Using machine learning to predict the production and quality of bio-oil from pyrolysis biomass

Document Type : Review

Authors

1 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 Agricultural Biotechnology Research Institute of Iran

3 Brain Engineering Research Center, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran

Abstract

Reducing the reserves of fossil energy sources serves as a warning sign for humanity. On the other hand, the increasing consumption of fossil fuels has led to significant environmental problems, such as global warming. These issues make the replacement of renewable energy sources with fossil fuels inevitable. Among various renewable energy sources, biomass is a reliable and sustainable resource. Thermochemical conversions of biomass are a promising method for converting raw biomass into liquid (bio-oil), solid (bio-char), and gas (biogas) fuels suitable for modern life. As one of the most important thermochemical conversions for efficient bio-oil production, pyrolysis has received significant attention. However, pyrolysis requires advanced equipment, precise product quantity, and quality measurement, which can be challenging and costly. Therefore, modeling has been extensively researched to enhance the performance and efficiency of pyrolysis. In recent years, machine learning has gained considerable attention in pyrolysis modeling, particularly for yield optimization, real-time monitoring, and process control. In addition to conventional techniques like artificial neural networks that capture nonlinear correlations between input and output values, combined machine learning models have been of particular interest for modeling and optimizing complex problems more effectively. This study provides a comprehensive overview of the research conducted on the application of machine learning in pyrolysis process modeling and assesses the prospects of this technology. These machine learning models have provided R2 between 0.26 in the weakest case and 0.99 in the best case for predicting bio-oil production. These values have been presented between 0.6 and 0.93 to predict the improvement of bio-oil quality modeling.

Keywords

Main Subjects

Using machine learning to predict the production and quality of bio-oil from pyrolysis biomass

EXTENDED ABSTRACT

Introduction

Reducing the reserves of fossil energy sources serves as a warning sign for humanity. On the other hand, the increasing consumption of fossil fuels has led to significant environmental problems, such as global warming. These issues make the replacement of renewable energy sources with fossil fuels inevitable. Among various renewable energy sources, biomass is a reliable and sustainable resource. Thermochemical conversions of biomass are a promising method for converting raw biomass into liquid (bio-oil), solid (bio-char), and gas (biogas) fuels suitable for modern life.

Biomass pyrolysis

Biomass pyrolysis is a process that involves heating organic matter in the absence of oxygen to produce bio-oil. The biofuel produced can be refined into transportation fuels or used as feedstock for chemical production. There are several different types of pyrolysis. Flash pyrolysis involves rapidly heating biomass to high temperatures without oxygen, forming a high-quality bio-oil. Slow pyrolysis, on the other hand, involves heating biomass at lower temperatures for longer periods, producing more biochar and less bio-oil. The pyrolysis process is complex and influenced by numerous variables, such as feedstock properties, heating rate, temperature, and residence time. Modeling can be a promising strategy to optimize the process parameters and improve product yield and quality.

Machine learning modeling

Machine learning is a field of artificial intelligence that uses algorithms to enable computers to learn from data and make predictions or decisions without being explicitly programmed. The process involves feeding data into a machine learning model, which then uses statistical analysis to identify patterns and relationships in the data. These patterns are used to make predictions or decisions about new, unseen data.

Supervised machine learning

Supervised machine learning is a type of machine learning where the algorithm is trained on labeled data, meaning the data has already been classified or grouped into different categories. The goal is to create a model to accurately predict the correct category or label for new, unseen data. There are two main subcategories of supervised machine learning: classification and regression. Classification involves predicting categorical labels, such as whether an email is spam or not. Regression, on the other hand, involves predicting a continuous numerical value. Several popular algorithms are used in supervised machine learning, including multi-linear regression, bagging regression decision trees, and random forest logistic regression.

Application of machine learning in the prediction of bio yield and quality prediction

Various studies have considered several independent inputs for modeling the pyrolysis process with machine learning methods. Regarding biomass composition, parameters such as the carbon content, hydrogen, oxygen, nitrogen, and sulfur of biomass, moisture content, ash content, and the amount of fixed carbon are considered effective parameters on the pyrolysis process in independent inputs. Operational input features include operating temperature, heating rate, reaction time, particle size, and input carrier fluid rate. Bio-oil yield, calorific value, carbon-to-hydrogen or carbon-to-oxygen ratio, sulfur and nitrogen in bio-oil, acidity, acid content, and various amounts of organic compounds have been considered dependence output parameters.

Application of parameter importance analysis in machine learning for predicting the production process and quality of bio-oil.

Although machine learning techniques can model complex phenomena, the nature of machine learning techniques, like a black box, makes interpreting results and identifying influential factors on the model very challenging. Therefore, the results of each machine learning model should be explained using advanced tools. These methods, such as Shapley Additive Explanations, provide a way to measure the impact of each independent input parameter of the pyrolysis process on the dependent response in the output of the machine learning models.

Results and Future Trends

Developing and expanding machine learning models for more complex processes and considering other influential parameters, such as catalyst properties on the biomass catalytic pyrolysis process, is possible. For instance, catalyst type, the amount used, and its structural and chemical properties greatly affect the product performance and quality improvement of bio-oil. Therefore, providing solutions in the future that can quantitatively incorporate important catalyst features as independent parameters during the modeling process and accurately model the effects of these factors on the outputs would be highly significant. Additionally, the use of machine learning modeling in semi-industrial and industrial systems for biomass pyrolysis can be investigated to improve the performance of these units.

References

Abnisa, F., Arami-Niya, A., Wan Daud, W. M. A., Sahu, J. N., & Noor, I. M. (2013). Utilization of oil palm tree residues to produce bio-oil and bio-char via pyrolysis. Energy Conversion and Management, 76, 1073–1082. https://doi.org/10.1016/j.enconman.2013.08.038
Akhtar, J., & Saidina Amin, N. (2012). A review on operating parameters for optimum liquid oil yield in biomass pyrolysis. Renewable and Sustainable Energy Reviews, 16(7), 5101–5109. https://doi.org/10.1016/j.rser.2012.05.033
Al-Sabawi, M., Chen, J., & Ng, S. (2012). Fluid Catalytic Cracking of Biomass-Derived Oils and Their Blends with Petroleum Feedstocks: A Review. Energy & Fuels, 26(9), 5355–5372. https://doi.org/10.1021/ef3006417
Awasthi, M. K., Sindhu, R., Sirohi, R., Kumar, V., Ahluwalia, V., Binod, P., Juneja, A., Kumar, D., Yan, B., Sarsaiya, S., Zhang, Z., Pandey, A., & Taherzadeh, M. J. (2022). Agricultural waste biorefinery development towards circular bioeconomy. Renewable and Sustainable Energy Reviews, 158, 112122. https://doi.org/10.1016/j.rser.2022.112122
Basu, P. (2018). Biomass Gasification, Pyrolysis and Torrefaction. Elsevier. https://doi.org/10.1016/C2016-0-04056-1
Campbell, R. M., Anderson, N. M., Daugaard, D. E., & Naughton, H. T. (2018). Financial viability of biofuel and biochar production from forest biomass in the face of market price volatility and uncertainty. Applied Energy, 230, 330–343. https://doi.org/10.1016/j.apenergy.2018.08.085
Carpenter, D., Westover, T. L., Czernik, S., & Jablonski, W. (2014). Biomass feedstocks for renewable fuel production: a review of the impacts of feedstock and pretreatment on the yield and product distribution of fast pyrolysis bio-oils and vapors. Green Chem., 16(2), 384–406. https://doi.org/10.1039/C3GC41631C
Chakraborty, B., & Murphy, S. A. (2014). Dynamic Treatment Regimes. Annual Review of Statistics and Its Application, 1(1), 447–464. https://doi.org/10.1146/annurev-statistics-022513-115553
Chen, D., Cen, K., Zhuang, X., Gan, Z., Zhou, J., Zhang, Y., & Zhang, H. (2022). Insight into biomass pyrolysis mechanism based on cellulose, hemicellulose, and lignin: Evolution of volatiles and kinetics, elucidation of reaction pathways, and characterization of gas, biochar and bio‐oil. Combustion and Flame, 242, 112142. https://doi.org/10.1016/j.combustflame.2022.112142
Chen, W.-H., Lin, B.-J., Lin, Y.-Y., Chu, Y.-S., Ubando, A. T., Show, P. L., Ong, H. C., Chang, J.-S., Ho, S.-H., Culaba, A. B., Pétrissans, A., & Pétrissans, M. (2021). Progress in biomass torrefaction: Principles, applications and challenges. Progress in Energy and Combustion Science, 82, 100887. https://doi.org/10.1016/j.pecs.2020.100887
Chen, X., Zhang, H., Song, Y., & Xiao, R. (2018). Prediction of product distribution and bio-oil heating value of biomass fast pyrolysis. Chemical Engineering and Processing - Process Intensification, 130(February), 36–42. https://doi.org/10.1016/j.cep.2018.05.018
Crisci, C., Ghattas, B., & Perera, G. (2012). A review of supervised machine learning algorithms and their applications to ecological data. Ecological Modelling, 240, 113–122. https://doi.org/10.1016/j.ecolmodel.2012.03.001
De Clercq, R., Dusselier, M., & Sels, B. F. (2017). Heterogeneous catalysis for bio-based polyester monomers from cellulosic biomass: advances, challenges and prospects. Green Chem., 19(21), 5012–5040. https://doi.org/10.1039/C7GC02040F
Di Blasi, C., Galgano, A., & Branca, C. (2009). Influences of the Chemical State of Alkaline Compounds and the Nature of Alkali Metal on Wood Pyrolysis. Industrial & Engineering Chemistry Research, 48(7), 3359–3369. https://doi.org/10.1021/ie801468y
Ding, Y., Ezekoye, O. A., Zhang, J., Wang, C., & Lu, S. (2018). The effect of chemical reaction kinetic parameters on the bench-scale pyrolysis of lignocellulosic biomass. Fuel, 232, 147–153. https://doi.org/https://doi.org/10.1016/j.fuel.2018.05.140
Dong, Z., Bai, X., Xu, D., & Li, W. (2023). Machine learning prediction of pyrolytic products of lignocellulosic biomass based on physicochemical characteristics and pyrolysis conditions. Bioresource Technology, 367, 128182. https://doi.org/10.1016/j.biortech.2022.128182
Foong, S. Y., Liew, R. K., Yang, Y., Cheng, Y. W., Yek, P. N. Y., Wan Mahari, W. A., Lee, X. Y., Han, C. S., Vo, D.-V. N., Van Le, Q., Aghbashlo, M., Tabatabaei, M., Sonne, C., Peng, W., & Lam, S. S. (2020). Valorization of biomass waste to engineered activated biochar by microwave pyrolysis: Progress, challenges, and future directions. Chemical Engineering Journal, 389, 124401. https://doi.org/10.1016/j.cej.2020.124401
Gani, A., & Naruse, I. (2007). Effect of cellulose and lignin content on pyrolysis and combustion characteristics for several types of biomass. Renewable Energy, 32(4), 649–661. https://doi.org/10.1016/j.renene.2006.02.017
Gautam, P., Neha, Upadhyay, S. N., & Dubey, S. K. (2020). Bio-methanol as a renewable fuel from waste biomass: Current trends and future perspective. Fuel, 273, 117783. https://doi.org/10.1016/j.fuel.2020.117783
Ghafarian, F., Wieland, R., Lüttschwager, D., & Nendel, C. (2022). Application of extreme gradient boosting and Shapley Additive explanations to predict temperature regimes inside forests from standard open-field meteorological data. Environmental Modelling & Software, 156, 105466. https://doi.org/10.1016/j.envsoft.2022.105466
Harrison, J. H., Gilbertson, J. R., Hanna, M. G., Olson, N. H., Seheult, J. N., Sorace, J. M., & Stram, M. N. (2021). Introduction to Artificial Intelligence and Machine Learning for Pathology. Archives of Pathology & Laboratory Medicine, 145(10), 1228–1254. https://doi.org/10.5858/arpa.2020-0541-CP
Hassan, E. M., Steele, P. H., & Ingram, L. (2009). Characterization of Fast Pyrolysis Bio-oils Produced from Pretreated Pine Wood. Applied Biochemistry and Biotechnology, 154(1–3), 3–13. https://doi.org/10.1007/s12010-008-8445-3
Haykiri-Acma, H. (2006). The role of particle size in the non-isothermal pyrolysis of hazelnut shell. Journal of Analytical and Applied Pyrolysis, 75(2), 211–216. https://doi.org/10.1016/j.jaap.2005.06.002
Hodge, V. J., & Austin, J. (2004). A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, 22(2), 85–126. https://doi.org/10.1007/s10462-004-4304-y
Hough, B. R., Beck, D. A. C., Schwartz, D. T., & Pfaendtner, J. (2017). Application of machine learning to pyrolysis reaction networks: Reducing model solution time to enable process optimization. Computers and Chemical Engineering, 104, 56–63. https://doi.org/10.1016/j.compchemeng.2017.04.012
Kaczor, Z., Buliński, Z., & Werle, S. (2020). Modelling approaches to waste biomass pyrolysis: a review. Renewable Energy, 159, 427–443. https://doi.org/10.1016/j.renene.2020.05.110
Kasmuri, N. H., Kamarudin, S. K., Abdullah, S. R. S., Hasan, H. A., & Som, A. M. (2019). Integrated advanced nonlinear neural network-simulink control system for production of bio-methanol from sugar cane bagasse via pyrolysis. Energy, 168(2019), 261–272. https://doi.org/10.1016/j.energy.2018.11.056
Khan, M., Raza Naqvi, S., Ullah, Z., Ali Ammar Taqvi, S., Nouman Aslam Khan, M., Farooq, W., Taqi Mehran, M., Juchelková, D., & Štěpanec, L. (2023). Applications of machine learning in thermochemical conversion of biomass-A review. Fuel, 332, 126055. https://doi.org/10.1016/j.fuel.2022.126055
Kim, D., & Philen, M. (2011). Damage classification using Adaboost machine learning for structural health monitoring (M. Tomizuka (ed.); p. 79812A). https://doi.org/10.1117/12.882016
Kumar, P., Barrett, D. M., Delwiche, M. J., & Stroeve, P. (2009). Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production. Industrial & Engineering Chemistry Research, 48(8), 3713–3729. https://doi.org/10.1021/ie801542g
Kumar Sharma, A., Kumar Ghodke, P., Goyal, N., Nethaji, S., & Chen, W.-H. (2022). Machine learning technology in biohydrogen production from agriculture waste: Recent advances and future perspectives. Bioresource Technology, 364, 128076. https://doi.org/10.1016/j.biortech.2022.128076
Leng, E., He, B., Chen, J., Liao, G., Ma, Y., Zhang, F., Liu, S., & E, J. (2021). Prediction of three-phase product distribution and bio-oil heating value of biomass fast pyrolysis based on machine learning. Energy, 236, 121401. https://doi.org/10.1016/j.energy.2021.121401
Li, S., Cheng, S., & Cross, J. S. (2020). Homogeneous and Heterogeneous Catalysis Impact on Pyrolyzed Cellulose to Produce Bio-Oil. Catalysts, 10(2), 178. https://doi.org/10.3390/catal10020178
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Liu, C., Wang, H., Karim, A. M., Sun, J., & Wang, Y. (2014). Catalytic fast pyrolysis of lignocellulosic biomass. Chem. Soc. Rev., 43(22), 7594–7623. https://doi.org/10.1039/C3CS60414D
Liu, M., Liu, X., Li, J., Ding, C., & Jiang, J. (2014). Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm. International Journal of Applied Earth Observation and Geoinformation, 33, 192–202. https://doi.org/10.1016/j.jag.2014.05.009
Madadian, E., Haelssig, J. B., & Pegg, M. (2020). A Comparison of Thermal Processing Strategies for Landfill Reclamation: Methods, Products, and a Promising Path Forward. Resources, Conservation and Recycling, 160, 104876. https://doi.org/10.1016/j.resconrec.2020.104876
Madhu, P., Matheswaran, M. M., & Periyanayagi, G. (2017). Optimization and characterization of bio-oil produced from cotton shell by flash pyrolysis using artificial neural network. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 39(23), 2173–2180. https://doi.org/10.1080/15567036.2017.1403508
Mateus, M. M., Bordado, J. M., & Galhano dos Santos, R. (2021). Estimation of higher heating value (HHV) of bio-oils from thermochemical liquefaction by linear correlation. Fuel, 302, 121149. https://doi.org/10.1016/j.fuel.2021.121149
Mathur, J., Baruah, B., & Tiwari, P. (2023). Prediction of bio‐oil yield during pyrolysis of lignocellulosic biomass using machine learning algorithms. The Canadian Journal of Chemical Engineering, 101(5), 2457–2471. https://doi.org/10.1002/cjce.24674
Maulud, D., & Abdulazeez, A. M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(4), 140–147. https://doi.org/10.38094/jastt1457
Murali, N., Kucukkaya, A., Petukhova, A., Onofrey, J., & Chapiro, J. (2020). Supervised Machine Learning in Oncology: A Clinician’s Guide. Digestive Disease Interventions, 04(01), 073–081. https://doi.org/10.1055/s-0040-1705097
Nie, P., Roccotelli, M., Fanti, M. P., Ming, Z., & Li, Z. (2021). Prediction of home energy consumption based on gradient boosting regression tree. Energy Reports, 7, 1246–1255. https://doi.org/10.1016/j.egyr.2021.02.006
Norouzi, O., Taghavi, S., Arku, P., Jafarian, S., Signoretto, M., & Dutta, A. (2021). What is the best catalyst for biomass pyrolysis? Journal of Analytical and Applied Pyrolysis, 158, 105280. https://doi.org/10.1016/j.jaap.2021.105280
Özbay, G., & KÖKTEN, E. S. (2019). Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach. Journal of Polytechnic, 0900, 0–3. https://doi.org/10.2339/politeknik.659136
Pütün, A. E., Özbay, N., Apaydın Varol, E., Uzun, B. B., & Ateş, F. (2007). Rapid and slow pyrolysis of pistachio shell: effect of pyrolysis conditions on the product yields and characterization of the liquid product. International Journal of Energy Research, 31(5), 506–514. https://doi.org/10.1002/er.1263
Raghavendra. N, S., & Deka, P. C. (2014). Support vector machine applications in the field of hydrology: A review. Applied Soft Computing, 19, 372–386. https://doi.org/10.1016/j.asoc.2014.02.002
Rahman, M. M., Liu, R., & Cai, J. (2018). Catalytic fast pyrolysis of biomass over zeolites for high quality bio-oil – A review. Fuel Processing Technology, 180, 32–46. https://doi.org/10.1016/j.fuproc.2018.08.002
Rashidi, H. H., Albahra, S., Robertson, S., Tran, N. K., & Hu, B. (2023). Common statistical concepts in the supervised Machine Learning arena. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1130229
Rzychoń, M., Żogała, A., & Róg, L. (2021). Experimental study and extreme gradient boosting (XGBoost) based prediction of caking ability of coal blends. Journal of Analytical and Applied Pyrolysis, 156, 105020. https://doi.org/10.1016/j.jaap.2021.105020
Salehi, E., Abedi, J., & Harding, T. (2009). Bio-oil from Sawdust: Pyrolysis of Sawdust in a Fixed-Bed System. Energy & Fuels, 23(7), 3767–3772. https://doi.org/10.1021/ef900112b
Samanpour, A. R., Ruegenberg, A., & Ahlers, R. (2018). The Future of Machine Learning and Predictive Analytics. In Digital Marketplaces Unleashed (pp. 297–309). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-49275-8_30
Seo, M. W., Lee, S. H., Nam, H., Lee, D., Tokmurzin, D., Wang, S., & Park, Y.-K. (2022). Recent advances of thermochemical conversion processes for biorefinery. Bioresource Technology, 343, 126109. https://doi.org/10.1016/j.biortech.2021.126109
Shafizadeh, A., Rastegari, H., Shahbeik, H., Mobli, H., Pan, J., Peng, W., Li, G., Tabatabaei, M., & Aghbashlo, M. (2023a). A critical review of the use of nanomaterials in the biomass pyrolysis process. Journal of Cleaner Production, 400, 136705. https://doi.org/10.1016/j.jclepro.2023.136705
Shafizadeh, A., Shahbeig, H., Hossein Nadian, M., Mobli, H., Dowlati, M., Kumar Gupta, V., Peng, W., Shiung Lam, S., Tabatabaei, M., & Aghbashlo, M. (2022). Machine learning predicts and optimizes hydrothermal liquefaction of biomass. Chemical Engineering Journal, 136579. https://doi.org/10.1016/j.cej.2022.136579
Shafizadeh, A., Shahbeik, H., Nadian, M. H., Gupta, V. K., Nizami, A.-S., Lam, S. S., Peng, W., Pan, J., Tabatabaei, M., & Aghbashlo, M. (2023b). Turning hazardous volatile matter compounds into fuel by catalytic steam reforming: An evolutionary machine learning approach. Journal of Cleaner Production, 413, 137329. https://doi.org/10.1016/j.jclepro.2023.137329
Shafizadeh, A., Shahbeik, H., Rafiee, S., Moradi, A., Shahbaz, M., Madadi, M., Li, C., Peng, W., Tabatabaei, M., & Aghbashlo, M. (2023c). Machine learning-based characterization of hydrochar from biomass: Implications for sustainable energy and material production. Fuel, 347, 128467. https://doi.org/10.1016/j.fuel.2023.128467
Shahbeik, H., Rafiee, S., Shafizadeh, A., Jeddi, D., Jafary, T., Lam, S. S., Pan, J., Tabatabaei, M., & Aghbashlo, M. (2022). Characterizing sludge pyrolysis by machine learning: Towards sustainable bioenergy production from wastes. Renewable Energy, 199, 1078–1092. https://doi.org/10.1016/j.renene.2022.09.022
Shahbeik, H., Shafizadeh, A., Gupta, V. K., Lam, S. S., Rastegari, H., Peng, W., Pan, J., Tabatabaei, M., & Aghbashlo, M. (2023). Using nanocatalysts to upgrade pyrolysis bio-oil: A critical review. Journal of Cleaner Production, 413, 137473. https://doi.org/10.1016/j.jclepro.2023.137473
Shen, J., Yan, M., Fang, M., & Gao, X. (2022). Machine learning-based modeling approaches for estimating pyrolysis products of varied biomass and operating conditions. Bioresource Technology Reports, 20, 101285. https://doi.org/10.1016/j.biteb.2022.101285
Somvanshi, M., Chavan, P., Tambade, S., & Shinde, S. V. (2016). A review of machine learning techniques using decision tree and support vector machine. 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), 1–7. https://doi.org/10.1109/ICCUBEA.2016.7860040
Su, S., & Wang, J. (2023). Machine learning prediction of contents of oxygenated components in bio-oil using extreme gradient boosting method under different pyrolysis conditions. Bioresource Technology, 379, 129040. https://doi.org/10.1016/j.biortech.2023.129040
Surendra, K. C., Angelidaki, I., & Khanal, S. K. (2022). Bioconversion of waste-to-resources (BWR-2021): Valorization of industrial and agro-wastes to fuel, feed, fertilizer, and biobased products. Bioresource Technology, 347, 126739. https://doi.org/10.1016/j.biortech.2022.126739
Suriapparao, D. V., & Vinu, R. (2018). Effects of Biomass Particle Size on Slow Pyrolysis Kinetics and Fast Pyrolysis Product Distribution. Waste and Biomass Valorization, 9(3), 465–477. https://doi.org/10.1007/s12649-016-9815-7
Tang, Q., Chen, Y., Yang, H., Liu, M., Xiao, H., Wu, Z., Chen, H., & Naqvi, S. R. (2020). Prediction of Bio-oil Yield and Hydrogen Contents Based on Machine Learning Method: Effect of Biomass Compositions and Pyrolysis Conditions. Energy & Fuels, 34(9), 11050–11060. https://doi.org/10.1021/acs.energyfuels.0c01893
Taşar, Ş. (2022). Estimation of pyrolysis liquid product yield and its hydrogen content for biomass resources by combined evaluation of pyrolysis conditions with proximate-ultimate analysis data: A machine learning application. Journal of Analytical and Applied Pyrolysis, 165, 105546. https://doi.org/10.1016/j.jaap.2022.105546
Trinh, T. N., Jensen, P. A., Dam-Johansen, K., Knudsen, N. O., & Sørensen, H. R. (2013). Influence of the Pyrolysis Temperature on Sewage Sludge Product Distribution, Bio-Oil, and Char Properties. Energy & Fuels, 27(3), 1419–1427. https://doi.org/10.1021/ef301944r
Varma, A. K., Shankar, R., & Mondal, P. (2018). A Review on Pyrolysis of Biomass and the Impacts of Operating Conditions on Product Yield, Quality, and Upgradation. In Recent Advancements in Biofuels and Bioenergy Utilization (pp. 227–259). Springer Singapore. https://doi.org/10.1007/978-981-13-1307-3_10
Velvizhi, G., Balakumar, K., Shetti, N. P., Ahmad, E., Kishore Pant, K., & Aminabhavi, T. M. (2022). Integrated biorefinery processes for conversion of lignocellulosic biomass to value added materials: Paving a path towards circular economy. Bioresource Technology, 343, 126151. https://doi.org/10.1016/j.biortech.2021.126151
Venderbosch, R., & Prins, W. (2010). Fast pyrolysis technology development. Biofuels, Bioproducts and Biorefining, 4(2), 178–208. https://doi.org/10.1002/bbb.205
Wang, L., Zhou, X., Zhu, X., Dong, Z., & Guo, W. (2016). Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal, 4(3), 212–219. https://doi.org/10.1016/j.cj.2016.01.008
Wurzer, C., & Mašek, O. (2021). Feedstock doping using iron rich waste increases the pyrolysis gas yield and adsorption performance of magnetic biochar for emerging contaminants. Bioresource Technology, 321, 124473. https://doi.org/10.1016/j.biortech.2020.124473
Xie, Q., Wang, G., Peng, Z., & Lian, Y. (2018). Machine Learning Methods for Real-Time Blood Pressure Measurement Based on Photoplethysmography. 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 1–5. https://doi.org/10.1109/ICDSP.2018.8631690
Xing, J., Luo, K., Wang, H., Gao, Z., & Fan, J. (2019). A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches. Energy, 188, 116077. https://doi.org/10.1016/j.energy.2019.116077
Xiong, Z., Guo, J., Chaiwat, W., Deng, W., Hu, X., Han, H., Chen, Y., Xu, K., Su, S., Hu, S., Wang, Y., & Xiang, J. (2020). Assessing the chemical composition of heavy components in bio-oils from the pyrolysis of cellulose, hemicellulose and lignin at slow and fast heating rates. Fuel Processing Technology, 199, 106299. https://doi.org/10.1016/j.fuproc.2019.106299
Yang, K., Wu, K., & Zhang, H. (2022a). Machine learning prediction of the yield and oxygen content of bio-oil via biomass characteristics and pyrolysis conditions. Energy, 254, 124320. https://doi.org/10.1016/j.energy.2022.124320
Yang, Q., Mašek, O., Zhao, L., Nan, H., Yu, S., Yin, J., Li, Z., & Cao, X. (2021). Country-level potential of carbon sequestration and environmental benefits by utilizing crop residues for biochar implementation. Applied Energy, 282, 116275. https://doi.org/10.1016/j.apenergy.2020.116275
Yang, Y., Shahbeik, H., Shafizadeh, A., Masoudnia, N., Rafiee, S., Zhang, Y., Pan, J., Tabatabaei, M., & Aghbashlo, M. (2022b). Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries. Renewable Energy, 201, 70–86. https://doi.org/10.1016/j.renene.2022.11.028
Yang, Y., Shahbeik, H., Shafizadeh, A., Rafiee, S., Hafezi, A., Du, X., Pan, J., Tabatabaei, M., & Aghbashlo, M. (2023). Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning. Energy, 278, 127881. https://doi.org/https://doi.org/10.1016/j.energy.2023.127881
Ying, X. (2019). An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series, 1168, 022022. https://doi.org/10.1088/1742-6596/1168/2/022022
Zeng, X., Shao, H., Pan, R., Wang, B., Deng, Q., Zhang, C., & Suo, T. (2022). Real-time damage analysis of 2D C/SiC composite based on spectral characters of acoustic emission signals using pattern recognition. Acta Mechanica Sinica, 38(10), 422177. https://doi.org/10.1007/s10409-022-22177-x
Zhang, T., Cao, D., Feng, X., Zhu, J., Lu, X., Mu, L., & Qian, H. (2022). Machine learning prediction of bio-oil characteristics quantitatively relating to biomass compositions and pyrolysis conditions. Fuel, 312, 122812. https://doi.org/10.1016/j.fuel.2021.122812
Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2021). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12(1), 469–477. https://doi.org/10.1016/j.gsf.2020.03.007
Zhou, S., Tang, S., Li, G., Xin, S., Huang, F., Liu, X., Mi, T., Huang, K., & Zeng, L. (2023). Catalytic fast pyrolysis of herbal medicine wastes over zeolite catalyst for aromatic hydrocarbons production. Fuel, 333, 126311. https://doi.org/10.1016/j.fuel.2022.126311
Iranian Journal of Biosystems Engineering
Volume 54, Issue 1
March 2023
Pages 87-113

Files

History

  • Receive Date: 25 May 2023
  • Revise Date: 24 October 2023
  • Accept Date: 29 October 2023

Share

How to cite

Statistics