Producer Gas Research Articles

Automation, Robotics Transforming How Industry Drills Wells

Automation, which can generate significant returns on investment, is increasingly mainstream, while robotics, which can improve safety on the jobsite, is gaining traction more slowly. Both play a role in safely finding and producing oil and gas, but neither would be able to shine without advances in technology. And while some worry automation will take jobs away—US dockworkers struck briefly in early October partly due to such fears—experts say the intent is to aid humans in their jobs and free them up for other, more meaningful tasks. Milos Milosevic, senior director of digital well construction at Halliburton, told JPT implementing automation and robotics generates a number of benefits, including resource optimization, operation transparency, and consistency. “As soon as you introduce automation, as soon as you introduce robotics, what you get is consistency, and what you get is transparency, because now we are putting all the knowledge in people’s heads in an automated system which executes every time the same,” he said, adding that leads to faster time to first oil, fewer emissions, and reduced hazards on the jobsite. “We want to have operations where we can be confident that every time, it’s going to go as we plan, and that the automation is going to help us deal with unexpected things that will happen in any kind of execution,” Milosevic said. “All of that creates value.” While uptake of automated processes is increasing, he noted many opportunities for further development remain. Beyond the benefit of using automation to remove people from having to perform routine operations, automation can produce outcomes that are very difficult for people to achieve, he said, such as integrating massive amounts of data and acting on that information. Knowledge can be distilled into software, which can then be trained to follow rules so the software recognizes when something is happening faster than a human can. He cited well construction activities as an example. “You don’t want to get stuck; you don’t want to have a well control event.” Typically, a person would watch a model, evaluate how a well was shaping up, and take action if data indicates irregularities or potential issues with the well. “That knowledge and that decision-making is distilled now into software,” Milosevic said. The goal is to aid experts by providing tools that allow them to spend their attention on important things. “They’re getting the alerts. They’re getting automated recommendations of what to do. In certain cases, the automation takes over and does certain responses that the driller, for example, would acknowledge and see that this happened without the necessary intervention from a human. So, we are augmenting, really, what the person does,” he said. The result is that the software can “worry” about the safety of equipment, such as by alerting people to emerging issues in order to prevent unsafe operations of machines and let the software focus on when actions are taken, instead requiring the people to do so. Milosevic said such software “orchestrates higher levels of working. When do we do certain things? When do we turn on certain pressure zones? When do you drill harder or not?”

Experimental Evaluation of Diesel Engine Performance using Producer Gas and Conventional Fuel: A Comparative Study

Experimental Evaluation of Diesel Engine Performance using Producer Gas and Conventional Fuel: A Comparative Study

Investigations on performance of gasifier engine integration using three different biomasses as feedstocks

The integration of a downdraft gasifier with modified internal combustion engines presents a feasible solution for heat and power production, such as off-grid areas. This study conducted an experimental analysis to evaluate combustion in a modified engine. The morphological characteristics of the agricultural residue used in the gasifier were analyzed using X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscopy (SEM), along with an examination of the slagging inorganic constituents. The engine used in the experiments was a modified single-cylinder design paired with a downdraft gasifier. Three types of biomass feedstocks—eucalyptus, corncob, and pearl millet in pellet form —were tested. The performance of the downdraft gasifier was stabilized, and the engine was run independently on each type of producer gas at a constant speed under different loads to determine the optimum load. Cylinder pressure measurements were taken to estimate the heat release rate.The study found that power generation was lower than typical woody biomasses due to the lower calorific value of the producer gas from the tested feedstocks. Additionally, lower cylinder pressures and heat release rates were recorded, along with longer combustion durations. The varying proportions of producer gas affected combustion stability. Among the studied ashes, pearl millet corncob (PMC) exhibited the highest slagging tendency, followed by corncob (CC). Morphological analysis indicated that the eucalyptus residue's increased porosity was caused by the volatiles changing, forming pores and tube-like structures. An aliphatic C–H peak (3000–2500 cm⁻1) is absent from the residue, indicating that the aliphatic structure was disturbed during thermochemical conversion. During the gasification process, the residue's crystallinity reduced as it split down.

Performance analysis of a pilot gasification system of biomass with stepwise intake of air-steam considering waste heat utilization

Although the fixed-bed gasifier is widely used because of their simple structure, easy operation, and strong adaptability to biomass type, its economy is weakened by their low bioenergy conversion ability and heat utilization efficiency. Therefore, a novel biomass gasifier with stepwise intake of air–steam is proposed, which also innovatively integrates the concept of multi-stage utilization of waste heat generated during the gasification process into its design. And a pilot system comprising the novel biomass gasifier with a biomass consumption of 30 kg/h has been built. A series of continuous air-steam gasification experiments on pine wood particles shown that, the high-temperature steam in the reduction zone enhanced the ring opening of aromatic hydrocarbons, decarboxylation, dehydroxylation, and the conversion of long-chain to short-chain hydrocarbons, thus increasing the yield of small-molecule gases. The steam reforming reaction played an active role in increasing the hydrogen content, the gasification gas's production and low calorific value. And the relationship among the reaction rates on the surface of char should be VC + H2O＞VC+2H2O＞VC + CO2＞VC+2H2. The minimum input temperature of steam was 350 °C, otherwise more char would be consumed in the oxidation zone to provide heat. The proposed steam-air gasification system achieved optimal performance with an air-to-steam mass ratio (A/S) of approximately 8, a steam temperature of 400 °C, and an air-to-biomass mass ratio (A/B) of 1.2. The average gas production could reach 2.67 m3/kg, with the average low calorific value of 5.1 MJ/m3. The average hydrogen content could reach 20 %, with the average H2/CO ratio of 2. The average effective gasification efficiency could reach 69.72 %. Compared with air gasification, the proposed air-steam gasification was equivalent to the use of 1 kWh of electricity to produce 2.4 kWh of electricity. Combining market research, the price of steam produced by the proposed system was approximately 70 CNY/t less than when using natural gas. This study aims to provide a technical reference for improving the performance of biomass gasification in practical applications.

Analysis of 27 supervised machine learning models for the co-gasification assessment of peanut shell and spent tea residue in an open-core downdraft gasifier

The producer gas (PG) obtained through thermochemical processing of the various renewable biomass types contributes to Sustainable Development Goal-7 (SDG-7). Hence, this study examined 27 supervised and multiple-input single-output ML models in terms of performance metrics and accuracy to predict CO, H2, CH4, and CO2 compositions, as well as PG's HHV in the co-gasification of peanut shell (PS) and spent tea residue (STR). Multiple trial experiments were tested with various equivalence ratios (ERs) and mixing ratios (MRs) on a 90 m3/h gas-yield open-core, down-draft gasifier with air as the gasification medium. About 18 models were chosen after evaluating their performance metrics in estimating relevant parameters. The accuracy of those models is assessed based on 12 sample runs with varying ER and MR. The findings revealed that 16 models from the linear, neural network, support vector machine, Gaussian process, tree, and ensemble categories were reliable ML models. The chosen 16 models' R2 values for CO, H2, CH4, CO2, and HHVPG are above 0.931, 0.937, 0.962, 0.806, and 0.971, respectively, with the exception of TRE. Additionally, the models demonstrated a prediction accuracy of >95 % for all of the investigated parameters in the co-gasification of PS and STR when compared to random experimental runs.

Investigation on varied sewage sludge producer gas and methane blend equivalence ratios operated SI engine intact with Miller cycle strategy

Decentralised power generation through a gasifier-engine genset can reduce both reliance on power grid and pollution. However, the low calorific value of the produced gaseous fuel moderates engine power and efficiency, which is a big challenge for its utilisation. For early and cost-effective solution prediction, a simulation tool of adequate precision can effectively envisage dual-fuel (DF) mode engine performance. Thus, this study simulates DF-SI engines with late inlet valve closures (LIVCs) to improve thermal efficiency using the Miller cycle strategy. Simulation is performed using quasi-dimensional thermodynamic modelling (QDTM). After validating the model using referred experimental results, QDTM was employed to study the parametric impacts of inputs (blends, LIVC and equivalence ratio) variations on performance and emissions response parameters. Power improves by blending sewage sludge-based producer gas with methane. Multi-objective optimisation through the response surface method (RSM) determined the optimum operating parameters as 0.814 equivalence ratio, 68.23% SSPG-blend and 55.9° CA (ABDC) LIVC. Corresponding optimal responses were 5.906 bar IMEP, 3.85 kW BP, 28.6% BTE, 12.596 MJ/kWh BSEC, 0.134 V% CO and 2045.29 ppm NO emissions. ANOVA-based optimal results presented a composite desirability of 0.843 with a 95% confidence level. Conclusively, methane mix and LIVC approach increase the power and efficiency of the DF-SI engine; additionally, optimisation guides to balance power and emission trade-off.

Research and Application of Intelligent Gel Chemical Sand Protection System in Sand Producing Gas Wells

Research and Application of Intelligent Gel Chemical Sand Protection System in Sand Producing Gas Wells

Experimental analysis of the effects of feedstock composition on the plastic and biomass Co-gasification process

Co-gasification is a feasible approach to manage fast-growing plastic and biomass waste issues. The current work examines air gasification of plastic waste blending with biomass. The influence of plastic-type and biomass characteristics on product distribution under the same operating condition was studied. Ethylene-vinyl acetate, high-density polyethylene, and polypropylene were the investigated plastics while the biomass materials considered were pine sawdust (PS), used ground coffee (UGC), and wheat straw (WS) are compared. Additionally, co-gasification and thermogravimetric analyses (TGA) of plastic and individual biomass components, namely, cellulose, hemicellulose, and lignin, were conducted to identify the nature of the synergy arising from plastic and biomass integration. Similarities in the chemical structure and composition of the investigated plastics resulted in the type of plastic marginally influencing product distribution. The gas composition included 4.6 vol% CH4, 37.5 % CO2, 5.3 % CnHm, 42 % CO, and 10.8 % H2, with yields of 5.9 wt% char, 39.5 wt% gas, and 54.8 wt% tar. Conversely, the type of biomass had a substantial effect on both gas composition and product yields. WS and UGC, characterized by higher hemicellulose and lignin content, generated more hydrogen with 14.4 and 13.3 vol%, respectively, and exhibited lower tar content (45.7 and 47.8 wt%) compared to PS, which has a higher cellulose content and yielded 54.8 wt% tar. During EVA/cellulose co-gasification, the predominant gases produced are CO and CO2, with H2 concentration of 7.2 vol%. In contrast, co-gasification of EVA/xylan and EVA/lignin yields 13.5 and 19.9 vol% hydrogen, respectively. Interactions and synergism between the plastic and biomass were found to be more intensified when the biomass contained greater proportions of cellulose and lignin. Co-gasification and TGA analyses indicated that plastic/cellulose and plastic/lignin interactions in the gas and solid phases (volatile-volatile and volatile-char interactions) and plastic/xylan interactions in the gas phase (volatile-volatile interactions) were significant. Modeling the predictability of the output data revealed that if interactions between the plastic and biomass components are accounted for, the co-gasification results are predictable with a credible accuracy for all biomass types. The predictability model offers a practical approach for selecting an appropriate co-gasification feedstock combination to give a targeted process performance.

Following fuel conversion during biomass gasification using tunable diode laser absorption spectroscopy diagnostics

The efficiency of the gasification process and product quality largely depend on the degree of fuel conversion. We present the real-time in-situ tunable diode laser measurements of main carbon and oxygen-containing species in the hot reactor core of a pilot-scale entrained flow biomass gasifier (EFG). The concentrations of CO, CO2, CH4, C2H2, H2O, soot, and gas temperature were measured during the air and oxygen-enriched gasification of stem wood at varying equivalence ratios. The experiments were made at the upper and lower optical ports inside a 4 m long, ceramic-lined, atmospheric EFG, allowing to access the degree of the fuel conversion inside the reactor. The exhaust composition was measured by micro-GC, FTIR, and low-pressure impactor. There was a good agreement between the data measured inside the reactor and at the exhaust for oxygen-enriched gasification implying that the chemical reactions are practically frozen downstream the optical ports. For air, the data indicated that the gasification reactions are still active at the measurement locations. Significant concentrations of C2H2, up to 5000 ppm, were found inside the reactor.

Multi-objective optimization of a dual fuel CI engine powered with syngas and pilot diesel using TLBO algorithm: A metaheuristic approach

Abstract The thermochemical conversion of biomass into producer gas presents an attractive alternative fuel option for compression ignition (CI) engines, making biomass gasification a critical driver for achieving sustainable development goals. Considering the application of producer gas (PG) in CI engine, the most potential gases include H2 and CO as main fuel compounds and it is crucial to comprehensively understand the impact of these two gas components on the engine behaviour. Nowadays, artificial intelligence-powered models are frequently applied for simulating engines that run on a single type of fuel. However, their usage is not as common when it comes to modeling dual-fuel CI engines run on synthetic producer gas or syngas. The present study explores the feasibility of optimizing operational parameters, such as engine load and syngas composition, in improving the efficiency and lowering the levels of pollutants emitted by a 3.5 kW CI engine operated under dual fuel (DF) mode using syngas as primary fuel and diesel as pilot fuel. The performance and emission characteristics of syngas (H2:CO) is examined by studying its behaviour in four different combinations. The compositions of syngas are prepared based on the volumetric percentage of the H2 and CO and is inducted into the combustion chamber using a novel venturi-type air-gas mixer. In the present study, an intelligent metaheuristics-based optimization algorithm i.e., Teaching–Learning Based Optimization (TLBO) is developed and introduced, to develop a predictive model within constrained range of engine operating conditions. Further, the algorithm is used to estimate multiple engine performance characteristics simultaneously viz., brake thermal efficiency (BTE), unburned hydrocarbons (HC), and carbon monoxide (CO). The resultant findings identify the optimal engine load of 68.87% and the ideal syngas composition of 63.9% H2 and 49.5% CO as key parameters for maximizing engine efficiency while minimizing exhaust emission. At these optimized operating condition, 19.49% BTE is observed, while HC and CO emission was found to be 384.6 ppm and 445.33 ppm respectively. This shows the effective and efficiency of the proposed algorithm.

Modeling of a heat-integrated biomass downdraft gasifier: Influence of feed moisture and air flow

A model for a heat-integrated biomass downdraft gasifier is developed and used to study the influence of changes in biomass moisture content and gasifier air flow. This one-dimensional steady-state model accounts for pyrolysis, combustion and gasification reaction kinetics as well as transport phenomena occurring within the gasifier and heat integration system. The gasifier is divided into four zones for solving the ordinary differential equations (ODEs), because each zone has different geometry for the reactor or heating system. The material and energy balance ODEs are solved as a boundary value problem (BVP), ensuring that conditions for the producer gas at the bottom of the reactor match the conditions of the countercurrent annulus gas, which is used for heating. The model also accounts for the preheating of the biomass using exhaust gas from an associated engine used to generate electricity from the producer gas. The model predicts the process gas temperature, flow rate and composition and was validated using two experimental runs with different control inputs. The model predictions show good agreement with the data. Simulations with the highest feed moisture result in lower reactor temperatures and simulations with the highest air flow result in the highest reactor temperatures.

Performance and environmental sustainability studies on a dual-fuel IC engine working with producer gas of variable calorific values from rural biomass.

Decentralized power generation using renewable gaseous biofuels faces challenges due to their inconsistent quality and availability. Mixing producer gas (PG) with diesel as a secondary fuel is a promising option, but the non-stable calorific value (CV) of PG from different biomasses poses a serious problem for the efficient operation of a dual-fuel engine. This study aims to examine how the CV of PG from various biomasses affects a 3.75-kW dual-fuel IC engine's performance. The experimental facility, which has a dual-fuel engine and a 115-kW thermal gasifier, tested the blends of diesel and PG from different biomasses. We chose biomass based on its availability and PG's CV of 3.4, 4.4, 5.2, and 6.3MJ/Nm3. For this range of CV, the efficiency, energy consumption, and fuel replacement of a dual-fuel engine vary between 20.9 and 26.6%, 17.3 and 13.5MJ/kWh, and 10.8 and 76.9%, respectively. Furthermore, the blend with the maximum CV of PG had a 69.64% lower specific diesel consumption and an 86% higher diesel replacement rate than the blend with the lowest CV of PG. In terms of emission characteristics, the comparison showed a 2.02-7.06% reduction in NOx and a 4.05-55.6% increase in hydrocarbons (HCs) for the tested conditions. The overall observations demonstrated that a significant enhancement in a dual-fuel engine's performance is possible with a higher CV of PG. However, the emissions trade-offs demand additional optimization studies, as well as case-based research, in order to integrate renewable energy and emission management in smaller-scale applications across more geographies.

Experimental Biomass Gasification in Updraft Gasifier with Gas Outlet at Reduction Zone and Air Supply using Suction Blower

Rice husk gasification is increasingly attractive, particularly with updraft gasifier type, because of its simple construction and ease of operation. However, updraft gasifier has a disadvantage of generating substantial amounts of tar. Tar will decompose into combustible gas when exposed to high temperatures. The reduction zone has a high temperature for tar decomposition to occur. Therefore, in this research, updraft gasifier was modified by positioning gas outlet at the reduction zone and inducing gasification air supply using a blower. Modifications are made by moving the gas outlet from the top to the middle or reduction area. The initial start-up of the system uses a blown air supply system, then after the syngas are produced by the operation, it is replaced with a sucked air supply. Two blowers are used, namely an exhalation or blowing blower for initial start and a suction blower for continuous operation. The fuel used was low bulk density, specifically rice husks. The aim was to characterize the modified gasifier, focusing on parameters such as operating time, duration of gas combustion, air-to-rice husks ratio, and flame color. Typically, the experiments were conducted under constant of air velocity and fuel quantity. The results showed that the average operating time, duration of flammable gas, and air-to-rice husks ratio were 74.25 minutes, 52.28 minutes, and 7.6 kg air/kg husk, respectively, and the flame produced was a bluish-yellow color that indicates a reduction tar.

Temperature Profile of a Biomass Gasification Power System

Abstract Biomass has been used as a renewable energy resource for years. The coconut tree, considered woody biomass, grows in the tropics and is abundant in the Philippines. To determine the performance of the biomass gasification power system (BGPS) using torrefied coconut shells (TCS), a 1.8-kVA BGPS was designed, fabricated, and tested. The TCS with a calorific value of 34.37 MJ/kg was used as feedstock to profile the temperature of BGPS. The temperature increased from the feeder at 79 °C to a maximum of 904 °C at the combustion zone and goes down to 779 °C at the reduction zone, which causes the breaking of the chemical structure of the torrefied coconut shells to release carbon monoxide (CO), hydrogen (H2) and other contaminants that form part the producer gas. The temperature at the inlet of the internal combustion engine (ICE) generating set had reduced to 37 °C. To improve the performance of the BGPS, it is recommended to review and enhance the cooler’s design to lessen the temperature of the producer gas to at least 25 °C.

Pyrolysis of biomass to produce H-rich gas facilitated by simultaneously occurring magnesite decomposition

This study investigated how biomass pyrolysis varies with the different fractions of magnesite mixing into biomass. The pyrolysis occurred with simultaneous decomposition of magnesite without use of any gasification reagent, and was analyzed in terms of producer gas yield and quality. As magnesite fraction increased from 0 to 30 %, the yield of producer gas and its calorific value increased from 48.6 % to 66.3 % and 8.29 to 8.93 MJ/Nm3, respectively. The carbon and hydrogen conversion increased from 44.1 % to 60.7 % and 43.1 % to 66.2 %, respectively. The characterization results revealed that magnesite particles facilitated the conversion of fixed carbon and the thermal/catalytic cracking of tar to produce H-rich gas. The in-situ generated CO2 from magnesite decomposition could be reduced to CO/CH4 in the reductive atmosphere of the pyrolysis products. This study proposes the concept of converting low-energy–density biomass into gas without oxygen and provides a novel approach for producing H-rich gas from biomass.

Enhancing CO2 Injection Efficiency: Rock-Breaking Characteristics of Particle Jet Impact in Bottom Hole

Storing CO2 in oil and gas reservoirs offers a dual benefit: it reduces atmospheric CO2 concentration while simultaneously enhancing oil displacement efficiency and increasing crude oil production. This is achieved by injecting CO2 into producing oil and gas wells. Employing particle jet technology at the bottom of CO2 injection wells significantly expands the bottom hole diameter, thereby improving CO2 injection efficiency and storage safety. To further investigate the rock-breaking characteristics and efficiency, a finite element model for particle jet rock breaking is established by utilizing the smoothed particle hydrodynamics (SPH) method. Specifically, this new model considers the high temperature and confining pressure conditions present at the bottom hole. The dynamic response and fracturing effects of rock subjected to a particle jet are also revealed. The results indicate that particle jet impact rebound significantly influences the size of the impact crater, with the maximum first principal stress primarily concentrated on the crater’s surface. The impact creates a “v”-shaped crater on the rock surface, with both depth and volume increasing proportionally to jet inlet velocity and particle diameter. However, beyond a key particle concentration of 3%, the increase in depth and volume becomes less pronounced. Confining pressure is found to hinder particle impact rock-breaking efficiency, while high temperatures contribute to larger impact depths and breaking volumes. This research can provide theoretical support and parameter guidance for the practical application of particle impact technology in enhancing CO2 injection efficiency at the bottom hole.

Experimental Investigation of Producer Gas Effects on Coated Pipelines in Biomass Downdraft Gasifier System

Abstract This research examines the performance of epoxy, ceramic, and graphene coatings on stainless steel 316 in a producer gas environment, focusing on corrosion and erosion resistance. This research aimed to identify the most effective coating for applications in harsh gasification environments. In this research, various analyses, including microstructural examination, hardness and weight measurements, FESEM analysis, and EDAX analysis, were conducted to evaluate the performance of the coatings. The producer gas was passed on to all the coated samples for a period of 100 hours. The significant findings include the superior corrosion and erosion resistance of ceramic-coated stainless steel, as evidenced by low weight fluctuations, maintained hardness levels, and elemental stability. Graphene coatings exhibit high hardness but increased porosity, raising concerns about durability, while epoxy coatings are vulnerable to gas-induced structural alterations. The inclusion rating analysis underscored the ceramic coatings' ability to preserve consistent material properties. Overall, ceramic coatings have emerged as the preferred option for gasification environments due to their structural resilience, inclusion integrity, and elemental stability. Considering the inclusion integrity, mechanical strength, weight stability, and elemental stability, ceramic-coated stainless steel 316 samples exhibit better resistance toward producer gas influence. This research contributes valuable insights for material selection in applications exposed to harsh gasification environments, emphasizing the importance of coating selection for long-term durability and performance.

Optimizing the model-prediction of date palm fronds-derived producer gas and third generation biodiesel powered dual-fuel engine by employing Bayesian-optimized Boosted Regression Trees for enhanced prognostics

The global issue concerning the dual problem of economic energy shortage as well as greenhouse gas emission needs urgent attention. Due to its abundance and carbon neutrality, waste biomass is a good energy conversion feedstock. This research suggests a unique method for using date palm frond waste biomass-derived producer gas and algal biodiesel as sustainable diesel engine fuels. The Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees were employed to optimize engine combustion and emission performance. This was further enhanced with the application of k-cross fold to prevent the model from overfitting during training. The working of dual-fuel engines is a complex phenomenon owing to the employment of two different types of fuel one is liquid (biodiesel + diesel) while the other one is gaseous (Producer gas). The present endeavor was undertaken to handle this with the application of advanced data learning methods. The engine was tested at different compression ratios, engine loads, and fuel injection pressures for brake thermal efficiency, diesel fuel substitution, and emission. Both the Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees could predict the engine output with good prediction efficiency with comparable performance, however, the Bayesian-optimized Boosted Regression Trees-based models were more robust with a coefficient of determination value in the range of 0.9617–0.9999 and mean squared errors as low as 0.001. The mean absolute percentage error was very low in the range of 0.0316–5.231. The Theil’s U2 values were in the range of 0.0029–0.0126.

Characteristics of hydrogen energy yield in steam gasification of coffee residues.

Coffee residues (CRs) were gasified using a laboratory-scale fluidized bed gasifier with an air/steam mixture as the carrier gas. The gasification was conducted at an equivalence ratio (ER) of 0.3, and different operation temperatures (700, 800, and 900 °C) and steam-to-biomass (S/B) ratios (0, 0.75, and 1.5) were applied. Increasing temperature without steam boosted H2 and CO concentrations in producer gas, raising lower heating value (LHV) and cold gas efficiency (CGE) through endothermic reactions like Boudouard, tar cracking, and water-gas formation. At 900 °C, gas had LHV of 3.76 MJ/Nm3 and CGE of 22.47%. It was elevating temperature from 700 to 900 °C and S/B ratio to 1.5 raised H2 and CO concentrations from 2.04 to 8.60% and from 9.56 to 11.8%, respectively. This also increased LHV from 2.23 to 3.89 MJ/Nm3 and CGE from 11.28 to 25.08%. The steam gasification reaction was found to increase the H2 concentration and was thus considered effective in converting CRs to syngas and increasing energy production. Overall, the study successfully demonstrated the feasibility of steam gasification as a means of converting coffee residues to syngas and increasing energy production. The results also highlighted the importance of operating temperature and S/B ratio in improving the gasification process.

CFDs Modeling and Simulation of Wheat Straw Pellet Combustion in a 10 kW Fixed-Bed Downdraft Reactor

This research paper presents a comprehensive study on the combustion of wheat straw pellets in a 10 kW fixed-bed reactor through a Computational Fluid Dynamics (CFDs) simulation and experimental validation. The developed 2D CFDs model in ANSYS meshing simulates the combustion process in ANSYS Fluent software 2021 R2. The investigation evaluates key parameters such as equivalence ratio, heating value, and temperature distribution within the reactor to enhance gas production efficiency. The simulated results, including combustion temperature and produced gases (CO2, CO, CH4), demonstrate a significant agreement with experimental combustion data. The impact of the equivalence ratio on the conversion efficiency and lower heating value (LHV) is systematically explored, revealing that an equivalence ratio of 0.35 is optimal for maximum gas production efficiency. The resulting producer gas composition at this optimum condition includes CO (~27.67%), CH4 (~3.29%), CO2 (~11.09%), H2 (~11.09%), and N2 (~51%). The findings contribute valuable insights into improving the efficiency of fixed-bed reactors, offering essential information on performance parameters for sustainable and optimized combustion.