本論文發展一理論模型，用於整合太陽輻射熱和合成氣燃燒的雙熱能混合發電系統。本研究包含詳細的子系統模型，包括雙反射式聚光型太陽追蹤系統、鼓泡式流體化床氣化爐、雙熱源混合能源吸收器、史特靈引擎和發電機。應用反應曲面法來建立史特靈引擎的加熱頭壁面溫度、整體系統效率和電功率輸出的預測模型。通過中心合成設計（CCD）實驗設計和反應曲面法，檢查操作參數（如太陽輻照度、循環水和合成氣的質量流率、史特靈引擎充填壓力和發電機的負載扭矩）對系統的影響。本論文亦探討使用NSGA-II和VSCGM方法進行多目標函數的最佳化，並比較兩者間的最佳化結果和該方法之計算效率。結果顯示，較高的太陽輻照度和合成氣質量流率往往會增加史特靈引擎的加熱頭壁面溫度和電功率輸出，表明這兩個操作參數可以達成相互協調，以保持恆定的功率輸出的目標。水的質量流率可調整史特靈引擎的加熱頭壁面溫度，並最佳化整體系統效率。調整史特靈引擎之充填壓力和發電機的負載扭矩，可以調整整體系統效率和電功率輸出。NSGA-II和VSCGM的最佳解分別為1714.76 W（效率63.7%）和1717.06 W（效率63.66%）。VSCGM在計算速度上顯示了顯著優勢，表明其在實時優化應用中的潛力。

This dissertation presents a theoretical model of a hybrid power system that integrates solar radiation and syngas combustion. The study incorporates detailed models of subsystems, including a dual-reflection solar dish, a fluidized bed gasifier, a hybrid energy receiver, a Stirling engine, and a generator. Response Surface Methodology (RSM) is applied to build prediction models of the heater wall temperature of the Stirling engine, overall system efficiency and electrical power generation. Using Central Composite Design (CCD) experimental design and RSM, the interplay of operating variables such as solar irradiance, mass flow rates of water and syngas, charged pressure, and load torque is examined for their impact on system performance. The dissertation further explores multi-objective optimization using NSGA-II and VSCGM methods, comparing their optimal results and computational efficiencies. The results show that higher solar irradiance and mass flow rate of syngas tend to increase both the heater wall temperature of the Stirling engine and electrical power, indicating that these two variables could coordinate with each other to maintain a constant power output. The mass flow rate of water offers the adjustment of the heater wall temperature of the Stirling engine and the optimization of the overall system efficiency. Adjusting the charged pressure and load torque provide a mechanism for finely tuning the overall system efficiency and electrical power. The optimal solutions for NSGA-II and VSCGM were 1714.76 W at 63.7% efficiency and 1717.06 W at 63.66% efficiency, respectively. VSCGM demonstrated a significant advantage in computational speed, suggesting its potential for real-time optimization applications.

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