Optimal Operation Research Articles

Optimizing the final rest period of sludge treatment wetlands: Assessment of biosolids quality

Wastewater Treatment Plants (WWTPs) generate a substantial amount of sludge as a residue of the wastewater treatment process. The management of this sludge requires the adoption of cost-effective and environmentally friendly techniques. Sludge Treatment Wetlands (STW) have emerged as an effective means to manage and treat sludge, resulting in the production of biosolids suitable for agricultural purposes. This study aimed to ascertain the optimal operation in terms of final rest period for STWs in the Mediterranean region to boost the production of biosolids for agricultural applications. Two WWTPs in Spain were monitored for a period of 9 months, during which several physic-chemical parameters were monitored to determine the optimal final rest period. The findings revealed that, when sludge feeding period was stopped in summer, a final rest period of 6 months was sufficient to reach biosolids suitable for agricultural re-use. These biosolids displayed a content of 53% total solids (TS), a mineralization rate of 40% volatile solids (VS/TS), a stabilization degree (SD) of 40%, and a high nutrient content. Moreover, the presence of heavy metals, pathogens, antimicrobial resistance genes (ARGs), antibiotics, and pesticides was generally low. Overall, this study demonstrates that, in the Mediterranean Region, 6 months of rest period starting in summer are appropriate for sludge treatment wetlands to effectively generate biosolids with suitable characteristics to be safely re-used as a bio-based fertilizer.

Ethanol recognition based on carbon quantum dots sensitized Ti3C2Tx MXene and its enhancement effect of ultraviolet condition under low temperature

The deteriorating air quality makes it particularly important to detect all kinds of harmful gases in the air. In this study, the Ti3C2Tx MXene/CQDs composite was formed by modifying carbon quantum dots (CQDs) to the surface of Ti3C2Tx MXene, in which the Ti3C2Tx MXene sensitized by CQDs achieved enhanced recognition of ethanol. After a series of characterizations, it was confirmed that CQDs were indeed modified on the surface of Ti3C2Tx MXene. The gas sensing test results show that the sensor based on Ti3C2Tx MXene/CQDs composite exhibits excellent response performance to ethanol, particularly achieving a high response value of 15.38–50 ppm ethanol at the optimal operating temperature of 140 °C. Furthermore, this composite also demonstrates excellent repeatability and a stable response relationship towards ethanol. Finally, it is found by comparison that the recovery time of the sensor under ultraviolet (UV) irradiation is significantly shortened, which further verifies that the sensor has wider application potential under UV condition. The experimental results show that the Ti3C2Tx MXene/CQDs composite significantly improves the efficiency and ability of detecting ethanol by optimization of photoresponsive performance, enhancement of electrical conductivity, and increase in specific surface area. This paper provides practical research methods and ideas for the development of novel sensors based on CQDs and Ti3C2Tx MXene.

Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks.

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem's non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios.

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.

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.

Optimal operation of three-phase unbalanced active distribution system based on semidefinite relaxation and convex-concave procedure

The three-phase distribution optimal power flow (D-OPF) problem has attracted much attention in recent years due to practical requirements for managing distributed energy resources and multi-energy customers in unbalanced active distribution systems. To deal with the strong nonconvexity of the three-phase OPF problem, this paper develops the semidefinite programming (SDP) formulation of three-phase OPF problems considering the mutual coupling in multi-phase networks. Instead of directly dropping the rank-one constraints, a sequential convex optimization algorithm based on the convex-concave procedure (CCP) is proposed for recovering a feasible and optimal solution of three-phase power flow. The proposed iterative algorithm not only has high computational efficiency but also guarantees the optimality when the semidefinite relaxation or second-order cone relaxation method is inexact. Case studies on various IEEE testing distribution systems verify that the proposed algorithm recovers the feasible and optimal power flow solution. It also exhibits a relatively low number of iterations and short solving time in large-scale IEEE testing systems.

Experimental Study on Performance of Cotton Small Arch Shed Recovery Machine

Most of the small-arch-shed-recycling machines in China use manual disassembly and manual recycling, with low recycling efficiency and low mechanization. Therefore, this paper designs a small recovery machine for arch sheds, greatly improving the efficiency of the recycling of arch sheds, which can realize the lifting and collection of the arch shed rod and orderly recycling of the shed film. By performing univariate experimental studies in the field, on the basis of field experiments, we carried out an experimental analysis with machine speed and different soil moisture contents as the influencing factors and took the removal rate of the shed rod, the removal rate of the shed film, and the damage rate of seedlings as the test indexes. The test results show that the optimal parameter combination is a travel speed of 1.1 m/s, at which the operation effect is the best. The results show that under the optimal operation effect, the removal rate of the shed rod was 95.72%, the removal rate of the shed film was 98.63%, the seedling injury rate was only 2.11%, and the removal rate of the shed rod was only 4.01%, which met the requirements of the recovery operation of the arch shed and means that this approach is conducive to the recycling of the arch shed materials and the realization of sustainable development. In actual operation, the parameters should be adjusted according to the actual situation in the field to meet the different recovery needs of arch sheds.

Multi-Objective Optimization Operation of Multi-Agent Active Distribution Network Based on Analytical Target Cascading Method

Multi-Objective Optimization Operation of Multi-Agent Active Distribution Network Based on Analytical Target Cascading Method

Bi-Level Operation Optimization and Performance Analysis for a Distributed Energy System with Energy Network

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method.

Dynamic feed scheduling for optimised anaerobic digestion: An optimisation approach for better decision-making to enhance revenue and environmental benefits

Anaerobic digestion (AD) offers a sustainable solution for clean energy production, with the potential for significant revenue enhancement through enhanced decision-making. However, the complexity and limited flexibility of AD systems pose challenges in developing reliable optimisation methods. Changing feeding strategies provides opportunities for efficient feedstock utilisation and optimal gas production, especially in volatile gas markets.To provide better decision-making tools in AD for energy production, we propose an integrated site model for the dynamic behaviour of the AD process in a biomethane-to-grid system and optimise production based on predicted gas prices. The model includes methods for optimal feed co-digestion strategies and integrates these results into a scheduling model to identify the optimal feedstock acquisition, feeding pattern, and potential gas storage operation considering feedstock availability, properties, sustainability, and fluctuating gas demand under different pricing variations.The methodology was tested on a 150 tonnes per day farm-scale AD plant in the UK, processing energy crops and manure considering both environmental (global warming potential) and economic objectives. The results showed strong adaptability of the proposed feeding schedule to the general trend of gas prices over time. To address the challenge of immediate price peaks, typically unattainable due to the system's sluggish behaviour and high retention times, the impacts of on-site storage were explored, leading to annual revenue increases ranging from 2 % to 7.4 %, depending on the pricing scheme, which translates to a significant boost in terms of revenue.

Thermo-hydraulic performance of concentric tube heat exchangers with turbulent flow: Predictive correlations and iterative methods for pumping power and heat transfer

This research addresses the problem of predicting the thermo-hydraulic performance of concentric tube heat exchangers (CTHE) under turbulent flow conditions, a critical aspect in energy-efficient industrial systems such as HVAC, power generation, and chemical processing. Existing studies often lack accurate predictive methods for balancing heat transfer performance with pumping power requirements. To tackle this issue, novel correlations and an iterative Newton–Raphson method were developed for predicting pumping power and heat transfer rates. Three-dimensional CFD simulations of a water-to-water counter-flow CTHE were conducted, with Reynolds numbers ranging from 4000 to 8000 for both the hot and cold fluids. The simulations employed the Reynolds-Averaged Navier–Stokes (RANS) equations with the k−ω SST turbulence model. The results demonstrated that increasing the Reynolds number enhances both heat transfer rates and pumping power, with the cold fluid requiring consistently higher pumping power. New correlations were developed to predict pumping power, capturing the impact of both entry and fully developed flow regions. These correlations showed an average error of less than 2.33% when compared with the CFD data. The iterative Newton–Raphson method for predicting heat transfer rates demonstrated high accuracy, with an average error of 0.66% for heat transfer rate, 0.03% for hot fluid outlet temperature, and 0.01% for cold fluid outlet temperature. Additionally, we identified optimal operating conditions for efficient cooling and heating based on the heat capacity ratio (Cr). The novelty of this work lies in the development of new, highly accurate predictive correlations and iterative methods for optimizing CTHE performance, going beyond existing literature by providing comprehensive insights into the relationship between pumping power, heat transfer efficiency, and flow conditions.

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

Optimizing building energy efficiency with a combined cooling, heating, and power (CCHP) system driven by boiler waste heat recovery

In light of increasing challenges related to fossil fuel consumption, global warming, and rising energy costs, optimizing energy systems has become essential. This study introduces a multi-purpose system designed to capture and utilize waste heat from boiler flue gases at the Tous power plant for simultaneous power generation, cooling, and heating. The system integrates an Organic Rankine Cycle (ORC) for power generation and an Absorption Refrigeration Cycle (ARC) for cooling, with the added function of recovering waste heat to preheat natural gas for the boiler and power plant. The system's performance was rigorously analyzed through energy, exergy, and exergoeconomic assessments. To determine the optimal operating conditions, a multi-objective optimization was conducted using the TOPSIS technique, focusing on critical parameters such as flue gas exit temperature, natural gas inlet temperature, and turbine inlet pressures. The results identified optimal conditions with a flue gas exit temperature of 532.5 K, a natural gas inlet temperature of 285 K, and turbine inlet pressures of 2.54 MPa and 14.62 MPa for the first and second turbines, respectively. Under these conditions, the system achieved an exergy efficiency of 41.1 %, cooling capacity of 133.1 kW, power generation of 210.4 kW, heat transfer to natural gas of 979.6 kW, and operational costs of 8.53 $/h. This study highlights the potential of the proposed system to significantly enhance energy efficiency and reduce operational costs in practical applications, offering a sustainable solution for energy management.

Improving electric vehicle charging forecasting: A hybrid deep learning approach for probabilistic predictions

AbstractElectric vehicles (EVs) have gained significant attention recently. Despite their advantages, challenges in the power grid, such as providing necessary information for optimal operation, persist. High‐precision forecasting techniques are essential to address the nonlinear and complex behavior of EV charging. A hybrid structure based on deep learning, called LSTLNet, has been proposed. LSTLNet combines convolutional neural networks (CNN), gated recurrent neural networks (GRU), attention mechanisms (AM), and automatic regression (AR) models. This combination improves the deterministic forecasting model and addresses the weaknesses of CNN and GRU. Deterministic prediction, which determines only one point of consumption charge, is prone to error. Therefore, probabilistic forecasting, represented as a probability distribution function (PDF) containing comprehensive statistical information, is preferred. A smooth band limit maximum likelihood (SBLM) estimator is used to indirectly predict the PDF from the data. Comparative results with conventional shallow and deep methods for similar time series forecasting demonstrate the superiority of the proposed method for both deterministic and probabilistic forecasting.

Increasing the available water diversion volume of water source project through flood resource utilization: A case study of the middle route of the South-to-North water diversion project in China

Water supply operating rules are critical for guaranteeing the safety of the water supply in the source project and receiving areas and overcoming the uneven distribution of water resources among different regions. Previous studies have mainly focused on optimizing the water supply operating rules to balance multiple benefits under the current engineering design parameters. In this study, a framework is proposed in which the water supply operating rules for the resource reservoir are adapted through flood resource utilization. The correlations among seasonal floods are first analyzed by considering the influence of water diversion projects. The seasonal flood-limited water levels (FLWLs) are then re-designed in terms of a flood damage assessment index under the deduced most-probable seasonal floods. Finally, the water supply operating curves of the resource reservoir are optimized by adopting the recommended seasonal FLWLs. The middle route of the South-to-North Water Diversion Project in China is taken as a case study. The results show that the recommended seasonal FLWLs are 160.2 m in summer and 164.2 m in autumn, and that the annual average available water diversion volume can be increased by 0.52 billion m3 without increasing the flood risk. Compared with the current water supply operating rules, the optimal operating curves make the annual distribution of the available water diversion volume more uniform in different typical-year scenarios and reduce the surplus reservoir water. These findings are helpful for exploring the potential of water resource utilization.

On the relationship between electrical and electro-optical characteristics in vertical cavity surface emitting lasers for chip scale Rb atomic clocks

We report on a comprehensive study of the electrical and electro-optical properties of 795 nm vertical cavity surface emitting lasers (VCSELs) designed for chip scale Rb atomic clocks. We highlight several key findings including the observation that the current flow at moderate bias levels comprises several parallel paths which are identified by an analysis of the I−V characteristic also confirmed by a numerical simulation. Resistance is a key parameter in any VCSEL. We analyze it in detail at all bias levels and find that above transparency, when the VCSEL enters the high injection regime, the current flow mechanism is modified significantly from an exponential to a power law characteristic. Consequently, resistance attains a nonlinear contribution which is quadratic in the spontaneous emission regime and quasi-linear above the threshold. This nonlinear contribution is not considered in common models. The optoelectronic properties are strongly correlated with the electrical characteristics what allow to explain several peculiarities of the VCSEL performance. We designed and fabricated the VCSELs according to the requirements of miniature Rb atomic clocks, including optimal operation at high temperatures. Their minimum threshold occurs at 363 K where they emit at 794.7 nm. The modal and polarization discrimination in the bias range where these VCSELs operate in practical miniature atomic clocks is well above 30 dB.

Two-Stage Optimization Model Based on Neo4j-Dueling Deep Q Network

To alleviate the power flow congestion in active distribution networks (ADNs), this paper proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First, the Neo4j graph model was established as the training environment for Dueling DQN. Meanwhile, the power supply paths from the congestion point to the power source point were obtained using the Cypher language built into Neo4j, forming a load transfer space that served as the action space. Secondly, based on various constraints in the load transfer process, a reward and penalty function was formulated to establish the Dueling DQN training model. Finally, according to the ε−greedy action selection strategy, actions were selected from the action space and interacted with the Neo4j environment, resulting in the optimal load transfer operation sequence. In this paper, Python was used as the programming language, TensorFlow open-source software library was used to form a deep reinforcement network, and Py2neo toolkit was used to complete the linkage between the python platform and Neo4j. We conducted experiments on a real 79-node system, using three power flow congestion scenarios for validation. Under the three power flow congestion scenarios, the time required to obtain the results was 2.87 s, 4.37 s and 3.45 s, respectively. For scenario 1 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by about 56.0%, 76.0% and 55.7%, respectively. For scenario 2 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 41.7%, 72.9% and 56.7%, respectively. For scenario 3 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 13.6%, 47.1% and 37.7%, respectively. The experimental results show that the trained model can quickly and accurately derive the optimal load transfer operation sequence under different power flow congestion conditions, thereby validating the effectiveness of the proposed model.

Enhancing selective NH4+ recovery from wastewater using modified zeolite-based flow electrode capacitive deionization

Despite flow-electrode capacitive deionization (FCDI) is an emerging technology for desalination, contaminant removal, and resource recovery, the application of conventional FCDI in wastewater treatment is hindered by the electrode selectivity and material costs. In this study, we synthesized a low-cost ammonium (NH4+) adsorption electrode material by modifying zeolite using ethylenediaminetetraacetic acid disodium salt (EDTA-2Na). The flow electrode prepared by the mixture of EDTA-zeolite and carbon black exhibits a high selectivity and adsorption capacity for the recovery of NH4+ from wastewater. The NH4+ in wastewater passes through the ion exchange membrane and is rapidly adsorbed by the modified zeolite through ion exchange, while Na+ is retained in the electrolyte. The decrease in NH4+ concentration and the increase in Na+ concentration in the catholyte lead to a significant change in ion concentration gradient across the membrane. Consequently, the transmembrane selectivity between NH4+ and Na+ reached 3.46. We validated the feasibility of NH4+ recovery using FCDI with food waste fermentation supernatant. Under optimal operating conditions, 99.15 % of the NH4+ in the fermentation supernatant was removed, and 95.92 % of the NH4+ in the electrolyte was stored in the EDTA-zeolite. By gravitational settling, the NH4+-rich modified zeolite was separated from carbon black and could be utilized as nitrogen fertilizer. Meanwhile, the mixture of carbon black and brine was used to prepare a fresh electrode suspension. In brief, the FCDI system exhibits a satisfying NH4+ recovery performance and demonstrates a sustainable wastewater resource recovery strategy.

Photovoltaic Charge Lithography on Passive Dielectric Substrates Using Fe:LiNbO3 Stamps

AbstractPhotovoltaic Fe:LiNbO3 is an outstanding material platform able to photo‐generate versatile charge patterns, useful for a broad variety of applications. However, in some cases, its photorefractive effect, light absorption, and active ferroelectric properties may interfere with the optimum operation of certain devices based on Fe:LiNbO3. Here, a novel optoelectronic method is proposed and demonstrated to transfer photovoltaic charge patterns from Fe:LiNbO3 to non‐photovoltaic passive substrates, thus removing these possible limitations. The method, denominated as photovoltaic charge lithography (PVCL), resembles the operation of a stamp and does not require external high‐voltage supplies or electron/ion beams. Upon contact between the active Fe:LiNbO3 stamp and a passive dielectric substrate, the light‐induced charge pattern can be faithfully mirrored on the passive substrate. The imprinted pattern is probed and characterized by dielectrophoretic and electrophoretic particle trapping. The results reveal that the charge builds up on the passive substrate during contact, allowing charge tunability. Moreover, arbitrary charge distributions can be flexibly tailored, using scanning laser beams or spatially structured light. Overall, PVCL opens the possibility of printing complex 1D/2D charge patterns of controlled polarity on different passive dielectric materials, enhancing the technological potential of Fe:LiNbO3 photovoltaic platforms.

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.