Predictive modeling and optimization-based control of particulate polysilicon reactor systems for enhanced solar cell production

Abstract

Solar-grade silicon production has a pivotal role in the photovoltaic industry, especially in the manufacturing of solar panels. It represents approximately 20% of the total solar cell manufacturing cost. Consequently, reducing the production cost of solar-grade silicon is a primary factor in enhancing the solar manufacturing process. In particular, fluidized-bed reactors (FBR) for silane pyrolysis appear as a promising technology for solar-grade silicon production, representing a more energy-efficient process with more operational benefits than conventional technologies. However, controlling the FBR system is a challenging task due to the complex gas-solid interactions. Limited research has been conducted on developing control strategies for enhancing silicon production in FBR systems. This work develops a predictive modeling framework for silicon production in FBRs that can be used for real-time optimization and control purposes. The proposed model characterizes the particle size distribution of the product and the powder loss. Two different flow regime modeling approaches are considered to describe the silane pyrolysis reaction and characterize the deposition rate that contributes to particle growth. A discrete population balance equation is used to estimate the particle size distribution as a function of the deposition rate. The proposed model is compared against comprehensive models reported in the literature, showing satisfactory results. A nonlinear model predictive control is then utilized to regulate the system at the desired operating conditions. Detailed open-loop and closed-loop simulation studies demonstrate the successful integration of nonlinear MPC and the proposed predictive modeling approach.