Higher Heating Value Research Articles

Optimization of waste biomass demineralization through response surface methodology and enhancement of thermochemical and fusion properties

This study examines the impact of leaching with dilute hydrochloric acid solution on the reduction of ash content and the thermal degradation behavior of sugarcane bagasse. Response surface methodology (RSM) was used to statistically design the experiments and investigate the effect of three independent variables: treatment time, solid-to-liquid ratio, and reagent concentration. The leaching conditions were further optimized and experimentally validated for maximum ash reduction for suitability of treated biomass as feedstock for thermochemical conversion technologies. Reagent concentration and treatment time directly affected ash reduction, while the solid-to-liquid ratio inversely influenced it. Concentration had the highest impact, and treatment duration had the least. The maximum 78.2% ash reduction was achieved by treating the biomass with 1 M HCl for 80 min at a solid-to-liquid ratio of 50:1 (wt/vol). This ash reduction also resulted in a 9.82% increase in higher heating value (HHV). Hemicellulose hydrolysis during leaching was observed through chemical composition and Fourier-transform infrared spectroscopy (FTIR). Ash fusion temperatures increased, indicating more thermally stable biomass. Thermogravimetric analysis (TGA) showed elevated maximum degradation temperature and activation energy.

Efficient High Heating Value estimation using Latin Hypercube Sampling and Artificial Neural Network-based approach.

To maximize energy recovery in waste-to-energy (WTE) systems, the High Heating Value (HHV) of municipal solid waste (MSW) must be accurately estimated. To forecast the HHV of MSW, this study proposes a unique method that combines an Artificial Neural Network (ANN) model with Latin Hypercube Sampling (LHS), with a focus on Solan City, Himachal Pradesh, India. In the present study, the elemental characteristics of waste have been used to deal with uncertainty and to find the suitable parameters responsible for the HHV of the MSW. Initially, Latin Hypercube Sampling (LHS) has been used to deal with uncertainty in the elemental composition of MSW, which includes carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O) content. This elemental composition has been used as input parameters to the ANN model for predicting the HHV of MSW. The network 5-28-5-1 offered a minimum MAPE value of 2.18%, MSE, RMSE and R2 values are 0.012, 0.107 and 0.767, respectively. Thereafter, a synaptic weight approach was used to find the most significant parameters responsible for HHV in MSW. It was observed that carbon is the most suitable parameter for HHV of MSW. By dealing with the uncertainty in MSW characteristics, the integration of LHS strengthens the robustness of the model. The results offer an accurate and economical approach for HHV estimation, which will be useful for improving the MSW management and WTE conversion procedures.

Carbonization of Refuse-Derived Fuel Pellets with Biomass Incorporation to Solid Fuel Production

In this work, dry carbonization (DC) and hydrothermal carbonization (HTC) of refuse-derived fuel (RDF) pellets were conducted to evaluate the physical, chemical, and fuel properties of the produced chars. In the dry carbonization tests, biomass sawdust was incorporated in different proportions on the samples to minimize agglomeration caused by the melting of the plastic fraction. The experiments were carried out in a temperature of 400 °C (DC) and 250–300 °C (HTC), in a residence time of 30 min. The respective chars and hydrochars were characterized according to their mass yield, apparent density, proximate, elemental, and mineral composition, chlorine content, high heating value, thermogravimetric profile, and surface functional groups. The results showed that the dry carbonization of RDF pellets with biomass incorporation, followed by a washing step, resulted in the production of chars with improved properties such as higher fixed carbon and higher heating value (HHV) (25–26 MJ/kg) and lower ash and chlorine content. Additionally, the HTC experiments demonstrated that hydrochars showed improved properties without the need for biomass addition and washing, however, with no significant difference in the HHV (20–21 MJ/kg). Therefore, DC of RDF pellets with 10% biomass incorporation seems to be a promising option to overcome the constraints of RDF utilization as an alternative fuel.

Spent tea leaves and tea bags - promising biofuels?

The spent tea leaves used after the extraction of liquid from processed tea are insufficiently utilized waste, rich in essential amino acids, polyphenols, alkaloids, and minerals, produced in large quantities, e.g., in tea houses, hospitals, school canteens, etc. Therefore, this study is aimed at characterizing this waste, how it is processed into pellets and how it subsequently increases its energy potential by torrefaction. The influence of the method of tea packaging, i.e., loose tea leaves or tea bags, was also considered. Several types of tea waste were processed using a pilot plant pelletizing press and torrefaction stand. The prepared pellets were characterized by mechanical parameters, such as the pellet durability index, the wettability index, water resistance, hardness, or specific bulk density, and were integrated by FTIR measurements. Furthermore, pellet’s energy parameters, such as higher heating value, lower heating value, and elemental analysis, were compared. The results show that non-torrefied pellets have an average combustion heat of 19 KJ.kg-1, while torrefied pellets have a value of 9 KJ.kg-1 higher. Overall, the study demonstrates that spent tea leaves can be converted into a sustainable source of bioenergy and presents a solution for the treatment of this waste, as well as a renewable energy option.

Thermogravimetric Assessment of Biomass: Unravelling Kinetic, Chemical Composition and Combustion Profiles

Thermogravimetric analysis (TGA) was performed on six samples of pine wood, poplar sawdust and olive residue, and the kinetic parameters were evaluated by using isoconversional models. The hemicellulose, cellulose and lignin contents were also estimated using the Fraser–Suzuki deconvolution method. In addition, a range of thermodynamic parameters and combustion indices was calculated. Significant correlations were found between the kinetic, thermodynamic and combustion parameters. The ignition index showed an inverse relationship with the activation energy, whereas the burnout index correlated with enthalpy values for most samples. Higher heating rates during TGA increased ignition and combustion efficiencies but decreased combustion stability. Differences in behaviour were detected between the olive residues, which had a much higher lignin content (51.2–56.9%), and the woody biomass samples (24.2–29.2%). Moreover, the sample with the highest ash content also exhibited some distinctive characteristics, including the lowest high heating value and ignition index, coupled with the highest activation energy, indicating a less favourable combustion behaviour than the other samples. The particle size of the samples was also found to be critical for both combustion efficiency and safety.

Elephant Grass Cultivar BRS Capiaçu as Sustainable Biomass for Energy Generation in the Amazon Biome of the Mato Grosso State

Sustainable biomasses are vital to ensure preservation of the Amazon biome within the Mato Grosso State whilst enabling energy generation for the region and its population. Here, the potential of the elephant grass cultivar BRS Capiaçu as an alternative to replace native forest wood as biomass for energy generation is investigated, considering the whole process from plant cultivation to biomass characterisation in terms of productivity of green and dry mass per hectare; density, moisture, ash, volatile and fixed carbon content, as well as higher heating value (HHV). MANOVA indicates that the effects of plant parts and age on density and proximate analysis parameters are influenced by the plant parts and age interaction, whereas HHV can be considered similar between them. The cultivar BRS Capiaçu showed suitable energetic values (17,922 < HHV < 18,918 kJ.kg−1) compared to that of native Amazon wood. Energetic results combined with cultivation outputs of high productivity (dry mass production of 44.1 tonnes.ha−1 at 180 days) with a short cutting interval (3 months), adaptation to the region’s climate and soil, and the possibility of cultivation in areas currently consolidated for agriculture demonstrate the potential of BRS Capiaçu as biomass to reduce native wood usage and deforestation rates.

Evolution of solid residue composition during inert and oxidative biomass torrefaction

Biomass torrefaction is a thermochemical pretreatment that can be used to improve biomass properties, contributing to the energy densification of biomass or increasing its grindability, burning and gasification characteristics, porous structure, etc. While it is typically carried out under inert conditions, oxygen-lean torrefaction of biomass can reduce the process cost and complexity. This work proposes a novel extended 2-step kinetics mechanism capable of accurately predicting the evolution of the chemical composition of torrefied biomass. The model parameters are obtained from data collected from an extensive experimental campaign in which the chemical composition of torrefied biomass was measured at different temperatures, residence times, and oxygen fractions. The carbon mass fraction of the torrefied products was found to increase by up to 50 % under severe inert torrefaction conditions, i.e., high temperatures and long residence times, whereas the hydrogen fraction decreased. In the presence of oxygen during torrefaction, the carbon and hydrogen contents in the solid residue decrease compared with those produced under inert torrefaction. The extended 2-step model can also be used to determine the high heating value and energy densification of the torrefied biomass, with the latter ranging from 1 to 1.4 for the operating conditions tested.

THERMAL TREATMENT OF AGRICULTURAL WASTE AS AN ALTERNATIVE FOR PRODUCING SOLID BIOFUELS

Brazil stands out in terms of the composition of its energy matrix, with biofuels playing an important role, with many different raw materials requiring study. This article seeks to evaluate thermal treatment's effect on different biomass species to improve their energy characteristics, through an analysis of immediate chemical composition (as standardized by the American Society for Testing and Materials), spectroscopy in the infrared region, and thermogravimetry. This paper details the energy potential for each biomass, establishing the torrefaction processes with the best yield from agricultural residue of peanuts (Arachis hypogaea L.), cacao (Theobroma cacao L.), sugarcane (Saccharum spp.), coconut (Cocos nucifera L.), palm oil (Elaeis guineensis Jacq.), and papaya (Carica papaya L.). The thermal treatment of biomass proved effective in obtaining a solid biofuel with a higher heating value, as diagnosed through immediate chemical analyses.

Hydrothermal liquefaction: exploring feedstock for sustainable biofuel production

A number of technological strategies utilizing various types of biomass for the production of hydrocarbons have been put forth but their energy intensive methods are a concern for improved efficiency of biofuel production. Hydrothermal liquefaction (HTL) has emerged as a promising and feasible technology towards utilization of lignocellulosic biomass. The suitability of different biomass feedstock for HTL is intricately tied to their macromolecular composition and process parameters. The comprehensive analysis of feedstock for hydrothermal liquefaction (HTL) signal towards the immense potential of various biomass feedstock, such as corn stover, Miscanthus, pine biomass, Spirulina, sugarcane bagasse, rice bran etc. in contributing significantly to renewable energy production. The study emphasizes that the composition of biomass is critical in influencing bio-oil yield during the HTL process. Biomass components like cellulose, hemicellulose, and lignin, each play distinct roles in determining the efficiency of conversion. Specifically, feedstock with higher cellulose and hemicellulose content, such as Miscanthus and sugarcane bagasse, demonstrate superior bio-oil yields. The analysis of proximate factors affecting HTL efficiency reveals that moisture content, ash content and high heating value (HHV) are pivotal in optimizing the process. In addition to composition and physical characteristics, the article underscores the significance of growth conditions and nutrient utilization in cultivating biomass feedstock. Integrating HTL with biomass cultivation can create a sustainable, closed-loop system where nutrients from the HTL process are recycled back into cultivation. Biomass offers a renewable energy alternative, however it also poses challenges related to land use and potential competition with food production. Sustainable practices, such as utilizing agricultural and forestry residues and optimizing collection as well as storage processes, can alleviate some of these concerns. By optimizing feedstock selection, process parameters, and integrating sustainable practices, HTL can play a decisive role in advancing biofuel production and contributing to a more sustainable energy future. The interplay between biomass composition, processing efficiency, environmental impacts, and economic feasibility is essential for realizing the full potential of HTL technology in the bio-economy. The current analysis sheds light on the relationship of bio-oil yield with macromolecular components including cellulose, hemicellulose, and lignin as well as process parameters like ash content, moisture content, higher heating value, fixed carbon and volatiles. Focusing on process optimization, this study embodies a closer analysis of literature aimed at defining optimum strategies for enhancement of HTL.

Testing Pellets Fuel Production from Sewage Sludge of Palm Oil Industry

The wastes from the palm oil production process were used in this study, such as wastewater sludge and cake decenter or decanter waste. That moisture was less than 40% and increased density by the cold pelletizing process. The aim is to use pellet fuel for heating. Analysis of pellet fuel consists of physical properties, proximate (moisture, ash, volatile matter and fixed carbon) and heating value. The results showed that cake decenter and wastewater sludge had similar high volatile matter. The decenter cake had a higher heating value than the wastewater sludge at 26.649 and 14.965 MJ/kg, respectively. The ash of decenter cake was lower than wastewater at 11.71% and 22.34%, respectively. Blending both raw materials for pelletization increased the volatile matter that did not mix with another material. Therefore, both materials can be used to produce pellet fuel that has an optimal shape and size. That was hard to break. The results of chemical property testing show that it is within acceptable limits and can be used as pellet fuel.

Co-pyrolysis of biomass/polyurethane foam waste: Thermodynamic study using Aspen Plus

Due to the varieties and identical feature of solid waste, this research aims to consider the use of various feedstocks in pyrolysis process for liquid fuel production. The feedstock considered covers woody and non-woody biomass and plastic waste which are represented by sawdust (SD), palm leaf (PL) and polyurethane foam (PU) waste. In this research, both pure solid waste and the co-pyrolysis of biomass and plastic wastes were determined based on thermodynamics study. The model of pyrolysis process developed through Aspen Plus simulator was implemented to study the product yield, higher heating value (HHV) and energy consumption with a wider range of pyrolysis temperature and blending weight ratio. The simulation results clearly showed the use of pure PU waste can provide the highest oil yield (∼44%wt.) which is corresponded to highest HHV (∼28 MJ/kg). The pyrolysis, operating at 400oC, can provide the most significant quantity of oil. For the co-pyrolysis, the results revealed that more PU waste blended in both biomasses can improve both oil yield and HHV while the energy consumption is lower. From the simulation results, the optimal blending weight ratio of biomass and PU waste at 25:75 can provide suitable oil yield (∼43%wt.), HHV (∼26 MJ/kg) and energy consumption (243kW).

Hydrothermal bio-oil yield and higher heating value of high moisture and lipid biomass: Machine learning modeling and feature response behavior analysis

The yield and higher heating value (HHV) of bio-oil products are significant performance parameters for the hydrothermal conversion of high-water and high-lipid biomass. Machine learning (ML) modeling prediction is a fast and convenient means of obtaining performance parameters. An informative dataset with 243 samples was prepared, and two highly adapted ML algorithms were used: Random Forest (RF) and Extreme Gradient Boosting Tree (XGBoost). It is interesting to note that the developed ML models demonstrated great prediction ability; for example, the regression coefficient (R2) of the XGBoost model for yield and HHV prediction was as high as 0.942 and 0.940, respectively. Furthermore, partial dependence plots (PDP) and SHapley Additive exPlanations (SHAP) interpretability methodologies were adopted to address the main contributions of the feature identification and response behavior analysis of the features. The results demonstrated that the biomass composition had the greatest effect on bio-oil yield, with fat contributing up to 40 %. In contrast, the elemental composition had the most significant effect on the HHV of bio-oil. Notably, hydrogen content affected the HHV of up to 4.5 units. The interaction response behavior showed that the interaction of the process parameters with feedstock properties was most common and significant. The information obtained from the response mechanism can be used to enhance the subsequent hydrothermal fuel preparation process for bio-oils.

Enhancing bioenergy and feed production in Southern Thailand: An approach through Leucaena cultivation and hydrothermal carbonization

This study addresses sustainability challenges in Southern Thailand, particularly the scarcity of biomass fuel and animal feed. It investigates the integration of Leucaena leucocephala cultivation with hydrothermal carbonization. The research compares the biomass yield and economic feasibility of growing Leucaena as a sole crop versus intercropping it with Para rubber trees. Sole cropping Leucaena produces higher biomass yields and is more economically viable. The wood stem of Leucaena is competitive with other biomass fuels used in local power plants, while its leaves, with over 14 % protein content, meet local animal feed market standards. Additionally, branches, which constitute 15.15 % to 30.58 % of the total biomass, are usually left as residue but can be used for hydrochar production. The study examines the effects of temperature (235 °C and 265 °C) and retention time (1, 2, and 3 hours) on hydrochar properties. Optimal condition (265 °C for 1 hour) produces hydrochar with high heating value and energy yield. Using these branches for hydrochar can significantly boost total revenue, with hydrochar contributing 54.9 % to overall revenue (4522.00 USD/ha). Integrating Leucaena cultivation with hydrothermal carbonization offers a sustainable solution, enhancing revenue, supporting local energy and feed needs, and promoting environmental sustainability.

Unveiling the potential of operating time in improving machine learning models’ performance for waste biomass gasification systems

Biomass is a promising renewable energy source, especially regarding local energy production. Gasification is an outstanding thermochemical process for syngas production from biomass. The optimal modeling and control of syngas production from biomass gasification is vital in planning small-scale energy supply. Artificial intelligence methods are widely applied to reveal the nonlinear relations between the gasification parameters and the reaction products. In this study, special attention is paid to the effect of operating time as a predictor on the performances of the models formed, as its introduction provides the phase information to the model which allows the combination of different sequential experimental data. Unlike most preceding studies in which hold-out is preferred, nested cross-validation is utilized for model evaluation emphasizing the unbiased nature of the latter method. Least squares, gaussian kernel, generalized additive models, random forest, and artificial neural networks (ANN) are utilized to model the process's product syngas composition and high heating value (HHV). The significance of operating time on the gasification system is also reflected on the feature importance analysis which is performed. Along with the operating time, temperature is also shown to play a vital role on the hydrogen yield and calorific value of the products.

Evaluation of an empirical model for predicting the calorific value of biomass briquettes from candlenut shells and kesambi twigs

Biomass briquettes offer a sustainable alternative to conventional fossil fuels, contributing to renewable energy while reducing environmental impact. This research explores the development of biomass briquettes from candlenut shell charcoal (Aleurites moluccanus) and kesambi twigs (Schleichera oleosa), focusing on their physical properties and heating values. Using tapioca as a binder, briquettes were produced with varying ratios: A. Control (90% kesambi twigs + 10% adhesive), B. 3:1 (67.5% kesambi twigs + 22.5% candlenut shell charcoal + 10% adhesive), C. 1:1 (45% kesambi twigs + 45% candlenut shell charcoal + 10% adhesive), and D. 1:3 (22.5% kesambi twigs + 67.5% candlenut shell charcoal + 10% adhesive). The study aimed to optimize briquette composition for maximum density, compressive strength, and calorific value while minimizing moisture, ash, and volatile matter. Results indicated that a higher proportion of candlenut shell charcoal enhanced density, compressive strength, and fixed carbon content, with the highest calorific value exceeding 19 MJ/kg observed in the 1:3 ratio. Additionally, the study evaluated three empirical models for predicting the Higher Heating Value (HHV) of the briquettes, finding the Nhuchhen model to be the most accurate, with an R² value of 0.93, providing a reliable method for predicting calorific value.

Effect of Torrefaction Process on the Physicochemical Properties of Solid Fuel from Palm Kernel Shells

This research investigates the effects of torrefaction on the characteristics of solid fuel made from palm kernel seed (PKS). The torrefaction process was conducted on raw PKS to improve its energy density and combustion efficiency. Torrefaction was carried out at 400°C at different durations (30, 40, and 50 minutes), with the goal of improving the energy density and combustion efficiency of raw PKS. The torrefied PKS was then ground and sieved into particle sizes of 150mm and 300mm. After being blended with high-density polyethylene (HDPE), the torrefied PKS was compacted using hot press machine into solid fuel and tested for its physicochemical properties. The results indicated that PKS torrefied for 50 minutes with particle size of 300 mm exhibited optimal characteristics, with a high heating value (HHV) of 23.22 MJ/kg. The particle size plays a role, with finer particles (150mm) having lower HHV values compared to coarser particles (300mm). Additionally, the inclusion of HDPE affected the properties of the solid fuel. Morphological analysis using scanning electron microscopy provided insights into its structural features.

Predicting the higher heating value of products through solid yield in torrefaction process

Torrefaction is a widely employed method to upgrade biomass for energy purpose. The higher heating value (HHV) serves as a vital indicator for assessing the energy potential of biomass. Nevertheless, HHV measurement is a time-consuming and costly process. HHV of raw biomass, torrefied biomass, and biochar has been extensively estimated using the results of elemental analysis and/or proximate analysis. However, these data must be repeatedly measured for each target object. To reduce the efforts required for the measurement of each object, this study proposes, for the first time, the use of solid yield (the most easily obtainable data) to predict the HHV of torrefied biomass.A total of 215 sets of data were retrieved from different biomass types undergoing various torrefaction technologies. Artificial neural network (ANN) and hierarchical linear regression (HLR) were employed to develop prediction models. Solid yield demonstrated a clear correlation to HHV for each individual biomass; however, diverse biomass types exhibited significant variation. Therefore, the results of elemental analysis of raw feedstocks were incorporated to account for variations in feedstock types and to iterate the models. Generally, ANN is superior to HLR in predicting the HHV of torrefied biomass with the optimized model achieving an R2 of 0.9133. Additionally, two biomasses subjected to two torrefaction technologies were used to validate the model, and an R2 of 0.9676 was achieved.As a result, in a torrefaction process, once the raw biomass is analyzed, the HHV of any torrefied biomass can be predicted simply by measuring the weight of the product.

Hydrochar from co-hydrothermal carbonization of sewage sludge and sunflower stover: Synergistic effects and combustion characteristics

This study carried out co-hydrothermal carbonization (Co-HTC) on sewage sludge (SS) and sunflower straw (SFS) at various ratios (1:0, 3:1, 1:1, 1:3, 0:1) to prepare hydrochar. The raw samples and produced hydrochar were characterized using ultimate analysis, proximate analysis, Brunauer-Emmett-Teller (BET), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The potential synergistic impact during the reaction process were analyzed. As the SFS ratio increased, the fuel ratio of hydrothermal carbons rose from 0.29 to 0.37, while the higher heating value (HHV) improved from 7.44 (MJ/kg) to 20.86 (MJ/kg). The co-hydrothermal synergistic effect resulted in enhanced the hydrochar yield, energy yield, carbon sequestration rate and organic retention rate. Compared to raw sample, the surface of hydrochar exhibits a rougher and more fragmented structure, with an increased number of microspheres and micropores, resulted in an increased specific surface area. Thermogravimetric analysis (TGA) demonstrated that, in comparison to HTC of sludge alone, the Co-HTC of SS with SFS enhances the ignition temperature and delays the completion of combustion. Therefore, Co-HTC with SFS is an effective method for converting SS into clean solid fuel for energy applications.

Catalytic hydrothermal liquefaction of Azolla filiculoides into hydrocarbon rich bio-oil over a nickel catalyst in supercritical ethanol

Hydrothermal liquefaction (HTL) is one of the most promising thermochemical techniques for converting wet biomass into crude oil-like products (bio-oil). In this study, Catalytic hydrothermal liquefaction of Azolla filiculoides (AZ) was performed over a various loading of nickel (Ni) on magnesium oxide (MgO) catalyst for the higher and quality bio-oil production. The key operating parameters such as temperature, reaction holding time, amount of Ni on MgO supports catalyst, and reaction solvents were investigated in the presence of a hydrogen environment. There was a 12.8 wt% increase in bio-oil yield and a 6.3 wt% decrease in biochar yield with addition of 15 wt% Ni catalysts compared to the non-catalytic reaction bio-oil yield (44.0 wt%). Results confirmed the highest total bio-oil yield of 56.8 wt% was attained at 280 °C with the catalyst amount of 15 wt% at a residence time of 45 min. Gas chromatography-mass spectrometry (GC-MS), FT-IR, CHNS, TGA, and NMR analyses were performed on the bio-oil, identifying 32.8 % long-chain hydrocarbons (C12-C16) along with small amounts of alcohols, alkanes, and esters. The boiling point distribution revealed that bio-oil produced using the Ni/MgO catalyst contained a significantly higher proportion of diesel-range hydrocarbons (42.4 %). Furthermore, the bio-oil yield under ethanol solvent and Ni catalysts showed higher heating value (HHV) 42.2 MJ/kg. Overall in the presence of Ni hydrogenation efficient catalysts on MgO in the liquefaction reaction promoted the deoxygenation and hydrogenation reaction.

Polyculture microalgae and Zno/GAC-nanocomposite system for greywater treatment

The study addressed the need for sustainable greywater treatment, aiming to mitigate water scarcity and pollution. The potential of using microalgae cultivated in greywater with zinc oxide nanoparticles (ZnO/GAC-nanocomposite) was investigated for these purposes. A batch photobioreactor was employed for 15 d. The nanocomposite system significantly improved nutrient removal, with optimal removal efficiency of 80 % for TOC, 94.2 % for PO43−, and 99.6 % for NH4+. The presence of nanocomposites improved microalgae growth, achieving a density of 1.8 g/L compared to 0.83 g/L without nanocomposites. The microalgal biomass obtained had a high volatile matter content of 74.4 %, low ash content of 5.1 %, and fixed carbon of 20.4 %. The biomass includes 46.74 % carbon, 5.72 % hydrogen, and 38.75 % oxygen, with a high heating value of 18.32 MJ/kg. The reusability of ZnO/GAC-nanocomposite was also assessed, which maintained effective nutrient removal after four cycles, with NH4+, PO43−, and TOC removal rates of 86.1 %, 83.2 %, and 69.8 %, respectively. Comparison of treated greywater with a pH of 8.5, turbidity <4 NTU, COD, NH4+, PO43− of 34, 0.032, and 0.48 mg/L, respectively, with various reuse standards, indicated its potential reuse for toilet flushing.