our concept consists of choosing the positions with ANN that has the lowest loss from a multiplicity of ANNs, which were trained by the simulated temperature distributions along the outer crucible wall. Interestingly, the top ten ranks of accurate predictions contain positions around the crucible’s bottom to suggest the importance of measuring temperatures carefully around high-temperature gradients that is the boundary between different materials.
The direct carbothermic reduction process from high-purity silica is promising for next-generation low-cost silicon solar cells. In this process, the granulation process is essential to avoid blowout of the silica powder. In this study, we investigated the effect of binders on this reduction process using four kinds of binders. The real-time monitoring of the chamber pressure and quadrupole mass spectroscopic analysis indicated the sign of the blowout phenomena of the generated CO gas and decomposition gas of the binders. In the case of starch and sucrose, the strengths of granules were not enough to the process with the pressure of the generated CO gas, while the granules with enough strength, namely, the ones with polyvinyl alcohol (PVA) and carboxymethyl cellulose (CMC), resulted in silicon yield of 33.8% and 27.8%, respectively.
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