Abstract
Hydrothermal liquefaction (HTL) of lignocellulosic biomass has gained attention as a promising technology for the production of biofuels and other value-added products. HTL process optimization is complex and involves various parameters such as reaction time, temperature, and pressure. In recent years, machine learning (ML) approaches have been adopted as a tool to optimize and predict the HTL process performance. The purposes of this study were to investigate the ML-based prediction of bio-crude yield (BCY) and their higher heating values (HHVs) from HTL of lignocellulosic biomass and to elucidate the relationship of features affecting the output of interest. Pre-processing and normalization were applied to a dataset of 215 data points with 17 input features. Feature selection using the Shapley value method identified key predictors. ML models including multilayer perceptron, kernel ridge regression, random forest, and extreme gradient boosting (XGB) were trained and evaluated. XGB algorithm shows superior performance in predicting the yields and their calorific values to within 5–8% of experimental values. Temperature was the most influential feature for both BCY and HHV prediction accounting for about 30%, followed by other feedstock and operational characteristics. In addition, a user interface was presented for ease of use in the ML modeling.
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Data Availability
The datasets generated during and/or analyzed during the current study are available on reasonable request.
References
Barcena-Vazquez J, Caro K, Bermudez K et al (2023) Designing and evaluating Reto Global, a serious video game for supporting global warming awareness. Int J Hum Comput Stud 177:103080. https://doi.org/10.1016/j.ijhcs.2023.103080
Wang L, Wang L, Li Y et al (2023) A century-long analysis of global warming and earth temperature using a random walk with drift approach. Decis Analy J 7. https://doi.org/10.1016/j.dajour.2023.100237
Jiang L, Zhao Y, Yao Y et al (2023) Adding siderophores: a new strategy to reduce greenhouse gas emissions in composting. Bioresour Technol 384. https://doi.org/10.1016/j.biortech.2023.129319
Zhao J, ** system in the Loess Plateau, China. Eur J Agron 141. https://doi.org/10.1016/j.eja.2022.126619
Naaz F, Samuchiwal S, Dalvi V et al (2023) Hydrothermal liquefaction could be a sustainable approach for valorization of wastewater grown algal biomass into cleaner fuel. Energy Convers Manag 283. https://doi.org/10.1016/j.enconman.2023.116887
Zoppi G, Tito E, Bianco I et al (2023) Life cycle assessment of the biofuel production from lignocellulosic biomass in a hydrothermal liquefaction – aqueous phase reforming integrated biorefinery. Renew Energy 206:375–385. https://doi.org/10.1016/j.renene.2023.02.011
Kaliyan N, Morey RV, Tiffany DG (2011) Reducing life cycle greenhouse gas emissions of corn ethanol by integrating biomass to produce heat and power at ethanol plants. Biomass Bioenergy 35:1103–1113. https://doi.org/10.1016/j.biombioe.2010.11.035
Alola AA, Adebayo TS (2023) Analysing the waste management, industrial and agriculture greenhouse gas emissions of biomass, fossil fuel, and metallic ores utilization in Iceland. Sci Total Environ 887. https://doi.org/10.1016/j.scitotenv.2023.164115
Miranda AM, Sáez AA, Hoyos BS et al (2021) Improving microalgal biomass production with industrial CO2 for bio-oil obtention by hydrothermal liquefaction. Fuel 302. https://doi.org/10.1016/j.fuel.2021.121236
Wang H, Han X, Zeng Y et al (2023) Development of a global kinetic model based on chemical compositions of lignocellulosic biomass for predicting product yields from hydrothermal liquefaction. Renew Energy 215:118956. https://doi.org/10.1016/j.renene.2023.118956
Fan Q, Fu P, Song C et al (2023) Valorization of waste biomass through hydrothermal liquefaction: a review with focus on linking hydrothermal factors to products characteristics. Ind Crops Prod 191. https://doi.org/10.1016/j.indcrop.2022.116017
Peng W, Karimi Sadaghiani O (2023) Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms. Biomass Bioenergy 175. https://doi.org/10.1016/j.biombioe.2023.106884
Sonwai A, Pholchan P, Tippayawong N et al (2023) Machine learning approach for determining and optimizing influential factors of biogas production from lignocellulosic biomass. Bioresour Technol 383. https://doi.org/10.1016/j.biortech.2023.129235
Haq ZU, Ullah U, Khan MNA et al (2022) Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction. Bioresour Technol 363. https://doi.org/10.1016/j.biortech.2022.128008
Zhang W (2021) Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae. Bioresour Technol 342. https://doi.org/10.1016/j.biortech.2021.126011
Shafizadeh A (2022) Machine learning predicts and optimizes hydrothermal liquefaction of biomass. J Chem Eng 445. https://doi.org/10.1016/j.cej.2022.136579
Katongtung T, Onsree T, Tippayawong N (2022) Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes. Bioresour Technol 344. https://doi.org/10.1016/j.biortech.2021.126278
Onsree T, Tippayawong N (2021) Machine learning application to predict yields of solid products from biomass torrefaction. Renew Energy 167:425–432. https://doi.org/10.1016/j.renene.2020.11.099
Phromphithak S, Onsree T, Tippayawong N (2021) Machine learning prediction of cellulose-rich materials from biomass pretreatment with ionic liquid solvents. Bioresour Technol 323. https://doi.org/10.1016/j.biortech.2020.124642
Pedregosa F (2011) Scikit-learn: machine learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. http://scikit-learn.sourceforge.net
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery 785–794. https://doi.org/10.1145/2939672.2939785
Elmaz F, Yücel Ö, Mutlu AY (2020) Predictive modeling of biomass gasification with machine learning-based regression methods. Energy 191. https://doi.org/10.1016/j.energy.2019.116541
Đukanović M, Kašćelan L, Vuković S et al (2023) A machine learning approach for time series forecasting with application to debt risk of the Montenegrin electricity industry. Energy Rep 9:362–369. https://doi.org/10.1016/j.egyr.2023.05.240
Wang H, Yang J, Chen G et al (2023) Machine learning applications on air temperature prediction in the urban canopy layer: a critical review of 2011–2022. Urban Climate 49. https://doi.org/10.1016/j.uclim.2023.101499.
Mabula MJ, Kisanga D, Pamba S (2023) Application of machine learning algorithms and Sentinel-2 satellite for improved bathymetry retrieval in Lake Victoria, Tanzania. Egypt J Remote Sens Space Sci 26:619–627. https://doi.org/10.1016/j.ejrs.2023.07.003
Malakouti SM (2023) Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in predicting wind speed and energy generation. Intel Syst Appl 19. https://doi.org/10.1016/j.iswa.2023.200248
Zhang X, Liu CA (2023) Model averaging prediction by K-fold cross-validation. J Econom 235:280–301. https://doi.org/10.1016/j.jeconom.2022.04.007
Sumayli A (2023) Development of advanced machine learning models for optimization of methyl ester biofuel production from papaya oil: gaussian process regression (GPR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) regression models. Arab J Chem 16. https://doi.org/10.1016/j.arabjc.2023.104833
Semmad A, Bahoura M (2023) Scalable serial hardware architecture of multilayer perceptron neural network for automatic wheezing detection. Microprocess Microsyst 99. https://doi.org/10.1016/j.micpro.2023.104844.
Kumar PS, Laha SK, Kumaraswamidhas LA (2023) Assessment of rolling element bearing degradation based on dynamic time war**, kernel ridge regression and support vector regression. Appl Acoust 208. https://doi.org/10.1016/j.apacoust.2023.109389
Rezaei I, Amirshahi SH, Mahbadi AA (2023) Utilizing support vector and kernel ridge regression methods in spectral reconstruction. Results Opt 11: 100405. https://doi.org/10.1016/j.rio.2023.100405.
Ghosh A, Dey P (2021) Flood Severity assessment of the coastal tract situated between Muriganga and Saptamukhi estuaries of Sundarban delta of India using frequency ratio (FR), fuzzy logic (FL), logistic regression (LR) and random forest (RF) models. Reg Stud Mar Sci 42. https://doi.org/10.1016/j.rsma.2021.101624
Khajavi H, Rastgoo A (2023) Predicting the carbon dioxide emission caused by road transport using a random forest (RF) model combined by meta-heuristic algorithms. Sustain Cities Soc 93. https://doi.org/10.1016/j.scs.2023.104503
Le HA (2022) An extreme gradient boosting approach to estimate the shear strength of FRP reinforced concrete beams. Structures 45:1307–1321. https://doi.org/10.1016/j.istruc.2022.09.112
Jarajapu DC, Rathinasamy M, Agarwal A et al (2022) Design flood estimation using extreme gradient boosting-based on bayesian optimization. J Hydrol (Amst) 613. https://doi.org/10.1016/j.jhydrol.2022.128341
Dong L, Liu Z, Zhang K et al (2023) Affordable federated edge learning framework via efficient Shapley value estimation. Future Gen Comput Syst 147:339–349. https://doi.org/10.1016/j.future.2023.05.007
Louhichi M, Nesmaoui R, Mbarek M et al (2023) Shapley Values for Explaining the Black Box Nature of Machine Learning Model Clustering. Proc Comput Sci 220:806–811. https://doi.org/10.1016/j.procs.2023.03.107
Sharma N (2021) Effect of catalyst and temperature on the quality and productivity of HTL bio-oil from microalgae: a review. Renew Energy 174:810–822. https://doi.org/10.1016/j.renene.2021.04.147
Reddy HK (2016) Temperature effect on hydrothermal liquefaction of Nannochloropsis gaditana and Chlorella sp. Appl Energy 165:943–951. https://doi.org/10.1016/j.apenergy.2015.11.067
Biswas B, Arun Kumar A, Bisht Y et al (2017) Effects of temperature and solvent on hydrothermal liquefaction of Sargassum tenerrimum algae. Bioresour Technol 242:344–350. https://doi.org/10.1016/j.biortech.2017.03.045
Chen WT (2017) Effect of ash on hydrothermal liquefaction of high-ash content algal biomass. Algal Res 25:297–306. https://doi.org/10.1016/j.algal.2017.05.010
Duan P, Chang Z, Xu Y (2013) Hydrothermal processing of duckweed: Effect of reaction conditions on product distribution and composition. Bioresour Technol 135:710–719. https://doi.org/10.1016/j.biortech.2012.08.106
Tian C (2015) Hydrothermal liquefaction of harvested high-ash low-lipid algal biomass from Dianchi Lake: effects of operational parameters and relations of products. Bioresour Technol 184:336–343. https://doi.org/10.1016/j.biortech.2014.10.093
Tang X, Zhang C, Yang X (2020) Optimizing process of hydrothermal liquefaction of microalgae via flash heating and isolating aqueous extract from bio-crude. J Clean Prod 258. https://doi.org/10.1016/j.jclepro.2020.120660
Yoo G, Park MS, Yang JW et al (2015) Lipid content in microalgae determines the quality of biocrude and energy return on investment of hydrothermal liquefaction. Appl Energy 156:354–361. https://doi.org/10.1016/j.apenergy.2015.07.020
Amar VS (2021) Hydrothermal liquefaction (HTL) processing of unhydrolyzed solids (UHS) for hydrochar and its use for asymmetric supercapacitors with mixed (Mn,Ti)-Perovskite oxides. Renew Energy 173:329–341. https://doi.org/10.1016/j.renene.2021.03.126
Yiin CL (2022) A review on potential of green solvents in hydrothermal liquefaction (HTL) of lignin. Bioresour Technol 364. https://doi.org/10.1016/j.biortech.2022.128075
Djandja OS, Salami AA, Yuan H et al (2023) Machine learning prediction of bio-oil yield during solvothermal liquefaction of lignocellulosic biowaste. J Anal Appl Pyrol 175. https://doi.org/10.1016/j.jaap.2023.106209
Acknowledgements
This project is partially funded by the National Research Council of Thailand (NRCT), SCG Co., Ltd. and Fundamental Fund 2567: Chiang Mai University (CMU). The 1st author also wishes to thank the Teaching and Research Assistantships from the CMU Graduate School and the NRCT Research & Researchers for Industry PhD program.
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Katongtung, T., Phromphithak, S., Onsree, T. et al. Machine Learning Approach for Predicting Hydrothermal Liquefaction of Lignocellulosic Biomass. Bioenerg. Res. (2024). https://doi.org/10.1007/s12155-024-10773-0
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DOI: https://doi.org/10.1007/s12155-024-10773-0