混合時間是評定塔式生化反應器混合效果的一項重要指標。混合時間越短，反應器的混合效能越佳，系統越快達到混合均勻的平衡狀態。為有效掌握塔式反應器的混合過程，本研究提出一改善型巨觀混合模型(modified macromixing model)，分別從反應器內管的設計與選擇較佳進料位置的角度，來討論提昇塔式生化反應器混合效益的原理與方法。反應器內管的設計方面是以模型的最慢模態特徵值(slow mode eigenvalue)以及混合時間，作為檢視塔式生化反應器混合成效的標的。本研究成功地以模型的最慢模態特徵值與非對稱進料實驗，證明出網狀內管氣舉式反應器(airlift reactor with a net draft tube)在混合效益上優於氣舉式反應器(airlift reactor)與氣泡塔反應器(bubble column reactor)的理由。最慢模態特徵值的分析結果說明：在相同的單位體積流率下，相較於其他兩款反應器，網狀內管氣舉式反應器有最小的最慢模態特徵值。對於提升混合效益的實驗結果具體說明：網狀內管反應器較其他兩款塔式生化反應器有優越的軸向、徑向流量分配比。於選擇較佳進料位置的研究上：本研究提出一個最適化初始進料演算法與進料操作模式，以期在要求之時限內，達到系統內各處混合均勻的結果。此研究是根據改善型巨觀混合模型的解析解，應用奇異值分解演算法，反推本模型的最適化初始進料條件。以給定的時限作為限制條件，估算出最少進料口數量，計算對應的進料位置與各進料位置的最佳進料量，於要求時限內達成混合的要求。研究結果可得知，一般所採行的塔頂進料方式，是較差的進料位置。也驗證出多口進料方式，確實有提升混合效益的成果；比較最差與最佳的例子，其所需的混合時間差異高達三倍。進一步利用塔式生化反應器來進行米麴黴菌(Aspergillus oryzae)發酵生產麴酸的應用。實驗證明，氣泡塔與攪拌槽反應器在相同的培養條件下，能產出幾乎等量的麴酸；在同樣的通氣成本下，比較氣泡塔與網狀內管反應器的產酸效能，因網狀內管氣舉式反應器有較氣泡塔為佳的氧氣質傳係數，所以有較高的溶氧量，同時反應在麴酸7天的產量約為27克/升，高於氣泡塔反應器的20克/升之具體成果上。

A modified network-of-zones model was developed to investigate the mixing performance of the three tower-type bioreactors; airlift, bubble column and airlift with a net draft tube. A key parameter b, that characteristic of the interaction intensity between the neighboring uprising and down-coming streams, which played a decisive role in determining the mixing performance of the three reactors was identified. The concentration dynamics and mixing behavior of the reactors were studied with a maximum non-zero eigenvalue analysis (the slow mode analysis). The model predictions were validated against results from mixing experiments using heat pulse. The model prediction and the experimental results were in good agreement. The model revealed a superior mixing performance for the airlift reactor with a net draft tube when compared to the airlift and bubble column reactors and can be linked to an optimum mass transfer between the neighboring uprising and down-coming streams, provided by the net draft tube.
This optimum mass transfer was a direct result of the balanced flow distribution in the axial and radial directions. In the tower type reactors, poor mixing is an important issue when applied to aerobic fermentation processes. As an attempt to enhance the mixing efficiency, an algorithm based on the analytic solution of the modified network-of-zones model and the application of singular value decomposition was proposed to determine the optimal feeding locations and the corresponding amount of feed in the three tower type bioreactors. Feeding locations and the corresponding feed amount predicted by the algorithm were in good agreement with the experimental results. The proposed efficient algorithm can be applied for optimizing the nutrient feeding to the three tower type reactors for improving the mixing efficiency that will result in higher productivities.
In another study, kojic acid was produced in the two tower-type reactors, bubble column reactor and airlift reactor with a net draft tube, by using a mutated strain of Aspergillus oryzae under aerobic conditions. The kojic acid production after 7 days and at an airflow rate of 6 LPM, in the airlift reactor with a net draft tube was higher (27g/L) than that in the bubble column reactor (20g/L). This was due to the higher oxygen transfer capacity of the airlift reactor with a net draft tube. Airlift reactor with a net draft tube can be applied for the large-scale production of kojic acid, yielding productivity comparable with stirred tank fermentation and at lower production costs.

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