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Synergistic torrefaction and co-combustion of rice husk and spent coffee grounds: Thermo-kinetics, ash morphology, and waste-to-energy implications
(2026-09-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
Combining silica-rich agricultural residues such as rice husk (RH) with energy-dense food-processing waste like torrefied spent coffee grounds (TSCG) offers a circular strategy to exploit the complementary advantages of each biomass while improving fuel performance and combustion reliability. Therefore, the objective of this study is to systematically investigate the co-combustion behavior, kinetic performance, and ash morphology of RH and TSCG blends using thermogravimetric analysis across multiple blend ratios and heating rates. Optimal co-combustion characteristics were observed for blends containing 40–60% RH, demonstrated by comprehensive combustion index values up to 6.02 × 10–6%2 min–2 °C–3 and flammability indices exceeding 0.72 × 10–4% min–1 °C–2. Furthermore, these blends exhibited low activation energies during dehydration (150–161 kJ mol⁻¹), devolatilization (143–175 kJ mol⁻¹), and char oxidation (89–134 kJ mol⁻¹). Notably, these blends exhibit high carbon conversion efficiency, leading to a relatively low residual ash content (10.25–14.36%). Synergistic effects, characterized by experimental mass loss exceeding theoretical predictions by up to 2%, were confirmed above 300 °C. This synergistic behavior is attributed to the interaction between oxygenated volatiles released from RH and the catalytic effects of alkali and alkaline earth metals present in TSCG, along with the stabilizing role of silica in mitigating ash-related issues. Additionally, increasing the proportion of TSCG promoted earlier ignition (T<inf>i</inf> reduced to 213 °C) but prolonged the char combustion stage, resulting in higher burnout temperatures of up to 678 °C. Morphological and elemental ash analysis revealed that moderate Si content in 40–60% RH blends contributed to thermal stability while suppressing alkali-induced slag formation. Collectively, these results demonstrate that co-combustion of RH with TSCG significantly enhances combustion reactivity, promotes kinetic synergy, and improves thermal stability. These findings highlight the potential of RH–TSCG blends as efficient and environmentally sustainable fuels for bioenergy applications by utilizing readily available local biomass residues.
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Understanding drying processes of steam-blanched Wolffia globosa under microwave vacuum drying through data-driven and semi-theoretical modeling
(2026-06-15)
Pambudi, Suluh
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Churat, Chutikan
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Nitinarot, Manassanan
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Buntreekanok, Withitpong
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Saechua, Wanphut
Steam blanching and microwave vacuum drying (MVD) are increasingly applied to preserve heat-sensitive, protein-rich biomaterials such as Wolffia globosa . However, accurately modeling moisture removal under MVD remains challenging due to the highly nonlinear and stage-dependent nature of microwave-induced drying. This study investigates the combined effects of steam blanching pretreatment and microwave power (540, 720, and 900 W at 5 kPa) on the drying kinetics of W. globosa and evaluates the capability of machine-learning models to predict moisture evolution in comparison with conventional semi-theoretical models. The results indicated that steam-blanched samples consistently exhibited higher maximum drying rates across all power levels, increasing from 1.088 to 1.298 g<inf>w</inf>/(g<inf>dm</inf>·min) at 540 W and from 1.974 to 2.126 g<inf>w</inf>/(g<inf>dm</inf>·min) at 900 W. The pretreatment also reduced total drying time from 19.5 to 18.0 min at 540 W, while drying time remained unchanged at higher microwave powers. Among the evaluated semi-theoretical models, the Midilli equation provided the best fit to condition-specific experimental data (R2 = 0.9981–0.9994). However, the generalized Midilli model showed systematic underestimation at low moisture ratios across operating conditions (R2 = 0.9865, RMSE = 0.9990). In contrast, the k-nearest neighbors (k-NN) model demonstrated strong generalized predictive capability across multiple drying conditions, achieving R2 = 0.9798 and RMSE = 0.0481 on the testing dataset. Beyond improved predictive accuracy, the machine-learning framework effectively captured stage-dependent drying behavior patterns that conventional generalized semi-theoretical models failed to represent. These findings highlight the potential of data-driven approaches for modeling complex drying behavior under microwave vacuum conditions. The accurate prediction of moisture ratio may further support the optimization of MVD processes for W. globosa -based food ingredients.
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Combined ultrasound–hydrothermal pretreatment enhances moisture diffusivity, energy efficiency, and quality retention during hot air drying of holy basil (Ocimum sanctum L.)
(2026-06-01)
Pambudi, Suluh
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Khamon, Duanghathai
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Ngo, Tai Van
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Saechua, Wanphut
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Jongyingcharoen, Jiraporn Sripinyowanich
Drying of holy basil (Ocimum sanctum L.) is limited by slow internal moisture diffusion and severe degradation of heat-sensitive pigments and bioactive compounds during prolonged thermal exposure. This study investigates the combined effects of ultrasonic and hydrothermal pretreatments on moisture transport, energy efficiency, and quality retention during hot air drying at 60 °C. Ultrasonication (30 and 60 min), hot-water blanching, steam blanching, and their combinations were systematically evaluated through drying kinetics, effective moisture diffusivity (D<inf>eff</inf>), microstructural analysis, POD enzyme activity, specific energy consumption (SEC), and comprehensive quality assessment. Drying occurred entirely in the falling-rate regime, confirming diffusion-controlled moisture transport. The combined ultrasound–blanching treatment (US30B) exhibited the greatest changes in drying performance, enlarging stomatal openings and enhancing moisture transport during drying, which increased D<inf>eff</inf> nearly threefold (from 1.70 × 10−11 to 5.02 × 10−11 m2/s) and reduced drying time from 100 to 30 min. This transport intensification decreased total SEC by 11% while simultaneously minimizing shrinkage and enhancing rehydration capacity. Importantly, accelerated moisture removal may have reduced thermal and oxidative degradation, resulting in superior retention of chlorophyll, phenolics, flavonoids, and antioxidant activity (p ≤ 0.05), alongside improved green color stability. These findings suggest that pretreatment strategies that enhance internal moisture transport may improve both energy efficiency and functional quality in heat-sensitive leafy herbs. The combined ultrasound–hydrothermal pretreatment shows potential as a practical approach for improving drying performance and quality retention in herbal dehydration.
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Quantitative analysis based on image processing combined with machine learning and deep learning to determine the adulteration in nutmeg powder
(2025-10-01)
Sitorus, Agustami
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Pambudi, Suluh
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Boodnon, Wutthiphong
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Lapcharoensuk, Ravipat
The global demand for nutmeg powder has increased, raising the risk of adulteration and necessitating an efficient and cost-effective screening method. The objective of this study is to develop a calibration model to predict the adulteration of nutmeg powder by cinnamon powder using a novel approach by integrating image processing with machine learning (ML) and deep learning (DL). Eight regressors, including four ML regressors (multiple linear ridge regression–MLRR, partial least squares regression–PLSR, multi-layer perceptron–MLP, and adaptive boosting–ABS) and four DL regressors (convolutional neural networks–CNN, AlexNET, residual networks–ResNET, and GoogleNET), were employed to analyze 1800 images of adulteration samples ranging from 0 % to 35 % (w/w). Among ML models, MLP achieved the highest accuracy in prediction (R<inf>p</inf>²=0.922, RMSEP=2.804 %, RPD=3.59), while PLSR (R<inf>p</inf>²=0.876, RMSEP=3.538 %, RPD=2.84), MLRR (R<inf>p</inf>²=0.872, RMSEP=3.596 %, RPD=2.80), and ABS (R<inf>p</inf>²=0.849, RMSEP=3.904 %, RPD=2.58) underperformed. For DL, ResNET (R<inf>p</inf>²=0.882, RMSEP=3.416 %, RPD=2.91) surpassed CNN (R<inf>p</inf>²=0.876, RMSEP=3.505 %, RPD=2.84), AlexNET (R<inf>p</inf>²=0.801, RMSEP=4.429 %, RPD=2.24), and GoogleNET (R<inf>p</inf>²=0.751, RMSEP=4.963 %, RPD=2.00). The MLP's superiority highlights its compatibility with ORB-based feature extraction for nonlinear adulteration patterns, outperforming complex DL architectures. This research highlights the potential of image processing supported by ML and DL as a rapid and low-cost tool for future nutmeg powder adulteration screening.
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Explainable machine learning for predicting thermogravimetric analysis of oxidatively torrefied spent coffee grounds combustion
(2025-04-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
Understanding the combustion behavior of oxidatively torrefied spent coffee grounds (SCG) is crucial for advancing sustainable fuel technologies. This study introduces a novel, explainable machine learning (ML) framework as a cost-effective alternative to traditional thermogravimetric analysis (TGA) that is designed to accelerate the evaluation of oxidatively torrefied SCG combustion properties. Four ML models: artificial neural network (ANN), k-nearest neighbor (k-NN), random forest (RF), and decision tree (DT), were compared to predict TGA data using proximate analysis and combustion temperature (CT). Among the evaluated models, k-NN exhibited the highest performance, achieving near-perfect R2 values that exceeded 0.9904 and RMSE values below 0.9552 on the validation set for both TG (mass loss) and DTG (derivative mass loss). It also accurately predicted key combustion properties, including ignition, peak, and burnout temperature when tested on unknown data. LIME (Local Interpretable Model-agnostic Explanations) analysis revealed that CT was the most influential predictor for TG and DTG, enhancing model interpretability. The results highlight the effectiveness of the k-NN-LIME approach in analyzing the combustion of oxidatively torrefied SCG, offering a robust and explainable model with significant implications for bioenergy research and sustainable fuel development.
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Combustion study of rice husk under different heating rates by integrating thermogravimetric analysis and decision tree regression
(2025-01-15)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
This study investigates the combustion behavior of rice husk using thermogravimetric analysis coupled with decision tree regression. Results indicated that increasing heating rates caused elevated burnout (Tb) and peak temperatures (Tp) while extending the active combustion stage. The optimized decision tree model effectively predicts mass loss, demonstrated by a perfect coefficient of determination (R2) of 1 with a low root mean square error (RMSE) of 0.1993 on the validation set. The model's robustness suggested its potential for accurate mass loss prediction in rice husk combustion.
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Heating value prediction model of Acacia mangium Willd using near infrared spectroscopy
(2025-01-15)
Pambudi, Suluh
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Saechua, Wanphut
This study reported the prediction model of heating value for the Acacia mangium Willd which is the promoted as energy plant for the farmer to grow in the farm. Its heating value is approximately of 19 kJ/kg which provides high potential using for an alternative energy as biomass. The near infrared spectroscopy technique (NIR) is used to create model to predict the heating value of this biomass in order to reduce the investigated time. The results of model on the validation set were the coefficient of determination (R2) of 71%, RMSEP of 246 J/g, RPD 1.87 and bias of 33.7 J/g. These results showed the potential and possibility to apply the NIR prediction technique. The robust model was suggested to carried out for more accuracy and may be applied in the online system analysis.
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Machine learning based prediction and iso-conversional assessment of oxidatively torrefied spent coffee grounds pyrolysis
(2024-12-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
This research focused on developing a predictive model for mass loss during the pyrolysis of oxidatively torrefied spent coffee grounds (SCG) using machine learning techniques. Four algorithms were employed: artificial neural networks (ANN), k-nearest neighbors (k-NN), random forest (RF), and decision tree (DT), with the RF model demonstrating superior performance (R2 > 0.9981, RMSE <1.346) for both training and testing sets. The pyrolysis behavior, kinetics, and thermodynamics of SCG were also investigated using thermogravimetric analysis (TGA) under an inert atmosphere at different heating rates. Higher heating rates in TGA cause T<inf>peak</inf> values to shift to higher temperatures with increased DTG<inf>peak</inf> values, while also resulting in lower T<inf>onset</inf> and higher T<inf>offset</inf>. Kinetic analysis, using the Flynn-Wall-Ozawa (FWO) method, was identified as the most suitable approach for determining activation energy (E<inf>a</inf>), with values ranging from 192.66 to 288.13 kJ mol−1, indicating differences in energy requirements for pyrolysis across samples. Thermodynamic analysis further revealed that both raw SCG and oxidatively torrefied SCG pyrolysis were endothermic reactions. These findings contribute valuable insights into the optimization of biomass conversion technologies, highlighting the potential of machine learning in improving predictive accuracy and efficiency in thermal behavior modeling. This research advances sustainable bioenergy production by promoting the use of SCG, an abundant waste material, as a renewable feedstock in pyrolysis-based processes.
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Thermochemical treatment of spent coffee grounds via torrefaction: A statistical evidence of biochar properties similarity between inert and oxidative conditions
(2024-03-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
This study used several statistical analyses to explore the impact of both inert and oxidative conditions on the characteristics of biochar derived from the torrefaction of spent coffee grounds (SCG). The study also considered variations in torrefaction temperature and residence time. Various fuel analyses were conducted, including high heating value (HHV), torrefaction index, proximate characteristics, thermogravimetric analysis (TGA), hygroscopicity, and Fourier-transform infrared spectroscopy (FTIR). The analysis of variance (ANOVA) revealed that the influence of both inert and oxidative conditions on HHV and mass yield was insignificant (p ≥ 0.05). Moreover, considering the same temperature and residence time, principal component analysis (PCA) and hierarchical cluster analysis (HCA) identified oxidative and inert conditions belonging to the same group or cluster. This finding indicated that neither atmospheric condition significantly affected the characteristics of the biochar measured in this research. Therefore, oxidative torrefaction offers significant advantages as it can produce biochar of comparable quality under inert conditions.
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A thermogravimetric assessment of eco-friendly biochar from oxidative torrefaction of spent coffee grounds: Combustion behavior, kinetic parameters, and potential emissions
(2024-02-01)
Pambudi, Suluh
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Saechua, Wanphut
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Jongyingcharoen, Jiraporn Sripinyowanich
This study investigated how temperature and residence time variations impact the combustion behavior, kinetic, and potential emissions of biochar produced through oxidative torrefaction of spent coffee grounds (SCG). The study examined the characteristics of combustion and kinetics by conducting thermogravimetry analysis under heating rates of 10 °C·min−1. The Coats-Redfern model was utilized to calculate kinetic parameters. While the emission indices were approximated using the data obtained from elemental analysis. The results indicated that biochar's comprehensive combustion index (C<inf>ci</inf>) from oxidative torrefaction was lower than that of raw SCG, suggesting stable combustion behavior. Moreover, with the escalation of torrefaction intensity, the activation energy (E<inf>a</inf>) values exhibited an upward trend for the char combustion stage, ranging from 22.08 kJ mol−1 to 38.46 kJ mol−1. Concurrently, the E<inf>a</inf> values pertaining to the oxidative pyrolysis stage decreased from 64.26 kJ mol−1 to 52.65 kJ mol−1. Besides, this study emphasized that the ash content of the biochar was lower than that of coal and remained consistent with the ash content of raw SCG (p > 0.05). Moreover, the study revealed that biochar from oxidative torrefaction emitted less CO<inf>2</inf> (67.35 g MJ−1) than lignite coal (76.55 g MJ−1). Additionally, biochar exhibited up to 27 times lower dust emissions than bituminous coal, emphasizing its eco-friendly fuel potential.

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