Theoretical Models Constructed by Artificial Intelligence Algorithms for Enhanced Lipid Production: Decision Support Tools

Yıl 2023, , 1195 - 1211, 28.12.2023

Aytun Onay

https://doi.org/10.17798/bitlisfen.1362136

Öz

Theoretical models that predict the lipid content of microalgae are an important tool for increasing lipid productivity. In this study, response surface methodology (RSM), RSM combined with artificial neural network (ANN), and RSM combined with ensemble learning algorithms (ELA) for regression were used to calculate the maximum lipid percentage (%) from Chlorella minutissima (C. minutissima). We defined one set of rules to achieve the highest lipid content and used trees.RandomTree (tRT) to simulate the process parameters under various conditions. Among the various models, results showed the optimum values of the root mean squared error (0.2156), mean absolute error (0.1167), and correlation coefficient (0.9961) in the tRT model. RSM combined with tRT estimated that the lipid percentage was 30.3% in wastewater (< 35%), lysozyme (≥ 3.5 U/mL), and chitinase (< 15 U/mL) concentrations, achieving the best model based on experimental data. The optimal values of wastewater concentration, chitinase, and lysozyme were 20% (v/v), 5 U/mL, and 10 U/mL, respectively. Also, the if-then rules obtained from tRT were also used to test the process parameters. The tRT model served as a powerful tool to obtain maximum lipid content. The final rankings of the performance of various algorithms were determined. Furthermore, the models developed can be used by the fuel industry to achieve cost-effective, large-scale production of lipid content and biodiesel.

Anahtar Kelimeler

Artificial intelligence algorithms, Biodiesel, Chlorella minutissima, Ensemble learning algorithms, Microalgal lipid content, Response surface methodology

Kaynakça

• [1] S. Anto, S.S. Mukherjee, R. Muthappa, T. Mathimani, G. Deviram, S.S. Kumar, T.N. Verma, A. Pugazhendhi, “Algae as green energy reserve: Technological outlook on biofuel production,” Chemosphere, vol. 242, 125079, 2020.
• [2] A. Garg, S. Jain, “Process parameter optimization of biodiesel production from algal oil by response surface methodology and artificial neural networks,” Fuel, vol. 277, 118254, 2020.
• [3] G. Srivastava, A.K. Paul, V.V. Goud, “Optimization of non-catalytic transesterification of microalgae oil to biodiesel under supercritical methanol condition,” Energy Convers Manag, vol. 156, pp. 269–278, 2018.
• [4] M. Karimi, “Exergy-based optimization of direct conversion of microalgae biomass to biodiesel,” J Clean Prod, vol. 141, pp. 50–55, 2017.
• [5] S. Nagappan, S. Devendran, P.C. Tsai, H.U. Dahms, V.K. Ponnusamy, “Potential of two-stage cultivation in microalgae biofuel production,” Fuel, vol. 252, pp. 339–349, 2019.
• [6] M. Nayak, G. Dhanarajan, R. Dineshkumar, R. Sen, “Artificial intelligence driven process optimization for cleaner production of biomass with co-valorization of wastewater and flue gas in an algal biorefinery,” J Clean Prod, vol. 201, pp. 1092–1100, 2018.
• [7] S. Chakravarty, N. Mallick, “Optimization of lipid accumulation in an aboriginal green microalga Selenastrum sp. GA66 for biodiesel production,” Biomass and Bioenergy, vol. 126, pp. 1–13, 2019.
• [8] W.B. Kong, S.F. Hua, H. Cao, Y.W. Mu, H. Yang, H. Song, C.G. Xia, “Optimization of mixotrophic medium components for biomass production and biochemical composition biosynthesis by Chlorella vulgaris using response surface methodology,” J Taiwan Inst Chem Eng, vol. 43, pp. 360–367, 2012.
• [9] S. Singh, J.P. Chakraborty, M.K. Mondal, “Optimization of process parameters for torrefaction of Acacia nilotica using response surface methodology and characteristics of torrefied biomass as upgraded fuel,” Energy, vol. 186, 115865, 2019.
• [10] U. Suparmaniam, M.K. Lam, Y. Uemura, J.W. Lim, K.T. Lee, S.H. Shuit, “Insights into the microalgae cultivation technology and harvesting process for biofuel production: A review,” Renew Sustain Energy Rev, vol. 115, 109361, 2019.
• [11] S.O. Ajala, M.L. Alexander, “Multi-objective optimization studies of microalgae dewatering by utilizing bio-based alkali: a case study of response surface methodology (RSM) and genetic algorithm (GA)”. SN Appl Sci, vol. 2, pp. 1–20, 2020.
• [12] A. Kirrolia, N.R. Bishnoi, R. Singh, “Response surface methodology as a decision-making tool for optimization of culture conditions of green microalgae Chlorella spp. for biodiesel production,” Ann Microbiol, vol. 64, pp. 1133–1147, 2014.
• [13] G. Satpati, S.K. Mallick, R. Pal, “An alternative high-throughput staining method for detection of neutral lipids in green microalgae for biodiesel applications,” Biotechnol Bioprocess Eng. vol. 20, pp. 1044–1055, 2015.
• [14] F.J. Chu, T.J. Wan, T.Y. Pai, H.W. Lin, S.H. Liu, C.F. Huang, “Use of magnetic fields and nitrate concentration to optimize the growth and lipid yield of Nannochloropsis oculata,” J Environ Manage, vol. 253, 109680, 2020.
• [15] M.F. Kamaroddin, A. Rahaman, D.J.Gilmour, W.B. Zimmerman, “Optimization and cost estimation of microalgal lipid extraction using ozone-rich microbubbles for biodiesel production,” Biocatal Agric Biotechnol, vol. 23, 101462, 2020.
• [16] Supriyanto, R. Noguchi, T. Ahamed, D.S. Rani, K. Sakurai, M.A. Nasution, D.S. Wibawa, M. Demura, M.M. Watanabe, “Artificial neural networks model for estimating growth of polyculture microalgae in an open raceway pond,” Biosyst Eng, vol. 177, pp. 122–129.
• [17] E. Baldev, D. Mubarakali, K. Saravanakumar, C. Arutselvan, N.S. Alharbi, S.A. Alharbi, V. Sivasubramanian, N. Thajuddin, “Unveiling algal cultivation using raceway ponds for biodiesel production and its quality assessment,” Renew Energy, vol. 123, pp. 486–498, 2018.
• [18] C. Zhang, Y. Zhang, B. Zhuang, X. Zhou, “Strategic enhancement of algal biomass, nutrient uptake and lipid through statistical optimization of nutrient supplementation in coupling Scenedesmus obliquus-like microalgae cultivation and municipal wastewater treatment,” Bioresour Technol, vol. 171, pp. 71–79, 2014.
• [19] P. Polburee, S. Limtong, “Economical lipid production from crude glycerol using Rhodosporidiobolus fluvialis DMKU-RK253 in a two-stage cultivation under non-sterile conditions,” Biomass and Bioenergy, vol. 138, 105597, 2020.
• [20] M. Tourang, M. Baghdadi, A. Torang, S. Sarkhosh, “Optimization of carbohydrate productivity of Spirulina microalgae as a potential feedstock for bioethanol production,” Int J Environ Sci Technol, vol. 16, pp. 1303-1318, 2017.
• [21] C. Huang, H. Wu, R. feng Li, M. hua Zong, “Improving lipid production from bagasse hydrolysate with Trichosporon fermentans by response surface methodology,” N Biotechnol, vol. 29, pp. 372–378, 2012.
• [22] M. Mäkelä, “Experimental design and response surface methodology in energy applications: A tutorial review,” Energy Convers Manag, vol. 151, pp. 630–640, 2017.
• [23] S.K. Yellapu, J. Bezawada, R. Kaur, M. Kuttiraja, R.D. Tyagi, “Detergent assisted lipid extraction from wet yeast biomass for biodiesel: A response surface methodology approach,” Bioresour Technol, vol. 218, pp. 667–673, 2016.
• [24] A. Onay, “Investigation of biomass productivity from Nannochloropsis gaditana via response surface methodology using MATLAB,” Energy Reports, vol. 6, pp. 44-49, 2020.
• [25] S.M. Huang, C.H. Kuo, C.A. Chen, Y.C. Liu, C.J. Shieh, “RSM and ANN modeling-based optimization approach for the development of ultrasound-assisted liposome encapsulation of piceid,” Ultrason Sonochem, vol. 36, pp. 112–122, 2017.
• [26] A.A. Ayoola, F.K. Hymore, C.A. Omonhinmin, O.C. Olawole, O.S.I. Fayomi, D. Babatunde, O. Fagbiele, “Analysis of waste groundnut oil biodiesel production using response surface methodology and artificial neural network,” Chem Data Collect, vol. 22, 100238, 2019.
• [27] M. Mondal, A. Ghosh, K. Gayen, G. Halder, O.N. Tiwari, “Carbon dioxide bio-fixation by Chlorella sp. BTA 9031 towards biomass and lipid production: Optimization using Central Composite Design approach,” J CO2 Util, vol. 22, pp. 317–329, 2017.
• [28] M.A. Alam, J. Wu, J. Xu, Z. Wang, “Enhanced isolation of lipids from microalgal biomass with high water content for biodiesel production,” Bioresour Technol, vol. 291, 121834, 2019.
• [29] N.B. Ishola, A.A. Okeleye, A.S. Osunleke, E. Betiku, “Process modeling and optimization of sorrel biodiesel synthesis using barium hydroxide as a base heterogeneous catalyst: appraisal of response surface methodology, neural network and neuro-fuzzy system,” Neural Comput Appl, vol. 31, pp. 4929–4943, 2019.
• [30] S. González, S. García, J.D. Ser, L. Rokach, F. Herrera, “A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities,” Inf Fusion, vol. 64, pp. 205–237, 2020.
• [31] S. Das, R. Chakraborty, A. Maitra, “A random forest algorithm for nowcasting of intense precipitation events,” Adv Sp Res, vol. 60, pp. 1271–1282, 2017.
• [32] L. Wang, X. Zhou, X. Zhu, Z. Dong, W. Guo, “Estimation of biomass in wheat using random forest regression algorithm and remote sensing data,” Crop J, vol. 4, pp. 212–219, 2016.
• [33] L. Rokach, “Decision forest: Twenty years of research,” Inf Fusion, vol. 27, pp. 111–125, 2016.
• [34] A. Onay, M. Onay, “A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques,” Current Computer-Aided Drug Design, vol. 16, pp. 407-419, 2020.
• [35] Y. Song, L. Wang, X. Qiang, W. Gu, Z. Ma, G. Wang, “The promising way to treat wastewater by microalgae: Approaches, mechanisms, applications and challenges,” Journal of Water Process Engineering, vol. 49, 103012, 2022.
• [36] W.N.A. Kadir, M.K. Lam, Y. Uemura, J.W. Lim, K.T. Lee, “Harvesting and pre-treatment of microalgae cultivated in wastewater for biodiesel production: A review,” Energy Conversion and Management, vol. 171, pp. 1416-1429, 2018.
• [37] S. I. Khan, I. Hashmi, S.J. Khan, R. Henderson, “Performance and optimization of lab-scale membrane bioreactors for synthetic municipal wastewater,” Desalin Water Treat, vol. 57, pp. 29193–29200, 2016.
• [38] F. Jordi, M. Lees , G.M. Sloane-Stanley, “A simple method for the isolation and purification of total lipids from animal tissues,” J Biol Chem, vol. 226, pp. 497-509, 1957.
• [39] A. Cutler, D.R. Cutler, J.R. Stevens, “Ensemble Machine Learning,” Ensemble Mach Learn, 2012.
• [40] L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5–32, 2001.
• [41] M. Muthuraj, N. Chandra, B. Palabhanvi, V. Kumar, D. Das, “Process Engineering for High-Cell-Density Cultivation of Lipid Rich Microalgal Biomass of Chlorella sp. FC2 IITG,” Bioenergy Res, vol. 8, pp. 726–739, 2015.
• [42] H.A. Thanaa, S.E.G. Mamdouh, H.E.G. Dina, E.A. Ghada, E.T. Amir, “Improvement of lipid production from an oil-producing filamentous fungus, Penicillium brevicompactum NRC 829, through central composite statistical design,” Ann Microbiol, vol. 67, pp. 601–613, 2017.
• [43] M. Dammak, S.M. Haase, R. Miladi, F.B. Amor, M. Barkallah, D. Gosset, C. Pichon, B. Huchzermeyer, I. Fendri, M. Denis, S. Abdelkafi, “Enhanced lipid and biomass production by a newly isolated and identified marine microalga,” Lipids Health Dis, vol. 15, pp. 1–13, 2016.
• [44] A. Onay, “Optimization of lipid content of Nannochloropsis gaditana via quadratic models using Matlab Simulink,” Energy Reports, vol. 6, pp. 128–133, 2020.
• [45] L. Khaouane, C. Si-Moussa, S. Hanini, O. Benkortbi, “Optimization of culture conditions for the production of pleuromutilin from pleurotus mutilus using a hybrid method based on central composite design, neural network, and particle swarm optimization,” Biotechnol Bioprocess Eng, vol. 17, pp. 1048–1054, 2012.
• [46] M.N.A. Sohedein, W.A.A.Q.I. Wan-Mohtar, Y. Hui-Yin, Z. Ilham, J.S. Chang, S. Supramani, P. Siew-Moi, “Optimisation of biomass and lipid production of a tropical thraustochytrid Aurantiochytrium sp. UMACC-T023 in submerged-liquid fermentation for large-scale biodiesel production,” Biocatal Agric Biotechnol, vol. 23, 101496, 2020.
• [47] S. Chakravarty, N. Mallick, “Optimization of lipid accumulation in an aboriginal green microalga Selenastrum sp. GA66 for biodiesel production,” Biomass and Bioenergy, vol. 126, pp. 1–13, 2019.
• [48] Y. Zhang, X. Kong, Z. Wang, Y. Sun, S. Zhu, L. Li, P. Lv, “Optimization of enzymatic hydrolysis for effective lipid extraction from microalgae Scenedesmus sp.,” Renew Energy, vol. 125, pp. 1049–1057, 2018.
• [49] L. Zhang, B. Chao, X. Zhang, “Modeling and optimization of microbial lipid fermentation from cellulosic ethanol wastewater by Rhodotorula glutinis based on the support vector machine,” Bioresour Technol, vol. 301, 122781, 2020.
• [50] A.M. Nassef, H. Rezk, M.A. Abdelkareem, A. Alaswad, A. Olabi, “Application of fuzzy modelling and Particle Swarm Optimization to enhance lipid extraction from microalgae,” Sustain Energy Technol Assessments, vol. 35, pp. 73–79, 2019.
• [51] J. Ferrell and V. Sarisky-Reed, “National Algal Biofuels Technology Roadmap,” U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Biomass Program, 2010.
• [52] S. Ali, T. Abuhmed, S. El-Sappagh, K. Muhammad, J. M. Alonso-Moral, R. Confalonieri, R. Guidotti, J. Del Ser, N. Díaz-Rodríguez, F. Herrera, “Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence,” Information Fusion, vol. 99, 101805, 2023.