Mechanistic Modeling of Growth and Polyhydroxybutyrate Production by Cupriavidus necator H16 in Chemolithoautotrophic Cultivation
- 주제(키워드) Cupriavidus necator H16 , Mathematical modeling , PHB production prediction , Non-linear square method , Simulation model
- 발행기관 서강대학교 일반대학원
- 지도교수 나정걸
- 발행년도 2026
- 학위수여년월 2026. 8
- 학위명 석사
- 학과 및 전공 일반대학원 화공생명공학과
- 세부분야 해당없음
- 실제URI http://www.dcollection.net/handler/sogang/000000083294
- UCI I804:11029-000000083294
- 본문언어 영어
- 저작권 논문은 저작권에 의해 보호받습니다.
초록(요약문)
Driven by the urgent global mandate to mitigate climate change and transition toward a sustainable, carbon-neutral society intensify, Carbon Capture and Utilization （CCU） technologies targeting the recovery and valorization of industrial greenhouse gases have garnered significant interest. Biological gas-to-product conversion represents a promising pathway; it operates under mild conditions, effectively preventing secondary carbon emissions, and exhibits high tolerance to varying gas compositions, which minimizes costly raw material pretreatment. Among prospective biocatalysts, the hydrogen-oxidizing bacterium Cupriavidus necator H16 offers a compelling solution to the dual challenges of greenhouse gas emissions and plastic accumulation, distinguished by its rapid growth rate relative to other autotrophs and its capacity to accumulate the biodegradable bioplastic, polyhydroxybutyrate （PHB）. To facilitate the commercialization of gasbased bioconversion, rigorous quantitative analysis and process optimization are required, demanding the development of mechanistic models capable of accurately depicting the physiological state of the microorganism. To address this requirement, this study established a comprehensive mathematical model that couples the growth and PHB accumulation kinetics of C. necator H16 with the gas-liquid mass transfer dynamics inside the bioreactor. In a multi-substrate system involving carbon dioxide, hydrogen, and oxygen, the consumption kinetics of each substrate were formulated using interactive Monod-type kinetics, while a dedicated kinetic expression governed by nitrogen limitation was integrated to describe PHB accumulation. By incorporating intracellular growth, PHB synthesis, and the liquid-phase transport and consumption of gaseous substrates into unified mass balance equations, the model achieves a high level of physical fidelity and practical applicability. To evaluate the predictive reliability of the model, simulations were validated against empirical gas fermentation datasets obtained under oxygen-transfer limiting conditions and during a 60-hour long-term cultivation. The model demonstrated exceptional predictive accuracy across all validation scenarios, yielding a coefficient of determination （R2 ） of 0.98 or higher for both active biomass and PHB accumulation trajectories, firmly establishing its robustness. The developed mechanistic model provides a rigorous framework for process design and optimization, particularly regarding gas composition and feed flow rates, thereby facilitating the industrial scale-up of biodegradable polymer production from biological greenhouse gas conversion using C. necator H16.
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Contents
Contents............................................................................................................................. 6
List of figures..................................................................................................................... 8
List of tables..................................................................................................................... 10
Abstract............................................................................................................................ 11
1. Introduction................................................................................................................. 13
2. Materials and Methods............................................................................................... 15
2.1 Media and Cultivation............................................................................................. 15
2.1.1 Precultrues........................................................................................................ 15
2.1.2 Gas Fermentation ............................................................................................. 16
2.2 PHB analysis ......................................................................................................... 18
2.3 Theory and Models ............................................................................................... 18
2.3.1 Mathematical Models ..................................................................................... 18
2.3.2 Optimization Algorithm................................................................................... 24
3. Results and Discussion................................................................................................ 25
3.1 Simulation Results .................................................................................................. 25
3.2 Simulation Model Validation.................................................................................. 28
3.2.1 Test for Modeling accuracy under Constant KLa............................................. 28
3.2.2 Test for Modeling accuracy by Long-Term Batch Fermentation .................... 31
4. Conclusion.................................................................................................................... 35
5. References.................................................................................................................... 36
