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  1. 1
    Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

    Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm by Mohd Zain, Mohamad Zihin, Kanesan, Jeevan, Kendall, Graham, Chuah, Joon Huang

    Published 2018
    “…DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. …”
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  2. 2
    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain

    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

    Published 2018
    “…Though DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation, BSA edged DE and other recent metaheuristics to emerge as superior optimization method in this work. …”
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  3. 3
    Model predictive control on fed-batch penicillin fermentation process

    Model predictive control on fed-batch penicillin fermentation process by Chew, Li Mei

    Published 2009
    “…In order to obtain best optimization result for the fed-batch penicillin fermentation process, two optimization algorithms were selected. …”
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    Undergraduates Project Papers
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  4. 4
    Comparison product optimization performance of fed-batch fermentation processes for penicillin g production

    Comparison product optimization performance of fed-batch fermentation processes for penicillin g production by Fazli Abdul Samad, Noor Asma, Ahmad, Arshad

    Published 2006
    “…In order to carry out this procedure, two optimization algorithms were selected. First, dynamic optimization using direct shooting method and second is implementation single step ahead Dynamic Matrix Control (DMC). …”
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  5. 5
    Comparison product optimization performance of fed-batch fermentation processes for penicillin G production

    Comparison product optimization performance of fed-batch fermentation processes for penicillin G production by Noor Asma Fazli, Abdul Samad, Arshad, Ahmad

    Published 2006
    “…In order to carry out this procedure, two optimization algorithms were selected. First, dynamic optimization using direct shooting method and second is implementation single step ahead Dynamic Matrix Control (DMC). …”
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  6. 6
    Application of artificial neural network to simulate phenolic content and antioxidant activity during spontaneous fermentation of Carica papaya leaf / Nur Syahira Mohammad and Mohamad Sufian So’aib

    Application of artificial neural network to simulate phenolic content and antioxidant activity during spontaneous fermentation of Carica papaya leaf / Nur Syahira Mohammad and Moha... by Mohammad, Nur Syahira, So’aib, Mohamad Sufian

    Published 2020
    “…Trial and error method were used to develop the ANN model. The transfer function used in this research project was hyperbolic tangent sigmoid with the Levenberg- Marquadt algorithm training function. …”
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  7. 7
    Application of artificial neural network on prediction of microbial population and species during spontaneous fermentation of carica papaya leaf / Muhammad Hafiz Ridwan and Mohamad Sufian So’aib

    Application of artificial neural network on prediction of microbial population and species during spontaneous fermentation of carica papaya leaf / Muhammad Hafiz Ridwan and Mohamad... by Ridwan, Muhammad Hafiz, So’aib, Mohamad Sufian

    Published 2020
    “…Microbial population and species during spontaneous fermentation process of Carica Papaya leaf was unpredictable. …”
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  8. 8
    Nonlinear partial differential equations model related to ethanol production

    Nonlinear partial differential equations model related to ethanol production by Ahmad Izul Fakhruddin, Azimi, Norazaliza, Mohd Jamil

    Published 2019
    “…Since the model is nonlinear partial differential equations (PDEs), Gear's algorithm, a numerical method was employed to solve the system while the Nelder-Mead method is utilised to estimate the value of the parameters. …”
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  9. 9
    Mathematical Modelling and Optimisation of Hydrogen Production from Photo-Fermentation in Microbial Electrolysis Cell using Sago Waste with Neural Network Algorithm

    Mathematical Modelling and Optimisation of Hydrogen Production from Photo-Fermentation in Microbial Electrolysis Cell using Sago Waste with Neural Network Algorithm by Then, Mun Yip

    Published 2020
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    Final Year Project Report / IMRAD
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  10. 10
    Parametric Optimization for the Maximization of Hydrogen Production by Enterobacter Cloacae

    Parametric Optimization for the Maximization of Hydrogen Production by Enterobacter Cloacae by Saidin, Mohamad Shakir Amri

    Published 2015
    “…By using data from the previous research, the parameters are optimized by using three numerical methods, simulated annealing, pattern search and Genetic algorithm. …”
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  11. 11
    Development of an artisanal fermentation: case studies on Carica papaya leaf and Garcinia Mangostana pericarp / Mohamad Sufian So’aib

    Development of an artisanal fermentation: case studies on Carica papaya leaf and Garcinia Mangostana pericarp / Mohamad Sufian So’aib by So’aib, Mohamad Sufian

    Published 2020
    “…The beneficial effects of the fermentation were observed in terms of enhanced TPC of the fermented CPL (of 5-L fermenter origin) at 48.42±0.31 mg GAE/g dry mass vs 12.13±0.39 mg GAE/g dry mass of the unfermented extract, whereas the antioxidant capacity of the fermented CPL was 467.38±4.09 mM TE/g dry mass, higher than the unfermented CPL i.e. 275.46±3.09 mM TE/g dry mass. …”
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  12. 12
    A review unveiling various machine learning algorithms adopted for biohydrogen productions from microalgae

    A review unveiling various machine learning algorithms adopted for biohydrogen productions from microalgae by Mohamad Zulfadhli Ahmad Sobri, Alya Redhwan, Fuad Ameen, Lim, Jun Wei, Liew, Chin Seng, Mong, Guo Ren, Hanita Daud, Rajalingam Sokkalingam, Ho, Chii-Dong, Anwar Usman, D. H. Nagaraju, Pasupuleti Visweswara Rao

    Published 2023
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  13. 13
    A review of advanced optimization strategies for fermentative biohydrogen production processes

    A review of advanced optimization strategies for fermentative biohydrogen production processes by Mohd Farid, Bin Atan, MOHAMAD AFIQ, MOHD ASRUL, Hafizah, Abdul Halim Yun, Josephine, Chang Hui Lai

    Published 2022
    “…Despite significant reports on the optimization of hydrogen production from fermentation, fewer studies have been made for the case using artificial intelligence algorithms. …”
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  14. 14
    Product optimization of a fed-batch fermentation process

    Product optimization of a fed-batch fermentation process by Arshad, Ahmad, Noor Asma Fazli, Abdul Samad, Mohd Kamaruddin, Abd Hamid

    Published 2007
    “…These are optimal control policy using direct-shooting algorithm and unconstrained Dynamic Matrix Control (DMC). …”
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  15. 15
    Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production

    Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production by Mamat, Nor Hana, Mohd Noor, Samsul Bahari, Che Soh, Azura, Ab Rashid, Ahmad Hazri, Jufika Ahmad, Nur Liyana, Mohd Yusuff, Ishak

    Published 2018
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  16. 16
    Stochastic Modelling Of Bioethanol Fermentation By Saccharomyces Cerevisiae Grown In Oil Palm Residues

    Stochastic Modelling Of Bioethanol Fermentation By Saccharomyces Cerevisiae Grown In Oil Palm Residues by Samsudin , Mohd Dinie Muhaimin

    Published 2015
    “…In order to improve bioethanol production, very high gravity (VHG) fermentation was then carried out using concentrated OPT sap and POME. …”
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  17. 17
    A study on solution of matrix riccati differential equations using ant colony programming and simulink / Mohd Zahurin Mohamed Kamali

    A study on solution of matrix riccati differential equations using ant colony programming and simulink / Mohd Zahurin Mohamed Kamali by Mohamed Kamali, Mohd Zahurin

    Published 2015
    “…Illustrative examples are shown to prove the effectiveness of the proposed algorithm. Moreover, the proposed method have been well applied to biological and engineering problems such as linear and nonlinear singular systems, human immunodeficiency virus (HIV) models, microbial growth model and ethanol fermentation process. …”
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  18. 18
    Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor

    Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor by Mamat, Nor Hana, Mohd Noor, Samsul Bahari, Che Soh, Azura, Taip, Farah Saleena, Ab Rashid, Ahmad Hazri, Jufika Ahmad, Nur Liyana, Mohd Yusuff, Ishak

    Published 2017
    “…The backpropagation neural network model with Lavenberg Marquardt learning algorithm was developed using 1476 samples real process dataset obtained from a fermentation process in a 200L bioreactor. …”
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  19. 19
    Particle swarm optimization-based model-free adaptive control for time-varying batch processes

    Particle swarm optimization-based model-free adaptive control for time-varying batch processes by Wang, Zhao, A.S., Sadun, N.A., Jalaludin, J., Jalani, S.N.H., Arifin, Mohamed Sunar, N., Muhammad Ashraf, Fauzi

    Published 2024
    “…Finally, by comparing it with the existing methods, a typical batch fermentation was applied to verify that PSO-MFAC had a good control effect. …”
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  20. 20
    Repeated fed-batch cultivation of nitrogen-fixing bacterium, Bacillus sphaericus UPMB10, using glycerol as the carbon source

    Repeated fed-batch cultivation of nitrogen-fixing bacterium, Bacillus sphaericus UPMB10, using glycerol as the carbon source by Ariff, Arbakariya, Ooi, Tze Chean, Mohd Saud, Halimi, Shamsuddin, Zulkifli

    Published 2010
    “…The exponential fed-batch cultivation of Bacillus sphaericus UPMB10 in 2 l stirred tank fermenter was performed by feeding the initial batch culture with 14 g l-1 of glycerol according to the algorithm aimed at controlling the specific growth rate (µ) of the bacterium. …”
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