Design and Implementation of an Intelligent Sphingan Bioreactor Architecture Based on Cyber-Physical Fusion

Guofan Jiang; Weina Zhao; Mingyv Wang

doi:10.54097/rax9t681

Authors

Guofan Jiang
Weina Zhao
Mingyv Wang

DOI:

https://doi.org/10.54097/rax9t681

Keywords:

Cyber-Physical Fusion, Sphingomonas, Sphingan, Intelligent Bioreactor, Microbial Electro-Fenton, Oily Wastewater Treatment

Abstract

Aiming at the problems of low degrading bacteria activity, difficult demulsification of emulsified oil, extensive process regulation, and weak working condition adaptability in traditional bioreactors for ship engine room oily wastewater, an intelligent bioreactor scheme integrating Cyber-Physical System (CPS), Sphingomonas-produced sphingan, and Microbial Electro-Fenton (MEF) coupled degradation is proposed. This reactor takes Sphingomonas as the core high-efficiency degrading strain, and uses sphingan biosurfactant produced by its metabolism to realize in-situ demulsification, solubilization and high-efficiency degradation of emulsified oil. A four-layer CPS closed-loop architecture of perception-transmission-decision-execution is adopted to regulate strain growth, gum production efficiency and MEF degradation working conditions in real time, coupling the first-stage Microbial Fuel Cell (MFC) biodegradation and the second-stage Electro-Fenton (EF) advanced oxidation process. At the hardware level, the integration of Sphingomonas culture module, MEF reaction unit and CPS perception control hardware is completed; at the software level, modules for strain activity monitoring, gum production optimization and intelligent regulation of degradation process are developed. Performance test results show that the sphingan production of Sphingomonas in the reactor is stable, the demulsification rate of emulsified oil reaches 91.8%, the total oil pollution degradation rate reaches 93.42%, and the COD removal rate reaches 90.8%. The reactor can adapt to water quality fluctuations and achieve stable discharge up to standard.

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References

[1] Dai H L, Gu L N, Zhao Z Q, et al. Research and application progress of ship oily wastewater treatment technology[J]. Journal of Jiangsu University of Science and Technology (Natural Science Edition), 2022, 36(03):99-108.

[2] He M F, Huang X F, Liu W Q, et al. Water quality and quantity characteristics of ship oily wastewater and its in-situ treatment technology[J]. Ship Engineering, 2021, 43(06):114-122+142. DOI:10.13788/j.cnki.cbgc.2021.06.21.

[3] Wang Z L. Study on electrokinetic coupled biotechnology for treatment of ship oily wastewater[D]. Shandong Jiaotong University, 2024. DOI:10.27864/d.cnki.gsjtd.2024.000172.

[4] Mkilima T, Zharkenov Y, Abduova A, et al. Hybrid microbial fuel cell–Photo-Fenton system for treatment and energy recovery from textile wastewater[J]. Journal of King Saud University – Engineering Sciences, 2025, 37(8):57. DOI:10. 1007/ S44444-025-00082-Y.

[5] Lv J, Zhao Q, Wei L, et al. A novel twin microbial fuel cell powered electro-Fenton system (twin-MFCⓅEFs) for waste activated sludge treatment: reduction, stabilization, and resource utilization[J]. Chemical Engineering Journal, 2025, 522:167427. DOI:10.1016/J.CEJ.2025.167427.

[6] Zhang J, Yao Y N, Wang Q, et al. Optimization of oil degradation conditions by Sphingomonas and Bacillus subtilis[J]. China Port Science and Technology, 2024, 6(03):91-96.

[7] Li B C. Biodegradation of sphingan WL and preparation of biopolymer surfactant[D]. China University of Petroleum (East China), 2022. DOI:10.27644/d.cnki.gsydu.2022.001963.

[8] Gao, Chang H. Potential of welan gum to enhance oil recovery[J]. Journal of PetroleumExploration & Production Technology, 2014, 5(2): 1-4.

[9] Wang Jiaqi, Chen Xiaoyi, Li Xianzhen. Structural Characteristics, Biosynthetic Strategies and Application Prospects of Sphingan Gum[J/OL]. Journal of Microbiology, 1-19[2025-10-05]. https://link.cnki.net/urlid/ 21.1186. Q. 20250915.1708.002.

[10] Hang S, Bo X, Mingchuan Z, et al. Theory, Applications, and Challenges of Cyber-Physical Systems 2021[J]. Complexity, 2022. DOI:10.1155/2022/9861298.

[11] Mkilima T. Intelligent hybrid electro-bioreactor networks for wastewater valorisation: AI-driven bioelectrochemical treatment, multi-scale resource recovery, and circular water-energy-nutrient systems[J]. Chemical Engineering Journal, 2026, 527:171716. DOI:10.1016/J.CEJ.2025.171716.

[12] Liu H, Yao Y, Yuan X, et al. Advances and challenges in synergistic fenton-microbial fuel cell systems for emerging contaminants removal: Mechanisms, configurations, and applications [J]. Bioresource Technology, 2025, 440:133499. DOI:10.1016/J.BIORTECH.2025.133499.

[13] Oliveira V W, Silva F C M, Araújo R B, et al. Assessment of homogeneous electro-Fenton process coupled with microbial fuel cell utilizing Serratia sp. AC-11 for glyphosate degradation in aqueous phase[J]. Journal of Environmental Management, 2024, 370:122797. DOI:10.1016/J.JENVMAN.2024.122797.

[14] Chen M ,Xu H ,Tian F , et al.The impact of R&D innovation strategy on the sustainable development of intelligent manufacturing: evidence from a quasi-natural experiment in China[J].Future Business Journal,2026,12(1):96-96.DOI: 10. 1186/S43093-026-00808-7.

[15] Müller A, Schmidt T. Towards Autonomous Biomanufacturing: Siemens' Digital Twin Approach for Process Optimization[J]. Biotechnology and Bioengineering, 2022.

[16] Lee J ,Kim H ,Lim R H , et al.Large-scale smart bioreactor with fully integrated wireless multivariate sensors and electronics for long-term in situ monitoring of stem cell culture.[J].Science advances,2024,10(7):eadk6714-eadk6714.DOI: 10.1126/ SCIADV. ADK6714.

[17] Pisani M ,Carbonell P. Challenges and Opportunities in Smart Biosensing for Biomanufacturing.[J].ACS synthetic biology, 2025, DOI:10.1021/ACSSYNBIO.5C00203.

[18] Petzold J C ,Mukhopadhyay A .From bench to biofactory: high-throughput technologies and automated workflows to accelerate biomanufacturing. [J]. Current opinion in biotechnology,2025,94103320.DOI:10.1016/J.COPBIO.2025.103320..

[19] Xia Jianye, Long Dongjiao, Chen Min, et al. Fermentation Process Optimization in Intelligent Biomanufacturing: Online Detection, Artificial Intelligence and Digital Twin Technology [J]. Chinese Journal of Biotechnology, 2025, 41(03): 1179-1196. DOI:10.13345/j.cjb.250032.

[20] Xu Tao. Research on Control and Optimization of Penicillin Fermentation Process Based on Reinforcement Learning[D]. Hebei University of Science and Technology, 2025. DOI:10.27107/d.cnki.ghbku.2025.000125.

[21] Cao Hanqi. Data-driven Multi-stage Modeling and Optimal Regulation of Biological Fermentation Process[D]. Jiangnan University, 2025. DOI: 10.27169/d.cnki.gwqgu.2025.002416.

[22] Dong B, Shahbaz A, Karavasilis L T. Integration of cyber-physical systems and digital twins in structural engineering: Applications, challenges and future advancements[J]. Journal of Building Engineering, 2026, 119:115259. DOI:10.1016/J. JOBE. 2026.115259.

[23] Gram-Negative Bacteria - Sphingomonas; Findings from University of Calgary Provides New Data on Sphingomonas (A Microbial Exopolysaccharide Produced by Sphingomonas Species for Enhanced Heavy Oil Recovery at High Temperature and High Salinity)[J]. Energy Weekly News, 2017, 244.

[24] Jieying L, Jinfeng D, Zhenyin H, et al. Physicochemical and Structural Characterization of Alkali-Treated Biopolymer Sphingan WL Gum from Marine Sphingomonas sp. WG[J]. ACS Omega, 2023, 8(7):7163-7171. DOI:10.1021/ ACSOMEGA. 3C00171.

[25] Lv J, Zhao Q, Jiang J, et al. Sludge dewaterability improvement with microbial fuel cell powered electro-Fenton system (MFCⓅEFs): Performance and mechanisms[J]. The Science of the Total Environment, 2024, 923:171422. DOI:10.1016/J. SCITOTENV.2024.171422.