Investigation of the chemical exergy of torrefied biomass from raw biomass by means of machine learning

Kartal, Furkan; Özveren, Uğur

doi:10.1016/j.biombioe.2022.106383

Cited by 18 publications

(6 citation statements)

References 60 publications

(68 reference statements)

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“…Actually, exergybased methods have been widely used in many fields, and a standard analytical framework has been developed [59]. Most recently, thanks to the powerful capacity of presenting detailed and reliable energy information, exergy-based methods have also been used for energy performance prediction and predictive control with the help of machine learning [60][61][62][63]. Besides, from the perspective of data fusion, exergy of compressed air can be regarded as a data fusion criterion of pressure, flowrate, and temperature.…”

Section: Exergy Of Compressed Airmentioning

confidence: 99%

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Wang,

Yang,

et al. 2023

TJFP

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Pneumatic systems are widely used in industrial production sectors. Increasing penetrations of Intelligent Manufacturing and Green Manufacturing are highlighting the drawbacks of pneumatic technology in terms of particularly low energy efficiency and low-level fault diagnosis intelligence. Here we propose that a combined energy-based maintenance and fault diagnostic approach for pneumatic systems could be a game-changer for pneumatics. In this study, a pneumatic cylinder with internal and external leakages is examined and a typical pneumatic experimental system is built. Exergy is adopted for evaluating the available energy of compressed air. Data-driven machine learning models, SAE + SoftMax neural network model and SAE + SVM model, are developed for fault detection and diagnosis. By comparing different machine learning methods with various pressure, flowrate, and exergy data, it is found that the diagnostic accuracy when using pressure and flowrate data is highly dependent on operating conditions, while the diagnostic accuracy when using exergy data is always high regardless of operating conditions. This indicates the promise of developing an exergy-based maintenance paradigm in pneumatic systems. Besides, with exergy and machine learning, more downstream faults can be detected and diagnosed with fewer upstream sensors. This study is the first attempt to develop an exergy-based maintenance paradigm in pneumatic systems. We hope it could inspire the following investigations in other energy domains.

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Section: Exergy Of Compressed Airmentioning

confidence: 99%

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Wang,

Yang,

et al. 2023

TJFP

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“…1,2 Through processes such as combustion, gasification, or fermentation, biomass can be converted into clean and sustainable energy. 3,4 By adopting biomass energy and implementing biochar into agricultural landscapes, societies can get closer to net-zero emissions. Net zero, or the balance of greenhouse gas (GHG) emissions and removal or offset, is a critical goal in addressing climate change and ensuring a sustainable future.…”

Section: Introductionmentioning

confidence: 99%

“…Biomass, which includes organic matter derived from plants, animals, or microorganisms, provides a sustainable energy source that can significantly reduce reliance on fossil fuels. , Through processes such as combustion, gasification, or fermentation, biomass can be converted into clean and sustainable energy. , By adopting biomass energy and implementing biochar into agricultural landscapes, societies can get closer to net-zero emissions. Net zero, or the balance of greenhouse gas (GHG) emissions and removal or offset, is a critical goal in addressing climate change and ensuring a sustainable future. , Biomass energy, which has the potential to replace fossil fuels, contributes to carbon sequestration by effectively removing carbon dioxide from the environment, whereas biochar application contributes to carbon sequestration by successfully removing CO 2 from the atmosphere. , These integrated approaches demonstrate that biomass and biochar have the potential to contribute considerably to the sustainable development goals (SDGs), particularly those concentrating on sustainable energy, combating climate change, and sustainable cities and communities. , By leveraging the strength of biomass, integrating biochar into agricultural systems, and striving for net-zero emissions, societies can make significant progress toward a more sustainable and resilient future. , …”

Section: Introductionmentioning

confidence: 99%

Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

Le,

Pandey,

Sirohi

et al. 2023

Energy Fuels

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Biochar is found to possess a large number of applications in energy and environmental areas. However, biochar could be produced from a variety of sources, showing that biochar yield and proximate analysis outcomes could change over a wide range. Thus, developing a high-accuracy machine learning-based tool is very necessary to predict biochar characteristics. In this study, a hybrid technique was developed by blending modern machine learning (ML) algorithms with cooperative game theory-based Shapley Additive exPlanations (SHAP). SHAP analysis was employed to help improve interpretability while offering insights into the decision-making process. In the ML models, linear regression was employed as the baseline regression method, and more advanced methodologies like AdaBoost and boosted regression tree (BRT) were employed. The developed prediction models were evaluated on a battery of statistical metrics, and all ML models were observed as robust enough. Among all three models, the BRT-based model delivered the best prediction performance with R 2 in the range of 0.982 to 0.999 during the model training phase and 0.968 to 0.988 during the model test. The value of the mean squared error was also quite low (0.89 to 9.168) for BRT-based models. SHAP analysis quantified the value of each input element to the expected results and provided a more in-depth understanding of the underlying dynamics. The SHAP analysis helped to reveal that temperature was the main factor affecting the response predictions. The hybrid technique proposed here provides substantial insights into the biochar manufacturing process, allowing for improved control of biochar properties and increasing the use of this sustainable and flexible material in numerous applications.

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“…11 Due to these advantages, ML can be applied to the prediction of torrefied biomass properties to avoid repetitive experiments. For torrefied biomass, the artificial neural network (ANN) models were most frequently used to predict the yields, 12,13 the CHO contents and HHV, 9 and the exergy 14 of torrefied biomass. In addition to the ANNs, several other models have also been developed to predict the properties of torrefied product.…”

Section: Introductionmentioning

confidence: 99%

Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm

2023

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Investigation of the chemical exergy of torrefied biomass from raw biomass by means of machine learning

Cited by 18 publications

References 60 publications

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations

Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm

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