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•Biofuel properties and reactivity of lignocellulosic biomass via TGA are studied.•Biofuel characteristics and operational conditions are critical factors in TGA.•TGA may evaluate ...proximate analysis components within biofuel in 5 methods.•TGA illustrates the measurement combustion indexes utilizing TGA/DTG curves.•Potential development of TGA-AI integration effectively carried out with R2 >95 %.
Biomass is an organic substance widely available in nature as a fresh or a waste material considered renewable energy that aligns with the zero-carbon scheme to reduce the dependency on fossil fuels. However, after conversion, biomass's physical or chemical properties highly affect biofuel characteristics. A variety of instruments can be used to figure out biofuel reactivity. Considering commonly adopted instruments, thermogravimetric analysis (TGA) is a simple, fast, and efficient way to determine biofuel properties and reactivity. The TGA method has the capability to analyze the biofuel properties (proximate analysis: moisture, volatile matter, fixed carbon, and ash) and combustion features of biomass (such as ignition, reactivity, etc). Most importantly, the TG curvatures (TGA and DTG) reveal the behavior of the biofuel during the thermodegradation process. As a consequence, the quality and quantity analyses on the biofuel properties and reactivity can be investigated comprehensively. Moreover, some TGA integration with artificial intelligence (AI) has been studied to better understand biofuel management and technology for future development. The outcome for the TGA-AI integration may obtain an excellent result with the fit quality value R2 >95 %. This study aims to comprehensively review relevant research using TGA to analyze the lignocellulosic biofuel properties and reactivity. Moreover, the discussion in this study is extended to perspective, challenges, and future work.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•The ash removal efficiencies from acid and water washings are 58.45 and 36.29%, respectively.•Biomass’s specific chemical bioexergy from optimal torrefaction shows a 3-fold ...enhancement.•Taguchi and ANOVA suggest that washing and catalyst have a significant influence.•The best performance of the ANN model is designed in 1 hidden layer and 3 neurons.•The ANN model with a quick propagation algorithm for the training poses excellent predictions.
The artificial neural network (ANN) in artificial intelligence (AI) is a computational model that portrays how nerve cells (neurons) work in the human brain. Meanwhile, specific chemical bioexergy (SCB) is a vital indicator to provide essential information on how high the actual energy may be contained within biomass fuel (biofuel). In this study, the Taguchi method, analysis of variance (ANOVA), and artificial neural network (AI-ANN) are utilized to predict the SCB of biofuels from the spent mushroom substrate (SMS) torrefaction via microwave-assisted heating (MAH). In Taguchi’s orthogonal array, washing, catalyst, and power operating parameters are considered. Acid and water washings lower the SMS’s ash content by 58.45 % and 36.29 %, respectively. The optimum conditions for combining with acid washing, a catalyst with higher Fe2O3 (35 %), and microwave power 540 W render the highest total SCB in biofuels (biochar + bio-oil) of 47.90 MJ·kg−1, which is close to the SCB of crude oil derivatives (41–49 MJ·kg−1). The enhancement of biomass’s SCB value from optimal torrefaction approximately 3 folds (256.30 %) from 12.84 (raw) to 47.90 MJ·kg−1. The ANN model with an architecture of 1 hidden layer (sigmoid activation function) with 3 neurons and the output layer (piecewise linear activation function) with a quick propagation algorithm for the training process of all layers poses excellent prediction with high accuracy R2 = 1. This result demonstrates that ANN with the designed scheme is suitable for predicting the SCB of SMS-derived biofuels.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•The highest total biofuel yield by thermochemical processes reaches up to 99.42%•The highest maximum temperature of heating is obtained from smaller particle size.•Taguchi and ANOVA ...analyses suggest that catalyst has a critical influence.•The ANN model has high fit quality values of biochar and bio-oil yields.•A quick propagation algorithm is successfully applied to supervise the ANN model.
Artificial neural network (ANN) is one kind of artificial intelligence in the computing system that aims to process information as the way neurons in the human brain. In this study, the combination of the Taguchi method and ANN are used to maximize and predict biofuel yield from spent mushroom substrate torrefaction and pyrolysis via microwave irradiation. The Taguchi method is utilized to design the multiple factors (particle size, catalyst, power, and magnetic agent) and levels of experiment parameters. The highest total biofuel yield (biochar + bio-oil) is 99.42%, accomplished by a combination of 355 µm particle size, 300 mg·g-SMS-1 catalyst, 900 W power, and 300 mg·g-SMS-1 magnetic agent. ANN with one hidden layer shows the outstanding linear regression predictions for the highest biofuel yields (biochar 0.9999 and bio-oil 0.9998). This high linear regression indicates that ANN with a quick propagation algorithm is an appropriate approach for predicting biofuel conversion.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Machine learning is developed to predict the torrefaction severity index of torrefied biomass.•MARS model identifies the temperature as the most influential factor on TSI value.•Two ...hidden layers with 85 neurons provide a better performance of the ANN model.•The best-fit quality (R2) values of MARS and ANN are 0.9851 and 0.9784, respectively.•Three-dimensional TSI profiles from MARS and ANN are almost equivalent.
Machine learning (ML) is one type of artificial intelligence (AI) commonly used for computer programming. Multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) are two common and popular tools in AI that allow the user to analyze the pattern of complex data. The torrefaction severity index (TSI) is an index to define torrefied biomass quality at different torrefaction conditions. In this study, MARS and ANN models are applied to predict TSI. The considered input parameters in predictions using MARS and ANN approaches comprise feedstock type, temperature, and duration. The MARS model indicates that temperature is the most influential factor on TSI, followed by duration and feedstock type. In contrast, the ANN model reveals that the feedstock type is a dominant factor, and temperature and duration are not important. The performance of the ANN model is evaluated in three different combinations of numbers of hidden layers and neurons. It shows 2 hidden layers along with 85 neurons giving the best performance. The highest R2 values in MARS and ANN are 0.9851 and 0.9784, respectively. The relative root means square error analysis shows that both MARS and ANN have good fit quality with the relative errors of 1.49% and 2.16%, respectively. Overall, the comparison reflects that MARS is a more suitable model for predicting solid biofuel’s TSI. The general observation suggests that the ANN lacks sensitivity to the input parameter. Nevertheless, ANN performance may be improved by adjusting the number of hidden layers and neurons.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Glucose production of the thermochemical WT of five types of biomass is predicted.•The statistical evaluations of ANOVA and RSM are applied.•AI analysis of NN, MARS, and DT models ...are successfully utilized in the bioenergy field.•The predicted highest glucose concentration is 15.216 g·L-1.•The relative error between the prediction and the experiment by the NN model is 5.55%.
Artificial intelligence (AI) has become the future trend for prediction after the data is provided to machine learning. This study uses data analysis to optimize the experiment, find the best-operating conditions, and obtain the maximum glucose concentration for bioethanol production where wet torrefaction (WT) is used to perform biomass pretreatment. Forty-nine (49) sets of data are split into training and test data in the ratio of 7:4. Glucose concentrations from five different feedstocks are trained and predicted using a neural network (NN) and multivariate adaptive regression splines (MARS), followed by a decision tree (DT) to predict the classification of the materials. The predicted NN results are better than MARS, so the NN training is used for the glucose prediction along with the Box-Behnken design (BBD) experiment. The BBD experiment is performed with the parameters of temperature (170, 175, and 180 °C), reaction time (10, 20, and 30 min), and sulfuric acid concentration (0, 0.01, and 0.02 M) for the WT of sorghum distillery residue. By adding the BBD experimental data in NN training, the fit quality of the model is improved to 99.78 %. The NN model predicts that the highest glucose concentration occurring at the optimal conditions (i.e., 173 °C, 10.5 min, and 0.02 M sulfuric acid) is 15.216 g/L with a relative error of 5.55 % between the prediction and experiment. These resuts indicate that NN is an appropriate approach to predicting glucose production from biomass WT for bioethanol production. Additionally, the analysis of variance (ANOVA) evaluation shows that the order of the vital parameter for glucose concentration is sulfuric acid, followed by reaction time and temperature.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through ...biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.
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•Artificial intelligence application for biowaste-to-bioenergy is reviewed.•Feedstock selection and conversion pathways are significant topics in bioenergy systems.•AI types of connectionism and statistical learning models are frequently implemented.•AI models provide computing data analysis with a high-accuracy prediction.•Model adjustment and configuration tuning are still challenging in AI development.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Pyrolysis of protein, lipid, and carbohydrates from microalgae is investigated.•The kinetics model gives a high fit quality for three pseudo-components.•A synergistic effect is found ...in the thermodegradation of model carbohydrates.•Glucose is the dominant species in the synergistic effect of carbohydrates.•Four distinguished regions are discovered in the mixture of all the investigated constituents.
Microalgae offer unique potentials for developing advanced biorefineries, including third-generation biofuel production, wastewater treatment, and animal and aquaculture feed production. The thermodegradation of protein, lipid, and carbohydrates plays a vital role in the thermochemical conversion of microalgae for biofuel production. This work aims to investigate the kinetics and the interaction of extracted protein and lipid as well as model carbohydrates from microalgae to assist the development of microalgae conversion techniques, which have not been studied so far. Thermogravimetric analysis is integrated with an independent parallel reaction (IPR) and particle swarm optimization (PSO) method to explore the pyrolysis kinetics of three constituents (protein, lipid, and carbohydrates). The calorific values of the three constituents show that protein (5.33 MJ·kg−1) is not a suitable biofuel feedstock. In contrast, lipid (34.22 MJ·kg−1) and carbohydrates (15.37–15.84 MJ·kg−1) are considered as potential feedstocks for liquid and solid biofuel production, respectively. The pyrolysis processes suggest that the thermodegradation extent follows the order of carbohydrates > protein > lipid. The application of the IPR-PSO method on the pyrolysis kinetics of microalgae in three pseudo-components obtains a high fit quality (>96%) for all cases, indicating that the method is suitable to predict the kinetics parameters of the three constituents of microalgae. The effect analysis reveals that the synergistic effect accounts for about 50% of the total mass of the thermodegradation process of model carbohydrates, occuring at 200–320 °C. Meanwhile, the theoretical and experimental thermogravimetric analysis curve of combination of the three constituents suggests that there are four regions detected, including strong synergistic effect, weak antagonistic effect, weak synergistic effect, and strong antagonistic effect, respectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•The four pyrolysis variants are reviewed for analytical Py-GC/MS of biomass.•Pyrolysis products of different feedstocks under different conditions are reviewed.•The effects of adding ...catalysts, co-pyrolyzed feedstock, or both are summarized.•Current challenges and perspectives are provided for analytical Py-GC/MS.
Biomass pyrolysis has garnered significant attention as a sustainable energy production method utilizing various biomass feedstocks. Pyrolysate is any product generated from the pyrolysis process, including solid, liquid, and gas types. This review focuses on the application of analytical pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) in the context of four pyrolysis modes: single feedstock pyrolysis, co-pyrolysis, catalytic pyrolysis, and catalytic co-pyrolysis to gain insights into the characteristics of the pyrolysates. A comprehensive understanding of each pyrolysis mode's unique products, benefits, and limitations is achieved by analyzing the pyrolysates of different feedstocks, including lignocellulosic and algal biomass. Moreover, this study discusses the integration of Py-GC/MS with techniques such as density function theory (DFT), which focuses on estimating the reactions’ activation energies or kinetic studies concentrating on the reaction rate and mechanism to gain further insight into pyrolysis mechanisms. Lastly, design of experiment (DoE) techniques are proposed for pyrolysis optimization to obtain a more comprehensive assessment of the parameter's influence on pyrolysis factors and levels.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fine particulate matter (PM_(2.5)) has been found to be harmful when inhaled by people, which has caused enormous health problems. It is found at high levels at public bus stations, where many ...passengers and workers may be exposed to PM_(2.5) emissions from idling diesel engines. This study evaluated the restriction on idling vehicles as a strategy to control PM_(2.5) levels at bus stations by measuring the PM_(2.5) and the chemical properties at both upwind and exposure sites for comparable data. The sampling took place on weekends and weekdays and before and after the idling restriction was applied. Originally, the exposure site showed a PM_(2.5) level that was 7% higher, non-neutralized nitrate content, anthropogenic metal elements, and higher mobile source contributions, as evaluated by a chemical mass balance model (CMB8.2). After the prohibition on idling heavy-duty diesel vehicles, the PM_(2.5) mass concentrations at the exposure site were reduced to levels comparable to those at the upwind site. Additionally, the nitrate content was reduced in the background. Moreover, the contributions of several anthropogenic metals (Zn, Pb, Mn, Cu, Cr, V, Ni, and Ti) in PM_(2.5) were reduced while those of crustal elements (Na, Mg, Al, K, and Ca) significantly increased after the restriction. Finally, the mobile contribution decreased to only 33.7-34.5%. Consequently, these findings verify that the prohibition policy on idling vehicles works well as a control strategy to manage the PM_(2.5) emissions at local hotspots such as bus stations.
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