DOI: https://doi.org/10.14710/ijred.2023.47831

Consistent Regime-Switching Lasso Model of the Biomass Proximate Analysis Higher Heating Value

Department of Computer Engineering and Financial Technology, School of Engineering, University of the Thai Chamber of Commerce, Bangkok, Thailand

Received: 22 Jul 2022; Revised: 8 Sep 2022; Accepted: 27 Sep 2022; Available online: 14 Oct 2022; Published: 1 Jan 2023.
Editor(s): Rock Keey Liew
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Prediction accuracy is crucial for higher heating value (HHV) models to promote renewable biomass energy, especially its consistency is crucial when retraining data and knowledge of the range are unavailable. Current HHV models lack consistency in accuracy and interpretability due to various reasons. Thus, this study aimed to construct an interpretable and consistent proximate-based biomass HHV model on a wide-range dataset. The model, regime-lasso, integrated the concepts of regime-switching, lasso regression, and federated averaging to construct a consistent HHV model. The regime-switching partitioned the dataset into optimal regimes, and the lasso trained the regime models. The regime-lasso model is a collection of these models. It provided root  mean square error of 0.4430– 0.9050, mean absolute error of 0.2743–0.6867, and average absolute error of 1.512–4.5894% in the literature’s wide-range datasets. The Kruskal–Wallis test confirmed the in-sample performance consistency at α=0.05, regardless of the training sets. In the out-of-sample situations without retraining, the model preserved its accuracy in six out of 11 datasets at α = 0.01. The interpretability of regime-lasso indicated the regime characteristic to be a factor of inconsistent prediction. The increase in FC had the maximum positive impact on HHV in the 2nd and 3rd regimes, while the increase in ASH negatively impacted the 1st and 2nd regimes. VM variation had neutral effects in all regimes. The regime-lasso solves the issues of accuracy declination and addresses the challenges in sensitivity analysis of the HHV model. The prediction accuracy issues of the model’s direct implementation were fixed.
Fulltext View|Download
Keywords: consistency; prediction; higher heating value; proximate analysis

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Experimental Evaluation of Thermohydraulic Performance of Tubular Solar Air Heater Techno-Economic Assessment of a 100 kWp Solar Rooftop PV System for Five Hospitals in Central Southern Thailand An Effect of Wind Veer on Wind Turbine Performance The Various Designs of Storage Solar Collectors: A Review More recent articles
Most cited articles
CFD Investigation of A New Elliptical-Bladed Multistage Savonius Rotors Optimization and Molecular Characterization of Syngas Fermenting Anaerobic Mixed Microbial Consortium TERI SA1 Historic Developments, Current Technologies and Potential of Nanotechnology to Develop Next Generation Solar Cells with Improved Efficiency Combustion characteristics of diesel engine using producer gas and blends of Jatropha methyl ester with diesel in mixed fuel mode Implications of Charcoal Briquette Produced by Local Communities on Livelihoods and Environment in Nairobi- Kenya More cited articles
  1. Akkaya, E. (2016). ANFIS based prediction model for biomass heating value using proximate analysis components. Fuel, 180, 687–693; https://doi.org/10.1016/j.fuel.2016.04.112
  2. Akkaya, A. V. (2013). Predicting coal heating values using proximate analysis via a neural network approach. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 35(3), 253–260; https://doi.org/10.1080/15567036.2010.509090
  3. Boumanchar, I., Charafeddine, K., Chhiti, Y., M’hamdi Alaoui, F. E., Sahibed-dine, A., Bentiss, F., Jama, C., & Bensitel, M. (2019). Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming. Biomass Conversion and Biorefinery, 9(3), 499–509; https://doi.org/10.1007/s13399-019-00386-5
  4. Chun-Yang Yin. (2011). Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel, 90(3), 1128–1132; https://doi.org/10.1016/j.fuel.2010.11.031
  5. Cordero, T., Marquez, F., Rodriguez-Mirasol, J., & Rodriguez, J. (2001). Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis. Fuel, 80(11), 1567–1571; https://doi.org/10.1016/S0016-2361(01)00034-5
  6. Core Writing Team, R. K. P. and L. A. M. (2015). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In IPCC. https://doi.org/10.1016/S0022-0248(00)00575-3
  7. Dashti, A., Noushabadi, A. S., Raji, M., Razmi, A., Ceylan, S., & Mohammadi, A. H. (2019). Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation. Fuel, 257, 115931; https://doi.org/10.1016/j.fuel.2019.115931
  8. Estiati, I., Freire, F. B., Freire, J. T., Aguado, R., & Olazar, M. (2016). Fitting performance of artificial neural networks and empirical correlations to estimate higher heating values of biomass. Fuel, 180, 377–383; https://doi.org/10.1016/j.fuel.2016.04.051
  9. Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22; https://doi.org/10.18637/jss.v033.i01
  10. Ghugare, S. B., Tiwary, S., Elangovan, V., & Tambe, S. S. (2014). Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms. Bioenergy Research, 7(2), 681–692; https://doi.org/10.1007/s12155-013-9393-5
  11. Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018). Federated learning for mobile keyboard prediction. arXiv preprint arXiv, 1811.03604, Nov 8, 2018; https://doi.org/10.48550/arXiv.1811.03604
  12. Kijkarncharoensin, A. (2022a). A regime-switching lasso model of the biomass higher heating value on the proximate analysis. Codeocean. https://doi.org/https://doi.org/10.24433/CO.9945805.v2
  13. Kijkarncharoensin, A. (2022b). Proximate_Analysis. Mendeley Data, V2; https://doi.org/10.17632/g36dhg826s.2
  14. Kijkarncharoensin, A., & Innet, S. (2022a). Performance Inconsistencies in Biomass Higher Heating Value Models for Ultimate Analysis. The Journal of King Mongkut’s University of Technology North Bangkok, 1–13 (Inpress)
  15. Kijkarncharoensin, A., & Innet, S. (2022b). An unsupervised learning technique to classify the biomass thermal properties on the proximate analysis. Proc. of the International Conference on Electrical, Computer and Energy Technologies (ICECET 2022), July, 20–22
  16. Liao, Y. (2017). Machine Learning in Macro-Economic Series Forecasting. International Journal of Economics and Finance, 9(12), 71; https://doi.org/10.5539/ijef.v9n12p71
  17. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of Machine Learning Research, 54, 10. https://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf
  18. Mela, C. F., & Kopalle, P. K. (2002). The impact of collinearity on regression analysis: The asymmetric effect of negative and positive correlations. Applied Economics, 34(6), 667–677; https://doi.org/10.1080/00036840110058482
  19. Mohammed, I. Y., Kazi, F. K., Suzana, Y., Alshareef, I. M., & Chin, S. A. (2014). Higher Heating Values (HHV) Prediction Model from Biomass Proximate Analysis Data. Conference: International Conference & Exhibition on Clean Energy, October; https://doi.org/10.13140/RG.2.1.1979.9525
  20. Nhuchhen, D. R., & Abdul Salam, P. (2012). Estimation of higher heating value of biomass from proximate analysis: A new approach. Fuel, 99, 55–63; https://doi.org/10.1016/j.fuel.2012.04.015
  21. Nhuchhen, D. R., & Afzal, M. T. (2017). HHV predicting correlations for torrefied biomass using proximate and ultimate analyses. Bioengineering, 4(1); https://doi.org/10.3390/bioengineering4010007
  22. Parikh, J., Channiwala, S. A., & Ghosal, G. K. (2005). A correlation for calculating HHV from proximate analysis of solid fuels. Fuel, 84(5), 487–494; https://doi.org/10.1016/j.fuel.2004.10.010
  23. Qian, C., Li, Q., Zhang, Z., Wang, X., Hu, J., & Cao, W. (2020). Prediction of higher heating values of biochar from proximate and ultimate analysis. Fuel, 265(September 2019), 116925; https://doi.org/10.1016/j.fuel.2019.116925
  24. Qian, X., Lee, S., Soto, A. M., & Chen, G. (2018). Regression model to predict the higher heating value of poultry waste from proximate analysis. Resources, 7(3); https://doi.org/10.3390/resources7030039
  25. Samadi, S. H., Ghobadian, B., & Nosrati, M. (2021). Prediction of higher heating value of biomass materials based on proximate analysis using gradient boosted regression trees method. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 43(6), 672–681; https://doi.org/10.1080/15567036.2019.1630521
  26. Soponpongpipat, N., Sittikul, D., & Sae-Ueng, U. (2015). Higher heating value prediction of torrefaction char produced from non-woody biomass. Frontiers in Energy, 9(4), 461–471; https://doi.org/10.1007/s11708-015-0377-3
  27. Taki, M., & Rohani, A. (2022). Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation. Case Studies in Thermal Engineering, 31(January), 101823; https://doi.org/10.1016/j.csite.2022.101823
  28. Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267–288
  29. Uzun, H., Yıldız, Z., Goldfarb, J. L., & Ceylan, S. (2017). Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis. Bioresource Technology, 234, 122–130; https://doi.org/10.1016/j.biortech.2017.03.015
  30. 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

Last update:

No citation recorded.

Last update: 2024-12-26 09:23:24

No citation recorded.