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S K, Ravi YK, Kumar G, Kadapakkam Nandabalan Y, J RB. Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 353:120135. [PMID: 38286068 DOI: 10.1016/j.jenvman.2024.120135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 12/16/2023] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Abstract
The microalgae can be converted into biofuels, biochemicals, and bioactive compounds in a biorefinery. Recently, designing and executing more viable and sustainable biofuel production from microalgal biomass is one of the vital challenges in the development of biorefinery. Scalable cultivation of microalgae is mandatory for commercializing and industrializing the biorefinery. The intrinsic complication in cultivation of microalgae is the physiological and operational factors that renders challenging impact to enable a smooth and profitable operation. However, this aim can only be successful via a simulation prospect. Machine learning tools provides advanced approaches for evaluating, predicting, and controlling uncertainties in microalgal biorefinery for sustainable biofuel production. The present review provides a critical evaluation of the most progressing machine learning tools that validate a potential to be employed in microalgal biorefinery. These tools are highly potential for their extensive evaluation on microalgal screening and classification. However, the application of these tools for optimization of microalgal biomass cultivation in industries in order to increase the biomass production, is still in its initial stages. Integrated hybrid machine learning tools can aid the industries to function efficiently with least resources. Some of the challenges, and perspectives of machine learning tools are discussed. Besides, future prospects are also emphasized. Though, most of the research reports on machine learning tools are not appropriate to gather generalized information, standard protocols and strategies must be developed to design generalized machine learning tools. On a whole, this review offers a perspective information about digitalized microalgal exploitation in a microalgal biorefinery.
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Affiliation(s)
- Kavitha S
- Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, 641021, India
| | - Yukesh Kannah Ravi
- Centre for Organic and Nanohybrid Electronics, Silesian University of Technology, Konarskiego 22B, 44-100, Gliwice, Poland
| | - Gopalakrishnan Kumar
- School of Civil and Environmental Engineering, Yonsei University, Seoul, 03722, Republic of Korea; Institute of Chemistry, Bioscience and Environmental Engineering, Faculty of Science and Technology, University of Stavanger, Box 8600 Forus, 4036 Stavanger, Norway
| | - Yogalakshmi Kadapakkam Nandabalan
- Department of Environmental Science and Technology, School of Environment and Earth Sciences, Central University of Punjab, VPO Ghudda, Bathinda, 151401, Punjab, India
| | - Rajesh Banu J
- Department of Biotechnology, Central University of Tamil Nadu, Neelakudi, Thiruvarur, 610005, Tamil Nadu, India.
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Oruganti RK, Biji AP, Lanuyanger T, Show PL, Sriariyanun M, Upadhyayula VKK, Gadhamshetty V, Bhattacharyya D. Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 876:162797. [PMID: 36907394 DOI: 10.1016/j.scitotenv.2023.162797] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 02/23/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.
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Affiliation(s)
- Raj Kumar Oruganti
- Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy 502284, Telangana, India
| | - Alka Pulimoottil Biji
- Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy 502284, Telangana, India
| | - Tiamenla Lanuyanger
- Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy 502284, Telangana, India
| | - Pau Loke Show
- Department of Chemical Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Malinee Sriariyanun
- Biorefinery and Process Automation Engineering Center, Department of Chemical and Process Engineering, The Sirindhorn Thai-German International Graduate School of Engineering, King Mongkut's University of Technology North Bangkok, Thailand
| | | | - Venkataramana Gadhamshetty
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, USA; 2-Dimensional Materials for Biofilm Engineering Science and Technology (2D-BEST) Center, South Dakota Mines, Rapid City, SD 57701, USA
| | - Debraj Bhattacharyya
- Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy 502284, Telangana, India.
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Ajeng AA, Rosli NSM, Abdullah R, Yaacob JS, Qi NC, Loke SP. Resource recovery from hydroponic wastewaters using microalgae-based biorefineries: A circular bioeconomy perspective. J Biotechnol 2022; 360:11-22. [PMID: 36272573 DOI: 10.1016/j.jbiotec.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/09/2022] [Accepted: 10/17/2022] [Indexed: 11/07/2022]
Abstract
As the world's population grows, it is necessary to rethink how countries throughout the world produce food in order to replace the conventional and unsustainable agricultural techniques. Microalgae cultivation using a nutrient-rich solution from hydroponic systems not only presents a novel approach to solving problems pertaining to the impact of the discharges on the natural environment but also provides a plethora of other biotechnological applications particularly in the productions of high value-added products and plants growth stimulants, which can be potentially assimilated into the circular bioeconomy (CBE) in the hydroponic sector. In this review, the potential and practicability of microalgae to be merged into hydroponics CBE are reviewed. Overall, the integration of microalgal biorefineries in hydroponics systems can be realized after considering their Technology Readiness Level and System Readiness Level beforehand. Several suggestions on strains and hydroponics system improvement using existing biotechnological tools, Artificial Intelligence (AI) and nanobiotechnology in support of the CBE will be covered.
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Affiliation(s)
- Aaronn Avit Ajeng
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Noor Sharina Mohd Rosli
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Rosazlin Abdullah
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; Centre for Research in Biotechnology for Agriculture (CEBAR), Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Jamilah Syafawati Yaacob
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; Centre for Research in Biotechnology for Agriculture (CEBAR), Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Ng Cai Qi
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Show Pau Loke
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.
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Teng SY, Yew GY, Sukačová K, Show PL, Máša V, Chang JS. Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products. Biotechnol Adv 2020; 44:107631. [PMID: 32931875 DOI: 10.1016/j.biotechadv.2020.107631] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 09/08/2020] [Accepted: 09/08/2020] [Indexed: 12/18/2022]
Abstract
With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a "domino effect" in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.
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Affiliation(s)
- Sin Yong Teng
- Brno University of Technology, Institute of Process Engineering, Technická 2896/2, 616 69, Brno, Czech Republic.
| | - Guo Yong Yew
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor, Malaysia.
| | - Kateřina Sukačová
- Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, Brno 603 00, Czech Republic.
| | - Pau Loke Show
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor, Malaysia.
| | - Vítězslav Máša
- Brno University of Technology, Institute of Process Engineering, Technická 2896/2, 616 69, Brno, Czech Republic.
| | - Jo-Shu Chang
- Department of Chemical and Materials Engineering, College of Engineering, Tunghai University, Taichung 407, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan 701, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407, Taiwan.
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Zero-waste biorefinery of oleaginous microalgae as promising sources of biofuels and biochemicals through direct transesterification and acid hydrolysis. Process Biochem 2020. [DOI: 10.1016/j.procbio.2020.02.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Ebadinezhad B, Ebrahimi S, Shokrkar H. Evaluation of microbial fuel cell performance utilizing sequential batch feeding of different substrates. J Electroanal Chem (Lausanne) 2019. [DOI: 10.1016/j.jelechem.2019.02.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Marine algal carbohydrates as carbon sources for the production of biochemicals and biomaterials. Biotechnol Adv 2018; 36:798-817. [DOI: 10.1016/j.biotechadv.2018.02.006] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 02/04/2018] [Accepted: 02/06/2018] [Indexed: 12/30/2022]
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