太陽能由於擁有穩定及低污染的主要優勢,因此發展太陽能科技有著長遠的效益。如何製造可靠度高並且具成本效益的太陽能系統便成為全球能源策略上的主要目標。在太陽能系統的製程中,其中一個顯著的因子即為矽原料的組合。本研究針對矽原料的組合對太陽能轉換率的影響作探討。

為達成本研究目標,本論文探討六個課題:(1)估計太陽能的轉換率。(2)將種類眾多的原料適當的分類。(3)整理可能影響太陽能轉換率的其他因子。(4)建立原料與其他相關因子對太陽能轉換率的迴歸模型。(5)為接近實際製程狀況,加入製程限制條件於迴歸模型中。
本研究主要的貢獻包括三個方面(a)可供工廠中實用的太陽能轉換率估計值。(b)建立迴歸模型(包含原料與長晶爐兩個顯著因子,可解釋太陽能轉換率變異的60%)(c)分析發現長晶爐與原料配方間有交互作用;也就是,不同的長晶爐有不同的最佳投料組合。未來研究仍應基於以上課題之邏輯做延伸,包括原料品質的明確定義及分類,建立更準確的迴歸模型,以達到太陽能轉換率最大化的原始目標。

Solar energy technologies have enormous long-term benefits due to their major advantages such as sustainability and low pollution. Figuring out how to produce highly reliable and cost-effective photovoltaic systems (PVS) has become a prime objective for energy policy worldwide. One significant factor in the PVS manufacturing process is the combination of various silicon raw materials. This research focuses on how different solar silicon-raw-materials combinations affect solar conversion efficiency.

To address our central question, in this thesis we address six issues: (1) estimating the solar conversion efficiency in the factory; (2) classifying a few types of combinations for silicon raw materials; (3) exploring factors other than silicon-raw-materials-combinations that might affect solar conversion efficiency; (4) constructing the regression model of the solar conversion efficiency as functions of silicon raw materials and the other significant factors; and (5) including necessary restrictions in the regression models to meet reality.

The primary contributions of this research encompasses three areas: (a) the creation of better estimators for solar conversion efficiency; (b) the construction of a regression model of solar conversion efficiency as functions of two significant factors (silicon raw materials and the crystal growth furnace--these two significant factors explain 60% of the variation in the proposed regression model); and (c) the investigation of interactions between crystal growth furnaces and the recipes of silicon raw materials (i.e., different crystal growth furnaces have different optimal combinations of silicon raw materials). Some avenues of future research based on this work should include determination of the well-defined quality of different silicon-raw-materials combinations and construction of a more effective regression model based on the well-defined quality of different silicon-raw-materials combinations.

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