Abstract
Biogas desulfurization using biochar is complex and highly nonlinear, affected by various variables and their interactions. Moreover, achieving maximum adsorption capacity and investigating the simultaneous effects of different variables on the efficiency of the adsorption process is challenging. In this study, machine learning algorithms were successfully applied to predict the biochar hydrogen sulfide adsorption capacity in biogas purification. Three supervised machine learning models were devised and evaluated in three-step model development to determine biochars' hydrogen sulfide adsorption capacity. In each model, a feature selection procedure was used in combination with feature important analysis to extract the most influential parameters on the hydrogen sulfide adsorption capacity and improve the total accuracy of models. The exhaustive feature selection method was used to find the best subset of features in each machine learning algorithm. The models used twenty features as input variables and were trained to learn complex relationships between these variables and the target variable. Based on features important and Shapley Additive Explanation analysis, the biochar surface's pH and the feedstock H/C molar ratio were among the most influential parameters in the adsorption process. The gradient boosting regression model was the most accurate prediction model reaching R2 scores of 0.998, 0.91, and 0.81 in the training, testing, and fivefold cross-validation sets, respectively. Overall, the study demonstrates the significance of machine learning in predicting and optimizing the biochar Hydrogen Sulfide adsorption process, which can be an asset in selecting appropriate biochar for removing hydrogen sulfide from biogas streams.
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References
Altmann, A., Toloşi, L., Sander, O., & Lengauer, T. (2010). Permutation importance: A corrected feature importance measure. Bioinformatics, 26, 1340–1347.
Al-Wabel, M. I., Al-Omran, A., El-Naggar, A. H., Nadeem, M., & Usman, A. R. (2013). Pyrolysis temperature induced changes in characteristics and chemical composition of biochar produced from conocarpus wastes. Bioresource Technology, 131, 374–379.
Arbuckle, W. B., & Ho, Y.-F. (1990). Adsorber column diameter: particle diameter ratio requirements. Research Journal of the Water Pollution Control Federation, 62, 88–90.
Auslander, N., Zhang, G., Lee, J. S., Frederick, D. T., Miao, B., Moll, T., Tian, T., Wei, Z., Madan, S., Sullivan, R. J., Boland, G., Flaherty, K., Herlyn, M., & Ruppin, E. (2018). Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nature Medicine, 24, 1545–1549.
Bakshi, S., Banik, C., & Laird, D. A. (2020). Estimating the organic oxygen content of biochar. Scientific Reports, 10, 13082.
Bhagat, S. K., Tung, T. M., & Yaseen, Z. M. (2020). Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. Journal of Cleaner Production, 250, 119473.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Cha, D., Park, S., Kim, M. S., Kim, T., Hong, S. W., Cho, K. H., & Lee, C. (2020). Prediction of oxidant exposures and micropollutant abatement during ozonation using a machine learning method. Environmental Science & Technology, 55, 709–718.
Choudhury, A., & Lansing, S. (2021). Adsorption of hydrogen sulfide in biogas using a novel iron-impregnated biochar scrubbing system. Journal of Environmental Chemical Engineering, 9, 104837.
Crespo Márquez, A. (2022). The curse of dimensionality, Digital maintenance management (pp. 67–86). Springer.
Cui, X., Hao, H., Zhang, C., He, Z., & Yang, X. (2016). Capacity and mechanisms of ammonium and cadmium sorption on different wetland-plant derived biochars. Science of the Total Environment, 539, 566–575.
Ding, W., Dong, X., Ime, I. M., Gao, B., & Ma, L. Q. (2014). Pyrolytic temperatures impact lead sorption mechanisms by bagasse biochars. Chemosphere, 105, 68–74.
Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813.
Florent, M., Policicchio, A., Niewiadomski, S., & Bandosz, T. J. (2020). Exploring the options for the improvement of H2S adsorption on sludge derived adsorbents: Building the composite with porous carbons. Journal of Cleaner Production, 249, 119412.
Ghaedi, M., Ghaedi, A. M., Negintaji, E., Ansari, A., Vafaei, A., & Rajabi, M. (2014). Random forest model for removal of bromophenol blue using activated carbon obtained from Astragalus bisulcatus tree. Journal of Industrial and Engineering Chemistry, 20, 1793–1803.
Guo, H.-n, Wu, S.-b, Tian, Y.-j, Zhang, J., & Liu, H.-t. (2021). Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review. Bioresource Technology, 319, 124114.
Gupta, R. P., Turk, B. S., Portzer, J. W., & Cicero, D. C. (2001). Desulfurization of syngas in a transport reactor. Environmental Progress & Sustainable Energy, 20, 187–195.
Han, X., Chen, H., Liu, Y., & Pan, J. (2020). Study on removal of gaseous hydrogen sulfide based on macroalgae biochars. Journal of Natural Gas Science and Engineering, 73, 103068.
Katyal, S., Thambimuthu, K., & Valix, M. (2003). Carbonisation of bagasse in a fixed bed reactor: Influence of process variables on char yield and characteristics. Renewable Energy, 28, 713–725.
Lakshmi, D., Akhil, D., Kartik, A., Gopinath, K. P., Arun, J., Bhatnagar, A., Rinklebe, J., Kim, W., & Muthusamy, G. (2021). Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar. Science of the Total Environment, 801, 149623.
Li, Y., Gupta, R., & You, S. (2022). Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass. Bioresource Technology, 359, 127511.
Miles J (2005). R‐squared, adjusted R‐squared. Encyclopedia of statistics in behavioral science
Mulu, E., M’Arimi, M. M., & Rose, R. C. (2022). Purification and upgrade of biogas using biomass-derived adsorbents. Advances in phytochemistry (pp. 286–295). Textile and Renewable Energy Research for Industrial Growth. CRC Press.
Nowicki, P., Skibiszewska, P., & Pietrzak, R. (2014). Hydrogen sulphide removal on carbonaceous adsorbents prepared from coffee industry waste materials. Chemical Engineering Journal, 248, 208–215.
Nuraiti, T., Tengku Izhar, T. N., Goh, Z., Kee, N. F., Saad, M., Zamree, S., Abd Rahim, I., Zakarya, I. A., Rizam, M., Besom, C., & Syafiuddin, A. (2022). Adsorption of hydrogen sulfide (H2S) from municipal solid waste by using biochars. Biointerface Research in Applied Chemistry, 12, 8057–8069.
Ohtani, K. (2000). Bootstrapping R2 and adjusted R2 in regression analysis. Economic Modelling, 17, 473–483.
Okoro, O. V., & Sun, Z. (2019). Desulphurisation of biogas: a systematic qualitative and economic-based quantitative review of alternative strategies. ChemEngineering, 3, 76.
Ortiz, F. G., Aguilera, P., & Ollero, P. (2014). Biogas desulfurization by adsorption on thermally treated sewage-sludge. Separation and Purification Technology, 123, 200–213.
Palansooriya, K. N., Li, J., Dissanayake, P. D., Suvarna, M., Li, L., Yuan, X., Sarkar, B., Tsang, D. C., Rinklebe, J., & Wang, X. (2022). Prediction of soil heavy metal immobilization by biochar using machine learning. Environmental Science & Technology, 56, 4187–4198.
Pathy, A., Meher, S., & BalasubramanianMeher, P. (2020). Predicting algal biochar yield using eXtreme gradient boosting (XGB) algorithm of machine learning methods. Algal Research, 50, 102006.
Pekel, E. (2020). Estimation of soil moisture using decision tree regression. Theoretical and Applied Climatology, 139, 1111–1119.
Ros, A., Lillo-Ródenas, M. A., Canals-Batlle, C., Fuente, E., Montes-Morán, M. A., Martin, M. J., & Linares-Solano, A. (2007). A new generation of sludge-based adsorbents for H2S abatement at room temperature. Environmental Science & Technology, 41, 4375–4381.
Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B. W., Aslam, W., & Choi, G. S. (2020). COVID-19 future forecasting using supervised machine learning models. IEEE Access, 8, 101489–101499.
Sahota, S., Vijay, V. K., Subbarao, P. M. V., Chandra, R., Ghosh, P., Shah, G., Kapoor, R., Vijay, V., Koutu, V., & Thakur, I. S. (2018). Characterization of leaf waste based biochar for cost effective hydrogen sulphide removal from biogas. Bioresource Technology, 250, 635–641.
Sawalha, H., Maghalseh, M., Qutaina, J., Junaidi, K., & Rene, E. R. (2020). Removal of hydrogen sulfide from biogas using activated carbon synthesized from different locally available biomass wastes-a case study from Palestine. Bioengineered, 11, 607–618.
Sethupathi, S., Zhang, M., Rajapaksha, A. U., Lee, S. R., Mohamad Nor, N., Mohamed, A. R., Al-Wabel, M., Lee, S. S., & Ok, Y. S. (2017). Biochars as potential adsorbers of CH4, CO2 and H2S. Sustainability, 9, 121.
Shang, G., Li, Q., Liu, L., Chen, P., & Huang, X. (2016). Adsorption of hydrogen sulfide by biochars derived from pyrolysis of different agricultural/forestry wastes. Journal of the Air & Waste Management Association, 66, 8–16.
Shang, G., Shen, G., Liu, L., Chen, Q., & Xu, Z. (2013). Kinetics and mechanisms of hydrogen sulfide adsorption by biochars. Bioresource Technology, 133, 495–499.
Shang, G., Shen, G., Wang, T., & Chen, Q. (2012). Effectiveness and mechanisms of hydrogen sulfide adsorption by camphor-derived biochar. Journal of the Air & Waste Management Association, 62, 873–879.
Skerman, A. G., Heubeck, S., Batstone, D. J., & Tait, S. (2017). Low-cost filter media for removal of hydrogen sulphide from piggery biogas. Process Safety and Environmental Protection, 105, 117–126.
Su, L., Chen, M., Zhuo, G., Ji, R., Wang, S., Zhang, L., Zhang, M., & Li, H. (2021). Comparison of biochar materials derived from coconut husks and various types of livestock manure, and their potential for use in removal of h2s from biogas. Sustainability, 13, 6262.
Sun, X., Shan, R., Li, X., Pan, J., Liu, X., Deng, R., & Song, J. (2017a). Characterization of 60 types of Chinese biomass waste and resultant biochars in terms of their candidacy for soil application. Gcb Bioenergy, 9, 1423–1435.
Sun, Y., Yang, G., Zhang, L., & Sun, Z. (2017b). Preparation of high performance H 2 S removal biochar by direct fluidized bed carbonization using potato peel waste. Process Safety and Environmental Protection, 107, 281–288.
Sun, Y., Zhang, J. P., Wen, C., & Zhang, L. (2016). An enhanced approach for biochar preparation using fluidized bed and its application for H2S removal. Chemical Engineering and Processing: Process Intensification, 104, 1–12.
Surra, E., Costa Nogueira, M., Bernardo, M., Lapa, N., Esteves, I., & Fonseca, I. (2019). New adsorbents from maize cob wastes and anaerobic digestate for H2S removal from biogas. Waste Management, 94, 136–145.
Tomczyk, A., Sokołowska, Z., & Boguta, P. (2020). Biochar physicochemical properties: Pyrolysis temperature and feedstock kind effects. Reviews in Environmental Science and Bio/technology, 19, 191–215.
Tuerhong, T., & Kuerban, Z. (2022). Preparation and characterization of cattle manure-based activated carbon for hydrogen sulfide removal at room temperature. ACS Sustainable Chemistry & Engineering, 10, 107177.
Yang, X., Yuan, C., He, S., Jiang, D., Cao, B., & Wang, S. (2023). Machine learning prediction of specific capacitance in biomass derived carbon materials: Effects of activation and biochar characteristics. Fuel, 331, 125718.
Yuan, X., Suvarna, M., Low, S., Dissanayake, P. D., Lee, K. B., Li, J., Wang, X., & Ok, Y. S. (2021). Applied machine learning for prediction of CO2 adsorption on biomass waste-derived porous carbons. Environmental Science & Technology, 55, 11925–11936.
Zama, E. F., Zhu, Y.-G., Reid, B. J., & Sun, G.-X. (2017). The role of biochar properties in influencing the sorption and desorption of Pb(II), Cd(II) and As(III) in aqueous solution. Journal of Cleaner Production, 148, 127–136.
Zhu, X., Li, Y., & Wang, X. (2019a). Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. Bioresource Technology, 288, 121527.
Zhu, X., Tsang, D. C. W., Wang, L., Su, Z., Hou, D., Li, L., & Shang, J. (2020). Machine learning exploration of the critical factors for CO2 adsorption capacity on porous carbon materials at different pressures. Journal of Cleaner Production, 273, 122915.
Zhu, X., Wang, X., & Ok, Y. S. (2019b). The application of machine learning methods for prediction of metal sorption onto biochars. Journal of Hazardous Materials, 378, 120727.
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All authors contributed to the study's conception and design. AB conceptualization, methodology of machine learning work, writing and editing of the manuscript, software, FQ conceptualization, supervision, methodology, writing—review and editing of the manuscript, all authors read and approved the final manuscript.
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Banisheikholeslami, A., Qaderi, F. Applied machine learning to the determination of biochar hydrogen sulfide adsorption capacity. Mach Learn 113, 3419–3441 (2024). https://doi.org/10.1007/s10994-023-06446-2
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DOI: https://doi.org/10.1007/s10994-023-06446-2