Machine learning-based characterization of hydrochar from biomass: Implications for sustainable energy and material production

• URL: http://arxiv.org/abs/2305.16348v1
• Date: Wed, 24 May 2023 18:55:54 GMT
• Title: Machine learning-based characterization of hydrochar from biomass: Implications for sustainable energy and material production
• Authors: Alireza Shafizadeh, Hossein Shahbeik, Shahin Rafiee, Aysooda Moradi, Mohammadreza Shahbaz, Meysam Madadi, Cheng Li, Wanxi Peng, Meisam Tabatabaei, Mortaza Aghbashlo
• Abstract summary: Hydrothermal carbonization (HTC) is a process that converts biomass into versatile hydrochar without the need for prior drying. This study aims to develop a model that characterizes hydrochar produced from different biomass sources under varying reaction processing parameters.
• Score: 7.7276891733684465
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hydrothermal carbonization (HTC) is a process that converts biomass into versatile hydrochar without the need for prior drying. The physicochemical properties of hydrochar are influenced by biomass properties and processing parameters, making it challenging to optimize for specific applications through trial-and-error experiments. To save time and money, machine learning can be used to develop a model that characterizes hydrochar produced from different biomass sources under varying reaction processing parameters. Thus, this study aims to develop an inclusive model to characterize hydrochar using a database covering a range of biomass types and reaction processing parameters. The quality and quantity of hydrochar are predicted using two models (decision tree regression and support vector regression). The decision tree regression model outperforms the support vector regression model in terms of forecast accuracy (R2 > 0.88, RMSE < 6.848, and MAE < 4.718). Using an evolutionary algorithm, optimum inputs are identified based on cost functions provided by the selected model to optimize hydrochar for energy production, soil amendment, and pollutant adsorption, resulting in hydrochar yields of 84.31%, 84.91%, and 80.40%, respectively. The feature importance analysis reveals that biomass ash/carbon content and operating temperature are the primary factors affecting hydrochar production in the HTC process.

Related papers

• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring. Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors. In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type. Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning [0.0]
  We use superstatistical methods and machine learning to predict dissolved oxygen levels in the River Thames. For long-term forecasting, the Informer model consistently delivers superior performance.
  arXiv  Detail & Related papers  (2025-01-10T16:54:52Z)
• Approaches for enhancing extrapolability in process-based and data-driven models in hydrology [0.16735447464058464]
  This paper reviews and compares methods for assessing and enhancing the extrapolability of process-based and data-driven hydrological models. Key strategies include the use of leave-one-out cross-validation and similarity-based methods to evaluate model performance in ungauged regions. Deep learning, transfer learning, and domain adaptation techniques are also promising in their potential to improve model predictions in data-sparse and extreme conditions.
  arXiv  Detail & Related papers  (2024-08-13T17:59:24Z)
• Retrosynthesis prediction enhanced by in-silico reaction data augmentation [66.5643280109899]
  We present RetroWISE, a framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation. On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models.
  arXiv  Detail & Related papers  (2024-01-31T07:40:37Z)
• Using evolutionary machine learning to characterize and optimize co-pyrolysis of biomass feedstocks and polymeric wastes [14.894507238371768]
  Co-pyrolysis is a promising strategy for improving the quantity and quality parameters of the resulting liquid fuel. Machine learning (ML) provides capabilities to cope with such issues by leveraging on existing data. This work aims to introduce an evolutionary ML approach to quantify the (by)products of the biomass-polymer co-pyrolysis process.
  arXiv  Detail & Related papers  (2023-05-24T19:59:21Z)
• Machine Guided Discovery of Novel Carbon Capture Solvents [48.7576911714538]
  Machine learning offers a promising method for reducing the time and resource burdens of materials development. We have developed an end-to-end "discovery cycle" to select new aqueous amines compatible with the commercially viable acid gas scrubbing carbon capture. The prediction process shows 60% accuracy against experiment for both material parameters and 80% for a single parameter on an external test set.
  arXiv  Detail & Related papers  (2023-03-24T18:32:38Z)
• Using Multivariate Linear Regression for Biochemical Oxygen Demand Prediction in Waste Water [1.9843222704723806]
  The goal of this work is to examine the capability of MLR in prediction of Biochemical Oxygen Demand (BOD) in waste water through four input variables. The four input variables have higher correlation strength to BOD out of the seven parameters examined for the strength of correlation. It was found that increasing the percentage of the training set above 80% of the dataset improved the accuracy of the model only but did not have a significant impact on the prediction capacity of the model.
  arXiv  Detail & Related papers  (2022-09-08T14:41:02Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Prediction of terephthalic acid (TPA) yield in aqueous hydrolysis of polyethylene terephthalate (PET) [0.0]
  Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) Modeling PET hydrolysis by considering the effective factors can provide useful information for material scientists. For the first time, 381 experimental data were gathered to model the aqueous hydrolysis of PET.
  arXiv  Detail & Related papers  (2022-01-29T20:51:36Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea [58.720142291102135]
  The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focus on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.
  arXiv  Detail & Related papers  (2021-03-03T18:51:46Z)
• Unassisted Noise Reduction of Chemical Reaction Data Sets [59.127921057012564]
  We propose a machine learning-based, unassisted approach to remove chemically wrong entries from data sets. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets.
  arXiv  Detail & Related papers  (2021-02-02T09:34:34Z)
• A multivariate water quality parameter prediction model using recurrent neural network [0.30458514384586394]
  This research is to develop a water quality prediction model based on water quality parameters. The model was developed using a recurrent neural network (RNN), Long Short-Term Memory (LSTM) and historical water quality data. The single step model attained an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean Squared Error (RMSE) of 0.227 mg/L.
  arXiv  Detail & Related papers  (2020-03-25T16:49:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.