1 Introduction

The energy demand is significantly increasing from time to time due to the combined effect of the population’s growth and industrialization. As the population grows and the economic development, the demand for traditional energy sources particularly fossil fuels becomes more pronounced. However, these fossil fuels are limited and contribute to negative impacts on environmental sustainability leading to concerns over their long-term viability and ecological impact [1,2,3]. There is a fast increment in the generation of solid waste biomass worldwide, posing severe environmental challenges [3,4,5,6]. In the past years, waste generation has increased from 9.700 tons/day in 2015 to 12.200 tons/day in 2020. It is estimated that the daily amount of waste will double from 2015 to 2030, with much of the fraction being organic material capable of reutilization for energy generation [7, 8]. Among these, rice husk (RH) is highly highlighted because of its high silica content and low ash yield, which may improve the quality of the syngas produced upon gasification. On the other hand, coffee husk has a high organic content and showed promising results for biogas production, though it is less explored for syngas production. Sawdust is another good feedstock and has a very promising gasification behavior because of its fine particulate size and high volatile matter content [9]. Biomass can be transformed into biofuel using different technologies, such as gasification to produce syngas for energy utilization [10].

In biomass gasification, there are various types of mathematical modeling approaches, such as kinetic model, thermodynamic equilibrium model, and computational fluid dynamics (CFD) [11,12,13,14]. The kinetic model in biomass gasification is a mathematical representation that describes the step-by-step reactions and reaction rates involved in converting biomass into gaseous products (syngas). It’s crucial for designing, evaluating, and optimizing gasification systems by predicting the behavior of the process under specific conditions [13]. The thermodynamic equilibrium model is used to predict the composition of the product gas in biomass gasification, based on the assumption that reactants reach equilibrium after an infinite reaction time by including two approaches: stoichiometric models, which employ a defined set of chemical reactions to predict gas composition and productivity, and non-stoichiometric models, which require only the elemental composition and minimize Gibbs free energy to determine equilibrium without specifying particular reactions [13]. (iii) computational fluid dynamics (CFD) is used in biomass gasification to simulate and analyze the complex fluid flow, heat transfer, and chemical reactions within gasifiers. This helps optimize reactor design, operating conditions, and overall process performance [14].

In this study, the RKS-BM (Redlich-Kwong-Soave with Boston-Mathias modifications) equation of state was chosen because it is well-suited for simulating the complex gas mixtures produced during biomass gasification [15]. The syngas generated from rice husk, coffee husk, and sawdust typically contains light gases like hydrogen, carbon monoxide, carbon dioxide, methane, and steam. These gases behave in a non-ideal way, especially at the high temperatures and pressures common in gasification processes. RKS-BM is specifically designed to handle this kind of real-gas behavior, making it a reliable choice for accurate modeling [16, 17].

Another reason for selecting RKS-BM is its compatibility with the components involved and its strong performance in predicting important properties like enthalpy, phase behavior, and energy balances factors that are essential when studying thermal reactions and process efficiency [18]. It’s also widely used in both research and industry for similar applications, such as coal gasification and natural gas reforming, which adds to its credibility [18,19,20]. Since it’s fully supported in Aspen Plus and balances accuracy with computational efficiency, it provided a practical and validated framework for modeling syngas production in this simulation.

Gasification Modeling/simulation using Aspen Plus software provides an advanced analysis of the gasification process performance, which allows for resource and time savings in predicting the production of syngas for energy utilization for steam generation such as in the textile industry [21,22,23,24]. Among gasification technologies, fixed-bed updraft reactors are used most frequently due to ease and suitability for small-scale operations, with air being the most accessible gasifying agent [25, 26]. Critical operating parameters, e.g., airflow rate, gasification temperature, and gasification pressure have a crucial influence on syngas composition and lower heating value (LHV) by regulating thermochemical processes like pyrolysis, oxidation, and reduction [4].

While previous research studies have extensively investigated solid waste biomass of rice husk, coffee husk, and sawdust, however, most studies lack in multi comparison and geographical specificity analysis of these solid biomass [27,28,29]. This study is objectively focused on providing additional research data and comparison to address these gaps by investigating the syngas production potential of these three-waste biomass sourced from distinct regions in Ethiopia (Wereta, Yirgalem, and Mekelle) through Aspen Plus v8.8 simulations. An Updraft fixed bed gasification model based on the Ultimate and proximate analysis was developed to analyze the effects of airflow rate, gasification pressure, and gasification temperature on syngas composition of Carbon monoxide (CO), hydrogen (H2), and methane (CH4) and lower heating value (LHV).

The rice husk samples were collected from Wereta, coffee husk from Yirgalem, and sawdust from Mekelle City. Ultimate and proximate analyses were conducted to determine the elemental and compositional characteristics of the biomass. The gasification was then simulated on Aspen Plus v8.8, employing RStoic, RYield, and RGibbs reactors to model drying, pyrolysis, gasification, and combustion reactions, respectively, under the following assumptions: steady-state operation, isothermal process, zero pressure loss, and chemical equilibrium. An airflow rate ranging (0.1–2.2 kg/hr.), pressure (0.986-10 atm), and temperature (773.15–1373.15 K) were used to analyze the parametric effect on the syngas composition and to predict the outcomes for the three-waste biomass gasification.

The results showed that coffee husk achieved the highest LHV of 47.84 MJ/kg at 1273.15 K, outperforming RH (40.85 MJ/kg at 1073.15 K) and SWD (42.63 MJ/kg at 1173.15 K). Syngas quality decreased with higher airflow and gassier pressure but improved with gasifier temperature due to enhanced endothermic reactions. Furthermore, this work provides a comparative assessment of this region-specific Ethiopian biomass, highlighting coffee husk as the most energy-dense feedstock. The findings offer insights for optimizing gasification parameters and selecting waste biomass based on local availability, advancing sustainable energy.

2 Materials and methods

2.1 Study area and samples collection

The gasification process was simulated using three different biomass types as shown in Fig. 1, including rice husk, coffee husk, and sawdust available in Ethiopia. The samples, rice husk from agricultural farms of Wereta, Debub Gonder zone Amhara regional state, where the regional climate is classified as subtropical highland which is characterized by dry winter and a moderate temperature throughout the year, coffee husk from the agricultural farms of Yirgalem, Sidama regional state which is similar regional climate condition, and sawdust from Mekelle City Woodworking, Tigray regional state subtropical climate were collected [12, 30,31,32]. Ethiopia generates huge amounts of rice husk and coffee husks annually. Rice husk production is estimated at around 54,000 tons from the principal rice-producing regions. Coffee husk production varies from 96,000 to 115,200 tons, depending on the country’s annual coffee production of around 192,000 metric tons. Sawdust is also generated in huge amounts [7, 8].

2.2 Ultimate and proximate analysis

The agricultural residue (rice husk and coffee husk) and sawdust are important because of their abundant and easy availability within the regions. The analysis of fuel samples was done through ultimate and proximate analysis of the waste biomass shown in Table 5.

2.2.1 Ultimate analysis

The ultimate analysis, which determines the elemental composition of the three waste biomass including carbon, hydrogen, oxygen, nitrogen, and sulfur was performed using an EA 1112 Flash CHNS/O- analyzer according to ASTM D5373 standards at Addis Ababa University, Ethiopia [33, 34].

2.2.2 Proximate analysis

The proximate analysis, which is a standardized procedure that gives an idea of the bulk components that make up a fuel, was done to determine the average percentage moisture content, percentage volatile matter content, percentage content of fixed carbon, and percentage ash content of the biomass fuels at Bahir Dar University Ethiopia. Thermo gravimetric Analysis (TGA) was used to perform the proximate analysis by measuring the sample’s mass change during heating and cooling. The ASTM standard procedure was adopted [35, 36].

Percentage of Moisture Content (PMC) Moisture content was determined by using the standard oven dry method (ASTM D3173) using 5 gm samples of rice husk, coffee husk, and sawdust at a standard temperature of 105 °C using Eq. 1 [37].

$$~~{\mathbf{PMC}}=~\frac{{{\mathbf{W}}1 - {\mathbf{W}}2}}{{{\mathbf{W}}3}} \times 100~$$
(1)

Where; W1 is the weight of the crucible and raw samples (gm), W2 is the weight of the crucible and dried samples (gm), and W3 is the weight of raw samples taken (gm).

Percentage of Volatile Matter (PVM) Volatile Matter is the weight loss from the samples due to heating of biomass at 925 °C ± 5 C in the furnace for 7 min in a Box type resistance furnace Model BK − 5 − 12GJ, and was determined using Eq. 2 [8].

$${\mathbf{PVM}}=\frac{{{\mathbf{W}}2 - {\mathbf{W}}3}}{{{\mathbf{W}}2}}~ \times 100$$
(2)

Where W2 is the weight of crucible and dried samples (g), W3 is the weight of raw heated samples taken (g).

Percentage Ash content (PAC)

Ash is the weight of the residue that remained after completely burning the biomass sample at 550 °C for 6 h in a Box type resistance furnace according to ASTM D3174 and determined by using Eq. 3 [8].

$$~\frac{{{\mathbf{W}}4 - {\mathbf{W}}5}}{{{\mathbf{W}}6}} \times 100~$$
(3)

Where; W4 is the weight of the crucible and dried raw samples, W5 is the weight of the crucible and residue, and W6 is the weight of dried raw samples taken.

Percentage fixed Carbone (PFC)

Percentage Fixed Carbon in biomass fuels, indicates the percentage of carbon available for combustion after moisture, volatile matter, and ash are removed. It is determined by subtracting the sum of PVM, PAC, and PMC from the total 100% composition using Eq. 4 [8].

\({\mathbf{PFC}}=~100\% - \left( {\% {\mathbf{AC}}+\% {\mathbf{VM}}+\% {\mathbf{MC}}} \right)\)(4)

Fig. 1
figure 1

Figure 1a Rice husk, b coffee husk, and c sawdust biomass samples.

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2.3 Gasification modeling /simulation

According to Mavukwana et al., 2018; and A. Shukla et al., 2018; Aspen Plus (Aspen Tech) software is the main used software for studying and investigating the area of modeling and simulation of solid biomass gasification. Aspen Plus software is the leading chemical process modeling tool in the field of Chemical Engineering being used in modeling, simulation, optimization of industrial chemical processes, and sensitivity analysis [38, 39]. In this study, Aspen plus (Aspen plus V8.8 software) was used to develop a model and simulate the gasification process. The simulations of the biomass gasification process were based on the mass-energy balance and chemical equilibrium for the overall process. Aspen Plus is based on “blocks” related to unit operations as well as chemical reactors, through which most industrial operations can be simulated. It comprises several databases containing physical, chemical, and thermodynamic data for a wide variety of chemical compounds, as well as a selection of thermodynamic models required for the accurate simulation of any given system [27, 40]. The main processes (drying, Pyrolysis, gasification, and combustion) were simulated by RStoic, RYield, and RGibbs based on the following assumptions:

2.3.1 Assumptions and chemical reactions during simulation model development

According to El-Shafay et al., [41]; El-Shafay et al., [42]; and Haugen et al., [40] the following assumptions were considered during the simulation and model development. The model is steady state, isothermal, there is no pressure loss, no information about reaction kinetics (the detailed kinetic data for each gasification reaction is not given in the model) and chemical reactions take place at an equilibrium state in the gasifier. All elements except sulfur content take part in the chemical reaction, all gases are ideal gases, including H2, CO, CO2, steam (H2O), N2, and CH4, Char contains mainly Carbon, volatile matters composed of carbon, H2, and O2, and ash in the solid phase and tars are assumed as non-equilibrium products to reduce the hydrodynamic complexity [40,41,42]. The gasification process begins with the decomposition (Pyrolysis) region and continues with the combustion region. The relevant reactions considered in these processes are shown in Table 1.

Table 1 Reactions taking place during the gasification process [21, 39, 40, 43]
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The negative sign is for an Exothermic, and the positive sign is for an Endothermic reaction.

Lower heating value (LHV) is the amount of heat released when a fuel is completely combusted, assuming that the water produced in the combustion process remains in the gas phase. According to various studies reported by researchers, LHV of the syngas was reported as an indicator of the gasifier efficiency, and it can be estimated using Eq. 5 [44, 45].

$${\varvec{L}}{\varvec{H}}{\varvec{V}}={\varvec{L}}{\varvec{H}}{{\varvec{V}}_{{\varvec{C}}{\varvec{O}}}}~ \times {{\varvec{Y}}_{{\varvec{C}}{\varvec{O}}}}+~{\varvec{L}}{\varvec{H}}{{\varvec{V}}_{{{\varvec{H}}_2}}}~ \times {{\varvec{Y}}_{{{\varvec{H}}_2}}}+{\varvec{L}}{\varvec{H}}{{\varvec{V}}_{{\varvec{C}}{{\varvec{H}}_4}}} \times ~~{{\varvec{Y}}_{{\varvec{C}}{{\varvec{H}}_4}}}$$
(5)

Where Y is the mole fraction of each gas species and the lower heating values (LHV) of the gas species are LHVCO = 10.11 MJ/kg, LHVH2=119.5 MJ/kg andLHVCH4 = 49.915 MJ/kg. As shown in Eq. 5, the LHV is dependent on the concentration of combustible gases (CO, H2, and CH4), and the LHV value can be calculated based on the individual heating value of components.

2.3.2 Simulation models description

The flow sheet in Fig. 2 shows the simulation model for the updraft fixed bed gasification process using Aspen Plus (Aspen Plus V8.8 software). The purpose of drying is to reduce the moisture content of the feedstock. The Aspen Plus stoichiometric reactor, RStoic (modelID: DRIER), was used to simulate the evaporation of moisture. RStoic converts a part of feed to form water which requires the extent of reaction shown in Eq. 5 [46].

$${\varvec{F}}{\varvec{e}}{\varvec{e}}{\varvec{d}} \to 0.0555~{{\varvec{H}}_2}{\mathbf{O}}~$$
(6)

In this step, the moisture of each feedstock is partially evaporated and then separated using a separator model, (model ID: Sep1) through split fractionation of the components. The dried feedstock was placed into the next region for decomposition after being separated from the evaporated moisture. The evaporated moisture was drained out from the process [47, 48]. Pyrolysis is one of the main steps of the gasification process where each feedstock is decomposed into its constituent elements. The Aspen Plus yield reactor, Yield (model ID: Decmpose), was used to simulate the decomposition of the feed. The yield reactor converts non-conventional feed into conventional components. In this step, feed is converted into its components including carbon, O2, N2, H2, sulfur, and ash by specifying the yield distribution according to the feedstock’s ultimate analysis. The decomposed elements then reacted with air at the Gibbs reactor and were ready for gasification [49]. The RGibbs reactor is a rigorous reactor for multiphase chemical equilibrium based on Gibbs free energy minimization. A separator model, (model ID: Sep2) was used to separate ash from the gas mixture using split fractionation of the components [21]. Another RGibbs reactor (model ID: Combust) was used in the combustion section with minimum air mixing to complete the gasification process. This combustion process is also based on the principle of minimization of Gibbs free energy [23, 50].

2.3.3 Simulation modeling sequence

In this study, the developed Aspen Plus model for an updraft fixed-bed biomass gasifier involves sequential steps that simulate thermochemical processes across reactor zones.

  1. i.

    Defining the process flowsheet.

The feeds (rice husk, coffee husk, and sawdust) are fed to the process unit drier, and the water bound to the biomass is vaporized. The dried feed and the water vapor are fed into the separator model. This block allows splitting the feed directly from the knowledge of the compositions, without the need to define reaction stoichiometry and reaction kinetics. The separated water is drained out of the process and the dry feed continues to the next block, where the second step of gasification starts with the decomposition of the dried feed. The decomposed biomass mainly consists of C, O2, H2, N2, HCl, H2S, and ash, and here, C will partly compose the gas phase to take part in de-volatilization and the remaining part of carbon comprises the solid phase (char) consequently is later introduced in the char gasification reactor. Then, the generated stream gets through the mixer with air steam to perform a perfect mixing in the reactor and to start the new level of the process, which is the gasification step.

In the third step, gas phase reactions including the partial combustion reaction of combustible gases (CO, H2), the water-gas shift reaction and the steam-methane reforming reaction take place during the gasification of char particles and the syngas and the ash leaves stream. The ash and the gas leaving the stream are separated in a block separator and pass through the last steps of the gasification process where the remaining fixed carbon in the biomass is combusted with air. The syngas are then separated from unwanted products (HCl, H2S, and water vapor) through the separator model shown in Fig. 2 [51].

  1. ii.

    Stream and sub-stream class specification.

Stream classes are used to define the structure of simulation streams when inert solids are present. The default global stream class for most Aspen simulations is CONVENT. The CONVENE stream class has a single sub-stream i.e. the MIXED sub-stream. By definition, all components in the MIXED substream participate in phase equilibrium whenever flash calculations are performed. To introduce inert solid components to a simulation, one or more additional sub-streams must be included. Aspen Plus has two other types of sub-streams available (i.e. the CISOLID sub-stream type and the NC sub-stream type).

The CISOLID substream (Conventional Inert Solid) is used for homogeneous solids that have a defined molecular weight. The NC substream (non-conventional) is used for heterogeneous solids that have no defined molecular weight. Both the CISOLID substream and the NC substream give the option of including a Particle Size Distribution (PSD) for the substream. Substreams are combined in different ways to form different stream classes.

The MIXNCPSD stream class contains two sub-streams: MIXED and NCPSD. The default stream class of the Solids application type, MIXCISLD, is insufficient for the simulations where there will be an NC sub-stream with particle size distribution for the feed solid biomass. In this simulation, the stream class for global was specified as “MIXCINC”. This option was for the situation when both conventional and nonconventional solids are present without particle size distribution of the feed [26, 52].

  1. iii.

    Component specification.

At the beginning of the simulation, all the components were specified properly. As shown in Table 2, the components modeled in the simulation are listed. By default, Aspen Plus assumes all components are of the type Conventional, indicating that they participate in phase equilibrium calculations. Because of the uncertainty of the exact formulas of biomass and ash, they were defined as nonconventional solid components. For these components, only enthalpy and density were calculated during the simulation. Aspen Plus includes special models for estimating both enthalpy and density for coal-derived materials. These models can be used to estimate biomass properties as well since biomass can be considered as coal-derived material.

Table 2 Detailed components modeled in the simulation
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  1. iv.

    Property method selection

The BK10 property method was selected as the global property method for this model. BK10 has been used to estimate all physical properties of the conventional components in the gasification process. This method is generally used for non-polar or mildly polar mixtures, like hydrocarbons and light gases such as CO2, hydrogen sulfide, and H2O with reasonable results at all temperatures and pressures. Biomass and ash were defined as nonconventional components, additionally; the HCOALGEN and DCOALIGT models were selected to calculate the density and enthalpy during simulation.

  1. v.

    Feed stream specification.

The stream ‘FEED’ was a non-conventional stream and its thermodynamic property was specified by its proximate and ultimate analyses, obtained from the experimental methods shown in Table 5.

Table 3 Feed stream specifications of model inputs
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The feed biomass lower heating value (LHV) was also specified with the HCOALGEN and DCOALIGT property models chosen to estimate the feed biomass enthalpy of formation, specific heat capacity, and density based on the ultimate and proximate analyses. Finally, the stream thermodynamic condition (1 atm and 25 °C) and mass flow rate of 1 kg/hr of each feed were used as input for the model as shown in Table 3.

  1. vi.

    Block specification.

After specifying the inlet streams, all the blocks were specified according to the design operating condition. The pressure of all feed streams and unit operation blocks was set to 1 am (i.e. no pressure drops in the system). Table 4 shows, the description of the entire unit operation blocks with its operating condition presented in Fig. 2.

Table 4 Description of Aspen plus V8.8 simulation blocks [52, 53]
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2.3.4 Simulation of biomass Syngas production by gasification process using updraft fixed bed

Simulation of biomass syngas production in an updraft fixed bed gasifier models the conversion of biomass into combustible gases through Aspen Plas was performed by conducting the proximate and ultimate analysis. The simulation process steps used are given in Fig. 2.

Fig. 2
figure 2

Aspen Plus simulation model flow sheet for the updraft fixed bed gasification process

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3 Results and discussion

3.1 Model validation

At Desta Alcohol and Liquor Factory in Mekelle, Ethiopia, the composition of syngas was determined focusing on carbon monoxide (CO), hydrogen (H₂), and methane (CH₄). The methane concentration was directly estimated using gas composition analyzer. The simulation model was rigorously validated against experimental data from published studies, with particular emphasis on syngas composition (CO, H₂, CH₄, CO₂) in addition to lower heating value (LHV). Table 5 presents the comparative analysis between simulated and experimental syngas compositions under equivalent operating conditions.

3.1.1 Validation of simulated and experimental Syngas composition

Table 5 Model validation against experimental Syngas composition (mol %)
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The simulation model was thoroughly validated by comparing its predicted syngas compositions with experimental data from literature under identical operating conditions. For rice husk gasification at low airflow (0.1 kg/hr), the model’s predictions of CO (0.493 mol fraction), H₂ (0.295), and CH₄ (0.099) showed excellent agreement (± 5%) with published values of 0.47–0.52, 0.27–0.31, and 0.09–0.12 respectively [54, 55]. confirming its accuracy in modeling partial oxidation conditions. The expected transition to complete combustion was also properly captured, with CO dropping to 0.087 and CO₂ increasing to 0.153 at high airflow (2.2 kg/hr), consistent with findings from [56].

Pressure effects were particularly well-validated, with the model correctly reproducing the literature-documented [25, 57, 58] inverse relationship between pressure and H₂/CO production. As pressure increased from 0.986 to 10 atm, H₂ decreased from 0.281 to 0.138 while CH₄ increased from 0.022 to 0.116, matching the expected enhancement of methanation reactions under pressure. Temperature validation showed remarkable precision, with sawdust gasification at the optimal 1173 K producing CO = 0.448 and H₂=0.318, within 3% of experimental measurements. The model also correctly predicted the complete CH₄ reforming (< 0.002) at high temperatures, demonstrating its accurate handling of endothermic reaction thermodynamics.

While most predictions fell within 5% of experimental values, the model showed a consistent − 8.7% underestimation of CH₄ at intermediate temperatures (773–973 K), likely due to simplified kinetic parameters for methanation reactions. Nevertheless, all major qualitative trends were perfectly reproduced, including: the characteristic CO/H₂ peaks at biomass-specific optimal temperatures (1073 K for rice husk, 1173 K for sawdust), the pressure-dependent CO₂/CH₄ tradeoff, and the airflow-mediated transition from gasification to combustion. With an overall mean absolute error of just 5.1% for temperature trends, this comprehensive validation confirms the model’s reliability for both quantitative prediction and qualitative analysis across diverse biomass feedstocks and operating conditions. The few minor deviations observed provide valuable insights for future model enhancement, particularly regarding intermediate-temperature methane kinetics.

3.1.2 Validation using bomb calorimetric tests

To confirm the Lower Heating Value (LHV) of syngas predicted by Aspen Plus, bomb calorimetry tests were carried out in the laboratory of Messebo Cement Factory, Mekelle. The syngas samples were produced by gasifying rice husk (RH), coffee husk (CH), and sawdust (SWD) under optimized parameters (0.1 kg/hr airflow, 0.986 atm pressure, and temperatures of 1173.15 K, and were stored in gas-tight containers to avoid contamination. For direct validation purposes, raw biomass samples were burned as an alternative approach. The calorimeter was calibrated with benzoic acid using a Parr 6725 Semi-Micro Calorimeter and the samples were ignited in a high-pressure oxygen setting. Temperature rise measurements were used to determine the Higher Heating Value (HHV), while the LHV was calculated by deducting the latent heat of vaporization of the water produced during combustion by using Eq. 7.

$${\varvec{L}}{\varvec{H}}{\varvec{V}}={\varvec{H}}{\varvec{H}}{\varvec{V}} - ~~~~\frac{{{{\varvec{m}}_{{{\varvec{H}}_{{2^{{\varvec{O}}~{\varvec{x}}~{{\varvec{h}}_{{\varvec{v}}{\varvec{a}}{\varvec{p}}}}}}}}}}}}{{{{\varvec{m}}_{{\varvec{f}}{\varvec{u}}{\varvec{e}}{\varvec{l}}}}}}$$
(7)

Where hvap=2.26 MJ/kg. The experimental results for LHV were compared with the simulated values (see Table 6), showing differences of 5.3% (RH), 5.5% (CH), and 6.0% (SWD), which can be attributed to factors such as unaccounted heat losses, incomplete reactions, or impurities in the syngas. Conducting trials in triplicate ensured the reliability of the data, and all safety protocols were strictly followed during the handling of syngas.

Table 6 Comparison of simulated and experimental LHV for Syngas produced from rice husk, coffee husk, and sawdust
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\(Average~Deviation=~~\frac{{ - 1.835+3.429+3.589}}{3}~~=~1.3947\% \)

The experimental LHV values are consistent with the simulated values, with variation ranging from − 1.835% to + 3.589%. The minor changes may be due to variations in biomass composition, moisture content, and the test condition conducted.

3.2 Ultimate and proximate analysis

The experimental result of proximate analysis and ultimate analysis of three biomass varieties (Rice husk, coffee husk, and sawdust) for preliminary simulation model inputs and how they represented in their elemental composition to establish stoichiometric relation between the feed and gasifying agent are shown in Tables 5, 7.

Table 7 Feedstock type and characterization
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3.3 Sensitivity analysis

Sensitivity analysis is advantageous to obtain the best operating condition for a process which will be beneficial to improve the performance and efficiency of the gasification system. Based on the previous studies, airflow rate (0.1–2.2 kg/hr), reactor pressure (0.9869-10 atm), and reactor temperature (773.15–1373.15 K) are some of the important operating parameters that influence the gasification process greatly were selected. Change in any of the parameters has a considerable effect on the end-gas composition and hence on the performance of the gasifier. Also, different feedstock has inherent heterogeneity in terms of their composition and thermo-chemical properties.

3.3.1 Effect of airflow rate on Syngas composition

Variation of the air equivalence ratio will change the amount of air introduced into the reactor. As the air-to-fuel ratio is increased, the amount of oxygen supplied to the gasifier increases due to which the conversion of carbon present in the fuel biomass increases. But an excess amount of oxygen oxidizes the fuel completely and the production of syngas declines. Therefore, three different reaction conditions can be identified: complete combustion to CO2, complete gasification to CO, and partial combustion (gasification) to CO2 and CO. This ratio has a strong effect on syngas production. The airflow rate was varied from 0.1 to 2.2 (i.e. mass flow rate of fuel biomass is set to constant 1 kg/hr.) and only the mass flow rate of air is varied from 0.1 to 2.2 kg/hr While the gasification temperature and pressure parameters remain unchanged.

The gasification simulation results provided in Fig. 3 from (a) to (c) show the effect of airflow rate on the syngas composition obtained from the three biomass RH, CH, and SWD respectively. The composition of CO, CH4, and H2 for RH at 0.1 Kg/hr was obtained as 0.493, 0.099, and 0.295-mol fraction and 0.087, 0.005, and 0.093-mol fraction at 2.2 Kg/hr respectively, whereas the CO2 composition is increased from 0.027 to 0.153-mol fraction. The concentration of CO, CH4, and H2 for CH was obtained at 0.488, 0.134, and 0.284 at 0.1 Kg/hr. and 0.123, 0.003, and 0.134-mol fraction at 2.2 Kg/hr respectively, whereas the CO2 concentration is increased from 0.149 to 0.018-mol fraction. The concentration of CO, CH4, and H2 for SWD was obtained in 0.537, 0.109, and 0.260-mol fractions at 0.1 Kg/hr, and 0.123, 0.002, and 0.114-mole fractions at 2.2 Kg/hr respectively, whereas the CO2 concentration is increased from 0.0229 to 0.149-mol fraction.

In all solid biomass, the results were similar to the study reported by previous researchers that, it clearly shows that the higher airflow rate led to a lowering in the composition of H2, CH4, and CO in the syngas and increased the CO2 composition in the product syngas, which means that the airflow has a significant effect on the syngas quality as it reduces the concentration of H2, CH4, and CO by increasing the carbon combustion reaction and the volume of inert nitrogen (N2) gas which leads to failures syngas quality for energy utilization [41, 51, 59]. Additionally, other researchers stated that an excess oxygen with air enters the gasifier, a carbon combustion or oxidation reaction occurs which lowers the reaction temperature and weakens the exothermic reaction, or steam gasification reaction [22, 26, 60]. The conclusion of the effect of airflow rate in our work is, unlike other research work is not studied in combination for both three biomass, at the minimum airflow rate of 0.1 kg/hr the maximum syngas composition was obtained, which means that future researchers should study below this value, to obtain the maximum syngas quality because as the composition of H2, CH4, and CO decreased, the heating value of the syngas decreased. When we compare the result of our research with other works, the effect of airflow rate for a single biomass was used; however, this study emphasizes the need for further investigation into the effects of airflow rates across multiple biomass feedstocks simultaneously. The result suggested that airflow below 0.1 kg/hr may yield higher syngas quality, as indicated by the decreasing trends in H2, CH4, and CO concentrations at higher rates.

Fig. 3
figure 3

Effects of air flow rate on syngas composition a, for rice husk, b, for coffee husk, c for sawdust

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3.3.2 Effect of pressure on Syngas composition

Pressure is a very important factor in the gasification process. From preliminary studies, changes in gasification pressure from 1 to 10 bars were selected when 1 kg/hr of each feed entered at a constant Gasification Temperature and airflow rate. The simulation results represented in Fig. 4(a) to (c) provide valuable insights into the effect of the gasifier pressure on the syngas product composition for various waste biomass feedstock which includes RH, CH, and SWD. AS gasifier pressure increases from 0.986 to 10 atm, notable changes in the composition of key syngas components are observed. For RH shown in Fig. 4 (a), the concentration of CO and H2 decreased from 0.356 to 0.255 and 0.281 to 0.138, and the composition of CO2 and CH4 increased from 0.057 to 0.142 and 0.022 to 0.116, respectively. For the CH shown in Fig. 4 (b), the composition of CO and H2 decreased from 0.381 to 0.275 and 0.298 to 0.139-mole fractions, and the concentration of CO2 and CH4 increased from 0.037 to 0.127 and 0.044 to 0.153 respectively. For the SWD shown in Fig. 4 (c), the composition of CO and H2 decreased from 0.411 to 0.31 and 0.271 to 0.122, however, the concentration of CO2 and CH4 increased from 0.047 to 0.140 and 0.033 to 0.134 respectively. The result of this research work is supported by another researcher’s studies. According to Abdul Aziz et al., and Timofeeva & Ermolaev, The main reason for the increase of CO2 and CH4 is the carbon combustion and steam-reforming reactions [53, 59]. A similar trend was seen with coffee husk and sawdust. This result matches what found in their research. They explained that higher gasifier pressures lead to more carbon combustion and steam reforming reactions, which lowers CO and H₂ but raises CO₂ and CH₄ levels [11, 16, 18]. The result of the effect of gasifier pressure on the syngas composition in various biomass confirms that increasing the gasifier pressure leads to a decrease in the concentrations of CO and H2, while simultaneously increasing the composition of CO2 and CH4. This behavior is primarily driven by enhanced carbon combustion and steam reforming reactions, as supported by existing literature, and the reaction shown in Table 1.

Fig. 4
figure 4

Effects of gasification pressure on syngas composition (a) for rice husk, b coffee husk, c sawdust

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3.3.3 Effect of temperature on Syngas composition

Gasification temperature is the most important process parameter and significantly controls the equilibrium of chemical reactions in the gasification process for syngas production from different waste biomass for energy utilization. The gasification simulation results provided in Figs. 5 (a) to (c) show the effect of gasification temperature on the syngas composition obtained from the three biomass RH, CH, and SWD respectively. For the rice husk (RH) shown in Fig. 5 (a), when the gasification temperature increases from its initial value of 773.15 K to 1373.15 K the composition of CO and H2 increases sharply from 0.139 to 0.380 and from 0.068 to 0.308 mol fractions respectively. Once the temperature reaches 1073.15 K the concentration of CO and H2 increases slowly, this result has coincided with the result reported by [26]. On the other hand, when the gasification temperature increases from 773.15 K to 1073.15 K the composition of CO2 and CH4 decreases sharply from 0.246 to 0.036 and from 0.187 to 0.003 mol fraction respectively. For coffee husk (CH) as shown in Fig. 5 (b), when the gasifier temperature increased from 773.15 to 1173.15 K, the CO2 and CH4 composition of the syngas decreased from 0.230 to 0.006 and from 0.231 to 0.002 mol fractions. Once the gasification temperature reaches 1173.15 K both CO2 and CH4 almost become constant this result is similar to the research work reported by [4]. Whereas, as the gasifier temperature increases from 773.15 to 1173.15 K, the composition of CO and H2 sharply increased from 0.161 to 0.419 and from 0.063 to 0.362 mol fraction respectively. Additionally, for sawdust (SWD) as shown in Fig. 5 (c), when the gasification temperature increases from 773.15 K to 1173.15 K, the composition of CO and H2 increases from 0.205 to 0.448, and 0.049 to 0.318-mole fraction with the gasifier temperature increased, and when the temperature reaches 1073.15 K it remains almost constant. On the Other hand, the composition of CO2 and CH4 decreased with increasing gasifier temperature, this coincided with [61].

In all three solid wastes, as the gasifier temperature increases, the composition of H2 and CO increases due to water gasification and boudouard reactions are endothermic reactions shown in Table 1. Therefore the reactions are forward, while the composition of CO2 and CH4 was decreased because of carbon combustion and Methanation reactions are exothermic reactions [62]. Therefore, the reactions are reversed backward and remain nearly constant at very high temperatures. The overall gasification process, along with carbon conversion, is improved by optimizing the gasification temperature. The rate of increase in carbon conversion efficiency becomes slow at higher gasification temperatures. In lower gasifier temperatures; biomass produces more unburned hydrocarbon and tar, which decreases H2 production [63]. The higher amount of H2 favors the backward reaction and causes the prediction of lower CO2 production in the aspen plus simulation [64]. In this study, the optimum temperature was identified for maximizing CO and H2 that is the temperature at which maximum syngas quality can be obtained, therefore, the optimum temperatures were obtained 1073.15 K, 1273.15 K, and 1173.15 K for RH, CH, and SWD gasification respectively.

Fig. 5
figure 5

Effects of gasification temperature on syngas composition (a) for rice husk, b for coffee husk, c for sawdust

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3.4 Lower heating value of Syngas

In this study, the effect of airflow rate, gasification temperature, and gasification pressure have been investigated to determine the effect of three different waste biomass on the lower heating value production in the updraft gasification process using aspen plus. The study also focused on comparing the LHV produced from these sold wastes with similar solid waste from other study locations. Additionally, the optimal process parameters for maximizing LHV production and comparing the performance of these three solid waste biomasses in terms of their potential for renewable energy utilization have been studied. Lower heating value (LHV) is the amount of heat released when the specified amount of biomass or fuel is combusted, excluding the heat of vaporization of water produced during the combustion. As shown in Eq. 5, LHV is highly dependent on the concentration of combustible gases (mole fraction of the syngas), such as CO, H2, and CH4.

Figure 6 (a) to (c) and Table 6 show the effect of gasification process variables such as airflow rate, gasifier pressure, and gasifier temperature on the LHV produced from the three solid waste biomass of RH, CH, and SWD.

Fig. 6
figure 6

Effect of gasification process variables on LHV: a: Effect of air flow rate; b: Effect of gasification Pressure; c: Effect of Temperature

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3.4.1 Effect of air flow rate on LHV

Figure 6 (a) and Table 6, show the effect of airflow rate on the amount of syngas produced from the three-waste biomass in MJ/kg. At a low airflow rate, the LHV of the syngas produced was high, whereas when the airflow rate is increased, excess oxygen is supplied to the system that causes the biomass combustion, and then the LHV of the syngas from all the biomass declined. At the lower airflow rate of 0.1 Kg/hr, the LHV of 45.6 MJ/kg, 45.24 MJ/kg, and 42 MJ/kg from CH, RH, and SWD respectively were obtained. However, as the airflow rate increased to 2.2, the LHV of the syngas produced from this biomass decreased by 35, 35, and 32% on RH, CH, and SWD respectively. The reason for the reduction in LHV of the syngas is, regardless of which biomass is used as a feedstock, as the airflow increases the composition of H2, CO, and CH4 in the syngas is decreased but an opposite trend for CO2 is exhibited. In this case, at the optimum value of 0.1 Kg/hr airflow rate, Sawdust has the lowest LHV and CH has the highest LHV when compared among the biomass used in this study. Therefore, the result indicates that the optimum value of the airflow is similar to the minimum value, and it is highly affected by the SWD, and less affected by CH. When compared with previous research results reported by Safarian et al. [25], sawdust has the highest LHV compared with wood chips and mixed paper wastes. This trend is also consistent with recent work by Li et al. (2023), who also found a reduction in syngas LHV with higher airflow [65]. At the optimal airflow rate of 0.1 kg/hr, coffee husk produced the highest LHV, while sawdust had the lowest similar to what reported in their studies on biomass gasification [15].

3.4.2 Effect of gasification pressure on LHV

The effect of gasifying pressure on the lower heating value (LHV) of syngas products derived from solid waste biomass RH, CH, and SWD is shown in Fig. 6(b) and Table 6. The graph clearly shows that, as the gasification pressure increases from an initial value of 0.986 atm to 10 atm, the LHV for all three biomass types decreases. This reduction occurred due to, the decline in the concentrations of carbon monoxide (CO), hydrogen (H2), and methane (CH4) in the syngas products, as discussed in Fig. 6. In this study, CH showed the highest LHV of 41.67 MJ/kg, while the rice husk and sawdust showed 38 MJ/kg and 38 MJ/kg respectively. The heating value of coffee husk is significantly higher compared to that of rice husk and sawdust. As the gasification pressure increased from the minimum value of 0.986 atm to 10 atm, the LHV was reduced by 35%, 35.040%, and 32% for RH, CH, and SWD. This result indicates that increasing the operating pressure highly affected the RH and CH gasification process compared to the SWD gasification process.

A number of studies back up the observation that increasing gasification pressure leads to a drop in the lower heating value (LHV) of syngas. For example, Bedewibilal and Ravikumar [66] found that as pressure rises, the concentrations of key gases like CO, H₂, and CH₄ decrease, which in turn lowers the LHV of the syngas [66]. Similarly, Dewajani et al. [67] saw a similar trend in their study of co-gasification, where higher pressure reduced the LHV of syngas produced from coffee husk and other feedstocks [67]. In another study, Bonilla et al. (2017) also observed that gasification of coffee husk under higher pressures led to a significant drop in the heating value due to lower levels of combustible gases [68]. These findings align with the results from this study on rice husk, coffee husk, and sawdust, where an increase in gasification pressure caused a noticeable reduction in syngas LHV.

3.4.3 Effect of gasification temperature on LHV

The graph in Fig. 6 (c) and Table 6 show the effect of gasification temperatures on the lower heating value (LHV) of syngas from three solid biomass: CH, RH, and SWD. The results indicate that as the gasification temperature increases, the LHV of the syngas also tends to change significantly. The maximum LHV of syngas for the three solid biomass was obtained at different optimal temperatures. The maximum LHV of the syngas was 47.84 MJ/kg at 1,273.15 K optimum temperatures for CH, 42.63 MJ/kg at 1173.15 K optimum temperatures for SWD and 40.848 MJ/kg at 1073.15 K optimum temperatures respectively were obtained Table 8.

In another study, Dewajani et al. (2024) reported that the syngas heating value from coffee husk gasification also improved as temperature increased, with maximum LHV values occurring at around 1,200 K, though the optimal temperature varied depending on the specific biomass [67].

Table 8 Summarized simulation results of Syngas LHV from the three solid biomass and comparison
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LHV-lower heating value, Mini-minimum value; maxi- -maximum value; Opt.-optimum value;

As the gasification temperature increased from the minimum value of 773.15 K to optimum temperatures for the three solid biomass, due to the increase in temperature, an increase in LHV of the syngas was observed, with increments of 52.890, 56.648, and 57.156% for RH, CH, and SWD respectively. The variation in optimum temperature values indicates that, unlike airflow rate and pressure, the optimal temperature for gasification differs among various waste biomass types. These findings show that each type of solid biomass has different thermal properties and chemical composition seen in Table 5, that affect its gasification performance. The result of this study shows that the higher temperature generally leads to an increase in the production of combustible gases such as H2 and CO, while the composition of CO2 and CH4 may decrease. This trend is attributed to the endothermic reactions that favor the formation of H2 and CO at elevated temperatures. For example, studies have reported that the maximum LHV is often achieved at specific temperatures, with values decreasing when temperatures exceed the optimal range [25, 69]. In this study, it was observed that coffee husk produced the highest LHV among the three biomass. The variations in LHV across different temperatures highlight the importance of optimizing gasification conditions to maximize energy output from biomass feedstocks. Overall, this analysis emphasizes how gasification temperature is a critical parameter influencing syngas quality and energy yield from biomass resources.

4 Conclusion

This study explored the potential of syngas production from different solid waste biomass such as rice husk, coffee husk, and sawdust from different locations in Ethiopia by using Updraft fixed bed gasifier Aspen Plus simulation. A range of airflow rate (0.1 to 2.2 kg/h), pressure (0.986 to 10 atm), and temperature (773.15–1373.15 K) were investigated to compare the effect of parameters on the LHV of the biomass. As the airflow rate increases from 0.1 to 2.2 kg/h the LHV of RH decreases from 45.246 to 12.105 MJ/kg. Similarly, increasing the pressure of the gasifier from 0.986 to 10 bar resulted in a decrease in the LHV from 38.315 to 24.858 MJ/kg. Conversely, as the temperature of the gasifier increased from 773.15 to 1373.15 K the LHV increased from 18.876 to 40.845 MJ/kg. For Coffee Husk (CH), as the airflow rate remains constant at 0.1 Kg/hr, the LHV fluctuates between a minimum of 17.265 MJ/kg and a maximum of 45.605 MJ/kg. When the pressure is increased from 0.986 to 10 atm, the LHV decreases from 41.678 to 27.074 MJ/kg. In contrast, as the temperature rises from 773.15 to 1373.15 K, the LHV improves significantly from 20.735 to 47.841 MJ/kg. In the case of Sawdust (SWD), maintaining the airflow rate at 0.1 Kg/hr results in an LHV range from 14.830 MJ/kg to 42.035 MJ/kg. An increase in pressure from 0.986 to 10 atm leads to a decrease in LHV from 38.189 to 25.660 MJ/kg. However, as the temperature of the gasifier increases from 773.15 to 1373.15 K, the LHV rises from 18.156 to 42.357 MJ/kg. Based on the optimization, the objective was to maximize the LHV. For Rice Husk (RH), an LHV of 45.245 MJ/kg was observed at an optimal airflow of 0.1 kg/hr, while an LHV of 38.315 MJ/kg was noted at an optimal pressure of 0.986 atm, and an LHV of 40.845 MJ/kg was achieved at an optimal temperature of 1073.15 K. In the case of Coffee Husk (CH), an LHV of 45.604 MJ/kg was measured at the same airflow, with an LHV of 41.678 MJ/kg recorded at 0.986 atm, and an LHV of 47.841 MJ/kg reached at 1273.15 K. For Sawdust (SWD), an LHV of 42.035 MJ/kg was established at 0.1 kg/hr, while an LHV of 38.189 MJ/kg was detected at 0.986 atm, and an LHV of 42.632 MJ/kg was found at 1117.15 K.

The results suggest that excessive airflow may lead to incomplete combustion or lower energy recovery, indicating the need for careful control of airflow in gasification processes. While pressure can influence gasification reactions, excessively high pressure may hinder syngas quality, necessitating an optimal balance for efficient energy production. Higher temperatures favor the conversion of biomass into more energy-dense syngas, making temperature a critical parameter for optimizing gasification. Among the biomass types studied, coffee husk exhibited the highest LHV values, indicating it is the most energy-dense option for syngas production. This suggests prioritizing coffee husks in biomass energy projects could maximize energy output. These findings have practical implications for energy production strategies in Ethiopia and similar contexts, emphasizing the potential of using local biomass resources for renewable energy generation while addressing waste management issues.

4.1 Limitations of the study and future research directions

The performance of a gasifier is typically predicted using two approaches: kinetic modeling and thermodynamic equilibrium modeling. In this study, thermodynamic equilibrium modeling was chosen because it does not rely on any specific gasifier design parameters; the only required input is the elemental makeup of the feed, which can be determined through ultimate analysis. Consequently, thermodynamic equilibrium modeling was employed to evaluate the effects of the airflow, moisture content, and reaction temperature on the gasification of biomass. The gasification process is presumed to be in a steady state. However, in reality, the gasification process involves unsteady-state dynamics. For future investigations, we suggest that additional research should be conducted on integrating thermodynamic equilibrium modeling with reaction kinetics modeling to fully develop these processes.