Optimal Operating Parameters Research Articles

Simultaneous dehalogenation and oxidation of dichloroacetamide by Cu-modified CoFe-LDH in three-dimensional continuous flow aerated electrocatalytic reactor

Searching for catalysts and degradation systems for synchronous dehalogenation and oxidation of haloacetamides (HAcAms) is pressing. In this study, a novel three-dimensional continuous flow aerated electrocatalytic reactor (3D CFAER) system, which employed Cu-modified CoFe layered double hydroxides (CoFe-LDH) composite/activated carbon as a catalytic particle electrode for the degradation of dichloroacetamide (DCAcAm) was constructed. Under the experiment’s optimal operating parameters, the maximum degradation rate of DCAcAm reached 91.7 %. The superior performance for the degradation of DCAcAm was due to the enhanced electron transfer induced by the modification of Cu in CoFe-LDH. Moreover, Density Functional Theory (DFT) calculations indicated that Cu doping improved the ability of LDH to dissociate water, thereby enhancing its electrocatalytic performance. Scavenger experiments and Electron Paramagnetic Resonance (EPR) results revealed that •OH, O2•– and electrochemical direct dehalogenation were responsible for DCAcAm removal. Additionally, the electrocatalytic stability of the electrode was tested upon repeated use with DCAcAm degradation remaining as high as 81.7 % after five consecutive experiments. More importantly, in 3D CFAER, electrocatalytic treatment lasting 90 min reduced the TOC and COD of the actual chlorine disinfection wastewater by 64.1 % and 74.9 %, respectively. This study provides new theoretical basis and technical support for control of HAcAms.

Predicting the Performance of Lithium Adsorption and Recovery from Unconventional Water Sources with Machine Learning

Selective lithium (Li) recovery from unconventional water sources (UWS) (e.g., shale gas waters, geothermal brines, and rejected seawater desalination brines) using inorganic lithium-ion sieve (LIS) materials can address Li supply shortages and distribution issues. However, the development of high-performance LIS materials and the optimization of recovery-related operating parameters are hampered by the variety of production methods, intricate procedures, and experimental expenses. Machine learning (ML) techniques offer potential solutions for enhancing LIS material development. We collected literature data on Li adsorption, categorizing 16 parameters into adsorbent parameters, operating parameters, and solution components. Three tree-based algorithms—Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost)—were used to evaluate the impact of these parameters on lithium adsorption. The grouped random splitting method limited data leakage and mitigated overfitting. XGBoost demonstrated the best performance, with an R² of 0.98 and a root-mean-squared error (RMSE) of 1.72. The SHAP values highlighted that operating parameters were the most influential, followed by adsorbent parameters and coexisting ion concentrations. Therefore, focusing on optimizing operating parameters or making targeted improvements on LIS based on operating conditions will enhance LIS performances in UWS. These insights are crucial for optimizing Li adsorption processes and designing effective inorganic LIS materials.

Microbial symbiotic electrobioconversion of carbon dioxide to biopolymer (poly (3-hydroxybutyrate)) via single-step microbial electrosynthesis cell

This study proposes an novel single-stage microbial electrosynthesis system (MES) that bioelectrochemically converts CO2 into poly(3-hydroxybutyrate) (PHB). This innovative approach utilizes a symbiotic co-culture of electroactive acetogenic bacteria and PHB-accumulating bacteria within a single reactor, bypassing the need for separate acetate production and extraction. Systematic optimization of key operational parameters, including applied voltage, carbon source, and cathode material, was conducted to maximize PHB production. Results demonstrate that using a voltage of 2.5 V yielded a 7.14-fold increase in PHB content compared to open circuit conditions. Furthermore, functionalizing the cathode with a conductive and hydrophilic PEDOT: PSS polymer coating significantly enhanced the system’s performance, resulting in a 1.5-fold increase in both acetate and PHB production compared to unmodified carbon felt electrodes. The long-term stability and effectiveness of the co-culture system were validated through comprehensive microbial and metagenomic analyses. Results revealed a significant enrichment of CO2-utilizing electrotrophs within the cathode biofilm, including Acetobacterium and Clostridium. Concurrently, a 4 to 14-fold increase in the relative abundance of PHB-biosynthesizing bacteria, such as Pseudomonas, Rhodobacter and Caulobacter was observed in the planktonic phase. This study offers a promising pathway towards a circular bioeconomy by enabling the valorization of CO2 into valuable bio-based products via single-stage MES, utilizing a symbiotic co-culture.

Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability

Abstract In the contemporary era, marked by the increasing significance of sustainable energy sources, biomass gasification emerges as a highly promising technology for converting organic materials into valuable fuel, offering an environmentally friendly approach that not only mitigates waste but also addresses the growing energy demands. However, the effectiveness of biomass gasification is intricately tied to its predictability and efficiency, presenting a substantial challenge in achieving optimal operational parameters for this complex process. It is at this precise juncture that machine learning assumes a pivotal role, initiating a transformative paradigm shift in the approach to biomass gasification. This article delves into the convergence of machine learning and the prediction of biomass gasification and introduces two innovative hybrid models that amalgamate the Support Vector Regression (SVR) algorithm with Coot Optimization Algorithm (COA) and Walrus Optimization Algorithm (WaOA). These models harness nearby biomass data to forecast the elemental compositions of CH4 and C2Hn, thereby enhancing the precision and practicality of biomass gasification predictions, offering potential solutions to the intricate challenges within the domain. The SVWO model (SVR optimized with WaOA) is an effective tool for predicting these elemental compositions. SVWO exhibited outstanding performance with notable R 2 values of 0.992 for CH4 and 0.994 for C2Hn, emphasizing its exceptional accuracy. Additionally, the minimal RMSE values of 0.317 for CH4 and 0.136 for C2Hn underscore the precision of SVWO. This accuracy in SVWO’s predictions affirms its suitability for practical, real-world applications.

Iron Separation from Iron-Rich Manganese Ore Leachate: Comprehensive Optimization of Operating Parameters and Economic Viability.

In the current electrolytic manganese industry, iron separation and reuse from iron-rich manganese ore leachate (IRMOL) has become one of the most pressing challenges. This study aimed to investigate the optimal conditions for iron separation from IRMOL and to assess the economic and practical advantages of iron separation or removal in industrial manufacturing. To identify more cost-effective and technologically advanced production circumstances, we examined five key elements that weaken Fe(OH)3 colloidal production conditions in enterprises: reaction temperature, pH, crystal species, aging, and reaction time. The screening results showed that when the conditions were optimized, the efficiency of reducing manganese loss decreased from 6.15% to 4.69%. Additionally, the generation of iron-rich electrolytic manganese residue (IREMR) was decreased by 44.32%, and the filtration velocity of IREMR increased from 0.0030 to 0.0220 mL/(s·cm2) compared to the production conditions before optimization at the enterprise. Through multiphase equilibria modeling with Visual MINTEQ, we have determined that raising the temperature and pH levels increases the expenses associated with chemicals and energy usage and results in an elevation of Fe(OH)2+ concentration. This can lead to the creation of Fe(OH)3 colloidal, causing a high water content in IREMR, inefficient filtration, and significant loss of manganese. This strategy is highly significant for the production of electrolytic manganese and the reduction of electrolytic manganese residue.

Simulation Optimization and Experimental Validation of Hydraulic Impact in Pruning Machines

Abstract This study addresses the instability caused by hydraulic shocks in fruit tree pruning machines during operation and proposes an optimization scheme that combines simulation with experimentation. We investigated the mechanisms of load shock in the hydraulic system through mathematical modeling and LS-DYNA simulation analysis. The optimal operating parameters were determined using mechanical and orthogonal analyses: a forward speed of 4 km/h, a pruning tool angle of 60°, and a saw blade rotation speed of 1200 r/min.Additionally, we employed AMESim simulation to optimize the hydraulic system of the pruning machine by designing a dual accumulator configuration to effectively suppress hydraulic shocks.We validated the optimized system through simulations and experimental tests, demonstrating significant reductions in pressure fluctuations, with cutting efficiency improved by 30% and pressure fluctuations reduced by 26.7%.This study not only presents an innovative dual accumulator design but also validates its effectiveness in pruning machines through simulations and experiments, thereby enhancing the operational efficiency and stability of agricultural machinery.

Dynamic liquid level prediction for multiple oil wells based on transfer learning and multidimensional feature fusion network

Abstract Accurate prediction of the dynamic liquid level (DLL) in oil wells is crucial for the intelligent optimization of pumping systems. It not only provides real-time insights into the operational conditions of the pumping system but also supports the optimization of operational parameters with data. However, due to the long-term operation of oil wells and their complex internal environments, direct measurement of the DLL is challenging, leading to low reliability of the obtained data. Therefore, this paper conducts an in-depth analysis of the parameters involved in the pumping process, identifies the model's input features, and develops a DLL prediction model for multiple wells based on multidimensional feature fusion (MFF). This model captures the characteristics of DLL changes and the diversity of input features. To address the issues of slow model training and low prediction accuracy caused by insufficient datasets in practical applications, this paper integrates transfer learning techniques and proposes a new model, the DLL model for multiple wells based on Transfer Learning and Multidimensional Feature Fusion (TMFF). Initially, the Euclidean distance and Maximum mean discrepancy methods are employed to verify the feature similarity between the source and target domains, using highly similar DLL data as experimental data. By combining transfer learning techniques with the MFF model, the TMFF model is established. The model's capabilities are validated using field-collected data with broad representativeness. Experimental results demonstrate that the proposed MFF model possesses high accuracy and generalization capability. Additionally, the TMFF model effectively resolves the issue of insufficient data during model training. In summary, the methods proposed in this paper can provide accurate DLL data for practical applications in intelligent oilfields.

Mathematical Modeling and Indirect Carbon Emission Reduction Analysis of Urban Wastewater Treatment Systems Under Different Temperature Conditions

In the context of achieving the two-carbon target, this study utilized a wastewater treatment plant in Shenyang City as a case study to accurately calculate indirect emissions related to energy and chemical consumption within the energy-intensive wastewater treatment industry. Sumo software was employed for precise mathematical modeling. Considering the operational characteristics of wastewater treatment plants in cold regions, this study innovatively divided the annual operation cycle into two periods, namely normal temperature and low temperature, and determined the optimal operational parameters under a low-carbon mode. The results indicate that precise regulation of dissolved oxygen concentration to 0.5–1.5 mg/L (normal temperature period) and 1–2 mg/L (low temperature period) can significantly reduce carbon emissions related to electricity consumption by 13,781.9 t CO2-eq. From the perspective of chemical consumption, adjusting the dosage of polyaluminum chloride (PAC) to 75% and sodium acetate to 70% during the normal temperature period can lead to a reduction in indirect carbon emissions of 1614.4 t CO2-eq compared to the same period last year. During the low-temperature period, by reducing the dosage of polyaluminum chloride to 80% and sodium acetate to 75%, the indirect carbon emissions can be reduced by 1557.3 t CO2-eq compared to the corresponding period last year. After optimization, USD 1.49 million can be saved. This study simulated the operation conditions of cold-region urban wastewater treatment plants at different times to effectively control carbon emissions resulting from energy and chemical consumption in wastewater treatment. This result can provide innovative ideas for energy saving and carbon reduction in cold-region wastewater treatment plants.

Deep Learning Empowered Water Quality Assessment: Leveraging IoT Sensor Data with LSTM Models and Interpretability Techniques

Addressing the imperative demand for accurate water quality assessment, this paper delves into the application of deep learning techniques, specifically leveraging IoT sensor datasets for the classification and prediction of water quality parameters. The utilization of LSTM (Long Short-Term Memory) models navigates the intricacies inherent in environmental data, emphasizing the balance between model accuracy and interpretability. This equilibrium is achieved through the deployment of interpretability methods such as LIME, SHAP, Anchor, and LORE. Additionally, the incorporation of advanced parameter optimization techniques focuses on fine-tuning essential parameters like learning rates, batch sizes, and epochs to optimize model performance. This comprehensive approach ensures not only precise predictions but also enhances the transparency and interpretability of the model, addressing the critical need for actionable information in water quality management. The research significantly contributes to the convergence of deep learning, IoT, and environmental science, offering valuable tools for informed decision-making while highlighting the importance of fine-tuning parameters for optimal model performance

Multi-objective optimization towards cooling performance of teardrop section Kagome trusses array jet impingement composite cooling structure with cross-flow

Teardrop Section Kagome trusses array can be a spoiler for a new generation of advanced combustion chambers. In this study, multi-objective optimizations are applied to optimize a jet impingement composite cooling structure with cross-flow. Four structural parameters with five levels and three performance parameters are calculated in design of experiment and fitted by response surface methodology. The database is expanded by non-dominated sorting genetic algorithm-II, and then technique for order preference by similarity to an ideal solution is used to calculate the optimal structures under different operating conditions by different weights of the performance parameter. The conclusions are as follows: The minimal Equality of variances and fitting accuracy are 35.4371 and 93.6%. Diameter of the cross-section has the largest positive main effect on average Nusselt number and pressure loss coefficient, while angle between the rod and the horizontal plane has the largest positive effect on temperature uniformity coefficient. The interaction of trailing edge angle with angle between the front rod and the back rod in the horizontal plane is large. Case15 is chosen, where the weight ratio is 1:5:5, improvement of the corresponding optimized performance parameters of the simulation results with respect to the Origin of three performance parameters are 26.72%, −2.93%, 91.59% respectively. It provides a feasible reference for future advanced combustion chambers and accelerates their development process.

Optimizing energy density in dielectric elastomer generators: a reliability-dependent metric

Abstract Dielectric elastomer generators (DEGs) are soft transducers capable of converting mechanical energy into electrostatic energy. Increasing the mechanical stretch amplitude and the electric field imposed to the DEG leads to higher energy conversion at the cost of a reduced lifetime. Here, mechanical fatigue and electrical degradation were assessed on a silicone-based DEG, and the outcome was used to build an electro-mechanical reliability model. A novel metric, termed levelized energy density, has been introduced to carefully balance the conflicting objectives of high energy output and long-term reliability. Through a multi-dimensional anaylsis of this index, the optimal operating parameters (stretch amplitude and electric field) that maximize energy conversion can be derived. Energy densities reported in literature are generally obtained after pushing the DEG close to their intrinsic limits for a limited number of cycles. In our approach, more realistic values in the endurance domain are presented, which typically leads to a 9-fold decrease in energy density for a design life of 1 million cycles. This article not only addresses the challenge of optimizing DEG performance but also emphasizes the importance of considering realistic operational conditions to enhance reliability, ultimately contributing to the practical and sustainable deployment of these soft transducers in various applications.

Optimization of Operating Parameters for Straw Returning Machine Based on Vibration Characteristic Analysis

For the mechanized technical mode of total wheat straw returning to field, there are problems such as large vibration during the operation of the straw returning machine that, in turn, affect the effect of stubble breaking. This study took the Tongtian 1-JHY-220 straw returning machine as the research object to conduct field experiments, with wheat stubble height, forward velocity, and PTO speed as experimental parameters. And the vibration characteristics at different positions of the machine and the final stubble breaking rate were used as evaluation indicators. Combined with the orthogonal experiment and response surface analysis method, this article analyzes and discusses the influence of various parameters on vibration characteristics and operational effectiveness. The results show that PTO speed and wheat stubble height were the main factors affecting the vibration and operation quality of the straw returning machine. Low PTO speed and high stubble height can improve the stubble breaking rate of the straw returning machine and reduce its operation vibration. Furthermore, the multi-objective optimization results show that when the forward velocity in the range of 8.5–9 km/h, the PTO speed is 540 r/min, and the stubble height is in the range of 200–250 mm, the stubble breaking rate of the straw returning machine is greater than 86%. At this time, the total vibration of the straw returning machine and tractor rear axle is relatively small. This study can lay a foundation for further studying the impact of the vibration of the straw returning machine on the stubble breaking effect and provide a reference for the preparation of high-quality seedbed under conservation tillage.

Neural-driven viscosity modeling and fatigue optimization for cycloidal gear systems

This study presents a comprehensive approach to enhancing RV reducer performance. A novel RAM viscosity-pressure model is developed, combining traditional stability with neural network precision. Surface micro-morphological analysis is integrated with contact fatigue modeling, revealing critical insights into surface characteristics and their impact on fatigue. A neural proxy-based optimization strategy is employed to identify optimal operational parameters, significantly reducing fatigue risks. Additionally, a new method for real-time transmission ratio monitoring is introduced, validating the model's effectiveness and offering a robust framework for improving the reliability and lifespan of RV reducers in industrial applications.

Construction of visible-light-induced Fe2O3/g-C3N4 nanocomposites for the enhanced degradation of organic dyes: Optimization of operative parameters

This research aims to develop the highly promising Fe2O3/g-C3N4 photocatalyst through hydrothermal techniques to cope with the elevated amounts of bromophenol blue (BPB) dye in wastewater. The morphological studies were carried out by SEM analysis, while the crystallinity, structural behavior, and oxidation states of the prepared materials were determined by XRD, FTIR, and XPS analysis. The hydrodynamic size, surface area, and magnetic characteristics of the constructed materials were assessed through DLS, BET, and VSM analysis. The effectivity of the synthesized Fe2O3 and Fe2O3/g-C3N4-30 photocatalysts was appraised by the abatement of BPB under visible light radiation for 48 min. The results have shown an excellent degradation efficacy of Fe2O3/g-C3N4-30 photocatalyst with 98.39 % of BPB removal and a rate constant of 0.0757 min−1, which was much higher than Fe2O3 photocatalyst with 79.64 % of removal and a rate constant of 0.0335 min−1 under optimum conditions. The enhancement in degradation efficiency of the Fe2O3/g-C3N4-30 was due to the large surface area of Fe2O3/g-C3N4-30 (106.94 m2/g) as a result of g-C3N4 inclusion in the material, while the pure Fe2O3 unveiled a surface area of 89.67 m2/g. The impact of different reaction parameters on BPB degradation was also investigated, while the contribution of free radicals was corroborated through radical trapping experiments. The Fe2O3/g-C3N4-30 exhibited tremendous stability for repeated applications, with a loss of 8.03 % in efficiency after five consecutive experiments because of its easy magnetic separation. The experimental results have shown that synthesized Fe2O3/g-C3N4-30 photocatalysts could be used for the effective degradation of BPB from wastewater.

Catalytic performance of FeCo2O4 spinel cobaltite for degradation of ethylparaben in a peroxymonosulfate activation process: Response surface optimization, reaction kinetics and cost estimation

The presence of cosmetics and personal care products in water resources has become an increasing concern for environment due to their toxicity and persistence in aquatic medium. Peroxymonosulfate mediated advanced oxidation methods have a great potential for the efficient removal of complex organic compounds due to the generation of highly reactive radicals with high oxidation ability. Iron cobaltite, FeCo2O4 catalyst was used as a peroxymonosulfate (PMS) activator for the degradation of ethylparaben which was selected as a model personal care product ingredient. Paraben degradation was modelled and the optimum operating parameters were determined by using Box-Behnken design. The ethylparaben degradation efficiency was calculated as 94.6 % under the optimized conditions which were determined as 0.9 g/L FeCo2O4 loading, pH 6.15, and 0.26 g/L PMS dosage. The ethylparaben degradation reaction followed second-order reaction kinetics. The activation energy was evaluated as 40.1 kJ/mol. Quenching radical tests showed that the dominant reactive species were hydroxyl radicals. Toxicity of the treated samples in terms of L. sativum growth inhibition was evaluated as 1.5 %. The total cost of the treatment including the operating and the amortization costs was calculated as 0.91 €/L.

Optimization of operational parameters of marine methanol dual-fuel engine based on based on RSM- MOPSO

Methanol and natural gas, as green fuels for environmental protection, have shown great potential in the field of maritime transportation. This study explores how the methanol energy fraction (MEF), exhaust gas recirculation (EGR), excess air ratio (λ), and engine injection timing (EIT) influence combustion and emission traits in marine dual-fuel (DF) engine. Initially, a DF engine model was formulated through experiments using CONVERGE software, while the CHEMKIN program devised a chemical mechanism involving 100 compounds and 512 reactions. The response surface methodology (RSM) was subsequently utilized to design the experiment and develop the regression model. The improved multi-objective particle swarm optimization (MOPSO) algorithm was applied to optimize three critical variables: brake thermal efficiency (BTE), carbon monoxide (CO), and nitrogen oxide (NOx) emissions. Ultimately, feature-optimized scenarios from the Pareto front were chosen for comparative evaluation to derive the optimal configuration. The results show that when the decision variables MEF, EGR, λ and EIT are 22.33 %, 10 %, 1.81 and 11°CA BTDC respectively, there is a better trade-off between the three target variables. Compared with the original case, the change of ITE, NOx and CO emissions in the optimal case are +0.97 %, −39.53 % and −14.51 %, respectively. Overall, based on the intelligent optimization approach, the rational selection of DF engine parameters improved the ITE and most NOx and CO emissions were effectively suppressed.

The operating parameter optimization of spark duration effect on the performance and emission characteristics of direct-injection propane by genetic algorithm

It is challenging to optimize of the effect of spark discharge duration on low carbon direct injection combustion of propane from a specific aspect of the internal combustion engine. The strategy in this paper combines a genetic algorithm (GA) with an artificial neural network (ANN) to enhance efficiency of a large bore diesel engine modified with spark plug and fueled with propane direct injection under various spark timing durations and identify its engine performance parameters and corresponding conditions for operational control purpose. In-cylinder performance experiments were used to create 756 datasets for training, testing, and validating the ANN with 16 neurons on each hidden layer, respectively. The selected performance parameters were the heat release rate, turbulent kinetic energy, indicated mean effective pressure, and indicated thermal efficiency. The NOx and CO emissions are improved by 7.14 % and 0.74 %, respectively, by the optimized ANN model. In addition, compared to an experimental result, the model improves the indicated thermal efficiency, heat release rate, and indicated mean effective pressure by 1.35 %, 0.37 %, and 0.04 %, respectively. The combined of ANN-GA is effective in identifying the optimal operating parameters for in-cylinder performance and emission.

Computational Analysis of Permeance Prediction for Gas Separation Membrane Using Countercurrent Flow Model

Gas separation polymeric membranes have gained significant attention in various industries, including gas processing, petrochemicals, and environmental applications. Accurately predicting the permeance of these membranes is crucial for optimizing their design and performance. This paper presented a numerical prediction model for the permeance of a binary gas mixture under various process conditions, using only algebraic equations to minimize the calculation effort. The model considers input parameters such as feed and permeate flow rates, feed pressure, feed and permeate mole fraction, and membrane area. It demonstrates the behavior of permeance and selectivity in this study. The study also presented two case studies that utilize predicted selectivity to model counter-current flow and compare the results with experimental data from the literature. The first case study involves recovering helium from natural gas using an asymmetric high-flux membrane. The second case study separates air using nano-porous carbon as the membrane material. The numerical analysis successfully accurately predicted the permeance of gas separation polymeric membranes. The model accounted for variations in membrane thickness, feed composition, and operating conditions, providing valuable insights into the overall performance of the membrane system. It also allowed for the optimization of membrane design and operational parameters to enhance separation efficiency and productivity. The study showcased the effectiveness of numerical techniques in accurately predicting membrane performance. The developed model can be a valuable tool for membrane designers and engineers, enabling them to optimize the design and operation of gas separation systems.

Effect of physical field-assisted freezing on the quality attributes of frozen baked sweet potato and its optimization

Freezing plays a crucial role in ensuring the safety of foods over long storage periods. However, ice crystals within the cells can cause irreversible damages to tissue structures during the freezing process, resulting a significant problem on the quality. The present study aimed to investigate the effects of physical field-assisted freezing on the quality attributes of frozen baked sweet potato and identify the optimum operational parameters of quick-frozen process. Combination of magnetic field and ultrasound suggested the best results, with a 58.33% reduction in freezing time, 59.63% reduction in thawing loss, compared to IF. The hardness and the signal values of Sweetness and W2W of CF were 58.90%, 20.03%, and 27.07% higher than those of IF, which showed that CF have better maintenance of sample quality. Low field-nuclear magnetic resonance results showed that CF caused a decrease in the proportion of free water and an increase in the proportion of immobile water, and the water status are more similar to FB. Microscopic observations confirmed that the CF had the smallest and the most evenly distribution of ice crystals. Optimization of frozen baked sweet potato via response surface methodology, which revealed that the optimal conditions included a magnetic field intensity of 30 mT, an ultrasonic intensity of 320 W, and an ultrasonic time of 10 s. Therefore, treatment of combination with magnetic field and ultrasound is a promising novel method to improve the quality of frozen foods.

Performance analysis and multi-objective optimization for an integrated air separation, power generation, refrigeration and ice thermal storage system based on the LNG cold energy utilization

Against the backdrop of escalating resource depletion and the urgent quest for alternative sources, liquefied natural gas (LNG) is increasingly gaining prominence as a sustainable solution, particularly in refrigeration applications. However, its underutilization results in wasted resources. To efficiently harness the released cold energy from LNG gasification, this study proposes an integrated system comprising air separation, power generation, refrigeration, and ice thermal storage. The system undergoes optimization using the non-dominated sorting genetic algorithm II (NSGA-II) to determine the optimal operating parameters. The optimized system is comprehensively analyzed from energy, exergy, economic, and environmental perspectives. Results show that the system, with a 70t/h LNG capacity, achieves an energy efficiency of 42.52% and an exergy efficiency of 48.09%. Economically, the system incurs a cost of approximately 0.0711 $/kWh and can mitigate over 1.4504*107kg of CO2 emissions. Compared to traditional LNG utilization systems, the integrated system demonstrates a 22.32% improvement in energy efficiency, a 7.69% enhancement in exergy efficiency, and a cost reduction of 0.0049 $/kWh. In summary, this technologically advanced and economically viable system offers a significant alternative to optimize LNG cold energy utilization.