A Review Unveiling Various Machine Learning Algorithms Adopted for Biohydrogen Productions from Microalgae

被引:22
作者
Ahmad Sobri, Mohamad Zulfadhli [1 ]
Redhwan, Alya [2 ]
Ameen, Fuad [3 ]
Lim, Jun Wei [1 ,4 ]
Liew, Chin Seng [1 ]
Mong, Guo Ren [5 ]
Daud, Hanita [6 ]
Sokkalingam, Rajalingam [6 ]
Ho, Chii-Dong [7 ]
Usman, Anwar [8 ]
Nagaraju, D. H. [9 ]
Rao, Pasupuleti Visweswara [10 ,11 ]
机构
[1] Univ Teknol Petronas, Inst Self Sustainable Bldg, HICoE Ctr Biofuel & Biochem Res, Dept Fundamental & Appl Sci, Seri Iskandar 32610, Perak Darul Rid, Malaysia
[2] Princess Nourah Bint Abdulrahman Univ, Coll Hlth & Rehabil Sci, Dept Hlth, Riyadh, Saudi Arabia
[3] King Saud Univ, Coll Sci, Dept Bot & Microbiol, Riyadh 11451, Saudi Arabia
[4] Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept Biotechnol, Chennai 602105, India
[5] Xiamen Univ Malaysia, Sch Energy & Chem Engn, Sepang 43900, Selangor, Malaysia
[6] Univ Teknol Petronas, Inst Autonomous Syst, Dept Fundamental & Appl Sci, Math & Stat Sci, Seri Iskandar 32610, Perak Darul Rid, Malaysia
[7] Tamkang Univ, Dept Chem & Mat Engn, New Taipei 251, Taiwan
[8] Univ Brunei Darussalam, Fac Sci, Dept Chem, Gadong BE1410, Brunei
[9] REVA Univ, Sch Appl Sci, Dept Chem, Bangalore 560064, India
[10] REVA Univ, Ctr Int Relat & Res Collaborat, Bangalore 560064, India
[11] Univ Malaysia Sabah, Fac Med & Hlth Sci, Dept Biomed Sci, Kota Kinabalu 88400, Sabah, Malaysia
来源
FERMENTATION-BASEL | 2023年 / 9卷 / 03期
关键词
machine learning; biohydrogen; microalgae; nonlinear interaction; prediction; overfitting; ARTIFICIAL NEURAL-NETWORKS; HYDROGEN-PRODUCTION; GROWTH; BIOMASS;
D O I
10.3390/fermentation9030243
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Biohydrogen production from microalgae is a potential alternative energy source that is now intensively being researched. The complex natures of the biological processes involved have afflicted the accuracy of traditional modelling and optimization, besides being costly. Accordingly, machine learning algorithms have been employed to overcome setbacks, as these approaches have the capability to predict nonlinear interactions and handle multivariate data from microalgal biohydrogen studies. Thus, the review focuses on revealing the recent applications of machine learning techniques in microalgal biohydrogen production. The working principles of random forests, artificial neural networks, support vector machines, and regression algorithms are covered. The applications of these techniques are analyzed and compared for their effectiveness, advantages and disadvantages in the relationship studies, classification of results, and prediction of microalgal hydrogen production. These techniques have shown great performance despite limited data sets that are complex and nonlinear. However, the current techniques are still susceptible to overfitting, which could potentially reduce prediction performance. These could be potentially resolved or mitigated by comparing the methods, should the input data be limited.
引用
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页数:12
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[1]   Plant Microbiome Engineering: Hopes or Hypes [J].
Afridi, Muhammad Siddique ;
Ali, Sher ;
Salam, Abdul ;
Cesar Terra, Willian ;
Hafeez, Aqsa ;
Sumaira ;
Ali, Baber ;
S. AlTami, Mona ;
Ameen, Fuad ;
Ercisli, Sezai ;
Marc, Romina Alina ;
Medeiros, Flavio H. V. ;
Karunakaran, Rohini .
BIOLOGY-BASEL, 2022, 11 (12)
[2]  
Akkaya B., 2019, y-BIS Conference 2019: Recent Advances in Data Science and Business Analytics, P162
[3]  
Al Husnain L, 2023, Journal of the Saudi Society of Agricultural Sciences, V22, P214
[4]   Sodium hydroxide pre-treated Aspergillus flavus biomass for the removal of reactive black 5 and its toxicity evaluation [J].
Alaguprathana, M. ;
Poonkothai, M. ;
Ameen, Fuad ;
Bhat, Sartaj Ahmad ;
Mythili, R. ;
Sudhakar, C. .
ENVIRONMENTAL RESEARCH, 2022, 214
[5]  
Alalayah WM, 2014, REV CHIM-BUCHAREST, V65, P458
[6]   Efficacy of Gold Nanoparticles against Drug-Resistant Nosocomial Fungal Pathogens and Their Extracellular Enzymes: Resistance Profiling towards Established Antifungal Agents [J].
Almansob, Abobakr ;
Bahkali, Ali H. ;
Ameen, Fuad .
NANOMATERIALS, 2022, 12 (05)
[7]   Vermicomposting: A management tool to mitigate solid waste [J].
Alshehrei, Fatimah ;
Ameen, Fuad .
SAUDI JOURNAL OF BIOLOGICAL SCIENCES, 2021, 28 (06) :3284-3293
[8]   Highly active iron (II) oxide-zinc oxide nanocomposite synthesized Thymus vulgaris plant as bioreduction catalyst: Characterization, hydrogen evolution and photocatalytic degradation [J].
Ameen, Fuad ;
Altuner, Elif Esra ;
Tiri, Rima Nour Elhouda ;
Gulbagca, Fulya ;
Aygun, Aysenur ;
Sen, Fatih ;
Majrashi, Najwa ;
Orfali, Raha ;
Dragoi, Elena Niculina .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2023, 48 (55) :21139-21151
[9]   Treatment of heavy metal-polluted sewage sludge using biochar amendments and vermistabilization [J].
Ameen, Fuad ;
Al-Homaidan, Ali A. .
ENVIRONMENTAL MONITORING AND ASSESSMENT, 2022, 194 (12)
[10]   Anti-oxidant, anti-fungal and cytotoxic effects of silver nanoparticles synthesized using marine fungus Cladosporium halotolerans [J].
Ameen, Fuad ;
Al-Homaidan, Ali A. ;
Al-Sabri, Ahmed ;
Almansob, Abobakr ;
AlNAdhari, Saleh .
APPLIED NANOSCIENCE, 2021, 13 (1) :623-631