本文選題：釀酒酵母　切入點(diǎn)：畢赤酵母　出處：《江南大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：酵母分批補(bǔ)料培養(yǎng)中,碳源添加過量會(huì)導(dǎo)致副產(chǎn)物乙醇的大量積累,破壞細(xì)胞結(jié)構(gòu)及功能,降低碳源利用效率;碳源添加不足會(huì)限制細(xì)胞生長(zhǎng)。為解決這一矛盾,提出了一種基于差分進(jìn)化算法(Differential evolution algorithm,DE)的在線自適應(yīng)底物流加策略(DE-PID)。該策略以傳統(tǒng)比例-積分-微分(Proportional-Integral-Differential,PID)控制為基礎(chǔ),利用自回歸移動(dòng)平均模型(Autoregressive moving average model,ARMA)辨識(shí)酵母培養(yǎng)過程的動(dòng)力學(xué)特性,再根據(jù)ARMA系統(tǒng)辨識(shí)模型預(yù)測(cè)被控變量,以被控變量的預(yù)測(cè)值與設(shè)定值之間的誤差為目標(biāo)函數(shù)。利用DE算法求解目標(biāo)函數(shù)達(dá)到最小值時(shí)的PID控制參數(shù),實(shí)現(xiàn)碳源流加的在線自適應(yīng)控制。主要研究?jī)?nèi)容與結(jié)論總結(jié)如下:(1)以乙醇濃度為被控變量,利用已有的釀酒酵母培養(yǎng)模型,對(duì)傳統(tǒng)PID控制與DE-PID控制策略的性能進(jìn)行計(jì)算機(jī)仿真比較研究。結(jié)果表明,使用DE-PID策略時(shí),發(fā)酵液中的乙醇濃度能夠被穩(wěn)定地控制在1 g·L-1的低水平,細(xì)胞濃度達(dá)到34.45 g·L-1,比采用傳統(tǒng)PID策略的批次提高29%。(2)在實(shí)際的釀酒酵母分批補(bǔ)料培養(yǎng)中,利用DE-PID控制策略分別對(duì)乙醇濃度和呼吸商(RQ)實(shí)施定值控制。培養(yǎng)30 h,乙醇濃度均可以始終控制于低水平(0.02~2.35g·L-1)。乙醇濃度定值控制條件下的細(xì)胞密度僅為23.25 g-DCW·L-1;RQ定值控制條件下的細(xì)胞密度則達(dá)到47.75 g-DCW·L-1,比采用DO-Stat流加策略的對(duì)照批次提高了85.44%。(3)在重組畢赤酵母高密度培養(yǎng)過程中,利用DE-PID策略分別對(duì)乙醇濃度和溶解氧濃度(DO)實(shí)施定值控制,培養(yǎng)34 h后細(xì)胞密度分別達(dá)到112.25 g-DCW·L-1和113.25g-DCW·L-1,乙醇濃度也始終可以被限制在低水平(0.09~1.75 g·L-1)。與前期構(gòu)建的改良型DO-Stat甘油流加策略相比,DE-PID策略同樣能夠在抑制乙醇積累的同時(shí)獲得更高密度的細(xì)胞,DO控制水平穩(wěn)定,過程控制和細(xì)胞培養(yǎng)性能明顯改善。
[Abstract]:In yeast batch feeding culture, excessive addition of carbon sources will lead to a large amount of byproduct ethanol accumulation, destroy cell structure and function, reduce carbon source utilization efficiency, and carbon source deficiency will limit cell growth. An online adaptive bottom logistics strategy based on differential evolution algorithm (DE-PIDD) is proposed, which is based on the traditional proportional-integral-differential-integral-integral-integral-differential differential algorithm (PIDs) control. The autoregressive moving average model ARMA was used to identify the dynamic characteristics of yeast culture process, and then the controlled variables were predicted according to the ARMA system identification model. Taking the error between the predicted value and the set value of the controlled variable as the objective function, the DE algorithm is used to solve the PID control parameters when the objective function reaches the minimum value. The main research contents and conclusions are summarized as follows: (1) ethanol concentration is taken as the controlled variable, and the existing Saccharomyces cerevisiae culture model is used. The performance of traditional PID control strategy and DE-PID control strategy is compared by computer simulation. The results show that the ethanol concentration in fermentation broth can be stabilized at a low level of 1 g 路L ~ (-1) when using DE-PID strategy. The cell concentration reached 34.45 g 路L ~ (-1), 29.3% higher than that of the traditional PID strategy) in the actual batch feeding culture of Saccharomyces cerevisiae. DE-PID control strategy was used to control ethanol concentration and respiratory quotient (RQ) respectively. After 30 hours of culture, ethanol concentration could always be controlled at a low level of 0.02 ~ 2.35 g 路L ~ (-1). The cell density was only 23.25 g-DCW 路L ~ (-1) RQ under the control of alcohol concentration. The cell density reached 47.75g-DCW 路L -1, which was 85.44.4% higher than that of the control batch using DO-Stat flow-adding strategy in the process of high density culture of recombinant Pichia pastoris. The DE-PID strategy was used to control the concentration of ethanol and dissolved oxygen, respectively. The cell density reached 112.25 g-DCW 路L -1 and 113.25 g-DCW 路L -1 after 34 h culture, and ethanol concentration could always be limited to 0.09 ~ 1.75 g 路L ~ (-1) 路L ~ (-1). Compared with the modified DO-Stat glycerol infusion strategy, DE-PID strategy could also inhibit ethanol accumulation at the same time. A higher density of cells with stable control levels of do was obtained. Process control and cell culture performance were significantly improved.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TQ920.1;TQ223.122
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本文編號(hào)：1573316