• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.136-136
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• 2017

By-products has been considered lately in Total Mixed Ration (TMR) as an alternative to livestock feed around the world. This is due to the high cost of using forage as feed, less expense in exploring by-products of agriculture origin and environmental concerns with their disposal. However, by-products usually contain contaminants and the production process requires fermentation using a storage and fermentation tank. Animal feed is the start point of the food safety chain in the 'farm-to-fork' model. This necessitated a study to model a protocol that will culminate to safe feed production. Hazard analysis and critical control points (HACCP), a systematic preventive approach to food safety from biological, chemical and physical hazards in production processes that can cause the finished product to be unsafe was explored. Implementation of this model provides a mechanism that ensures product safety is continuously achieved. The entire production process of By-products feed production was evaluated using HACCP wizard software. This includes the plant layout, technical standards, storage and fermentation tank cleansing method, staff assignment, safety control method, and distribution. The potential biological, chemical, and physical hazards that may exist in every step of the production process were identified, and then critical control points (CCPs) were selected. This will ensure the safety of products made from livestock that consumes by-product feed. These includes cheese, milk, beef, etc.

• Journal of Microbiology and Biotechnology
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• v.21 no.6
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• pp.652-658
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• 2011

The growth kinetics of Streptomyces noursei NRRL 5126 was investigated under different aeration and agitation combinations in a 5.0 l stirred tank fermenter. Poly-${\varepsilon}$-lysine biosynthesis, cell mass formation, and glycerol utilization rates were affected markedly by both aeration and agitation. An agitation speed of 300 rpm and aeration rate at 2.0 vvm supported better yields of 1,622.81 mg/l with highest specific productivity of 15 mg/l.h. Fermentation kinetics performed under different aeration and agitation conditions showed poly- ${\varepsilon}$-lysine fermentation to be a growth-associated production. A constant DO at 40% in the growth phase and 20% in the production phase increased the poly-${\varepsilon}$-lysine yield as well as cell mass to their maximum values of 1,992.35 mg/l and 20.73 g/l, respectively. The oxygen transfer rate (OTR), oxygen utilization rate (OUR), and specific oxygen uptake rates ($qO_2$) in the fermentation broth increased in the growth phase and remained unchanged in the stationary phase.

• Environmental Engineering Research
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• v.19 no.4
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• pp.387-393
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• 2014

The purpose of this study is to evaluate integrated anaerobic hydrogen fermentation and membrane bioreactor (MBR) for on-site domestic wastewater treatment and resource recovery. A synthetic wastewater (COD 17,000 mg/L) was used as artificial brown water which will be discharged from urine diversion toilet and fed into a continuous stirred tank reactor (CSTR) type anaerobic reactor with inclined plate. The effluent of anaerobic reactor mixed with real household grey water (COD 700 mg/L) was further treated by MBR for reuse. An optimum condition maintained in anaerobic reactor was HRT of 8 hrs, pH 5.5, SRT of 5 days and temperature of $37^{\circ}C$. COD removal of 98% was achieved from the overall system. Total gas production rate and hydrogen content was 4.6 L/day and 52.4% respectively. COD mass balance described the COD distribution in the system via reactor byproducts and effluent COD concentration. The results of this study asserts that, anaerobic hydrogen fermentation combined with MBR is a potent system in stabilizing waste strength and clean hydrogen recovery which could be implemented for onsite domestic wastewater treatment and reuse.

• Journal of Biosystems Engineering
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• v.43 no.3
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• pp.229-236
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• 2018

Purpose: Makgeolli is a traditional alcoholic beverage made from rice with a fermentation starter called "nuruk." The concentration of alcohol in makgeolli depends on the temperature of the fermentation tank. It is important to monitor the alcohol concentration to manage the makgeolli production process. Methods: Data were collected from 84 makgeolli fermentation tanks over a year period. Independent variables included the temperatures of the tanks and the room where the tanks were located, as well as the quantity, acidity, and water concentration of the source. Software for the multilayer perceptron model (MLP) was written in Python using the Scikit-learn library. Results: Many models were created for which the optimization converged within 100 iterations, and their coefficients of determination $R^2$ were considerably high. The coefficient of determination $R^2$ of the best model with the training set and the test set were 0.94 and 0.93, respectively. The fact that the difference between them was very small indicated that the model was not overfitted. The maximum and minimum error was approximately 2% and the total MSE was 0.078%. Conclusions: The MLP model could help predict the alcohol concentration and to control the production process of makgeolli. In future research, the optimization of the production process will be studied based on the model.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.3
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• pp.244-250
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• 2002

This paper proposed a modified observer based on Busawon's high gain observer using an appropriate time depended function, which can be chosen to make each estimated state converge faster to its real value. The stability of the modified observer is proved by using Lyapunov function. The modified nonlinear observer is applied to estimate the states in stirred tank bioreactor: out-put substrate concentration, output biomass concentration and the specific growth rate of the process. The convergences of the modified observer and Busawon's observer are compared trough simulation results. As the results, the modified observer converges faster to its real value than the well-known Busawon's observer.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.74.3-74
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• 2001

This paper proposes a nonlinear adaptive controller based on back-stepping method for tracking reference substrate concentration by manipulating dilution rate in a continuous baker´s yeast cultivating process in stirred tank bioreactor. Control law is obtained from Lyapunov control function to ensure asymptotical stability of the system. The Haldane model for the specific growth rate depending on only substrate concentration is used in this paper. Due to the uncertainty of specific growth rate, it has been modified as a function including the unknown parameter with known bounded values. The substrate concentration in the bioreactor and feed line are measured. The deviation from the reference is observed when the external disturbance such as the change of the feed is introduced to the system ...

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.277-282
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• 2002

This paper proposes a nonlinear adaptive controller based on back-stepping method for tracking reference substrate concentration by manipulating dilution rate in a continuous baker's yeast cultivating process in stirred tank bioreactor. Control law is obtained from Lyapunov control function to ensure asymptotical stability of the system. The Haldane model for the specific growth rate depending on only substrate concentration is used in this paper. Due to the uncertainty of specific growth rate, it has been modified as a function including the unknown parameter with known bounded values. The substrate concentration in the bioreactor and feed line are measured. The deviation from the reference is observed when the external disturbance such as the change of the feed is introduced to the system. The effectiveness of the proposed controller is shown through simulation results in continuous system.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.2
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• pp.115-121
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• 2005

Using a general Saccharomyces cerevisiae as a model strain, continuous ethanol fermentation was carried out in a stirred tank bioreactor with a working volume of 1,500 mL. Three different gravity media containing glucose of 120, 200 and 280 g/L, respectively, supplemented with 5 g/L yeast extract and 3 g/L peptone, were fed into the fermentor at different dilution rates. Although complete steady states developed for low gravity medium containing 120 g/L glucose, quasi-steady states and oscillations of the fermented parameters, including residual glucose, ethanol and biomass were observed when high gravity medium containing 200 g/L glucose and very high gravity medium containing 280 g/L glucose were fed at the designated dilution rate of $0.027\;h^{-1}$. The observed quasi-steady states that incorporated these steady states, quasi-steady states and oscillations were proposed as these oscillations were of relatively short periods of time and their averages fluctuated up and down almost symmetrically. The continuous kinetic models that combined both the substrate and product inhibitions were developed and correlated for these observed quasi-steady states.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.125-128
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent recycled to the pigsty. This system significantly removes offensive smells (at both pigsty and treatment plant), BOD and other loads, and appears to be costeffective for the small-scale farms. The most dominant heterotrophs were Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp. in order 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 neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment such as population densities of heterotrophic and lactic acid bacteria, suspended solids (SS), COD, $NH_3-N$, ortho-P, and total-P) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.5
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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.