Operating Conditions Research Articles

Stator inter-turn shortcircuit fault diagnosis of induction motor based on characteristic current

Purpose This paper aims to facilitate reliable online diagnosis of early faults in the stator winding inter-turn short circuits of induction motors (IMs) under various operating conditions. Design/methodology/approach A novel fault characteristic component, the characteristic current amplitude, is proposed for the fault. Defined as the product of short-circuit coefficient and short-circuit current, the characteristic current is derived from the positive and negative-sequence components of the stator-side current and voltage. Findings Simulation models of the IMs pre- and postfault, along with an experimental platform for the motor’s inter-turn short circuit, were established. The characteristic current amplitude proves more robust against voltage unbalance and load variations, which offers enhanced reliability and sensitivity for early fault diagnosis of inter-turn short circuit in IMs stator windings. Originality/value A novel feature is proposed. Compared with negative-sequence current, which is considered as a traditional fault feature, the characteristic current amplitude exhibits a greater robustness against the imbalanced conditions, which simultaneously possesses the attributes of both reliability and expeditiousness in fault detection.

Formation of Coatings Containing Cr2AlC MAX Phase During Plasma Spraying of Mixture of Cr3C2+Al Powders

In this article, the structure formation and phase composition of coatings containing Cr2AlC MAX phase under the conditions of plasma spraying were studied. Mechanical mixtures of commercially available Cr3C2 and Al powders were used as a material for spraying. The amount of aluminium in the mixtures was 9 and 18 wt.%. As a result of studying physicochemical processes occurring during plasma spraying of mechanical mixtures of selected compositions, the formation of coatings containing Cr2AlC MAX phase was established, the synthesis of which occurs both at the stage of the particles flight of initial components in the plasma jet as a result of the collision and coagulation, and at the stage of a coating layer formation as a result of layering particles deformed during the collision–splats. It is shown that for the formation of a denser coating with a higher MAX phase content for spraying, it is rational to use a mixture of chromium carbide powders with 9 wt.% of aluminium. A coating with the composition 91Cr3C2-9Al (wt.%) has high corrosion resistance in operation conditions in a chloride-acetate solution, and by its indicators of corrosion resistance, is not inferior to the Cr3C2-NiCr coating, which is widely used in industry to protect parts from corrosion and wear. The obtained results show the possibility and feasibility of using mechanical mixtures of commercially available powders for the formation of coatings containing Cr2AlC MAX phase instead of expensive synthesized MAX-Cr2AlC powders.

A Rolling Bearing Fault Diagnosis Method Combining MSSSA-VMD with the Parallel Network of GASF-CNN and BiLSTM

Once the rolling bearing fails, it will threaten the normal operation of the whole rotating machinery. Therefore, it is very necessary to conduct research on rolling bearing fault diagnosis. This paper proposes a rolling bearing fault diagnosis method combining MSSSA-VMD (variational mode decomposition optimized by the improved salp swarm algorithm based on mixed strategy) with the parallel network of GASF-CNN (convolutional neural network based on Gramian angular summation field) and bi-directional long short-term memory (BiLSTM) to solve the problem of poor diagnostic performance for the rolling bearing faults caused by the respective limitations of existing fault diagnosis methods based on signal processing and deep learning. Firstly, MSSSA-VMD is proposed to solve the problem where the decomposition effect of VMD is not ideal due to improper parameter selection. Then, MSSSA-VMD is employed to preprocess and extract characteristics. Finally, the extracted characteristics are input into the parallel network of GASF-CNN and BiLSTM for diagnosis. In one channel of the parallel network, GASF is used to convert the characteristic vectors into a two-dimensional image, which is then fed into CNN for spatial characteristic extraction. In the other channel of the parallel network, the characteristic vectors are directly input into BiLSTM for temporal characteristic extraction. Experimental results demonstrate that the proposed method has good performance in terms of fault diagnosis performance under constant operating conditions, generalization ability under variable operating conditions and noise resistance.

Signal Detection by Sensors and Determination of Friction Coefficient During Brake Lining Movement

This article presents a laboratory device by which the course of two signals can be detected using two types of sensors—strain gauges and the DEWESoft DS-NET measuring apparatus. The values of the coefficient of friction of the brake lining when moving against the rotating shell of the brake drum were determined from the physical quantities sensed by tensometric sensors and transformed into electrical quantities. The friction coefficient of the brake lining on the circumference of the rotating brake disc shell can be calculated from the known values measured by the sensors, the design dimensions of the brake, and the revolutions of the rotating parts system. The values of the friction coefficient were measured during brake lining movement. A woven asbestos-free material, Beral 1126, which contained brass fibers and resin additives, showed slightly higher values when rotating at previously tested speeds compared to the friction coefficient values obtained when the brake drum rotation was uniformly delayed. The methodology for determining the friction coefficient of the brake lining allowed the laboratory device to verify its magnitude for different friction materials under various operating conditions.

Research on structural vertical stiffness evaluation of track-specific suspension bridge under the action of the no-load first train

The track-specific bridge has the characteristics of a large volume of passenger traffic, narrow transverse width, and large excitation. The long-term and high-frequency operating conditions put forward strict requirements for the safety and comfort of the bridge. Aiming at the problem of achieving fast, accurate, and intelligent evaluation of bridges under the coupling action of trains and temperatures, this paper proposes a method for evaluating the structural vertical stiffness of track-specific suspension bridges under the action of the no-load first train. Firstly, the [Formula: see text] (deformation-velocity-time) data of trains on the bridge are analyzed through linkage numerical simulation, track train transponder, and structural safety monitoring system. Secondly, the CART algorithm establishes a regression model for data correction. The mapping relationship between the modified deformation monitoring value and the theoretical calculation value is analyzed to realize the current structural vertical stiffness evaluation. Finally, it is verified by the world’s largest span self-anchored track-specific suspension bridge. The results show that the theoretical deformation value of the most unfavorable deformation section is 268.55 mm, the actual monitoring value is 232.80 mm, the difference is 35.75 mm, and the relative error is 13.3%. The R2 score of the CART regression model is 0.94, and the accuracy of the CART regression model is higher than that of other algorithm regression models. The modified dynamic check coefficient is less than 1, and the ratio of the modified residual deformation value to the maximum deformation value is 7.6%, which meets the specification requirement not exceeding 20%, indicating that the current structural vertical stiffness is in normal operating conditions.

Modelling, Analysis and Validation of Hydraulic Self-Adaptive Bearings for Elevated Floating Bridges

Conventional floating bridge systems used during emergency repairs, such as during wartime or after natural disasters, typically rely on passive rubber bearings or semi-active control systems. These methods often limit traffic speed, stability, and safety under dynamic conditions, including varying vehicle loads and fluctuating water levels. To address these challenges, this study proposes a novel Hydraulic Self-Adaptive Bearing System (HABS). The system integrates real-time position closed-loop control and a flexible support compensation method to enhance stability and adaptability to environmental changes. A modified three-variable controller is introduced to optimise load response, while a multi-state observer control strategy effectively reduces vibrations and improves traffic smoothness. A 1:15 scale prototype was constructed, and a co-simulation model combining MATLAB/Simulink and MSC Adams was developed to simulate various operational conditions. Results from both experiments and simulations demonstrate the HABS’s ability to adapt to varying loads and environmental disturbances, achieving a 72% reduction in displacement and a 54% reduction in acceleration. These improvements enhance traffic speed, stability, and safety, making the system a promising solution for emergency and floating bridges, providing superior performance under challenging and dynamic conditions.

Shear capacity model of RC beams based on MCMC approach and reliability analysis

The research question revolves around accurately predicting the shear capacity of RC beams and enhancing the reliability of such prediction models. This study aims to refine the accuracy of the RC beam shear capacity model. To augment the safety margin, the enhanced model underwent further calibration through a reliability analysis. The shear strength models, rooted in Bayesian theory’s Markov Chain Monte Carlo (MCMC) method and the Modified Compressive Field Theory (MCFT), were introduced. A research database was curated from test data of 782 RC beams sets under shear stress. From this database, six mechanism models were juxtaposed with the MCMC models for comparison. To meet the desired reliability indexes ([Formula: see text] = 3.2, 3.7, 4.0) in these models, resistance factors ([Formula: see text]) were incorporated. Both the Monte Carlo and First Order Second Moment (FOSM) methods were employed for the reliability analysis, considering inherent model errors and the probability distribution of primary variables. The effect of different elements on the target reliability index was assessed using a tornado diagram. Findings showed that the refined shear model displayed minimal variance when benchmarked against models from existing literature and national codes, yielding a Vtest/ Vcal ratio close to 1.0. The shear components aligned with established mechanical mechanism. Depending on various operational conditions and load combinations, resistance factors fluctuated from 0.377 to 0.516 in the RC beams without stirrup, while resistance factors fluctuated from 0.479 to 0.621 in the RC beams with stirrup. Key determinants, such as the inherent uncertainty in computing model, the live loads, and the cylinder compression strength, significantly influenced the reliability index.

Effect of Operational Parameters and Iron Ore Tailings Properties on Filter Cake Porosity

Objective: By evaluating operational filtration conditions, chemical, mineralogical, and particle size properties of the tailings, the study aimed to identify critical variables affecting porosity and provide predictive models for optimizing dewatering operations. Theoretical Framework: This research builds upon existing theories of solid-liquid separation and dewatering processes in mineral processing. Key references include classical works on filtration dynamics, particle size distribution, and cake porosity characterization. The study addresses gaps in literature regarding the relationship between tailings composition and filtration results. Method: The Leaf Test method was employed on 33 fresh slurry samples from the Brucutu plant to simulate industrial filtration conditions. Filtration cycle parameters such as cake formation and drying times were standardized. Porosity was calculated using Grace's equation and correlated with characterization data, including mineralogical composition, true density, and particle size. Statistical methods such as clustering, regression, and Random Forest modeling identified key predictors of porosity. Results and Discussion: The results indicated that porosity correlates strongly with silica content and certain mineralogical attributes, such as the presence of martitic hematite and quartz. Cluster analysis revealed two sample groups with distinct filtration characteristics. While operational parameters showed limited impact on porosity, the statistical models highlighted the significance of ore composition. Research Implications: This research provides a foundation for optimizing iron ore tailings filtration by identifying the key variables influencing porosity. The findings support more efficient dewatering techniques, contributing to sustainable tailings management. Further, the development of predictive models aids industrial operations in minimizing risks associated with tailings disposal. Originality/Value: This study is among the first to integrate statistical modeling and mineralogical characterization in exploring filter cake porosity. The results offer novel insights into the optimization of solid-liquid separation processes in the mining industry.

Good practices for the industrial cultivation of the cyanobacterium Arthrospira platensis under a greenhouse in a temperate zone (northern Italy)

Arthrospira, Spirulina, and Limnospira are cyanobacteria widely known as food supplements or additives and cultivated worldwide under the commercial name of spirulina. Many studies have been focused on the improvement of operational conditions for optimizing cell growth and harvesting. At present, greater attention is paid to obtaining a good-quality, possibly food-grade, product that can be added to different food formulations and to reducing the environmental impact by saving water and avoiding or minimizing the release of mineral salts in the environment. A few studies have addressed these aspects although, in most cases, through laboratory experiments or pilot plants. This study focused on the effects of medium recycling, monitored at each harvesting step, and nutrient replenishment on Arthrospira platensis growth, biomass, biochemical composition, and extracellular polysaccharide (EPS) production, in a production plant located in a temperate zone (northeastern Italy). Four out of the seven largest ponds (11,000 L each) of the plant were followed for a three-month semi-continuous cultivation, which included the period with the highest biomass productivity. Recycling the culture medium after biomass harvest effectively allowed the best nutritional status of the cells. Biomass productivity increased from June to August 2023 (mean values: 0.029 and 0.038 g L-1 d-1 at 25–26 °C and 27–29 °C, respectively). Protein, polysaccharides, and C-phycocyanin contents were 62, 22, and 12%, respectively, while EPS was < 7 mg L-1. The biochemical composition did not vary during the cultivation period, differently from previous studies performed with small culture volumes and for a short time.

Prediction of syngas production in the gasification process of biomass employing adaptive neuro-fuzzy inference system along with meta-heuristic algorithms

Abstract Compared to fossil fuels, biomass fuels have minimal sulfur content, lower ash production, and significantly reduced emissions. The global need to reduce dependence on imported energy sources and preserve dwindling fossil fuel reserves underscores the importance of utilizing alternative energy resources. Biomass, with its abundant availability, presents a promising source for syngas production, even though the gasification procedure requires substantial energy due to its endothermic nature. Challenges related to the efficiency of biomass gasification and compliance with environmental standards have hindered economic viability. Much attention has been focused on predictive modeling of biomass gasification procedures to address these issues, necessitating robust frameworks capable of predicting parameters under varying operating conditions. This article introduces two hybrid frameworks, which are combined versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Rabbits Optimization (ARO) and Crystal Structure Algorithm (CSA), based on proximate biomass values to predict elemental compositions (N2 and H2). These intelligent hybrid frameworks, trained with 70 % of biomass data, were further validated and tested with the remaining 15 % portions of the database. The frameworks were assessed based on some known performance metrics, namely, root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R 2), RMSE-observations standard deviation ratio (RSR), and Nash-Sutcliffe Efficiency (NSE). Developed single and two hybrid frameworks compared and obtained outcomes revealed that both introduced optimizers efficiently promoted N2 and H2 estimation by ANFIS, especially CSA. R 2 values for ANCS were a maximum of 0.993 in both targets’ predictions. Also, minimum RMSE values of 1.007 and 1.470 related to N2 and H2 prediction emphasized the accuracy of ANCS, which is capable of being used in real-world applications.

Cellulose-Based Sorbents: A Comprehensive Review of Current Advances in Water Remediation and Future Prospects

Cellulose-based sorbents are promising materials for wastewater treatment due to their environmental friendliness, biodegradability, and high sorption capacity. This paper presents an overview of cellulose modification methods, including carboxylation, amination, oxidation, graphene, and plasma treatments, as well as combined approaches. Their effect on key physicochemical properties, such as porosity, morphology, and chemical stability, is considered. Examples from the literature confirm the effectiveness of modified cellulose sorbents in removing heavy metal ions and organic pollutants from wastewater. The analysis shows that combined methods allow for creating materials with improved characteristics that are resistant to extreme operating conditions. The main advantages and disadvantages of cellulose sorbents, as well as challenges associated with their scalability and cost-effectiveness, are discussed. The paper emphasizes the importance of further research to advance these materials as a key element of sustainable water treatment technologies.

A Comprehensive Multi-Objective Optimization Study on the Thermodynamic Performance of a Supercritical CO2 Brayton Cycle Incorporating Multi-Stage Main Compressor Intermediate Cooling

This study proposes a supercritical carbon dioxide Brayton cycle incorporating multi-stage main compressor intermediate cooling (MMCIC sCO2 Brayton cycle), and conducts an in-depth investigation and discussion on the enhancement of its thermodynamic performance. With the aim of achieving the maximum power cycle thermal efficiency and the maximum specific net work, this study examines the variation of the Pareto frontier with respect to the number of intermediate cooling stages and critical operational parameters. The results indicate that the MMCIC sCO2 Brayton cycle offers significant advantages in improving power cycle thermal efficiency, reducing energy consumption, and mitigating the adverse effects associated with main compressor inlet temperature increasing. Under the investigated operational conditions, the optimal cycle performance is achieved with four intermediate cooling stages, yielding a maximum power cycle thermal efficiency of 67.85% and a maximum specific net work of 0.177 MW·kg−1. Cycles with two or three intermediate cooling stages also deliver competitive cycle performance, and can be regarded as alternative options. Additionally, increasing the turbine inlet temperature proves more effective for enhancing power cycle thermal efficiency, whereas increasing the turbine inlet pressure can substantially improve the specific net work. This study provides a feasible structural layout approach and research framework to improve the thermodynamic performance of the sCO2 Brayton cycle, offering a robust theoretical foundation and technical guidance for its implementation in power engineering.

Analysis and calculation of electromagnetic force of stator winding and damping bar during dynamic loading of pumped storage generator/motor

AbstractFrequently and rapidly dynamic conversion is one of the main differences between pumped‐storage motor and general hydro‐generator. In the process of changing operating conditions, in order to achieve fast dynamic conversion, the loading rate requirement of pumped storage motors is often much higher than that of conventional generators, which brings great impact on the key structural components of generators, it affects the safe operating life of motors directly. To solve the impact of dynamic loading rate on key structural components of pumped storage motors, for a pumped storage motor as an example, investigating different loading rates during the dynamic conversion of two main operating conditions on pumped storage motors (power generation and electric power) of pumped storage motors are investigated. Calculating accurately the distribution laws of electromagnetic forces of key structural components which contains stator tooth walls and damping strips during dynamic loading. The results show that the different loading rates will affect the force distribution of the key position directly, under the condition of generator state and motor state, the distribution of force on stator winding and damping bar present different laws, it will provide reference for the determination of multi‐state conversion loading rate of pumped storage motor and the improvement of pumped storage motor structure scheme.

Flow-Induced Stress Analysis of a Large Francis Turbine Under Different Loads in a Wide Operation Range

Francis turbines, being widely used in hydropower plants, operate under different loads which significantly affect their hydraulic characteristics and structural dynamics. It is essential to carry out the flow-induced dynamics analysis of the large prototype Francis turbines under different loads in a wide load operation range to optimize the hydraulic performance, ensure structural reliability, and prevent mechanical failure. This work analyzes the flow-induced dynamics of a large Francis turbine prototype with a rated power of 46 MW. Computer-aided design (CAD) models of the Francis turbine unit are first constructed, including the fluid and structural domains. After generating the computational meshes of the flow passages in the Francis turbine unit, Computational fluid dynamics (CFD) calculations are carried out under four typical operating conditions from 25% load to 100% load, and the pressure files obtained from CFD calculations are applied to the finite element model to analyze the flow-induced stresses of the runner. The results show that the pressure inside the Francis turbine runner decreases gradually from the spiral case to the draft tube under 25%, 50%, 75%, and 100% loads, but the local pressure distribution in the crown chamber of the Francis turbine unit varies under different loads. The locations of the maximum stress of the runner under the four different operating conditions vary with the power output. The flow-induced maximum stress of the runner at 25% load is located on the chamfer of the connection between the blade trailing edge and the crown. But from 50% load to 100% load, the maximum stress of the runner appears on the chamfer of the connection between the blade leading edge and the band. From 25% load to full load, the maximum stress of the unit is one-fifth of the yield stress of the runner material, and the runner will not be damaged during normal use. The calculation method with a fully three-dimensional fluid–structure interaction (FSI) method and the conclusions proposed in this study can provide important references for the design and evaluation of other hydraulic turbine units.

Real-Time Monitoring of Metal Ions during the Molten Salt Electrolysis Process by Optical Emission Spectrometry Based on Microplasma.

Molten salt electrolysis has been widely used in the production and separation of metals, but it still lacks in situ real-time analysis methods to monitor the electrolysis process. In this work, a microplasma spectroscopic real-time analysis (MIPECA) system is developed based on noncontact direct current (DC) glow discharge. With the MIPECA system, the atomic emission spectroscopy of Li and K could be obtained in situ in LiCl-KCl molten salt, and the impact of different operating conditions on spectral signals was investigated. Then, the characteristic spectra of ten representative elements, including alkali metals (Na, Cs), alkaline earth metals (Mg, Ca, Sr, Ba), transition metals (Ag, Cu, Ni), and lanthanide metals (Ce), were all successfully excited for qualitative analysis. Under the optimal operating conditions, quantitative analysis of Ag, Cs, and Sr was achieved with high sensitivity and low limits of detection (LOD) of about 0.063, 0.021, and 0.073 wt %, respectively. Finally, the electrolysis process of Ag+ in LiCl-KCl molten salt was real-time monitored by using this MIPECA system, showing its application potential in molten salt electrolysis.

Real-time wheel-rail adhesion anti-skid control model for high-speed articulated trains on long downhill slopes during braking

To improve the braking efficiency of articulated trains on long-steep downhill gradients under different wheel-rail adhesion levels, this paper develops a real-time train braking control system equipped with an optimal adhesion anti-slip controller. Simulation validation is conducted under both dry and wet wheel-rail contact conditions with different braking torques applied on a steep downhill slope. The anti-slip adhesion control system comprises two modules: an adhesion optimization module using a forgetting factor recursive least squares (FFRLS) method to determine creep force function slopes for enhancing wheel-rail adhesion utilization, and a controller employing a neural network with a Levenberg–Marquardt (L-M) algorithm. Through the co-simulation of the train-track coupling dynamic model and the anti-skid control system, the real-time adjustment of the braking torque of the train can be quickly corrected. The results indicate that the anti-skid control system demonstrates excellent self-adjustment capabilities under various operating conditions. It effectively controls wheel slip, achieving optimal wheel-rail adhesion. This ensures the shortest possible braking distance while maintaining train stability and improving train transmission efficiency.

Ecological Comparison of Airships to Other Types of Transport

Airships are lighter-than-air vehicles that rely on buoyant gases like helium or hydrogen to generate lift, which is achieved through static buoyancy, unlike aircraft that use aerodynamic lifts. In comparison to traditional modes of transport, such as trucks, trains, and aircraft, airships have the potential to fill niche roles in freight logistics, particularly in regions with congested or underdeveloped infrastructure. In this study, a hybrid airship, named Dynalifter, is compared to traditional transport modes such as rail, electric trains, aircraft, and airships. Its large transport capacity over long distances, combined with its ability to land on smaller airfields, positions it as a complement to existing modes of transportation, particularly for oversized or remote cargo deliveries. It offers unique advantages in operational flexibility, reduced infrastructure requirements, and suitability for cargo transport. The analysis shows that although airship emissions are not the most environmentally friendly in terms of emissions per ton-kilometer, they offer significant flexibility in operational conditions and cargo capacity, making them a viable option for specific types of cargo. Further research into hydrogen propulsion and advances in airship technology could improve their environmental performance, potentially making them a competitive alternative for low-emission cargo transportation.

Research on Temperature Field Analysis Method of High-Speed Bearing Chamber–Bearing System

The analysis of the temperature field of a high-speed bearing chamber–bearing system is very complex. We used the temperature field analysis method on a 40,000 rpm bearing chamber–bearing system by simulation, which builds on the finite volume method and introduces a decoupling method that separates fluid dynamics from the thermal analysis of the solid temperature field. Firstly, according to bearing operating conditions, the characteristics of the oil–air two-phase distribution in the bearing chamber are determined using the Volume of Fluid (VOF) method. The convective heat transfer boundary conditions derived from this analysis serve as the thermal boundary conditions for the subsequent thermal analysis. Secondly, considering the heat generation of the bearings and the thermal boundary conditions, a temperature field analysis model is formulated. The calculated results are found to be in close agreement with the actual test data, with an error of less than 10% under three operational conditions. Thirdly, the presented method to evaluate the temperature field of the bearing chamber–bearing system has not been studied in other published literature. Additionally, compared with the thermal fluid–structure interaction method, the method described in this paper can save 90.75% of calculation time, which significantly improves efficiency. Therefore, the above method is reliable for evaluating the temperature field of the bearing chamber–bearing system.

Analysis of the Operational Characteristics and Performance Comparison of Steam Screw Pressure Matcher Based on Twin‐Screw Expander

ABSTRACTTo address the discrepancy between the steam parameters extracted by pure condensing units/combined heat and power units and those required by users, this paper introduces the design of a steam screw pressure matcher(SSPM) and determines its optimal steam supply scheme. The SSPM primarily comprises a twin‐screw expander (TSE), twin‐screw compressor(TSC), electric motor, and generator. Initially, models were developed for a 300‐MW subcritical intermediate reheat condensing steam turbine unit, an SSPM, and a desuperheater and pressure reducer(DPR). Subsequently, these models are incorporated into the Ebsilon Professional software to simulate the operational characteristics of the SSPM. The performance parameters of the two options are then compared and analyzed. Simulation results reveal that the operational characteristics of the SSPM closely correlate with the mass flow rate of the TSE. As the TSE mass flow rate varies from 47 to 91 t/h, the efficiency of the TSE declines from 66.3% to 65.3%, while the power performed by the SSPM increases from −2457.978 to 1558.469 kW. Under identical operating conditions, the SSPM scheme exhibits a 6.2%–12.4% improvement in efficiency compared to the DPR scheme. Building upon the aforementioned analysis, the SSPM demonstrates effective cascade utilization of heat steam energy and exhibits favorable regulation characteristics under variable operating conditions. Contrasted with traditional DPR, the SSPM notably mitigates constraints on steam extraction by boilers and enhances the maximum generation efficiency of a single unit.

Computational Modeling of Biomass Fast Pyrolysis in Fluidized Beds with Eulerian Multifluid Approach

This study investigated the fast pyrolysis of biomass in fluidized-bed reactors using computational fluid dynamics (CFD) with an Eulerian multifluid approach. A detailed analysis was conducted on the influence of various modeling parameters, including hydrodynamic models, heat transfer correlations, and chemical kinetics, on the product yield. The simulation framework integrated 2D and 3D geometrical setups, with numerical experiments performed using OpenFOAM v11 and ANSYS Fluent v18.1 for cross-validation. While yield predictions exhibited limited sensitivity to drag and thermal models (with differences of less than 3% across configurations and computational codes), the results underline the paramount role of chemical kinetics in determining the distribution of bio-oil (TAR), biochar (CHAR), and syngas (GAS). Simplified kinetic schemes consistently underestimated TAR yields by up to 20% and overestimated CHAR and GAS yields compared to experimental data (which is shown for different biomass compositions and different operating conditions) and can be significantly improved by redefining the reaction scheme. Refined kinetic parameters improved TAR yield predictions to within 5% of experimental values while reducing discrepancies in GAS and CHAR outputs. These findings underscore the necessity of precise kinetic modeling to enhance the predictive accuracy of pyrolysis simulations.