Optimal Operating Parameters Research Articles

Study on the operating parameter optimization based on the temperature characteristics of fuel cell

Study on the operating parameter optimization based on the temperature characteristics of fuel cell

Energy, exergy and advanced exergy analyses on Garri “1” combined cycle power plant of Sudan

Thermal power plants account for 39% of Sudan's electricity grid. Consequently, enhancing the performance of these plants is crucial for bolstering the Sudanese energy sector. This paper presents an analysis of energy, conventional exergy, and advanced exergy for 180-MW Garri “1” combined cycle power plant in Sudan. The study focuses on assessing the enhancement potential of this power plant to provide guidance for performance improvement and identify optimal areas for enhancement within the plant's components. The energy and classical exergy analyses results indicated that the highest energy losses occur in the stacks (55.1%), while the largest source of exergy destruction is combustion chambers (50.98%). Furthermore, the lowest contributions to energy and exergy losses are from the deaerators (0.15% energy and 0.32% exergy) and auxiliary systems (0.22% energy and 2.85% exergy). The overall energy and exergy efficiencies of the plant were found to be 36.1% and 34.1%, respectively. The advanced exergy analysis exhibited that the ratios of unavoidable to avoidable exergy destruction and endogenous to exogenous exergy destruction are 87.95% to 12.05% and 68.3% to 31.7%, respectively. Moreover, it was shown that the primary source of avoidable endogenous exergy destruction is the combustion chambers (48.53%). Furthermore, the improvement potential in the plant's exergy efficiency was found to be only 3%, suggesting the need for optimization of operational parameters to enhance overall performance. The current paper offers valuable insights into enhancing efficiency of Sudanese energy sector, with implications for optimizing the energy sector in similar developing countries.

Instrumentation in Tractor for Evaluating Performance of a Tractor Drawn Rotavator

Performance measurement of agricultural machinery is essential for enhancing their efficiency for which they are operated. The performance related data helps the operator to select optimum operating parameters which reduce fuel consumption and increase field productivity. This study was aimed to develop various sensing units and install them on the tractor for evaluating performance of a tractor drawn rotavator. A microcontroller based embedded system was developed for continuous recording of wheel slip, actual forward speed, throttle opening, engine speed, velocity ratio, draft and power take-off (PTO) torque related data while carrying out tillage operation with rotavator. The wheel slip (from 1.5% to 35%) and actual forward speed of the tractor (up to 6 km h-1) was measured using inductive proximity sensors. For measuring throttle opening (0 to 100%), engine speed (750 to 2500 rpm), draft (65 to 350 kg) and PTO torque (up to 420 N-m) values, a potentiometer, magnetic pickup sensor, S-type loadcell and PTO torque transducer, respectively, were used. For computing velocity ratio, peripheral speed of rotavator was measured using PTO speed data received from PTO torque transducer, and actual forward speed of tractor was measured using inductive proximity sensor. The developed sensing units were calibrated under both dynamic and static conditions, and were also verified in laboratory and field conditions. Results showed that output signals received from the sensing units varied linearly with applied load. The mean absolute percentage error (MAPE) between measured and actual values was found to be below 5%. From the measured parameters, specific energy requirement and total power required for carrying out tillage operation could be computed. The power requirement was found to increase with increase in both gear and throttle settings. The specific energy requirement was minimum when rotavator was operated in L2 gear of the tractor. The minimum specific energy of 21 Wh m-3 was found at L2 gear and 75% throttle opening.

Response surface methodology and process optimization of spark plasma sintered parameters of Ti20Al20Cr5Nb5Ni19Cu12Co19 high entropy alloy

Study was carried out on the effect of milling and sintering process parameters on the relative density and microhardness property of a septenary non-equiatomic Ti20Al20Cr5Nb5Ni19Cu12Co19 high entropy alloy fabricated via spark plasma sintering (SPS) process at 100 °C/min constant heating rate, 5 min dwell time, and at a pressure of 50 MPa. A predictive model was developed through response surface methodology (RSM) using the SPS sintering temperature (ST) and milling time (MT) as the process variables. In order to reduce the number of experimental runs, the design of experiment (DOE) technique was used, to eventually avoid the trial-and-error process associated with conventional experimental procedures. The user-defined design (UDD) of the RSM was used to forecast the optimal operating parameters, and the outcome was validated by experiments. Findings indicate that MT and ST are important in achieving high densification, which leads to high densification and mechanical properties. Optimization model shows that, at MT of 7.20115 h and ST of 899.742 °C, desirable responses of 99.9 % relative density, percentage porosity of 0.0999994 % and a micro-hardness value of 835.21 HV can be attained.

Energy-efficient optimization design of bio-butanol fermentation broth purification process

To address the downstream processing challenges of the IBE (isopropanol-butanol-ethanol) system and obtain biobutanol products with a mass fraction of 0.9999, as well as obtain a mass fraction of 0.99975 for the IE (isopropanol-Ethanol) mixture product as a gasoline additive, this study proposes a four-column distillation process termed "Dehydration-Butanol-Extractive Four Column Distillation" (DBE-4CD). With the heat load as the optimization target, the DBE-4CD process was optimized to determine the optimal operating parameters. Based on the optimized process and considering the energy-saving potential of the dividing wall column, an “Azeotropic Dividing Wall-Extractive Three Column Distillation” (ADE-3CD) process was subsequently proposed to further enhance energy efficiency and reduce consumption. Compared to both conventional literature process and the DBE-4CD process, the total load of the ADE-3CD process decreased to 7433.5 kW, representing reductions of 20.35% and 10.11%, respectively. Additionally, the mass recovery rates of butanol and the IE mixture reached 99.90% and 99.64%, respectively, exceeding those of the conventional literature process, which were 99.11% and 99.18%.

Experimental Design, Statistical Analysis, and Modeling of the Reduction in Methane Emissions from Dam Lake Treatment Using Agro-Industrial Biochar: A New Methane Capture Index

This study aimed to reduce the methane (CH4) emissions originating from dam lake treatment using malt dust-derived biochar, which is an agro-industrial byproduct of the brewery industry. Optimum operating and water quality parameters for CH4 reduction were determined using statistical analyses based on the Box–Behnken design method. Also, a Monte Carlo simulation was performed to determine the correlation between CH4 emissions and operating parameters. According to the simulation, dissolved oxygen (DO) and the oxidation–reduction potential (ORP) had the highest correlation with CH4 emissions, with values of 92.03% and 94.57%, respectively. According to the Box–Behnken design methodology, the optimum operating parameters were 4 mg/L of dissolved oxygen, −359 mV of ORP, and 7.5 pH for the minimum CH4 emissions. There was a reported reduction of up to 19.4% in CH4 emissions for the dam lake treatment using malt dust-derived biochar. Finally, a new methane capture index, based on the biochar application (MCI), was developed and validated. The largest methane capture capacity was related to the malt dust-derived biochar produced at the lowest temperature (M1).

Reactive crystallization regulation for synthesizing NCM811 precursor by different impellers

The spherical and dense precursors with a narrow particle size distribution are essential to achieve high electrochemical performance in cathode materials. Four newly designed impellers, including the punched-flow turbine (PFT), punched-flow disc turbine (PFDT), curved-inclined-blade disc turbine (CIBDT), and 6-folded-pitched turbine (FPT-6), are applied to prepare the precursors to investigate the interplay among the fluid flow state, precursors’ morphology and growth, and physicochemical properties of cathodes. The flow and mixing features of double impellers are predicted using computational fluid dynamics, indicating that the FPT-6 plays a crucial role in crystal nucleation while PFDT facilitates crystal growth. This concurrence can be corroborated by examining the performance of precursors synthesized using these four impellers. It is first found that the electrochemical performance is distinctly enhanced by utilizing different impellers to regulate the specified nucleation and growth stage. By increasing the nucleation rate with double impellers of FPT-6, the tap density and sphericity of the precursor and cathode materials are significantly improved. The discharge capacity of cooperative agitators (FPT-6+PT) is 2.1 times that of the only propeller turbine at a high current density (10 C). The later growth stage is suitably regulated by FPT-6 or PFDT; properly extended reactive time can enhance the sphericity and electrochemical performance. The dual mixed-impeller (PT/CIBDT) combines intense mixing and shear ability, attaining a dense spheroid with a highly smooth surface. The cycle retention of this cathode is increased by 10.35% after 200 cycles at 1 C compared to a commercial counterpart. A regulatory strategy for the reactive co-precipitation process to enhance precursor stability is proposed here. Furthermore, the optimal operation parameters derived from this strategy can be effectively applied to commercial cathode materials.

Application of artificial neural network for peak load forecasting in 150 kV Semarang power system

Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544).

DEVELOPMENT OF A DRYING UNIT FOR THE PRODUCTION OF KURTA

The article is devoted to the development of a drying unit for the production of kurt, a traditional fermented milk product that is very popular in Kazakhstan. Kurt is not only a national product, but also has a high nutritional value, vitamin and mineral content. Regular consumption of kurt helps to restore beneficial intestinal microflora and is especially useful for pregnant women, children and the elderly. The traditional method of drying kurt in the open air has a number of disadvantages, such as dependence on weather conditions and low productivity. The developed convective drying unit allows solving these problems. It ensures uniform and rapid drying of kurt in controlled conditions, which improves quality and productivity. The unit is designed taking into account the principles of ease of maintenance, ease of operation and cost-effectiveness. It is affordable and cost-effective for individual entrepreneurs and small businesses. Availability, price, quality, productivity, maintainability, reliability, light weight and mobility of the equipment make it relevant for the development of small businesses in Kazakhstan. During the work, a number of tests were conducted on the performance of the drying unit. A sketch drawing was also developed, a drying unit was developed and the optimal operating parameters of the design were selected. The resulting drying unit is promising for implementation in the production of kurt, in the conditions of individual and farm enterprises. It will improve the quality and productivity of the product, as well as create new opportunities for small and medium businesses in Kazakhstan. Also, such equipment can give impetus to local gastronomic tourism, which is becoming an increasingly popular type of tourism, as tourists are looking for unique and authentic experiences. Offering tourists the opportunity to get acquainted with the local culture through its cuisine.

Properties of cement raw meals used as sorbents in a calcium looping process

Calcium looping (CaL) is a promising carbon capture and storage (CCS) technology that has the potential to significantly reduce CO2 emissions in cement production. Integrating CaL with cement production provides a viable solution to the high CO2 emissions generated during the calcination process. This study examines the behavior of two industrial cement raw meals from different sites (A-RM and B-RM) under CaL conditions, focusing on phase composition, particle size distribution, and clinker phase evolution up to 1450 °C. Calcination and CaL experiments were conducted in a CO2-rich atmosphere, with materials characterized using quantitative X-ray diffraction (Q-XRD) and high-temperature X-ray diffraction (HT-XRD). The results showed that both raw meals absorbed similar amounts of CO2 during the cyclic CaL experiments. A-RM formed C2S and other silicates, while B-RM retained more free CaO due to a less effective reaction with coarser quartz (SiO2) particles. HT-XRD revealed delayed clinker-phase evolution in the 1000–1400 °C range in CaL-treated raw meals. However, CaL-treated raw meals achieved low free CaO at 1450 °C, suggesting that optimal kiln conditions can produce the desired phase composition. These findings indicate that integrating CaL-treated raw meals into cement production requires careful optimization of operational parameters to maintain clinker quality and minimize energy consumption. Further research should focus on improving the efficiency and reactivity of CaL-treated raw meals to enhance their suitability for industrial cement production.

Extrapolation Framework and Characteristic Analysis of Load Spectrum for Agriculture General Power Machinery

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements.

Analysis and optimization of performance parameters of the 220Rn chamber in flow-field mode using computational fluid dynamics method

Analysis and optimization of performance parameters of the 220Rn chamber in flow-field mode using computational fluid dynamics method

A two-stage framework for quantifying the impact of operating parameters and optimizing power density and oxygen distribution quality of PEMFC

Proton exchange membrane fuel cell (PEMFC) exhibits significant promise in generating power from hydrogen energy. Operating parameters exert a direct influence on both the power output and the uniformity of oxygen distribution within the PEMFC. Therefore, quantifying the impact of operating parameters and identifying the optimal operating conditions are pivotal to enhance the performance and extend the lifespan of the PEMFC. To this end, a two-stage framework leveraging interpretable machine learning and multi-objective optimization is proposed. In the first stage, an interpretable surrogate model for the PEMFC is established. The impacts of single parameter and pairwise parameters on the power output and the oxygen distribution quality are quantified. Moreover, the decision variables are selected for the second stage. In the second stage, the optimal operating parameters are determined via multi-objective optimization. The results from the first stage suggest that operating voltage and pressure have the highest cumulative contribution for both power density and oxygen distribution quality. The influence mechanism of one operating parameter on the relationship between another operating parameter and the research target is clearly quantified. The findings from the second stage indicate that power density increases by 32 %, 27.36 %, and 32.58 % for three optimized solutions, respectively, while the standard deviation of oxygen molar concentration in the selected operating condition is reduced by 29.66 %.

Enhanced cadmium (Cd2+) removal from wastewater using integrated inclined plate settler and composite adsorbent coating

Bottlenecks inherent in batch and column adsorption configurations have impeded the implementation of the adsorption technique in large-scale wastewater treatment systems. This study mainly aimed to develop an innovative wastewater treatment prototype that integrates inclined plate settlers (IPS) and composite adsorbent coating (CAC). The objective is to enable the removal of Cd2+ from aqueous solutions in a continuous setup, thereby enhancing its practicality for large-scale applications. The combined IPS-CAC system was optimized at various angle of inclination (θ), influent flow rate (Q) and adsorbate initial concentration (Co) using the Box–Behnken Design (BBD) of the Response Surface Methodology (RSM). At optimized operating parameters (θ = 45°, Q = 5 ml/min and Ci = 1.87 mg/L) the IPS-CAC Cd2+ predicted (R2 = 0.9926) and experimental removal efficiencies were 75.8% and 69.7 ± 4.67%, respectively. The IPS-CAC breakthrough adsorption capacity was 9.6 mg/g. Comparing IPS-CAC performance with a tank without plates and IPS with plain plates, the Cd2+ removal efficiencies were 2.4 ± 0.1% and 4.6 ± 1.1%, respectively, confirming the synergistic effect of IPS and CAC. Additionally, breakthrough curves were acquired for various flow rates, cadmium influent concentrations, and plate inclination angles. Only a 10% decline in the removal effectiveness (from 69.7 to 59.7%) of the CAC after three adsorption–regeneration cycles was observed, indicating its stability for heavy metal removal. The results underpin the potential of using IPS-CAC for industrial wastewater treatment and enhancing the use of adsorption on a larger scale.

Design and Optimization of Key Parameters for a Machine Vision-Based Walnut Shell–Kernel Separation Device

The separation of walnut kernels and shells has long been regarded as a bottleneck, limiting processing efficiency, product quality, and industry advancement. In response to the challenges of improving separation accuracy and the inadequacy of existing equipment for meeting industry demands, this paper proposes an innovative walnut shell–kernel separation device based on machine vision technology. An experimental system was constructed, and key parameters were optimized to enhance its performance. The device comprises five main modules: material conveyance, image acquisition, control module, sorting module, and frame. Differential separation technology is used to convert the walnut material group into a stable particle flow, addressing the issue of missed selections due to material blockages. An enhanced YOLOv8n algorithm improves small object detection and interference resistance, enabling accurate identification of walnut kernels. The Box–Behnken Design and Artificial Neural Network prediction model was used to determine the optimal operating parameters for the device. Experimental results showed that effective differential separation was achieved when the dual-stage conveyor system operated at speeds of 0.2 m/s and 1 m/s. The improved I-YOLOv8n algorithm reached an accuracy of 98.8%. Using the neural network model, the optimal operational parameters were determined: an air pressure of 0.72 MPa, a jetting component angle of 10.16°, and a sorting height of 105.12 cm. Under these conditions, the device achieved an actual cleaning rate of 93.56%, demonstrating outstanding separation performance. Compared to traditional separation methods, this device offers significant advantages in terms of efficiency, quality, and ease of operation, providing new technological pathways and support for the automation and intelligent transformation of the walnut processing industry. In the future, the device is expected to undergo further improvements to meet broader market demand and serve as a reference for the separation of other agricultural products.

HeritageScript: A cutting-edge approach to historical manuscript script classification with CNN and vision transformer architectures

Determining the script of historical manuscripts is pivotal for understanding historical narratives, providing historians with vital insights into the past. In this study, our focus lies in developing an automated system for effectively identifying the script of historical documents using a deep learning approach. Leveraging the ClAMM dataset as the foundation for our system, we initiate the system with dataset preprocessing, employing two fundamental techniques: denoising through non-local means denoising and binarization using Canny-edge detection. These techniques prepare the document for keypoint detection facilitated by the Harris-corner detector, a feature-detection method. Subsequently, we cluster these keypoints utilizing the k-means algorithm and extract patches based on the identified features. The final step involves training these patches on deep learning models, with a comparative analysis between two architectures: Convolutional Neural Networks (CNN) and Vision Transformers (ViT). Given the absence of prior studies investigating the performance of vision transformers on historical manuscripts, our research fills this gap. The system undergoes a series of experiments to fine-tune its parameters for optimal performance. Our conclusive results demonstrate an average accuracy of 89.2 and 91.99% respectively of the CNN and ViT based proposed framework, surpassing the state of the art in historical script classification so far, and affirming the effectiveness of our automated script identification system.

Optimization of furfural production from xylose over sulfated titanium-niobium mixed oxides catalyst in biphasic system

The present study investigates the use of SO42-/TiO2Nb2O5 (STNO) catalyst prepared through the modified sol-gel method in the process of xylose dehydration to furfural. The reaction was carried out in a biphasic solvent consisting of toluene and water. The catalyst used in this study was subjected to several characterization methods, including Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The textural properties of the catalyst were evaluated by conducting N2 adsorption and desorption measurements using the Brunauer-Emmett-Teller (BET) method. The impact of catalyst dosage, resident time, xylose concentration, and reaction temperature in the dehydration of xylose to produce furfural was explored. The study employed response surface methodology to identify the optimal operational parameters that would result in the highest furfural selectivity. At a reaction temperature of 150 °C and a reaction time of 180 min, a maximum conversion of xylose of 98 mol%, furfural selectivity of 74 mol%, and a furfural yield of 63 mol% was obtained. The activation energy for the synthesized catalyst was determined to be 26.7 KJ/mol. The results of this investigation show the great potential that sulfated titanium-niobium mixed oxides have in transforming biomass resources into value-added compounds.

Research of a 0.14 THz Dual-Cavity Parallel Structure Extended Interaction Oscillator.

This paper presents a method to enhance extended interaction oscillator (EIO) output power based on a dual-cavity parallel structure (DCPS). This stucture consists of two conventional ladder-line structures in parallel through a connecting structure, which improves the coupling efficiency between the cavities. The dual output power fusion structure employs an H-T type combiner as the output coupler, which can effectively combine the two input waves in phase to further increase the output power. The dispersion characteristics, coupling impedance, and field distribution of the DCPS are investigated through numerical and simulation calculations, and the optimal operating parameters and output structure are obtained by PIC simulation. At an operating voltage of 12.6 kV, current density of 200 A/cm2, and longitudinal magnetic field of 0.5 T, the DCPS EIO exhibits an output power exceeding 600 W at a frequency of 140.6 GHz. This represents a nearly three-fold enhancement compared with the 195 W output of the conventional ladder-line EIO structure. These findings demonstrate the significant improvement in output power and interaction efficiency achieved by the DCPS for the EIO device.

Performance Analysis and Rapid Optimization of Vehicle ORC Systems Based on Numerical Simulation and Machine Learning

The organic Rankine cycle (ORC) system is an important technology for recovering energy from the waste heat of internal combustion engines, which is of significant importance for the improvement of fuel utilization. This study analyses the performance of vehicle ORC systems and proposes a rapid optimization method for enhancing vehicle ORC performance. This study constructed a numerical simulation model of an internal combustion engine-ORC waste heat recovery system based on GT-Suite software v2016. The impact of key operating parameters on the performance of two organic Rankine cycles: the simple organic Rankine cycle (SORC) and the recuperative organic Rankine cycle (RORC) was investigated. In order to facilitate real-time prediction and optimization of system performance, a data-driven rapid prediction model of the performance of the waste heat recovery system was constructed based on an artificial neural network. Meanwhile, the NSGA-II multi-objective algorithm was used to investigate the competitive relationship between different performance objective functions. Furthermore, the optimal operating parameters of the system were determined by utilizing the TOPSIS method. The results demonstrate that the highest thermal efficiencies of the SORC and RORC are 6.21% and 8.61%, respectively, the highest power outputs per unit heat transfer area (POPAs) are 6.98 kW/m2 and 8.99 kW/m2, respectively, the lowest unit electricity production costs (EPC) are 7.22 × 10−2 USD/kWh and 3.15 × 10−2 USD/kWh, respectively, and the lowest CO2 emissions are 2.85 ton CO2,eq and 3.11 ton CO2,eq, respectively. The optimization results show that the RORC exhibits superior thermodynamic and economic performance in comparison to the SORC, yet inferior environmental performance.

Unfolding multilevel agglomerative strategies for SVM classification: a case study in discriminating spectrally similar land covers

ABSTRACT In remote sensing applications, image classification algorithms normally require parameter optimization strategies to adapt to the complexities of the data and determine the model’s parameters for optimal performance. However, a unique set of hyper-parameters is generally chosen, without acknowledging the presence of subsets of classes that exhibit different levels of spectral similarity. A model used to distinguish between well-separable classes might not be optimal for delineating other classes. This study explores a classification method that extends the standard One-Against-One (OAO) and One-Against-All (OAA) strategies for Support Vector Machines (SVMs), based on agglomerative hierarchical clustering of spectrally-similar classes. The main objective is to provide additional classification scenarios where more detailed per-class analyses can be performed, rather than simple comparisons of global accuracies, particularly for hard-to-distinguish classes. Clusters of classes closely located in the feature space are initially classified using an OAA strategy, while each cluster undergoes further subdivision using an OAO approach. This strategy aims to identify the most effective sequence for incorporating subgroups of classes into the tuning process. The experimental results indicate that the proposed method can be efficiently applied to classify remote sensing data, producing varying levels of enhancement in per-class accuracies at each stage of the agglomerative process.