Key Operating Parameters Research Articles

Optimizing key parameters for grinding energy efficiency and modeling of particle size distribution in a stirred ball mill

Fine grinding using a stirred ball mill can enhance ore liberation but incurs high energy consumption, which can be minimized by optimizing operating conditions. This study explores the impact of key operational parameters—grinding time, stirrer tip speed, solid concentration, and feed size—on grinding efficiency, evaluated using specific energy inputs, in stirred milling of Egyptian copper ore. The particle size distribution (PSD) of ground products was simulated using the Gates–Gaudin-Schuhmann model (GGS) and the Rosin-Rammler-Benne (RRB) function. Taking minimum energy consumption into account, the finest particles (100% ~1 μm) were achieved at the maximum stirrer speed of 500 rpm and a moderate solid concentration of 33.3% after 17 h of grinding, consuming approximately 1225 kWh/t. Experimental data demonstrated a linear correlation between the natural logarithm of the cumulative retained fraction and particle size (µm). The proposed model accurately describes PSDs across different solid concentrations and grinding durations.

Modeling of parameters affecting the removal of chromium using polysulfone/graphene oxide membrane via response surface methodology.

In this study, an efficient membrane composed of polysulfone and graphene oxide was developed and evaluated for its efficacy in chromium adsorption. Characterization of the synthesized membrane involved comprehensive analyses including scanning electron microscopy (SEM), transmission electron microscopy (TEM), thermogravimetric analysis (TGA) and Fourier-transform infrared spectroscopy (FTIR) to assess its structural properties. Subsequently, the membrane's performance in removing chromium from aqueous solutions was scrutinized, considering key operational parameters. Response surface methodology (RSM) based on central composite design (CCD) was employed to optimize parameters. Additionally, the pH parameter revealed the most significant (F-value = 184.25) on the amount of chromium removal by the membrane process. The interaction between pH and contact time is the most significant among all interactions, with an F-value of 40.99. Moreover, the high R2 (97.58%) and adjusted R2 (95.41%) indicate the model effectively explains variance with minimal overfitting, confirming its strong predictive capability. Under optimized conditions (pH 5, initial concentration of 30 mg/L, and contact time of 40 min), the polysulfone/graphene oxide membrane exhibited an impressive removal efficiency of 81.1%. This study highlights the potential of polysulfone/graphene oxide membranes in effectively separating chromium from aqueous mediums, thereby suggesting a promising avenue for future research in addressing heavy metal pollution.√.

Comparative Analysis of Regression Methods for Estimation of Remaining Useful Life of Lithium Ion Battery

Lithium batteries play a critical role in modern technological applications, including electric vehicles and portable electronic devices. Ensuring accurate estimation of their remaining useful life is essential to improve system efficiency and reliability. This study focuses on predicting the remaining useful life of lithium batteries using advanced regression methods. Data were collected from lithium battery charge-discharge cycles, encompassing key operational parameters such as voltage, current, and temperature. The analysis employed several regression models, including linear regression, lasso regression, and Ridge regression, to identify relationships between these parameters and battery life. The models were evaluated based on estimation accuracy, with Root Mean Square Error (RMSE) as the primary performance metric. The findings demonstrate that regression methods can effectively capture non-linear relationships between input variables and the remaining useful life, with lasso and Ridge regression showing superior performance in reducing prediction errors. These results underscore the potential of regression-based approaches in providing robust and reliable estimations of battery life. The conclusions highlight the importance of these models for developing predictive battery management systems, which can optimize battery performance and extend their operational lifespan across various applications. This research establishes a solid foundation for future studies on intelligent battery health monitoring and management.

Methodology for diagnosing the technical condition of aviation gas turbine engines using recurrent neural networks (RNN) and long short-term memory networks (LSTM)

This study presents a method for diagnosing the technical condition of aviation gas turbine engines (GTE) using recurrent neural networks (RNN) and long short-term memory networks (LSTM). The primary focus is on comparing the effectiveness of these models for forecasting key operating parameters of GTEs, such as vibrations, turbine-inlet temperatures, and rotor speeds of low and high pressure. The research involved thorough data cleaning and normalization, including handling missing values, normalization using Min-Max Scaling, outlier removal, data decorrelation, and time series smoothing. The RNN and LSTM models were trained using the backpropagation through time (BPTT) algorithm to accurately forecast GTE operating parameters. The results show that both models demonstrate high forecasting accuracy, but the RNN models perform better in most parameters. For vibration parameters (VIB_N1FNT1, VIB_N1FNT2, VIB_N2FNT1, and VIB_N2FNT2), RNN models achieved lower RMSE and MAE values, confirming their higher accuracy. For temperature parameters (EGT1 and EGT2), RNN models also showed higher accuracy rates. Meanwhile, LSTM models achieved better results for some rotor speed parameters (N21 and N22). The findings emphasize the necessity of choosing the appropriate model based on the nature of data and the specifics of the parameters to be forecast. Future research may focus on developing hybrid approaches that combine the advantages of both models to achieve optimal results in diagnosing the technical condition of GTEs.

Optimisation of high-rate filamentous algal pond operating parameters for nutrient bioremediation of primary municipal wastewater

Effective management of operational parameters is crucial for optimising wastewater treatment in high-rate filamentous algal pond (HRFAP) systems. This study examined three key operational parameters - hydraulic retention time (HRT), stocking density, and harvest frequency - on the growth and nutrient bioremediation efficiency of Klebsormidium flaccidum cultivated in primary municipal wastewater in outdoor HRFAPs in summer and winter. Seasonal conditions significantly influenced biomass productivity, with productivity being 48.3 % higher in summer compared to winter across all experiments. The optimal HRT of 4 days for both seasons achieved the highest average reductions in total ammoniacal‑nitrogen (TAN) concentration (64.6 % day−1 ± 1.82 SE in summer, 32.3 % day−1 ± 1.82 SE in winter) and acceptable reductions in nitrate-N (66.6 % day−1 ± 3.54 SE in summer, 42.6 % day−1 ± 8.98 SE in winter) and dissolved reactive phosphorous (DRP, 19.8 % day−1 ± 1.01 SE in summer, 15 % day−1 ± 1.98 SE in winter). A stocking density of 0.25 g fresh weight (FW) L−1 was optimal in summer as it resulted in the highest reductions in TAN (75.9 % day−1 ± 2.49 SE), nitrate-N (43.8 % day−1 ± 3.31 SE), and DRP (21.6 % day−1 ± 1.37 SE). In winter, a stocking density of 0.5 g FW L−1 was optimal to mitigate the risk of primary wastewater toxicity during slower growth periods. Harvest frequency did not significantly affect nutrient removal rates across treatments and seasons; however, biomass productivity was significantly higher in summer with a 4-day harvest frequency (7.83 g dry weight (DW) m−2 day−1). Longer HRTs improved water quality variables, with the highest Escherichia coli (E. coli) reduction (99.9 %) observed with a stocking density of 0.25 g FW L−1 in winter. This study highlights the importance of seasonal optimisation of HRFAP operation to maximise biomass production and nutrient bioremediation for effective treatment of primary municipal wastewater.

Purification of mesenchymal stromal cell-derived small extracellular vesicles using ultrafiltration.

Mesenchymal stromal cell-derived small extracellular vesicles (MSC-sEVs) are pivotal for the curative effects of mesenchymal stromal cells, but their translation into clinical products is hindered by the technical challenges of scaled production and purification. Ultrafiltration, a pressure-driven membrane separation method, is well known as an efficient, scalable, and cost-effective approach for bioseparation. However, there has been little study so far that comprehensively evaluates the potential application of ultrafiltration for scaled sEV isolation and purification. In this study, the feasibility and effectiveness of ultrafiltration for MSC-sEV isolation and purification are studied, and the effects of key process design and operational parameters, including the membrane pore size, transmembrane pressure (TMP), stirring speed (shear rate), feed concentration, are quantified using a stirred cell setup. Results revealed that 500kDa molecular weight cut-off (MWCO) polyethersulfone membrane demonstrated superior suitability for MSC-sEV separation, yielding higher purity and productivity compared to 100 and 300kDa MWCO membranes of the same material. The MSC-sEV productivity and purity could also be improved by applying a moderate stirring speed and lower operational pressure, respectively. Isovolumetric diafiltration was incorporated to enhance the purity of MSC-sEVs, successfully removing about 99% of protein contaminants by six diafiltration volumes (DVs). Subsequently, a fed-batch ultra-diafiltration (UF/DF) process with optimised filtration parameters was developed and compared with the currently most used ultracentrifugation (UC) method, showing exceptional effectiveness and performance in the isolation of MSC-sEVs: it increased the recovery of MSC-sEV from 20.59% to 60.88% (about three folds increase) and nearly doubled the purity, while also reducing processing time from over 4h to 3.5h, with a potential further reduction to less than 2.5h through automation. The study concludes that ultrafiltration could be a promising method for both lab-scale preparation and industrial-scale manufacture of MSC-sEVs, offering advantages of high recovery, scalability, fast, and cost-effectiveness.

Research progress in the application of infrared blanching in fruit and vegetable drying process.

Fruits and vegetables offer substantial nutritional and health benefits, but their short shelf life necessitates effective preservation methods. Conventional drying techniques, while efficient, often lead to deterioration in food quality. Recent advancements highlight the potential of infrared blanching (IRB) as a preparatory process to improve drying outcomes. This review systematically evaluated the application of IRB for various fruits and vegetables, including tomatoes, potatoes, carrots, mangoes, apples, peaches, strawberries, grapes, and green beans. IRB demonstrated notable improvements in texture, color retention, and nutrient preservation in dried products. Key operational parameters for effective IRB include product thickness (2-5cm), treatment duration (30s to several minutes), and the distance from the infrared (IR) emitter (10-30cm). These factors collectively ensure efficient heat penetration and energy transfer. Regarding IR generators, far-IR heaters are advantageous due to their uniform heating capabilities, whereas near-IR heaters deliver rapid heating. Catalytic IR generators are also emerging as promising options for industrial-scale applications. This review further explores the principles and mechanisms of IRB, particularly its impact on drying kinetics and the retention of vitamins, antioxidants, and bioactive compounds. Evidence indicates that IRB can reduce drying times by up to 50%, increase drying rates, and lower energy consumption by approximately 17%, achieving energy efficiency levels of 80%-90%. However, limitations such as the shallow penetration depth of IR radiation remain challenging. Potential solutions, such as the development of hybrid blanching methods, are discussed to optimize the drying process further and enhance the quality of dried fruits and vegetables.

Sustainable Permeable Reactive Barrier Materials for Electrokinetic Remediation of Heavy Metals‐Contaminated Soil

AbstractThegreen and sustainable remediation technologies in curing heavy metals (HMs)‐contaminated soil require recyclable, cost‐effective, and sustainable materials to achieve good health, and sustainable goals. Electrokinetic remediation coupled with a permeable reactive barrier (EKR‐PRB) has been recognized as a viable technique for remedying HMs‐contaminated soil, owing to its passive operation, inexpensiveness, and environmental compatibility. However, most fillermaterials in PRB are expensive and environmentally unfriendly, affecting thesustainable development goals of the planet. This review comprehensivelyexamines the current progress on using waste/recyclable materials as fillermaterials in EKR‐PRB to remove toxic HMs from contaminated soil. These materialsare waste/recyclable materials, biochar, charcoals, and cork, which have shownhigh potential as EKR‐PRB fillers in extracting HM‐contaminated soil. Thesematerials provide a path to reduce both remediation costs and environmentalimpact, enhancing the practicality and sustainability of the EKR‐PRBapplication. The review commences with a brief discussion of the fundamentalsof EKR‐PRB and key operational parameters affecting the remediationperformance, with a focus on the ecological and economic benefits associatedwith these novel filler materials. Ultimately, it presents future perspectivesand outlines critical challenges in scaling up the application of sustainablePRB materials for effective and environmentally responsible soil remediation.

Experimental investigation on swirling annular liquid film breakup characteristics of prefilming airblast atomizer

In order to further improve the understanding of the liquid film breakup characteristics of prefilming airblast atomizer, the effects of key operating parameters and structural parameters (swirl intensity, diameter and number of injection holes on pilot stage, length of pre-film lip and sleeve angle) on the liquid film pattern of prefilming airblast atomizer were investigated using the high-speed shadow method and the Otsu’s method. The mechanism of liquid film breakage was analyzed, the results show that, with the increase of fuel injection pressure, the spray morphology experiences “columnar-shaped”, “tulip-shaped” and “fully cone-shaped”. Meanwhile, the main fluctuation frequency of liquid film varies from 100.5 Hz in the “columnar-shaped” and “tulip-shaped” to 25.12 Hz in the “fully cone-shaped”. With increasing relative pressure drops of swirler, the film breakup length and film fluctuation frequency decrease, while the film fluctuation amplitude increases. An increase in inner-stage swirl intensities will lead to a reduction in the film breakup distance, while an increase in outer-stage swirl intensities will mainly lead to a larger spray angle. The increase of the diameter and number of injection holes and the length of pre-film lip leads to the rise of the liquid film breakup length, while the decrease of sleeve angle mainly leads to the rise of spray angle, which makes the liquid film more easily broken.

Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment

With the rapid development of urbanization and the widespread adoption of wastewater treatment facilities, the volume of sludge produced has steadily increased. Hydrothermal oxidation (HTO) technology offers an effective solution for sludge reduction, harmless disposal, and resource recovery, making it a highly promising method for sludge treatment. In recent years, HTO has attracted significant attention due to its efficiency and environmental benefits. This paper provides a detailed explanation of the fundamental principles of HTO in sludge treatment, with a focus on the removal of organic pollutants, nitrogen transformation, and phosphorus recovery. The influence of key operational parameters, such as reaction temperature, time, initial oxygen pressure, and pH, on the performance of HTO treatment is also explored. In addition, the research status of HTO sludge treatment and an example of product recovery after treatment are also discussed. It examines the challenges associated with scaling up HTO for large-scale sludge treatment, along with potential research directions for future work. Special attention is given to the innovation of catalysts, with the goal of achieving self-catalysis in sludge treatment. Moreover, considering that ammonia nitrogen (NH3-N) is a major intermediate product in HTO, its removal, as well as the prediction and planning of other unintended products, remains a key issue. Further areas of interest include improving sludge dewatering performance and enhancing the production of valuable single carboxylic acids, which can boost resource recovery efficiency. This paper also highlights the diversification of sludge applications after HTO treatment. By providing insights into future development trends, this review offers valuable references for further research and practical applications. The ultimate goal is to support the development of HTO as a sustainable and efficient solution for sludge treatment, addressing environmental concerns while maximizing resource recovery opportunities.

Development of a Low-Cost Automated Injection Molding Device for Sustainable Plastic Recycling and Circular Economy Applications

In response to the critical demand for innovative solutions to tackle plastic pollution, this research presents a low-cost, fully automated plastic injection molding system designed to convert waste into sustainable products. Constructed entirely from repurposed materials, the apparatus focuses on processing high-density polyethylene (HDPE) efficiently without hydraulic components, thereby enhancing eco-friendliness and accessibility. Performance evaluations identified an optimal molding temperature of 200 °C, yielding consistent products with a minimal weight deviation of 4.17%. The key operational parameters included a motor speed of 525 RPM, a gear ratio of 1:30, and an inverter frequency of 105 Hz. Further tests showed that processing temperatures of 210 °C and 220 °C, with injection times of 15 to 35 s, yielded optimal surface finish and complete filling. The surface finish, assessed through image intensity variation, had a low coefficient of variation (≤ 5%), while computer vision evaluation confirmed the full filling of all specimens in this range. A laser-based overflow detection system has minimized material waste, proving effective in small-scale, community recycling. This study underscores the potential of low-cost automated systems to advance the practices of circular economies and enhance localized plastic waste management. Future research will focus on automation, temperature precision, material adaptability, and emissions management.

Offshore Wind‐Hydrogen Systems Fault Detection Based on CNN‐BiLSTM‐AM Algorithm

ABSTRACTThis study presents a novel deep learning‐based approach for fault detection in offshore wind‐hydrogen systems. A fault detection model is developed using convolutional neural networks (CNNs), bidirectional long short‐term memory networks (BiLSTMs), and an attention mechanism (AM). The model is trained on a dataset generated through fault injection techniques, which simulate real‐world faults in the system. Key operational parameters, such as wind speed and hydrogen production rate, are used to detect faults effectively. This paper reduces reliance on actual experiments, and introducing artificial faults allows system performance assessment under different fault scenarios, lowering project risks and costs. This work facilitates automatic feature extraction and high‐precision classification of time‐series fault data, which covers a fully automated learning process from data to fault detection. The outstanding performance of the method is validated through computation and result comparison.

DETERMINATION OF THE STABILITY PERIOD OF TURNING CUTTERS FOR HEAVY MACHINE TOOLS

To determine the optimal cutting modes under conditions of increased requirements for the stability of technological processes, it is necessary to take into account the value of the tool life with a given probability. In this paper, the stability dependence for prefabricated cutters used on heavy machine tools with maximum diameters Dmax = 1250-2500 mm is specified using the group argumentation method. The study presents a new mathematical model that establishes the relationship between tool fracture resistance and key operational parameters. This model incorporates the probabilistic nature of tool performance, which allows for a more accurate assessment of the impact of part size variation, cutting conditions, and process variability. The proposed relationship facilitates the determination of cutting modes that not only increase tool stability but also ensure the reliability and efficiency of heavy machine tools in industrial environments. This mathematical dependence makes it possible to take into account the variation of workpiece parameters and cutting modes, which is especially important when working with large-sized parts on heavy-duty machine tools. The results of the study are of practical importance for industry, as they make it possible to increase the sustainability and productivity of technological processes.

Optimization of operational parameters in the mechanochemical regeneration of sodium borohydride (NaBH[formula omitted])

In this study we investigate the mechanochemical regeneration of sodium borohydride (NaBH4) from a system comprising hydrated sodium metaborate ( ▪ ) and magnesium hydride (MgH2). We explore the individual and joint impact of key operational parameters (rotational speed, milling time, ball-to-powder ratio (BPR), and molar ratio) on the regeneration yield. Furthermore, a method for quantifying chemical conversion is introduced relying only on water and thus, offering environmental benefits. This approach additionally facilitates the production and storage of a “ready-to-use” NaBH4 solution with minimal losses at room temperature. Notably, a yield of 90% is achieved, with a 20% reduction in rotational speed compared to prior literature. This research contributes to sustainable hydrogen storage and presents practical advancements in mechanochemical processes.

Interpretable machine learning modeling of temperature rise in a medium voltage switchgear using multiphysics CFD analysis

In recent decades, leading companies and research groups have extensively conducted Multiphysics computational fluid dynamics (CFD) analyses to evaluate temperature rise in switchgear systems, aiming to meet type-testing requirements specified in IEC standards. However, the complex interaction of geometrical and operational parameters presents significant challenges in interpreting these methods. Artificial intelligence (AI) algorithms have gained attention in various engineering fields to address similar issues. This paper investigates the influence of four key manufacturing and operational parameters, both categorical and continuous, on temperature rise in a medium voltage (MV) switchgear case study. A CFD-based dataset was created from these parameters to target maximum temperature, facilitating the study's objective. Several models for temperature rise estimation, including extreme gradient boosting (XGBoost), support vector regression, decision tree, and random forest, were compared. An explainable artificial intelligence (XAI) technique, Shapley Additive Explanations (SHAP), was applied to the best-performing model to evaluate the importance of each feature in predicting maximum temperature. The results revealed that XGBoost provided the most accurate predictions, with a scatter band (SB) of ±1.01 and average R2 values of 99.98 % and 96.59 % for the training and testing sets, respectively. SHAP analysis identified the most significant variables affecting temperature prediction as current, air velocity, duct area, and switchgear conditions, in that order.

Assessing the economic and environmental performance of a closed-loop supply chain for waste tires: an industrial case study.

Recovery strategies for end-of-life (EoL) and end-of-use (EoU) products, such as reuse, remanufacturing, and recycling, present opportunities to enhance profitability and lower emissions while fostering new business prospects. However, significant uncertainties and stochasticity affect the quantity and quality of returned waste tires, costs associated with used tires and the retreading process, and the selling prices driven by second-hand market dynamics. This paper investigates the economic and environmental performance of a closed-loop supply chain (CLSC) for waste tire retreading and recycling considering the impact of these uncertainties using a Monte Carlo simulation-based methodology. The proposed methodology is implemented on an industrial case study in the United Arab Emirates (UAE) for quantifying the impact of uncertainties involved in the CLSC for tire retreading and recycling large tires of the 12R24 type, which are extensively used by buses and trucks in the UAE. The findings reveal that for an average of 13,200 tires reaching their EoL, the processes of retreading and recycling yield an average profit of $ 487 K. Furthermore, these initiatives are expected to result in the reduction of an average of 1457 tons of CO2 emissions. A scenario analysis is conducted to evaluate the impact of improvements in key operational parameters on the CLSC performance. Consequently, all stakeholders engaged in tire retreading would benefit by reducing costs, generating higher profits, and reducing emissions while creating new business opportunities.

A multi-objective analysis for enhanced energy and exergy performances of an integrated compressed air energy storage system using the meta-heuristic whale optimization algorithm

This study investigates the optimization of energy and exergy efficiencies in a compressed air energy storage integrated energy system using the meta-heuristic whale optimization algorithm. The analysis focuses on the effects of key operating parameters, including current density, utilization factor, and temperature, on the system's performance. The whale optimization algorithm identifies seven multi-objective optimum points, with processing conditions ranging from a current density of 3000 A/m2 to 5237 A/m2, utilization factors between 0.740 and 0.747, and temperatures from 700 °C to 900 °C. Desirability analysis reveals that some points, with a desirability value of 1, are the most optimal. Among these, one point characterized by a utilization factor of 0.747, a current density of 3000 A/m2, and a temperature of 700 °C, is identified as the optimal point. Under these conditions, the system achieves an energy efficiency of 66.63 % and an exergy efficiency of 34.85 %. The study highlights the significant potential of the meta-heuristic whale optimization algorithm in navigating complex search spaces to identify optimal processing conditions. The results underscore the importance of balancing electrical and thermal efficiencies to achieve overall system optimization using meta-heuristic optimization algorithms.

A New Prediction Model of Cutterhead Torque in Soil Strata Based on Ultra-Large Section EPB Pipe Jacking Machine

Cutterhead torque is a key operational parameter for earth pressure balance (EPB) TBM tunneling in soil strata. The effective management of cutterhead torque can significantly maintain face stability and ensure the tunneling machine operates steadily. The Shenzhen Metro Line 12 project at Shasan Station utilized the world’s largest rectangular pipe jacking machine for constructing the subway station. This project has enabled the collection of relevant data to analyze the factors influencing cutterhead torque and to establish a predictive model. The data encompass an abundant array of cutterhead design parameters, operational parameters, properties of the excavated soil, and environmental factors, revealing the distribution characteristics of cutterhead torque during tunneling. The correlation between various factors and cutterhead torque has been examined. By employing multiple regression analysis and a Levenberg–Marquardt (L-M) algorithm-based neural network, an optimal prediction model for EPB cutterhead torque has been developed. This prediction model incorporates various factors, including cutterhead diameter, RPM, soil chamber pressure, soil shear strength, and the soil consistency index. And the degree of influence of each factor on the cutter torque was also revealed. The prediction results demonstrated good accuracy compared to previous models, providing valuable insights and guidance for EPB TBMs or pipe jacking machines operating in soil strata. The current limitations of this model and suggestions for future work have also been addressed.

Intelligent control system and operational performance optimization of a municipal solid waste incineration power plant

This study proposed an integrated intelligent control system in one municipal solid waste incineration power plant to improve the system's control accuracy, economic performance and environmental impact, including the intelligent combustion, intelligent denitrification, intelligent desulfurization and intelligent soot-blowing control subsystems. The precise detection and adjustment of the key operational parameters was achieved by integrating these modules into the existing distributed control system. A comparative analysis of the operation data between pre-optimization and post-optimization states was conducted to assess the changes in the key operational and economic parameters. The results indicate that the introduction of the intelligent control module can significantly improve the system stability and parameter control accuracy, thereby enhancing the economic efficiency of the waste incineration power plant and reducing the operation workload and the pollutants emissions. Specifically, the standard deviations of main steam flowrate and pressure decreased by 45.1 % and 60.7 %, respectively. Furthermore, the consumptions of the ammonia water and lime slurry were reduced by 38.2 % and 23.2 %, respectively, while the auxiliary power consumption rate declined by two percentage points, and the power generation per ton of waste increased by 4.2 %. These improvements not only strengthen the economic benefits but also effectively reduce the pollutants emissions.

An emerging route for efficient conversion of monopotassium phosphate via a pilot-scale electrodialysis metathesis

This study explores energy-efficient KH₂PO₄ (KDP) preparation, examining its implications for sustainability, and offers insights on enhancement and benefits. For the first time, electrodialysis metathesis (EDM) process was considered as a potential technique for efficient conversion and cleaner production of KDP in this study. The effects of key operational parameters including applied voltage, initial feed concentration, concentration ratio, and volume ratio on the conversion performance of EDM were systematically investigated. The promising conditions were established with an applied voltage of 2.3 V per repeating unit, a concentration ratio of 0.5 M NH4Cl to 0.6 M KCl, and a critical 1.43-fold volume ratio, yielding a high-purity KDP solution of 96.65 % at 78.52 g/L with an energy consumption rate of 0.52 KWh/kg. An economic evaluation indicates a 15.4 % cost reduction compared to the market price, presenting the EDM process as a viable and leading technology for the production of high-purity KDP.