平板式集熱器之製程參數為影響其性能之關鍵，而設計及製造平板式集熱器時，影響平板式集熱器性能的關鍵製程參數為集熱管材質、吸熱板材質、集熱管數量、集熱管管徑、吸收膜使用類型及底部保溫材厚度等六種，品質特性為效率係數與熱散係數。因此本研究首先針對平板式集熱器不同之關鍵製程參數水準對品質特性之影響進行研究。本研究使用田口直交表規劃實驗，實驗完成後所得的各品質數據，透過田口品質工程的主效果分析與變異數分析理論得到對單一品質之最佳參數，並將實驗所得到的各品質數據，經由灰關聯生成進行數據前處理，再以灰關聯分析(Grey Relational Theory)配合熵測度(Entropy Measurement)找出最佳製程參數水準之組合，最後透過田口確認實驗與計算信賴區間，驗證實驗結果。
本研究並應用倒傳遞類神經網路結合Levenberg-Marquardt 演算法建構平板式集熱器製程參數之預測系統，將控制因子設為網路之輸入參數，而品質特性設為輸出參數，經過網路學習訓練，預測誤差率在5%以內，證明本研究所建構之預測系統具有極佳之預測能力。

Process parameters are critical to flat plate collector performance, and the key process parameters for designing and manufacturing a flat plate collector include collector materials, absorber materials, number of collectors, collector tube diameter, absorber film type, and understructure insulation material thickness. The quality characteristics are the efficiency coefficient and the heat loss coefficient. Therefore, this study examined the effect of various levels of key process parameters on flat plate collector quality. The Taguchi orthogonal array table was used to design the experiment. The main effect analysis and analysis of variance were conducted on quality data obtained from the experiment in order to determine the optimum parameters for single quality. Quality data from experiment were preprocessed by a grey relational generating operation, and the grey relational theory, coupled with entropy measurement, was employed to determine the optimum process parameter-level combination. Finally, Taguchi verification was carried out to verify experimental and computational confidence intervals and experimental results.
In addition, this study applied a back-propagation neural network and Levenberg-Marquardt algorithm to build the flat plate collector process parameter prediction system. It also set control factors as network input and quality characteristics as output, and conducted network learning training. The prediction error rate was within 5%, proving that the prediction system, as established in this study, has excellent prediction capability.

1. 王革華，能源與永續發展，新文京開發出版股份有限公司，台北(2008)
2. 經濟部能源局，中華民國九十八年能源統計手冊，台北(2010)
3.H. C. Hottel, and B. B. Woertz,“Performance of Flat-Plate Solar-Heat Collectors,＂Trans. ASME, Vol. 64, pp. 91-104 (1942)
4. H. P. Garg, and R. K. Agarwal,“Some Aspects of A Pv/t Collector/Forced Circulation Flat Plate Solar Water Heater with Solar Cells,＂Energy Conversion and Management, Vol.36, No. 2, pp. 87-99 (1995)
5. N. M. Nahar, and J. P. Gupta,“Studies on Gap Spacing between Absorber and Cover Glazing in Flat Plate Solar Collectors,＂International Journal of Energy Research, Vol. 13, No. 6, pp. 727-732 (1989)
6. H. M. Yeh, C. D. Ho, and C. W. Yeh,“Effect of Aspect Ratio on the Collector Efficiency of Sheet-and-Tube Solar Water Heaters with the Consideration of Hydraulic Dissipated Energy,＂Renewable Energy, Vol 28, No. 10, pp. 1575-1586 (2003)
7. W. Eisenmann, K. Vajen, and H. Ackermann,“On the Correlations between Collector Efficiency Factor and Material Content of Parallel Flow Flat-Plate Solar Collectors,＂Solar Energy, Vol 76, No. 4, pp. 381-387 (2004)
8. S. Farahat, F. Sarhaddi, and H. Ajam,“Exergetic Optimization of Flat Plate Solar Collectors,＂Renewable Energy, Vol 34, No 4, pp. 1169-1174 (2009)
9. S. W, Anderson, and K. Sedatole,“Designing Quality into Products: The Use of Accounting Data in New Product Development,＂Accounting Horizons, Vol. 12, No. 3, pp. 213-233 (1998)
10. D.M. Byrne, and S. Taguchi,“The Taguchi Approach to Parameter Design,”Quality Process, Vol. 20, No. 12, pp. 19-26 (1987)
11. W. L. Liu, S. H. Hsieh, W. J. Chen, P. I. Wei, and J. H. Lee,“Synthesis of the CuInSe2 Thin Film for Solar Cells Using the Electrodeposition Technique and Taguchi Method,”International Journal of Minerals, Metallurgy and Materials, Vol. 16, pp. 101-107 (2009)
12. S. I. Jun, T. E. McKnight, M. L. Simpson, and P. D. Rack,“A Statistical Parameter Study of Indium Tin Oxide Thin Films Deposited by Radio-Frequency Sputtering,”Thin Solid Films, Vol. 476, No. 1, pp. 59-64 (2005)
13. C. F. J. Kuo, H. M. Tu, S. W. Liang, and W. L. Tsai,“Optimization of Microcrystalline Silicon Thin Film Solar Cell Isolation Processing Parameters Using Ultraviolet Laser,”Optics & Laser Technology, Vol. 42, No. 6, pp. 945-955 (2010)
14. 鄧聚龍，灰色系統基本方法，華中理工大學出版社，武昌 (1987)
15. J. L. Deng,“Grey Informational Space,”The Journal of Grey System, Vol. 1, No. 2, pp. 103-117 (1989)
16. J. L. Deng,“Control Problems of Grey System,”Systems and Control Letters, Vol. 1, No 5, pp. 288-294 (1982)
17. J. L. Deng,“Introduction to Grey System Theory,”Journal of Grey System, Vol. 1, No. 1, pp. 1-24 (1989)
18. C. B. Chen, C. T. Lin, C. W. Chang, and C. P. Ho,“Grey Relation for Solving Multi-quality Characteristics Problems of Taguchi Methods,”Journal of Technology, Vol. 15, No. 1, pp. 25-33 (2000)
19. Y. S. Tarng, S. C. Juang, and C. H. Chang,“The Use of Grey-Based Taguchi Methods to Determine Submerged Arc Welding Process Parameters in Hardfacing,”Journal of Materials Processing Technology, Vol. 128, No. 1-3, pp. 1-6 (2002)
20. Y. M. Chiang, and H. H. Hsieh,“The Use of the Taguchi Method with Grey Relational Analysis to Optimize the Thin-Film Sputtering Process with Multiple Quality Characteristic in Color Filter Manufacturing,”Computers and Industrial Engineering, Vol. 56, No. 2, pp. 648-661 (2009)
21. S. J. Wu, S. W. Shiah, and W. L. Yu,“Parametric Analysis of Proton Exchange Membrane Fuel Cell Performance by Using the Taguchi Method and A Neural Network,”Renewable Energy, Vol. 34, No. 1, pp. 135-144 (2009)
22. M. Altan,“Reducing Shrinkage in Injection Moldings Via the Taguchi, ANOVA and Neural Network Methods,”Materials & Design, Vol. 31, No. 1, pp. 599-604 (2010)
23. M. S. Packiannather, P. R. Drake, and H. Rowlands,“Optimizing the Parameters of Multilayered Feedforward Neural Networks Through Taguchi Design of Experiments,”Quality and Reliability Engineering International, Vol. 16, No. 6, pp. 461-473 (2000)
24. M. D. Jean, C. D. Liu, and J. T. Wang,“Design and Development of Artificial Neural Networks for Depositing Powders in Coating Treatment,”Applied Surface Science, Vol. 245, No. 1-4, pp. 290-303 (2005)
25. A. Tortum, N. Yayla, C. Çelik, and M. Gökdağ,“The Investigation of Model Selection Criteria in Artificial Neural Networks by the Taguchi Method,”Physica A: Statistical Mechanics and its Applications, Vol. 386, No. 7, pp. 446-468 (2007)
26. S. Gunasekaran, B. Venkatesh, and B. S. D. Sagar,“Convergence Index for BPN Training,”Neurocomputing, Vol. 55, No. 3-4, pp. 711-719 (2003)
27. J. Zhao, F, and Wang,“Parameter Identification by Neural Network for Intelligent Deep Drawing of Axisymmetric Workpieces,”Journal of Materials Processing Technology, Vol. 166, No. 3, pp. 387-391, (2005)
28. V. Singh, I. Gupta, and H. O. Gupta,“ANN-Based Estimator for Distillation Using Levenberg-Marquardt Approach,”Engineering Applications of Artificial Intelligence, Vol. 20, No. 2, pp. 249-259 (2007)
29. 張國光、張育誠，「太陽能熱水器優缺點剖析」，能源報導，第33-36頁，2010 年3 月。
30. 張文師，太陽熱能利用與製冷，五南圖書出版股份有限公司，台北，第108~124頁 (2008)。
31. http://itseed.trps.cyc.edu.tw/resource/file/242.pdf
32. ISO 9806-1, Test Methods for Solar Collectors - Part 1: Thermal Performance of Glazed Liquid Heating Collectors Including Pressure Drop, (1994)
33. Sung H. Park, Jiju Antony, Robust Design for Quality Engineering and Six Sigma, World Scientific, Singapore (2008)
34. Phillip J. Ross, Taguchi Techniques for Quality Engineering, McGraw-Hill, New York (1996)
35. 蘇朝墩，產品穩健設計，中華民國品質學會，台北 (2002)。
36. 李輝煌，田口方法－品質設計的原理與實務，高立圖書有限公司，台北 (2000)。
37. 溫坤禮、趙忠賢、張宏志、陳曉瑩、溫惠筑，灰色理論，五南圖書出版公司，台北 (2009)。
38. 鄧聚龍，灰色系統理論與應用，高立圖書有限公司，台北 (2003)。
39. 葉怡成，類神經網路模式應用與實作，儒林圖書，台北 (2003)。
40. 王進德、蕭大全，類神經網路與模糊控制理論入門，全華科技圖書股份有限公司，台北 (1994)。
41. 羅華強，類神經網路-MATLAB 的應用，清蔚科技，台北 (2001)。