• Biotechnology and Bioprocess Engineering:BBE
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• 제11권5호
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• pp.432-441
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• 2006

2D fluorescence sensors produce a great deal of spectral data during fermentation processes, which can be analyzed using a variety of statistical techniques. Principal component analysis (PCA) and a self-organizing map (SOM) were used to analyze these 2D fluorescence spectra and to extract useful information from them. PCA resulted in scores and loadings that were visualized in the score-loading plots and used to monitor various fermentation processes with recombinant Escherichia coli and Saccharomyces cerevisiae. The SOM was found to be a useful and interpretative method of classifying the entire gamut of 2D fluorescence spectra and of selecting some significant combinations of excitation and emission wavelengths. The results, including the normalized weights and variances, indicated that the SOM network is capable of being used to interpret the fermentation processes monitored by a 2D fluorescence sensor.

• KSBB Journal
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• 제13권3호
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• pp.244-250
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• 1998

글루코스와 초산을 온라인 모니터링 하기위한 흐름주입분석기 술이 개발되었고 대장균발효공정에 이용되었다. 또한, Epoxy 고 분자 담체에 고정화된 GOD와 SOD 를 이용한 GOD-FIA와 SOD-FlA의 특성을 연구했다. 즉, FIA 의 조작온도, 운반용액 속의 첨 가제 (Triton, EDTA, natrium azid 등), 분석 시 료에 용 해된 신진대사물의 농도, pH, 초산측정시 사코진 농도 등에 따 라 고정화된 GOD와 SOD의 활성도, 즉 검출신호의 높이변화를 고찰했다. 최소배양액 과 복합배양액을 사용한 대장균 발효공정 에서 달루코스와 초산을 동시에 온라인 모니터링 하였으며 그결 과는 오프라인 분석결과와 잘 일치 하였다,

• Biotechnology and Bioprocess Engineering:BBE
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• 제2권2호
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• pp.69-72
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• 1997

The pH of microbial culture medium was calculated from equations of equilibrium, meterial balances for ionic components and electro-neutrality theory. Ammonium ion consumption and Acetic acid production are found out to be the major contributors for the alteration of the pH as well as the buffer capacity of the medium. By measuring the buffer capacity on-line, levels of acetic acid were estimated by a software sensor using pH signal in a fermentation process of E.coli growing in a minimal medium. The measured values of acetic acid showed good correlation to those of estimated by the software sensor.

• Biotechnology and Bioprocess Engineering:BBE
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• 제11권4호
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• pp.351-356
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• 2006

This study developed an artificial neural network (ANN) to estimate the growth of microorganisms during a fermentation process. The ANN relies solely on the cumulative consumption of alkali and the buffer capacity, which were measured on-line from the on/off control signal and pH values through automatic pH control. The two input variables were monitored on-line from a series of different batch cultivations and used to train the ANN to estimate biomass. The ANN was refined by optimizing the network structure and by adopting various algorithms for its training. The software estimator successfully generated growth profiles that showed good agreement with the measured biomass of separate batch cultures carried out between at 25 and $35^{\circ}C$.

• KSBB Journal
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• 제10권2호
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• pp.126-130
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• 1995

초산발효 중 에탄올 농도를 On-line으로 측정하기 위해 막가스 센서를 개발하였다. 에탄올이 함유된 발효액은 실리콘막을 통해 투과되고 Carrier gas로 사용된 Synthetic air에 의해 운반되어 반도체형 가 스센서에 감지되도록 설계하였다. 이 때 실리콘막의 두께가 O.5mm이고 Carrier gas의 유속이 20ml/mim이었을 때 막가스 센서의 감도가 가장 높았다. 막가스센셔의 저항치는 측정하고자 하는 에탄올 농 도에 따라 변하였고 이 저항치는 전위차로 변환되어 출력되었다. 제작된 막가스센서의 Calibration CUf ve를 작성하였고 실제로 조업 중인 초산 발효조의 발효액 중 에단올 농도의 On-line 측정이 가능하였 으며 이를 Gas chromatography에 의한 분석치와 비교한 결과 에단올 농도가 $0∼70g/\ell$의 범위에서 서로 상관관계를 나타내어 이러한 막가스센서가 초산발효와 같은 여러 생물공정의 모니터링과 제어에 이용이 가능함을 확인하였다.

• KSBB Journal
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• 제20권4호
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• pp.291-298
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• 2005

본 연구는 재조합 대장균과 S.cerevisiae의 발효공정에서 형광스펙트럼 데이터를 수집하였으며, SOM을 이용하여 형광스펙트럼 데이터를 특정 그룹으로 분류하고 발효공정을 분석하고자 하였다. 배출가스 내 이산화탄소농도와 세포농도 같은 공정변수들은 SOM 알고리즘으로부터 얻은 분산 및 정규화된 가중치들과 좋은 연관성을 나타내었다. 전체 스펙트럼 데이터의 분류는 생물공정 모델링을 위한 매우 중요한 단계인데 그 이유는 몇몇 여기파장과 방출파장의 유의한 조합들이 전체영역의 스펙트럼 데이터로부터 추출되기 때문이다. 예를 들면, 본 연구에서 SOM을 이용하여 추출한 98개의 스펙트럼 데이터의 예제들은 부분최소자승법이나 감독신경망 (supervised neural network)을 이용한 공정의 모델링에 사용될 수 있다.

• Biotechnology and Bioprocess Engineering:BBE
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• 제5권5호
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• pp.355-361
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others, and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order, Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis(PCA) was first applied to identify a relationship between input and output. The input would be microbial densities and the treatment parameters, such as population densities of heterotrophic and lactic acid bacteria, suspended solids(SS), COD, NH$_4$(sup)+-N, ortho-phosphorus (o-P), and total-phosphorus (T-P). then multi-layer neural networks were employed to model the treatment process for each tank. PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of imput. Neural network independently trained for each treatment tank and their subsequent combined data analysis allowed a successful prediction of the treatment system for at least two days.