Key fuel characteristics and techno-economic aspects of torrefied rubberwood biomass pellets produced by incorporating various cassava-based binders at varied doses.

Improving energy content and hydrophobic nature of woody biomass can be pursued through torrefaction. This gives torrefied biomass with a low bulk density, potentially increasing storage and transport costs. To overcome this issue, densifying the torrefied biomass is necessary. However, poor binding of particles makes densification challenging without using a binder. Therefore, the aim of this study was to investigate the physicochemical characteristics and techno-economic aspects of torrefied rubberwood biomass (TRWB) when pelletized using various cassava-based binders at different blending ratios. The selected binders included cassava starch (CS), cassava pulp (CP), and cassava chip (CC). Each binder at 5%, 10%, or 15% (wt.) was mixed with TRWB and water before pelletizing using a flat die machine. The results revealed that pelletizing TRWB with different cassava-based binders at various blending ratios influenced the physicochemical characteristics of the TRWB pellets, particularly dimensions, bulk density, fuel and atomic ratios, and energy content. The TRWB pellets demonstrated energy densities in the range of 7.95-11.39 GJ/m3, and their mechanical durability and fine content fell within acceptable ranges. The TRWB pellets maintained their shape during 120 min of water soaking, with water absorption levels varying by binder dose. The pelletizing ability, material, and energy costs of TRWB pellets depend on binder type and dose. CP can be applied as a binder for pelletizing torrefied rubberwood biomass. However, the mechanical durability of the product needs to be above the user requirement or standard.

Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes.

Feedstock properties play a crucial role in thermal conversion processes, where understanding the influence of these properties on treatment performance is essential for optimizing both feedstock selection and the overall process. In this study, a series of van Krevelen diagrams were generated to illustrate the impact of H/C and O/C ratios of feedstock on the products obtained from six commonly used thermal conversion techniques: torrefaction, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, pyrolysis, and gasification. Machine learning methods were employed, utilizing data, methods, and results from corresponding studies in this field. Furthermore, the reliability of the constructed van Krevelen diagrams was analyzed to assess their dependability. The van Krevelen diagrams developed in this work systematically provide visual representations of the relationships between feedstock and products in thermal conversion processes, thereby aiding in optimizing the selection of feedstock and the choice of thermal conversion technique.

Machine learning approach for determining and optimizing influential factors of biogas production from lignocellulosic biomass.

Machine learning (ML) was used to predict specific methane yields (SMY) with a dataset of 14 features from lignocellulosic biomass (LB) characteristics and operating conditions of completely mixed reactors under continuous feeding mode. The random forest (RF) model was best suited for predicting SMY with a coefficient of determination (R2) of 0.85 and root mean square error (RMSE) of 0.06. Biomass compositions greatly influenced SMYs from LB, and cellulose prevailed over lignin and biomass ratio as the most important feature. Impact of LB to manure ratio was assessed to optimize biogas production with the RF model. Under typical organic loading rates (OLR), optimum LB to manure ratio of 1:1 was identified. Experimental results confirmed influential factors revealed by the RF model and provided the highest SMY of 79.2% of the predicted value. Successful applications of ML for anaerobic digestion modelling and optimization specifically for LB were revealed in this work.

Unlocking the potential of transesterification catalysts for biodiesel production through machine learning approach.

The growing demand for fossil fuels has motivated the search for a renewable energy source, and biodiesel has emerged as a promising and environmentally friendly alternative. In this study, machine learning techniques were employed to predict the biodiesel yield from transesterification processes using three different catalysts: homogeneous, heterogeneous, and enzyme. Extreme gradient boosting algorithms showed the highest accuracy in predictions, with a coefficient of determination accuracy of nearly 0.98, as determined through a 10-fold cross-validation of the input data. The results indicated that linoleic acid, behenic acid, and reaction time were the most crucial factors affecting biodiesel yield predictions for homogeneous, heterogeneous, and enzyme catalysts, respectively. This research provides insights into the individual and combined effects of key factors on transesterification catalysts, contributing to a deeper understanding of the system.

Conversion of tobacco processing waste to biocrude oil via hydrothermal liquefaction in a multiple batch reactor.

Fossil fuels are the primary energy source of almost all societies and economies, but it is finite and scarce. The use of non-renewable fossil fuels threatens earth's environment. At the same time, waste from agricultural and industrial activities is increasing. Most of this waste is discarded or poorly managed, causing many other environmental issues. Converting waste to energy is a promising route to address these challenges. We investigated the hydrothermal liquefaction (HTL) of high moisture content, tobacco-processing waste in a multiple batch thermal reactor to produce biocrude oil. The effects of operating conditions were studied and optimized for maximum liquid biocrude oil yield. HTL operating conditions considered were temperatures from 280 to 340 °C and residence times from 15 to 45 min for a fixed ratio of biomass to deionized water of 1:3. The reaction temperature was found to affect the yields and distribution of products significantly. The maximum yield of the liquid biocrude oil obtained was more than 52% w/w at 310 °C and 15 min. Under these conditions, almost 90% of the energy was recovered in biocrude oil and solid products. The liquid fraction was mainly composed of phenols, ketones, and nitrogenous compounds. This study provides a potential framework for eco-technologies for biomass waste-to-energy conversion with respect to converting tobacco processing residues to liquid biofuels and biochemicals.

Exposing and understanding synergistic effects in co-pyrolysis of biomass and plastic waste via machine learning.

During co-pyrolysis of biomass with plastic waste, bio-oil yields (BOY) could be either induced or reduced significantly via synergistic effects (SE). However, investigating/ interpreting the SE and BOY in multidimensional domains is complicated and limited. This work applied XGBoost machine-learning and Shapley additive explanation (SHAP) to develop interpretable/ explainable models for predicting BOY and SE from co-pyrolysis of biomass and plastic waste using 26 input features. Imbalanced training datasets were improved by synthetic minority over-sampling technique. The prediction accuracy of XGBoost models was nearly 0.90 R2 for BOY while greater than 0.85 R2 for SE. By SHAP, individual impact and interaction of input features on the XGBoost models can be achieved. Although reaction temperature and biomass-to-plastic ratio were the top two important features, overall contributions of feedstock characteristics were more than 60 % in the system of co-pyrolysis. The finding provides a better understanding of co-pyrolysis and a way of further improvements.

Effect of print parameters on additive manufacturing of metallic parts: performance and sustainability aspects.

In this study, the effects of print parameters on the mechanical properties of additively manufactured metallic parts were investigated using a tensile test. The 17-4 PH stainless steel specimens with two print parameters, including infill density and pattern orientation, were fabricated by additive manufacturing (AM) using the bound metal deposition (BMD) technique. The mechanical properties considered in this study are the Young's modulus and ultimate tensile strength. The results demonstrate that the pattern orientations do not affect the Young's modulus of the infill specimen with the triangular pattern. In contrast, the ultimate strength significantly varies depending on the pattern orientations, where the samples with the pattern orientation of zero degrees yield the best ultimate strength. In fact, the mechanical properties of infill specimens increase with their infill density. However, when operating cost and time are considered, an index for estimating performance and sustainability is consequently established. The relationship between the normalized ultimate strength of an infill specimen and the relative density is defined as the weight efficiency. The index for assessing a sustainable product is characterized by the weight efficiency versus sustainable parameter(s). The index can help end users select an appropriate infill density for AM products by considering the operating cost and time. Different cost models, including material-only costs, direct costs, and total costs, can be included in the index model to assess a sustainable product in a particular cost context.

Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques.

Crop yield and its prediction are crucial in agricultural production planning. This study investigates and predicts arabica coffee yield in order to match the market demand, using artificial neural networks (ANN) and multiple linear regression (MLR). Data of six variables, including areas, productivity zones, rainfalls, relative humidity, and minimum and maximum temperature, were collected for the recent 180 months between 2004 and 2018. The predicted yield of the cherry coffee crop continuously increases each year. From the dataset, it was found that the prediction accuracy of the R2 and RMSE from ANN was 0.9524 and 0.0784 tons, respectively. The ANN model showed potential in determining the cherry coffee yields.

Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes.

Machine learning (ML) approach was applied for the prediction of biocrude yields (BY) and higher heating values (HHV) from hydrothermal liquefaction (HTL) of wet biomass and wastes using 17 input features from feedstock characteristics (biological and elemental properties) and operating conditions. Several novel ML algorithms were evaluated, based on 10-fold cross-validation, with 3 different sets of input features. An extreme gradient boosting (XGB) model proved to give the best prediction accuracy at nearly 0.9 R2 with normal root mean square error (NRMSE) of 0.16 for BY and about 0.87 R2 with NRMSE of about 0.04 for HHV. Temperature was found to be the most influential feature on the predictions for both BY and HHV. Meanwhile, feedstock characteristics contributed to the XGB model for more than 55%. Individual effects and interactions of most important features on the predictions were also exposed, leading to better understanding of the HTL system.

Compositional analysis of bio-oils from hydrothermal liquefaction of tobacco residues using two-dimensional gas chromatography and time-of-flight mass spectrometry.

Sustainable energy from biomass is one of the most promising alternative energy sources and is expected to partially replace fossil fuels. Tobacco industries have normally rid their processing residues by landfilling or incineration, affecting the environment negatively. These residues can be used to either extract high-value chemicals or generate bio-energy via hydrothermal liquefaction. The main liquid product or bio-oil consists of highly complicated chemicals. In this work, the bio-oil from hydrothermal liquefaction of tobacco processing residues was generated in a batch reactor at biomass-to-deionized water ratio of 1:3, temperature of 310°C, and 15 min residence time, yielding the maximum liquid products for more than 50% w/w. The liquid products were analyzed, using two-dimensional gas chromatography and time-of-flight mass spectrometry (GC × GC/TOF MS). This technique allowed for a highly efficient detection of numerous compounds. From the results, it was found that hydrothermal liquefaction can cleave biopolymers (cellulose, hemicellulose, and lignin) in tobacco residues successfully. The hydrothermal liquefaction liquid products can be separated into heavy organic, light organic, and aqueous phase fractions. By GC × GC/TOF MS, the biopolymers disintegrated into low molecular weight compounds and classified by their chemical derivatives and functional groups could be detected. The major chemical derivative/functional groups found were cyclic ketones and phenols for heavy organic and light organic, and carboxylic acids and N-containing compounds for the aqueous phase. Additionally, by the major compounds found in this work, simple pathway reactions occurring in the hydrothermal liquefaction reaction were proposed, leading to a better understanding of the hydrothermal liquefaction process for tobacco residues.

Biocrude oil production via hydrothermal liquefaction of food waste in a simplified high-throughput reactor.

In this study, the hydrothermal liquefaction of food waste was investigated for generating bio-oils or biocrudes using a simplified high-throughput reactor. Different operating conditions were parametrically tested for obtaining maximum yield of biocrudes at temperatures from 280 to 340 °C and sample-to-water mixture ratios from 1:3 to 1:7, with 30 min residence time. The biocrude yields were found to increase significantly with increasing temperature and mixture ratio. A maximum biocrude yield of approximately 40% w/w dry basis (from a total liquid product yield of over 56% w/w) and energy recovery of over 70% were obtained at 340 °C and a mixture ratio of 1:7. High heating values of the resulting biocrude and solid residue were remarkably better than those of the raw material. Fatty acids, amides, N-containing compounds, and cyclic ketones were identified to be the main components of the biocrudes using advanced GCxGC/TOF-MS.

Biogas production from high solids digestion of Pennisetum purpureum x Pennisetum typhoideum: Suitable conditions and microbial communities.

Effects of organic loading rates (OLRs), temperatures and effluent recirculation rates on biogas production from Giant Juncao Grass (GJG) using pilot-scale semi-continuously fed CSTRs were investigated. Thermophilic reactors could be stably operated at OLR up to 5.0 kg VS m-3 d-1, while damaged process stability was detected in mesophilic reactors at OLR of 4.0 kg VS m-3 d-1. Higher effluent recirculation rate (3:1) helped lessen negative effects of system being over-loaded, especially for mesophilic reactors. Microbial community analysis revealed that temperatures had the highest effect on bacterial community structure. Firmicutes were the dominant bacterial phyla found under high temperatures, while majority of archaea in all reactors belonged to the phylum Bathyarchaeota. Changes of microbial communities could partly explain system performance under different operating conditions. This study was the first to show GJG as a superior biogas feedstock to other energy crops thanks to its higher methane yields per planting area.

Production and characterization of bio-oils from fast pyrolysis of tobacco processing wastes in an ablative reactor under vacuum.

Application of advanced pyrolysis processes to agricultural waste for liquid production is gaining great attention, especially when it is applied to an economic crop like tobacco. In this work, tobacco residues were pyrolyzed in an ablative reactor under vacuum. The maximum bio-oil yield of 55% w/w was obtained at 600°C with a particle size of 10 mm at a blade rotation speed of 10 rpm. The physical properties of the products showed that the oil produced was of high quality with high carbon, hydrogen, and calorific value. Two-dimensional gas chromatography/time-of-flight mass spectrometric analysis results indicated that the oils were complex mixtures of alkanes, benzene derivative groups, and nitrogen-containing compounds. In addition, 13C NMR results confirmed that long aliphatic chain alkanes were evident. The alkanes were likely converted from furans that were decomposed from hemicelluloses. Ablative pyrolysis under vacuum proved to be a promising option for generating useful amount of bio-oils from tobacco residues.

Machine learning prediction of cellulose-rich materials from biomass pretreatment with ionic liquid solvents.

Ionic liquid solvents (ILSs) have been effectively utilized in biomass pretreatment to produce cellulose-rich materials (CRMs). Predicting CRM properties and evaluating multi-dimensional relationships in this system are necessary but complicated. In this work, machine learning algorithms were applied to predict CRM properties in terms of cellulose enrichment factor (CEF) and solid recovery (SR), using 23-feature datasets from biomass characteristics, operating conditions, ILSs identities, and catalyst. Random forest algorithm was found to have the highest prediction accuracy with RMSE and R2 of 0.22 and 0.94 for CEF, as well as 0.07 and 0.84 for SR, respectively. Highly influential features on making predictions were mainly from biomass characteristics andILS treatment'soperating conditions, totally contributed 80% on CEF and 60% on SR. One- and two-way partial dependence plots were used to explain/interpret the multi-dimensional relationships of the most important features. Our findings could be applied in designing new ILSs and optimizing the process conditions.

Bio-oils from vacuum ablative pyrolysis of torrefied tobacco residues.

Fast pyrolysis, in combination with torrefaction pretreatment, was used to convert tobacco residues to value-added bio-fuels and chemicals. Tobacco plant residues were torrefied at 220, 260, and 300 °C, before being pyrolyzed at 450, 500, 550, and 600 °C in a rotating blade ablative reactor under vacuum conditions to test the effects on product yields. With torrefaction, tobacco residues thermally decomposed 20-25% w/w at low temperatures. Torrefaction and pyrolysis temperatures were found to markedly affect pyrolytic product yields of bio-chars and bio-oils, while having no effect on gas-phase products. Bio-oil yields exhibited a direct relation with pyrolysis temperature and an inverse relation with torrefaction temperature. Bio-oils produced were separated into light and heavy oils and analyzed by GC-MS, and 1H and 13C NMR. Nicotine was found to be the main compound in the light and heavy oils along with several phenols and cresols in the heavy oil.

Torrefaction of pelletized corn residues with wet flue gas.

Corn residue pellets were torrefied with wet flue gas, simulated by steam (0-21% v/v), CO2 (12% v/v), and O2 (4% v/v), balanced with N2 as reactive gas, in a fixed bed reactor at 260 °C of temperature and at 10-40 min of residence time. The distribution and yields of torrefied pellets, liquid, and gas products were examined. The microstructural changes of torrefied pellets were evaluated by Raman spectroscopy and scanning electron microscopy, while the components of gas products were analyzed by mass spectrometry. Residence time and steam concentration in the reactive gas were found to have significant effects on the products yield distribution, the porosity of the torrefied pellets, and the concentrations of CO, CH4, H2, and CO2 in the gas products. At high steam concentrations, the decomposition reaction of hemicellulose and lignin in the raw pellets, and the formation of the graphene structures in torrefied pellets occurred faster.

Nonisothermal thermogravimetric analysis of Thai lignite with high CaO content.

Thermal behaviors and combustion kinetics of Thai lignite with different SO3-free CaO contents were investigated. Nonisothermal thermogravimetric method was carried out under oxygen environment at heating rates of 10, 30, and 50°C min⁻¹ from ambient up to 1300°C. Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods were adopted to estimate the apparent activation energy (E) for the thermal decomposition of these coals. Different thermal degradation behaviors were observed in lignites with low (14%) and high (42%) CaO content. Activation energy of the lignite combustion was found to vary with the conversion fraction. In comparison with the KAS method, higher E values were obtained by the FWO method for all conversions considered. High CaO lignite was observed to have higher activation energy than the low CaO coal.

Thermogravimetric analysis of giant sensitive plants under air atmosphere.

The aim of this work is to utilise thermal analysis to study the thermal degradation of giant sensitive plants (Mimosa pigra L.) or Mimosa under oxidative environment. Thermogravimetric method was used under air sweeping in dynamic conditions at the heating rates of 10, 30, and 50 degrees C/min, from room temperature to about 725 degrees C. Starting with dehydration step between 30 and 150 degrees C, the main thermal decomposition process under air showed two distinct degradation zones, corresponding to devolatilisation step between 200 and 375 degrees C and combustion step around 375-500 degrees C. Kinetic parameters in terms of apparent activation energy and pre-exponential factor were determined. Comparison was made against other biomass materials. Mass loss and mass loss rates were strongly affected by heating rate. It was found that an increase in heating rate resulted in a shift of thermograms to higher temperatures. As the heating rates increased, average devolatilisation and combustion rates were observed to increase while the activation energy showed slight increase.

Non-isothermal pyrolysis characteristics of giant sensitive plants using thermogravimetric analysis.

A giant sensitive plant (Mimosa pigra L.) or Mimosa is a fast growing woody weed that poses a major environmental problem in agricultural and wet land areas. It may have potential to be used as a renewable energy source. In this work, thermal behaviour of dried Mimosa was investigated under inert atmosphere in a thermogravimetric analyzer at the heating rates of 10, 30, and 50 degrees C/min from room temperature to 1000 degrees C. Pyrolysis kinetic parameters in terms of apparent activation energy and pre-exponential factor were determined. Two stages of major mass loss occurred during the thermal decomposition process, corresponding to degradation of cellulose and hemicellulose between 200-375 degrees C and decomposition of lignin around 375-700 degrees C. The weed mainly devolatilized around 200-400 degrees C, with total volatile yield of about 60%. The char in final residue was about 20%. Mass loss and mass loss rates were strongly affected by heating rate. It was found that an increase in heating rate resulted in a shift of thermograms to higher temperatures. As the heating rates increased, average devolatilization rates were observed to increase while the activation energy decreased.