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Kaur G, Nakamura K, Ogawa K, Wakui K. Monitoring of MBR fouling properties by filtration resistance and zeta potential measured for both filtration and backwashing directions. J Memb Sci 2023. [DOI: 10.1016/j.memsci.2023.121550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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2
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Xie D. Continuous biomanufacturing with microbes - upstream progresses and challenges. Curr Opin Biotechnol 2022; 78:102793. [PMID: 36088736 DOI: 10.1016/j.copbio.2022.102793] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/15/2022] [Accepted: 08/07/2022] [Indexed: 12/14/2022]
Abstract
Current biomanufacturing facilities are mainly built for batch or fed-batch operations, which are subject to low productivities and do not achieve the great bioconversion potential of the rewired cells generated via modern biotechnology. Continuous biomanufacturing should be the future directions for high-yield and low-cost manufacturing of various fermentation products. This review discusses the major challenges and the strategies for continuous biomanufacturing with microbes, which include minimizing contamination risk, enhancing genetic stability over a long-term continuous operation, achieving high product titer, rate, and yield simultaneously by decoupling cell growth from product formation, and using modeling approach to accelerate research and development of continuous biomanufacturing. New strain designs and process engineering strategies, including integration with artificial intelligence, are also discussed for intelligent and the next generation of continuous biomanufacturing.
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Affiliation(s)
- Dongming Xie
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA 01854, United States.
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Wang Y, Zhou Y, Qin Y, Wang L. Effect of environmental factors on the aflatoxin production by Aspergillus flavus during storage in upland rice seed using response surface methodology. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Dynamic Analysis and Control for a Bioreactor in Fractional Order. Symmetry (Basel) 2022. [DOI: 10.3390/sym14081609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
In this paper, a mathematical model was developed to describe the dynamic behavior of a bioreactor in which a fermentation process takes place. The analysis took into account the bioreactor temperature controlled by the refrigerant fluid flow through the reactor jacket. An optimal LQR control acting in the water flow through a jacket was used in order to maintain the reactor temperature during the process. For the control design, a reduced-order model of the system was considered. Given the heat transfer asymmetry observed in reactors, a model considering the fractional order heat exchange between the reactor and the jacket using the Riemann–Liouville differential operators was proposed. The numerical simulation demonstrated that the proposed control was efficient in maintaining the temperature at the desired levels and was robust for disturbances in the inlet temperature reactor. Additionally, the proposed control proved to be easy to apply in real life, bypassing the singularity problem and the difficulty of initial conditions for real applications that can be observed when considering Riemann–Liouville differential operators.
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Khoshhal Salestan S, Rahimpour A, Abedini R, Soleimanzade MA, Sadrzadeh M. A new approach toward modeling of mixed‐gas sorption in glassy polymers based on metaheuristic algorithms. JOURNAL OF POLYMER SCIENCE 2022. [DOI: 10.1002/pol.20210846] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
| | - Ahmad Rahimpour
- Department of Chemical Engineering Babol Noshirvani University of Technology Babol Iran
- Department of Mechanical Engineering, 10‐367 Donadeo Innovation Center for Engineering, Advanced Water Research Lab (AWRL) University of Alberta Edmonton Canada
| | - Reza Abedini
- Department of Chemical Engineering Babol Noshirvani University of Technology Babol Iran
| | - Mohammad Amin Soleimanzade
- Department of Mechanical Engineering, 10‐367 Donadeo Innovation Center for Engineering, Advanced Water Research Lab (AWRL) University of Alberta Edmonton Canada
| | - Mohtada Sadrzadeh
- Department of Mechanical Engineering, 10‐367 Donadeo Innovation Center for Engineering, Advanced Water Research Lab (AWRL) University of Alberta Edmonton Canada
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Behaviour of Aspergillus parasiticus in aflatoxin production as influenced by storage parameters using response surface methodology approach. Int J Food Microbiol 2021; 357:109369. [PMID: 34474198 DOI: 10.1016/j.ijfoodmicro.2021.109369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 08/09/2021] [Accepted: 08/20/2021] [Indexed: 01/20/2023]
Abstract
Aspergillus parasiticus is a pre-harvest and postharvest pathogen that is known to produce aflatoxin; however, it is less studied compared to A. flavus. Inappropriate storage conditions are a cause of food spoilage and growth of mycotoxigenic fungi especially in low moisture foods thus constituting hazards to health. Hence, this study investigated the behaviour of A. parasiticus on aflatoxin production in inoculated wheat flour as influenced by storage conditions using the response surface methodology. Twenty experimental runs consisting of independent variables (incubation temperature (A), time (B) and (C) moisture content) and responses (aflatoxin concentrations, i.e., AFB1, AFB2, AFG1, AFG2 and AFTOT) were developed. A central composite face-centered design was used with lower and upper limits: A (25-35 °C), B (7-15 days) and C (15-25%), while the non-inoculated wheat flour served as the negative control. Aflatoxin production was determined using High Performance Liquid Chromatography (HPLC) according to standard procedures. Numerical and graphical process variables were optimized, adequate models were predicted and optimal point prediction for aflatoxin concentration was determined. AFG1 concentrations ranged from 1.10 to 360.06 μg/g, AFG2 (0.91-446.94 μg/g), AFB2 (7.95-488.77 μg/g), AFB1 (17.21-20,666.6 μg/g) and AFTOT (15.91-21,851.09 μg/g). Aflatoxin concentration increased with increase in 'B' and 'A' but decreased with prolonged increase in 'B'. AFB1 concentrations in A. parasiticus inoculated wheat flour increased at prolonged 'B' and 'A' at constant moisture (12.09%). A reduced cubic model was significantly adequate to describe the relationship between process variables and responses (AFG1 and AFG2), cubic model (AFB1 and AFTOT) and a transformed square root cubic model for AFG2 concentrations (p ≤ 0.05). 'A' influenced AFG1 production more than 'C' while 'C' and 'A' had no significant effect on AFG2 production. Process variables 'AB' influenced AFB2 concentrations more than 'C' while 'A' had a more significant effect on the AFTOT production than 'B' (p ≤ 0.05). The predicted (R2) and adjusted coefficient of regression (adj R2) were in reasonable agreement. After optimal point prediction and validation, minimum aflatoxin concentration ≤ 0 μg/g could be achieved at the predicted conditions (A = 30.42 °C, B = 10.58 days and C = 14.49%) except in AFG2 (3.33 μg/g).
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Dave N, Varadavenkatesan T, Selvaraj R, Vinayagam R. Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 791:148429. [PMID: 34412402 DOI: 10.1016/j.scitotenv.2021.148429] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 06/04/2021] [Accepted: 06/09/2021] [Indexed: 06/13/2023]
Abstract
Third generation biomass (marine macroalgae) has been projected as a promising alternative energy resource for bioethanol production due to its high carbon and no lignin composition. However, the major challenge in the technologies of production lies in the fermentative bioconversion process. Therefore, in the present study the predictive ability of an integrated artificial neural network with genetic algorithm (ANN-GA) in the modelling of bioethanol production was investigated for an indigenous marine macroalgal biomass (Ulva prolifera) by a novel yeast strain, Saccharomyces cerevisiae NFCCI1248 using six fermentative parameters, viz., substrate concentration, fermentation time, inoculum size, temperature, agitation speed and pH. The experimental model was developed using one-variable-at-a-time (OVAT) method to analyze the effects of the fermentative parameters on bioethanol production and the obtained regression equation was used as a fitness function for the ANN-GA modelling. The ANN-GA model predicted a maximum bioethanol production at 30 g/L substrate, 48 h fermentation time, 10% (v/v) inoculum, 30 °C temperature, 50 rpm agitation speed and pH 6. The maximum experimental bioethanol yield obtained after applying ANN-GA was 0.242 ± 0.002 g/g RS, which was in close proximity with the predicted value (0.239 g/g RS). Hence, the developed ANN-GA model can be applied as an efficient approach for predicting the fermentative bioethanol production from macroalgal biomass.
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Affiliation(s)
- Niyam Dave
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Thivaharan Varadavenkatesan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
| | - Raja Selvaraj
- Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Ramesh Vinayagam
- Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
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Bader NB, Germec M, Turhan I. Ethanol production from different medium compositions of rice husk hydrolysate by using Scheffersomyces stipitis in a repeated-batch biofilm reactor and its modeling. Process Biochem 2021. [DOI: 10.1016/j.procbio.2020.09.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Vieira RC, De Farias Silva CE, da Silva LOM, Almeida RMRG, de Oliveira Carvalho F, dos Santos Silva MC. Kinetic modelling of ethanolic fermented tomato must (Lycopersicon esculentum Mill) in batch system: influence of sugar content in the chaptalization step and inoculum concentration. REACTION KINETICS MECHANISMS AND CATALYSIS 2020. [DOI: 10.1007/s11144-020-01810-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Ren HY, Kong F, Ma J, Zhao L, Xie GJ, Xing D, Guo WQ, Liu BF, Ren NQ. Continuous energy recovery and nutrients removal from molasses wastewater by synergistic system of dark fermentation and algal culture under various fermentation types. BIORESOURCE TECHNOLOGY 2018; 252:110-117. [PMID: 29306713 DOI: 10.1016/j.biortech.2017.12.092] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 12/26/2017] [Accepted: 12/27/2017] [Indexed: 06/07/2023]
Abstract
Synergistic system of dark fermentation and algal culture was initially operated at batch mode to investigate the energy production and nutrients removal from molasses wastewater in butyrate-type, ethanol-type and propionate-type fermentations. Butyrate-type fermentation was the most appropriate fermentation type for the synergistic system and exhibited the accumulative hydrogen volume of 658.3 mL L-1 and hydrogen yield of 131.7 mL g-1 COD. By-products from dark fermentation (mainly acetate and butyrate) were further used to cultivate oleaginous microalgae. The maximum algal biomass and lipid content reached 1.01 g L-1 and 38.5%, respectively. In continuous operation, the synergistic system was stable and efficient, and energy production increased from 8.77 kJ L-1 d-1 (dark fermentation) to 17.3 kJ L-1 d-1 (synergistic system). Total COD, TN and TP removal efficiencies in the synergistic system reached 91.1%, 89.1% and 85.7%, respectively. This study shows the potential of the synergistic system in energy recovery and wastewater treatment.
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Affiliation(s)
- Hong-Yu Ren
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Fanying Kong
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Jun Ma
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Lei Zhao
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Guo-Jun Xie
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Defeng Xing
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Wan-Qian Guo
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Bing-Feng Liu
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
| | - Nan-Qi Ren
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
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