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Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance

機譯：酶回收廠在線智能控制器：設計方法和性能

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This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity.
機譯：本文著重于開發(fā)用于從菠蘿皮中回收酶的智能控制器。蛋白水解菠蘿蛋白酶（EC 3.4.22.4）在低溫下用酒精在分批進料的夾套罐中沉淀。溫度控制對于避免不可逆的蛋白質(zhì)變性至關重要。模糊或神經(jīng)控制器提供了一種實現(xiàn)涵蓋動態(tài)和非線性過程的解決方案的方式。提出了模糊PI，神經(jīng)模糊和神經(jīng)網(wǎng)絡智能控制器性能的設計方法和比較研究。為了調(diào)整模糊PI Mamdani控制器，測試了各種語篇，規(guī)則庫和隸屬函數(shù)支持集。還開發(fā)和測試了基于Takagi-Sugeno規(guī)則的神經(jīng)模糊推理系統(tǒng)（ANFIS）和基于神經(jīng)建模的模型預測控制器。使用現(xiàn)場總線網(wǎng)絡架構，冷卻液變速泵由控制器驅(qū)動。實驗結果表明，與神經(jīng)預測控制相比，模糊控制器的有效性。模糊PI控制器顯示出降低的誤差參數(shù)（ITAE），更低的功耗和更好的酶活性恢復能力。

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