Intelligent And Metabolic Engineering Based On-line Adaptive Control And Fault Diagnosis Techniques For Typical Aerobic Fermentation Processes

Most of the bulk fermentation products, such as amino acids, enzymes, drug proteins, etc.are produced through aerobic fermentations. Besides strains breeding or screening, processcontrol technique is an alternative way for fermentation performance improvement. Aerobicfermentations suffer with many common problems, for example, off-line control has limitedpower in performance improvement, and implementing of on-line process control is difficultas many crucial fermentation parameters can not be on-line measured; the occurrence offermentation faults severely deteriorate fermentation stability and economics; extremely highoxygen supply load causes difficulty in dissolved oxygen concentration (DO) control and pureoxygen based aeration which largely increases operation cost and safety risk. Focusing onthose problems, in this thesis, drug proteins and glutamate fermentations by two typicalaerobic strains, recombinant Pichia patoris and Corynebacterium glutamicum, were used asthe prototypes, and the key techniques of intelligent and metabolic engineering based on-linestate prediction, adaptive control and fault diagnosis were investigated. The purpose ofdeveloping such key techniques is to supply a technological platform to the typical aerobicfermentation processes, to effectively improve targeted product concentrations, fermentationstabilities and performance. The major results of the dissertation were summarized as follows:(1) In porcine interferon-? (pIFN-?) production by Pichia pastoris, fermentationperformance is hardly to predict, as pIFN-? antiviral activity (pIFN-?-AVA) was difficult tomeasure. Prediction performance of various models, including polynomial regression basedmodel and ANN models (BP-ANN and “improved” ANN optimized by genetic algorithm),were compared, with the easily measurable parameters (induction time/temperature, DO, O2uptake rate OUR, CO2evolution rate CER, methanol consumption rate, and total proteinconcentration) as the inputs and pIFN-?-AVA as the output. Among the models, the improvedANN model indicated the most accurate and universal abilities. Sensitivity analysis suggestedthat CER, OUR and methanol consumption rate were closely correlated with pIFN-?-AVA.(2) pIFN-? production by MutSP. pastoris is unstable. Ethanol accumulation occurred iftraditional DO-Stat method was adopted for glycerol feeding during cultivation phase.Methanol induction could not be initiated if the cells were subject to high ethanolconcentration environment (>6g·L-1) for longer time (>4h). Analyzing the transcriptionallevels and activities of the key enzymes in methanol metabolism routes revealed that, thelong-term and high level accumulation of ethanol during cultivation phase irreversiblyrepressed the activity and transcriptional level of alcohol oxidase (AOX), which deterioratedpIFN-? fermentation stability in turn. Using a commercialized methanol electrode to on-linedetect ethanol, an on-line ethanol measurement based adaptive DO-Stat glycerol feedingstrategy was thus proposed. The strategy could alternatively utilize glycerol and theaccumulated ethanol to control ethanol concentrations at low level (2g L-1) without cellgrowth rate deterioration. The maximal pIFN-? concentration in runs without ethanolconcentration control was2.08g L-1, but unstable. pIFN-? concentrations in runs with low ethanol accumulation could be stabilized at higher levels of2.70~3.65g·L-1.Fermentationperformance could be successfully stabilized and enhanced.(3) Porcine circovirus Cap protein production by Mut+P. pastoris with strong AOXpromoter suffered with the problems: inefficient methanol utilization, extensively highoxygen supply requirement, difficulty in stably controlling DO, very low Cap protein titer.Based on DO response patterns against the addition of methanol or sorbitol with equal amount,a DO on-line measurement and fuzzy logical inference based automatic methanol/sorbitolco-feeding control system was proposed. With the aid of this control system by setting DOcontrol level at10%, key enzymes in methanol metabolism route were largely activated;methanol feeding rate was restricted at a moderate level; oxygen supply load was relieved;and DO was stably controlled at the desired level. Under this condition, methanol utilizationefficiency was greatly improved and accumulation of toxic intermediate metabolites wasrelieved. Cap protein concentration reached a level of198mg·L-1, which was much higherthan the maximal value of121mg·L-1in the runs by methanol induction. Induction could beconducted by air-aeration and overall fermentation performance enhanced significantly.(4) In glutamate fermentation by biotin-auxotroph C. glutamicum, the biotin contentvariation in culture medium severely affects fermentation stability. An intelligent patternrecognition based fault diagnosis system was thus proposed to solve the problem. A supportvector machine classifier (SVM) was firstly used to categorize the on-line measurableparameters (fermentation time, agitation rate, NH3consumption rate, OUR and CER) within amoving-window, and then the SVM was combined with fuzzy reasoning technique toconstruct an unique intelligent fault diagnosis system, which could classify the fermentationphysiological states into3catagories of biotin “in shortage”,“medium”, and “in excess”. Byexperimentally varying the initial biotin content to intently creat the three differentfermentation states and obtaining a large amount corresponding data, an artificial neuralnetwork (ANN) model was developed with initial biotin content and fermentation time as theinputs, the other on-line measurable parameters as the outputs. This ANN model couldautomatically generate a large amount of data pairs to simulate the effectiveness of the faultdiagnosis system. Simulation results indicated that the fault diagnosis system could clusterfermentation runs corresponding to different initial biotin content well and had goodfermentation states recognition ability.(5) The fault diagnosis system was applied to glutamate fermentation process for on-linefault diagnosis. In the cases of initial biotin content “medium”, the system did not send anywarning signals throughout the fermentations; while in the cases of improper initial biotincontents, the system could accurately identify the faults and their type in the earlyfermentation stage (6~8h), and rescue those failure-likelihood fermentations by adding purebiotin or tween40. After the rescue measures were taken, outputs of the system weregradually recovered to the normal range, final glutamate concentrations in all testing runsreached to normal levels of75~80g·L-1, and fermentation stability significantly enhanced.