(218g) Optimal Portfolio of Products in a Polycrystalline Silicon Refinery

Phenomena such as climate change, resources depletion, and waste pollution have prompted the industrial and academic sector to seek new and more sustainable approaches to energy supply. While there are numerous sustainable alternatives (biofuels, wind, geothermal, etc.), photovoltaic (PV) energy is considered one of the best renewable energy solutions [1] since it has a solid industrial development, being the first renewable based power technology to be commercialized at a large scale [2]. For years, the PV industry was highly dependent on the availability of polycrystalline silicon as scraps from the microelectronic industry. However, the increased demand for PV panels has driven the development of processes for refining polycrystalline silicon with solar grade purity [3]. The refining of polycrystalline silicon is rather energy intensive and generates large amount of secondary residual products [4]. Although the main product is polysilicon, there are a number of added value products that might be generated in the process to increase its profitability, such as tetraethoxysilane (TEOS), at different purities as well as chlorosilanes, including SiH₄, SiH₂Cl₂, and SiH₃Cl. In this context, the exploitation of the different by-products generated in the production of polycrystalline silicon (polysilicon) offers opportunities to increase the economic efficiency of the polycrystalline silicon production process

In this work, a silicon based refinery is conceptually designed using surrogate models for the major units to evaluate the portfolio of products. The modelling of the carboreduction reactor was developed by means of a experimental based subrogated model [5]. The second section is the reactor of the hydrogenation of silicon tetrachloride, that is modelled based on Gibbs free energy minimization [6]. The surrogate models for the purification of the chlorosilanes obtained from the previous reactor as well as the TEOS columns are developed from rigorous simulations using a stochastic optimization approach [4]. Finally, the conversion of trichlorosilane into polysilicon in a Siemens deposition reactor is modelled based on the work of Del Coso et al. [7]. The entire process is modeled in GAMS as an NLP model for the optimization of the operating conditions.

The optimal silicon refinery produces tetraethoxysilane and chlorosilanes in addition to the production of polysilicon. The proposed design reduces the cost for polycrystalline silicon to 6.86 $/kg, compared to a cost of 8.93 $/kg of polycrystalline silicon if the plant does not generate high value-added by-products, both below the commercial price, which is estimated at 10 $/kg. Therefore, the refinery is not only capable of meeting the market share requirements, but in a way the generation of different high added value by-products increases the plant profit compared to that of the net income earned by traditional polysilicon single-product plants.

References

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