Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation
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This study evaluates and compares several machine learning methods on the effects of different parameters in the hydrothermal carbonisation (HTC) process of macroalgae Sargassum horneri. Reaction temperature, residence time, biomass particle size, the amount of catalyst and loaded biomass were considered as inputs and three variables of BET, higher heating value (HHV) and energy recovery were regarded as outputs. For analysing the input parameters, the Taguchi method was used for experimental designs and the obtained results were utilised here as training sets. Various data mining approaches like support vector machine (SVM), group method of data handling, decision tree, random forest, radial basis function, adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) neural network were implemented to model the problem and two different optimisation techniques named BAT and Grasshopper Optimisation Algorithm (GOA) were employed with MLP and ANFIS model for optimising. By comparing different statistical parameters such as Average Absolute Relative Deviation (AARD), coefficient of determination (R2), Root Mean Square Error (RMSE) and Standard Deviation (SD), It is found out that SVM method has a considerably better performance relative to other methods for estimating both the BET, HHV and energy recovery parameters. Furthermore, coupling of MLP and ANFIS with GOA increases the accuracy of these models for BET, HHV and energy recovery estimations.
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Small particle sizes and higher catalyst loads were associated with higher BET surface areas and energy recovery efficiencies.
“For instance, small particle sizes and higher catalyst loads were associated with higher BET surface areas and energy recovery efficiencies, demonstrating the role of feedstock preparation and process intensification.”
From Algal-Based Carbonaceous Materials for Environmental Remediation: Advances in Wastewater Treatment, Carbon Sequestration, and Biofuel Applications (2025) · cites this paper as [29]
Machine learning models such as support vector machines and artificial neural networks optimized HTC parameters and enabled the design of hydrochars with tailored properties.
“The optimization of HTC parameters was achieved using machine learning models such as support vector machines and artificial neural networks.These predictive tools enhanced the understanding of complex parameter interdependencies, enabling the design of hydrochars with tailored properties for specific applications.”
From Algal-Based Carbonaceous Materials for Environmental Remediation: Advances in Wastewater Treatment, Carbon Sequestration, and Biofuel Applications (2025) · cites this paper as [29]
Rasam et al. (2021) found that HTC of Sargassum horneri under varying temperature (180-210°C), time (2-16 h), and catalyst loading (citric acid) produced hydrochars with BET surface area ranging from 0.6 to 31.8 m2/g and HHV ranging from 20.8 to 25.1 MJ/kg.
“A recent study by Rasam et al., 2021 [29], explored the HTC of Sargassum horneri under varying conditions of temperature (180-210 • C), time (2-16 h), and catalyst loading (citric acid).The hydrochar yield and properties were evaluated, including the Brunauer-Emmett-Teller (BET) surface area, higher heating value (HHV), and energy recovery.The BET surface area of the hydrochars varied sign”
From Algal-Based Carbonaceous Materials for Environmental Remediation: Advances in Wastewater Treatment, Carbon Sequestration, and Biofuel Applications (2025) · cites this paper as [29]
Energy densification ratios of S. horneri-derived hydrochar increased with reaction severity, indicating effective dehydration and decarboxylation during HTC.
“The energy densification ratios increased with reaction severity, indicating effective dehydration and decarboxylation during HTC.”
From Algal-Based Carbonaceous Materials for Environmental Remediation: Advances in Wastewater Treatment, Carbon Sequestration, and Biofuel Applications (2025) · cites this paper as [29]
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References 41 · Cited by 29
- Authors
- Rasam, S., Talebkeikhah, F., Talebkeikhah, M., Salimi, A., & Moraveji, M. K.
- Affiliations
- Amirkabir University of Technology (IR)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Moraveji, Mostafa Keshavarz
- Venue
- International Journal of Environmental & Analytical Chemistry
- Publisher
- Informa UK Limited
- Published
- 2019-12-16
- Volume
- 101
- Issue
- 14
- Pages
- 2297-2318
- ISSN
- 0306-7319 (ISSN-L)
- 1029-0397
- OpenAlex
- W2995823148
- Cited by
- 29
- References
- 41
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 7 · 2023: 5 · 2022: 5 · 2020: 4
- FWCI
- 0.79
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- cc-by-nc-nd
- Locations
- International Journal of Environmental & Analytical Chemistry (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Thermochemical Biomass Conversion Processes
- Physical Sciences › Environmental Science › Water Science and Technology › Adsorption and biosorption for pollutant removal
- Physical Sciences › Engineering › Biomedical Engineering › Biodiesel Production and Applications
- Keywords
- hydrochar, Sargassum horneri, hydrothermal carbonization, seaweed, BET surface area, higher heating value, energy recovery, support vector machine, adaptive neuro-fuzzy inference system, multilayer perceptron, bat algorithm, Taguchi method, process parameter optimization
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