Non-dominated Sorting Genetic Algorithm Research Articles

Developing a crash severity model based on multi objective evolutionary feature selection approaches

The main purpose of this article is assessing the capability of multiobjective optimisation evolutionary algorithms (MOEAs) along with machine learning method on predicting the crash injury severity with the approach of reducing input variables. Dataset of crashes which were occurred in Iran was analysed over the period of 2016–2020. The data were classified into two classes in terms of injury severity: fatal and non-fatal datasets. At first, general prediction models were created by inserting 31 available input variables into support vector machine (SVM) method based on two imbalanced and obtained balanced datasets. A cluster based under sampling technique was used to obtained balanced data. Then evolutionary algorithms of two objectives optimisation algorithms namely non-dominated sorting genetic algorithm (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEA/D), and pareto envelope-based selection algorithm (PESA-II) along with SVM were used to select features that significantly affect the severity of crashes and developed the simplified models. Then the simplified developed model was compared to the model which was prepared with random forest (RF) method in feature selection. It is shown that the simplified model developed by combined MOEAs and SVM is more appropriate to accurately predict the crash severity, considering the number of input variables in comparison to RF.

Prediction of fatigue life of laminated composites by integrating artificial neural network model and non-dominated sorting genetic algorithm

The present study introduces an artificial neural network (ANN) model coupled with the non-dominated sorting genetic algorithm (NSGA-II) to establish an advanced model for fatigue life prediction of laminated composites. The developed model was trained on a comprehensive collection of dependent experimental data of carbon/epoxy composites. This training encompassed 14 distinct cases, covering various stress concentration geometries, loading levels, and stacking sequences to ensure the model’s robustness and generality. A data augmentation technique was employed to generate similar data to enrich the datasets. The best-obtained accuracy from the model, as measured by the coefficient of determination (R2 score), was 88% and 90% for the test and validation datasets, respectively. While using the data augmentation method, these values improved to 97% and 98%, respectively. The results from the model were compared to those of more conventional regression machine learning algorithms, such as decision trees and gradient boosting. To emphasize the superior predictive accuracy of the developed model, it is essential to note that the best R2 score obtained for the test and validation datasets was 68% using conventional models.

Optimisation of process-induced residual stresses in composite laminates by different genetic algorithm and finite element simulation coupling methods

To address the residual stress induced during the cure of fibre reinforced thermoset polymer composites, two different approaches were suggested for coupling a non-dominated sorting genetic algorithm (NSGA-II) with finite element (FE) simulations based on a viscoelastic constitutive law. These two approaches were proposed with consideration of different ways of integrating NSGA-II and the FE model. In Approach A, NSGA-II was performed based on results from a series of simulations under various combinations of cure variables. Alternatively, Approach B employed NSGA-II to iteratively update and optimise the cure profile for subsequent simulations. Results indicated that both approaches achieved simultaneous reductions in cure time and macroscale residual stress, with Approach B showing further improvements due to the direct coupling between the NSGA-II and simulations. Specifically, the maximum residual stress and cure time optimised by Approach A were reduced by 5%–9% and 22%–50% respectively, while those obtained by Approach B were reduced by 7%–10% and 32%–49% respectively, compared to those based on the manufacturer recommended cure profile. The evolution of stress in composites based on optimised cure profiles from these two approaches was also elucidated. Additionally, microscale modelling further revealed a 3%–5% reduction in the average residual stress within a representative volume element (RVE) model was also shown, depending upon the approach adopted. Ultimately, by combining a NSGA-II and FE simulations, the optimisation of cure time and residual stress at the macroscale and cure time together with a reduction of microscale stress could be realised.

EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.

In recent years, electroencephalography (EEG) has been investigated for identifying brain disorders. This technique involves placing multiple electrodes (channels) on the scalp to measure the brain's activities. This study focuses on accurately detecting mild cognitive impairment (MCI) from the recorded EEG signals. To achieve this, this study first introduced discrete wavelet transform (DWT)-based approaches to generate reliable biomarkers for MCI. These approaches decompose each channel's signal using DWT into a set of distinct frequency band signals, then extract features using a non-linear measure such as band power, energy, or entropy. Various machine learning approaches then classify the generated features. We investigated these methods on EEGs recorded using 19 channels from 29 MCI patients and 32 healthy subjects. In the second step, the study explored the possibility of decreasing the number of EEG channels while preserving, or even enhancing, classification accuracy. We employed multi-objective optimization techniques, such as the non-dominated sorting genetic algorithm (NSGA) and particle swarm optimization (PSO), to achieve this. The results show that the generated DWT-based features resulted in high full-channel classification accuracy scores. Furthermore, selecting fewer channels carefully leads to better accuracy scores. For instance, with a DWT-based approach, the full-channel accuracy achieved was 99.84%. With only four channels selected by NSGA-II, NSGA-III, or PSO, the accuracy increased to 99.97%. Furthermore, NSGA-II selects five channels, achieving an accuracy of 100%. The results show that the suggested DWT-based approaches are promising to detect MCI, and picking the most useful EEG channels makes the accuracy even higher. The use of a small number of electrodes paves the way for EEG-based diagnosis in clinical practice.

Research on time-energy optimal trajectory planning of articulated heavy-duty robot

Articulated heavy-duty robots are more and more widely used in the construction environment. Aiming at the problems of low working efficiency and high energy consumption of this kind of robot, a multi-objective adaptive trajectory planning method combining path optimization and trajectory optimization is proposed to improve the working efficiency of the robot and reduce energy consumption. Firstly, based on kinematics and dynamic analysis considering friction, the energy consumption model of robot trajectory is constructed. Then, according to the posture of the key points in the known workspace, the optimal path points in the joint space are obtained by a path point solution method considering the joint load characteristics. On this basis, a multi-objective model considering running time and energy consumption is established to plan the interpolation trajectory of the joint space of the quintic B-spline curve. Finally, constrained by the velocity, acceleration and torque of the joint of the manipulator, the optimal solution is obtained by using the elite non-dominated sorting genetic algorithm (NSGA-II), and the optimal weight factor is selected by a multi-objective adaptive optimization method to obtain the optimal position-time series. The experimental results show that compared with the quintic polynomial trajectory optimization method, the energy consumption of the proposed method is reduced by 10.90% under the same working efficiency. The research results of this paper provide a new optimization idea for other target trajectory planning.

Extracting phonon coupling parameters from multi color center photoluminescence

Vibronic coupling of color centers plays a crucial role in their effectiveness for quantum technologies. This study presents a novel approach to extract key parameters, including the Huang–Rhys factor and the one-phonon coupling function, from photoluminescence spectra containing the signal from multiple color centers. Monotonic splines are used to represent the one-phonon coupling function, optimized using a variable-length non-dominated sorting genetic algorithm. Our method is applied to the combination of the well-studied negatively charged nitrogen-vacancy center and its less explored neutral counterpart in diamond, observed through photoluminescence at 14 K. Despite the complexity inherent in the theoretical framework governing phonon coupling in photoluminescence, we achieve robust fits, yielding a normalized root-mean-squared error of approximately 6%. The neutral nitrogen-vacancy center exhibits a Huang–Rhys factor of 3.0, while its negatively charged counterpart showcases a value of 3.95. The quasi-localized vibrations of both charge states are clearly present in the fitted one-phonon spectral function. Furthermore, the consistency of our methodology is bolstered by temperature-dependent photoluminescence, which was calculated up to numerical constants using fit-derived parameters, matching the experimental measurements up to 300 K.

Enhancing PEMEC Efficiency: A synergistic approach using CFD analysis and Machine learning for performance optimization

The proton exchange membrane electrolytic cell (PEMEC) is a clean, pollution-free, and promising device for producing hydrogen from water electrolysis. This paper enhances the electrochemical performance of the PEMEC by developing a comprehensive three-dimensional two-phase non-isothermal heat and mass transfer cell model. An analysis was conducted to study the influence of various operating parameters on the temperature safety and efficiency of the cell. The performance of the latter was optimized by adopting the machine learning technology, which aimed at identifying optimal conditions for improved performance. The obtained results showed that, when the inlet water flow rate and membrane thickness were increased, the average temperature of the anode catalyst layer (A-CL) was decreased by 1.4 K and 2.31 K, respectively. This allowed to improve the safety of the electrolytic cell. However, it would reduce the hydrogen and oxygen mole fraction in the electrolyzer. On the contrary, enhancing the porosity or thickness of the anode gas diffusion layer can increase the average A-CL temperature and decrease the oxygen and hydrogen mole fraction. Therefore, the backpropagation neural network and non-dominated sorting genetic algorithm were used to predict and optimize the performance of the electrolyzer, in order to maximize its temperature safety, electrochemical reaction rate, and hydrogen evolution efficiency as much as possible. The results of this study provide a theoretical foundation for the selection of suitable cell models according to different conditions in engineering applications. It also has an application value in energy saving and sustainable development.

A multifunctional inverter power quality coordinated optimization strategy based on comprehensive evaluation

The large-scale integration of renewable energy sources and power electronic devices has led to increasingly dispersed and networked characteristics of power quality disturbances in distribution systems. Traditional control devices, limited by their fixed-point control, no longer meet the development needs of modern distribution systems. Considering the distribution and structural characteristics of the current new-type sources and loads, a multifunctional inverter power quality coordinated control strategy based on comprehensive evaluation is proposed. This strategy aims to achieve power quality coordinated control by utilizing optimal compensation capacity while the grid-connected inverter provides active power output. Firstly, the traditional ipiq power quality detection method is analyzed and improved to enable detection of harmonics, reactive power, and three-phase imbalance currents in single-phase systems or three-phase four-wire systems. Secondly, in the power quality assessment, a balanced algorithm is employed to obtain the comprehensive evaluation index of power quality, thereby improving the deficiencies of single-weight evaluation. Next, to ensure the grid-connected inverter achieves optimal power quality coordinated control with minimal compensation capacity, an optimization compensation function is established between compensation capacity and comprehensive power quality index. This function is optimized using non-dominated sorting genetic algorithm to enhance equipment utilization and system economy. Finally, the feasibility and effectiveness of the proposed method are validated through simulations, demonstrating the achievement of optimal capacity configuration for power quality coordinated control while ensuring active power output by the grid-connected inverter.

Seismic retrofitting optimization model using fiber-reinforced polymer jacketing and NSGA-III

Fiber-reinforced polymer (FRP) is widely used for retrofitting structural elements due to its easy application. However, establishing a retrofit strategy is challenging due to conflicting objectives, such as cost and performance level, requiring optimization for effective decision-making. This study proposes a many-objective optimization model for seismic retrofitting using FRP and the Non-dominated Sorting Genetic Algorithm (NSGA)-III. The model's efficacy was demonstrated through two numerical examples: a reinforced concrete building retrofitted with FRP jackets and a masonry-infilled reinforced concrete building retrofitted with FRP bracings. Each example included three objective functions and multiple constraints. Nonlinear static pushover analysis provided optimal strategies to enhance base shear and energy dissipation while minimizing costs and retrofitting locations. Among the Pareto-optimal solutions, the optimal solution with the minimum Euclidean distance was selected. NSGA-III offered a wider distribution and more Pareto-optimal solutions compared to NSGA-II, demonstrating its potential in addressing many-objective problems related to retrofit decision-making.

A novel hybrid method integrating multi-objective optimization with emergy analysis for building renewal strategy

Building renewal has become a promising approach for improving energy efficiency and reaching carbon neutrality targets. Two renewal strategies, namely, the retrofitting of existing buildings and their demolition and reconstruction of a new one, are often explored and compared to determine which strategy is the optimal one. However, the decision is still uncertain due to the large domain space of strategies and the lack of comprehensive methods. This paper, therefore, presents a novel hybrid method to help decision-makers identify the optimal building renewal strategy based on the integration of multi-objective optimization algorithm with emergy analysis. A simulation-based non-dominated sorting genetic algorithm is developed to select the optimal retrofitting strategy, where dynamic simulation model is created in EnergyPlus software and non-dominated sorting genetic algorithm is written in Python. Then, the equivalent reconstruction strategy is projected by the optimal retrofitting strategy while meeting mandatory standards. A comparison between the optimal retrofitting strategy and the equivalent reconstruction strategy can be made by applying emergy analysis considering the whole life cycle phase. A fictional office building was chosen as a case study to illustrate the practical implementation of the proposed method. High-cost building retrofitting strategies were found to consistently yield the highest energy savings and thermal comfort, and the energy use decreased by 4.63 % to 48.25 %. The input emergy of the original building, retrofitting building and reconstruction building are 3.16E + 20 sej, 1.41E + 21 sej, and 1.46E + 21 sej, respectively, and the output emergy are 2.39E + 20 sej, 1.66E + 20 sej, and 4.76E + 19 sej. The results also indicate that the sustainability of the retrofitting strategy is better than that of the reconstruction strategy. This paper provides a guidance method and framework for decision-makers and policy-makers to identify the optimal building renewal strategy for reducing the energy use and achieving carbon peak targets.

Multi-period urban hydrogen refueling stations site selection and capacity planning with many-objective optimization for hydrogen supply chain

The location and quantity of hydrogen refueling stations (HRSs) play a crucial role in the development and promotion of hydrogen fuel cell vehicles (HFCVs). This study proposes a multi-period urban HRSs site selection and capacity planning with many-objective optimization framework for hydrogen supply chain (HSC). Firstly, the city's multi-period hydrogen requirement is predicted based on a generalized Bass diffusion model. Using publicly available data, including gas station network data, geographic information system (GIS) data, population data, and regional economic data, a spatially aggregated demand model is established to allocate hydrogen requirement at candidate sites in the city of the geographic grid model with a 1 km resolution. On this basis, four interrelated objective functions (total investment cost, hydrogen requirement coverage, risk coefficient, and environmental factors) are developed. The third-generation non-dominated sorting genetic algorithm (NSGA-III) and the technique for order preference by similarity to an ideal solution (TOPSIS) are employed to achieve multi-period urban HRSs site selection and capacity planning with many-objective optimization for HSC. By comparing the results of single-objective optimization focusing on minimum cost and maximum hydrogen requirement coverage, it is observed that many-objective optimization achieves a better balance among the four conflicting objectives. After comprehensive analysis, the distribution of HRSs exhibits a clustered structure, influenced by the population and economic structure of the city. In the initial stages of HFCV development, most stations serve the peripheral areas around the city center; while n the later stages of the optimization period, a few high-capacity HRSs begin to concentrate in the city center.

Precast concrete component yard layout optimization considering safety and efficiency

ABSTRACT The contradiction between the space demand for precast concrete components (PCCs) and the limited area of the construction site is reinforced by the increase in the prefabrication rate of prefabricated buildings, and a large number of PCC hoisting operations may have an adverse impact on the safety and efficiency of the construction site. Therefore, the importance of precast concrete component yard layout planning (PCCYLP) is becoming increasingly prominent. Most existing research on construction site layout planning (CSLP) has focused on all temporary facilities on the construction site and has neglected the particularity of PCC when solving the above problems. Based on this, this paper integrates the PCC hoisting efficiency and safety into the existing CSLP research and optimizes PCCYLP solutions by establishing a PCCYLP model. The model proposes a non-centralized PCC yard division method. Moreover, the surplus rectangle algorithm is used to calculate the yard sizes, which are input into the model in alternative ways. Engineering cases verify the effectiveness of the proposed model, and the non-dominated sorting genetic algorithm is selected for simulation. The results show that the method can enhance the scientific and reasonable layout solution and provide a favorable guarantee for subsequent safe and efficient construction activities.

New global multi-objective optimization of compact heat exchanger

This paper introduces a robust and global algorithm, the multi-objective version of the Set Trimming method (MOST). The application of MOST is demonstrated through optimization of a plate and fin heat exchanger. The results are compared with NSGA-II (Fast and elitism non-dominated sorting Genetic Algorithm) and an exhaustive search. The optimization focuses on effectiveness and annual cost, with seven selected design parameters and for six different cases. Results indicate that the Pareto optimal fronts obtained with NSGA-II are entirely dominated by those achieved using MOST in the all studied cases. Furthermore, MOST improves annual cost for the best economic point solution by up to 13.44% as compared to NSGA-II. Simultaneously, effectiveness is enhanced up to 0.04% when using MOST compared to NSGA-II. The computational time is also improved with the presented optimization algorithm compared to NSGA-II in the range of 78.6–88.8%.

Mechanical and thermal property analysis and optimization design of hybrid lattice structure based on triply periodic minimal surfaces

Hybrid lattice structures have significant potential in engineering applications owing to their outstanding mechanical and thermal properties. In this paper, an innovative design strategy for hybrid lattice structures is proposed involving the integration of primitive surfaces with truss lattice configurations. This approach allows for the full exploitation of the mechanical and thermal properties inherent in each component, leading to the development of new hybrid lattice structures. Firstly, the quasi-static compression and fluid-solid coupling heat conduction analysis of the hybrid lattice structure is carried out by numerical simulation. Then the effects of surface wall thickness, and truss diameter on the mechanical and thermal properties of the lattice structure are discussed. Additionally, the mechanical performance of the array hybrid lattice structure is evaluated through quasi-static compression experiments. These experimental results are compared with those from the numerical simulations, validating the accuracy of the simulation model. Finally, a multi-objective optimization model targeting specific elastic modulus and heat dissipation performance (indicated by Nusselt number) is established, in response to the demand of aviation and aerospace industries for lightweight, load-bearing, and heat-dissipating multi-functional integrated lattice design. The non-dominated sorting genetic algorithm is utilized to solve the optimal model, and the distribution of feasible solutions and Pareto front are obtained. The results show that the interpenetrating hybrid lattice structure has a greater improvement in mechanical and thermal properties than the primitive surfaces. This research holds significant implications for the design of multi-performance hybrid lattice structures.

A multi-objective trajectory planning approach for vibration suppression of a series–parallel hybrid flexible welding manipulator

Although the series–parallel hybrid welding manipulator has advantages over the traditional series manipulator, its must run continuously and smoothly according to a specific trajectory at the rated velocity. Considering a flexible manipulator with a complex series–parallel hybrid structure as the research object, this paper proposes a multi-objective approach to indirectly suppress the vibration. In view of the complex flexible mechanism with a multi-kinematic pair arrangement and a multi-motor drive, a system comprehensive elastodynamics model is established, and the effectiveness of the model is verified through the hammer test method. To achieve synchronous and coordinated planning of the end position and attitude, the spatial arc welding trajectory is planned on the basis of the synchronous planning controller and the dual-S velocity profile interpolation function. Then, a multi-objective optimization model considering the travel time, energy consumption and terminal amplitude fluctuations is proposed, the trajectory planning problem is transformed into a parameter optimization problem in the interpolation function, and then the elite non-dominated sorting genetic algorithm (NSGA-II) is employed to determine the optimal trajectory. The optimal trajectory thus obtained effectively suppresses the vibration of the system. Finally, compared with the classic fifth-degree polynomial trajectory planning method, the system vibration suppression effect is significant, which verifies the effectiveness and feasibility of the proposed method and provides a theoretical basis for further realizing real-time control.

Advancing road safety strategy development: A data-driven multi-objective optimisation integrated approach

Road traffic accidents pose a significant global health concern, with an alarming 1.19 million fatalities reported in 2021. Traditionally, strategies to address this challenge have relied on expert input and subjective evaluations. This study introduces an adaptive and novel two-stage model that minimizes expert interference by integrating multi-objective optimisation with association rule mining. This innovative approach provides a systematic framework to enhance the efficiency of decision-making and optimise resource allocation outcomes in road infrastructure management, facilitating adaptability to diverse objectives. A case study in Utrecht from iRAP validates the efficiency of the approach, demonstrating significant improvements across various objectives by using the non-dominant sorting genetic algorithm and enabling road local authorities to tailor investment plans to their specific road network characteristics by crash database mining. However, the methodology requires refinement, particularly in identifying risk levels considering the interactive effects of multiple road attributes. In conclusion, while representing a substantial advancement, further refinement is necessary to fully realize its potential in enhancing road safety.

Aerodynamic performance optimization of a UAV's airfoil at low-Reynolds number and transonic flow under the Martian carbon dioxide atmosphere

Unmanned aerial vehicles (UAV) on Mars operate in a thin atmosphere of carbon dioxide, where the flow Reynolds number and local sound velocity are significantly reduced compared to the Earth's atmosphere because of low gas density and temperature. Therefore, a Mars UAV with a rotary-wing design can operate in a state of low-Reynolds number (Re = 1-5 × 104) and transonic flow (Matip = 0.7–1.2), where flow separation and shock waves occur simultaneously. The interaction between flow separation and shock waves not only makes the entire flow field more complex but also seriously affects the aerodynamic performance of the airfoil, thereby reducing the payload of UAVs, which previous aircraft designs based on the thick atmospheric environment of the Earth have not considered. Therefore, in this study, a numerical simulation method is used to conduct airfoil optimization research on a characteristic airfoil under low-Reynolds number transonic flow (Re = 33200, Ma = 0.85) conditions to acquire additional insight, improve aerodynamic performance of the airfoil, and enhance the payload. The results were compared with the optimization results of subsonic flow (Re = 19500, Ma = 0.50). The Hicks-Henne bump functions method was selected for airfoil parameterization, and non-dominated sorting genetic algorithms (NSGA)-Ⅱ were used for optimization. Through an analysis of the pressure coefficient (Cp), surface friction coefficient (Cf), and density gradient, the number and intensity of oblique shock waves on the upper and lower surfaces were observed to significantly affect the aerodynamic performance of airfoils under low-Reynolds number flow. The changes in flow separation and transition positions do not directly affect the aerodynamic performance but weaken the oblique shock wave intensity induced by the separation point or directly transform strong shock waves into weaker compression waves because of the absence of flow separation. The optimization results for both subsonic and transonic flow point to a thinner overall shape with a flatter surface with the drag greatly reduced due to the disappearance of shock waves on the lower surface, which significantly improves aerodynamic performance. Compared with the original airfoil, at Ma = 0.85, this design can increase the lift coefficient (Cl) by at least 124 % and lift drag ratio (Cl/Cd) by 177 %.

Multi-objective optimization and thermodynamic assessment of a solar unit with a novel tube shape equipped with a helical tape

The performance of several heat transfer mechanisms, including solar energy sources, is being improved through optimization, which also effectively supports the most efficient feasible design. A numerical study has been done on a turbulator-enhanced flat plate solar collector (FPSC) while also introducing the utilization of a quad-lobed tube as a pioneering innovation in the system’s design. The incorporation of nanoparticles into the working fluid was implemented to enhance performance, with aluminum oxide particles selected for this purpose. Furthermore, assessing the second law and analyzing exergy generation was employed to discern irreversibility within the system. Optimization of both geometric and non-geometric elements has been accomplished through the integration of a multi-objective optimization (MOO) method with machine learning (ML). Random Forest (RF), LASSO Regression (LaR), and Support Vector Regression (SVR) are the machine learning models that were utilized in order to ascertain the correlations that exist between the system’s inputs and outputs. According to the findings, RF was found to be the most appropriate algorithm for capturing the impacts of parameter variations. This was demonstrated by the high coefficient of determination (R2) quantities that RF possessed, which were 0.95, 0.99, and 0.99 for friction factor (f), exergy loss (Xd), and second law effectiveness (ηII), respectively. However, the SVR and LaR algorithms showed significant deviations from the FPSC simulation results. The SVR algorithm achieved R2 values of 0.54 for f, 0.78 for Xd, and 0.84 for ηII, while the LaR algorithm achieved R2 values of 0.65 for f, 0.99 for Xd, and 0.98 for ηII. This research aims to find the optimal input parameters that minimize f and Xd while maximizing ηII. Therefore, to determine the Pareto optimal solutions the non-dominated sorting genetic algorithm (NSGA-II) has been utilized. The result was an illustration of the Pareto set, which stands for a collection of optimal solutions. Every solution in this collection achieved the desiredbalance, obtaining the best outcomes without compromising any objectives.

Process comparison and performance evaluation of different entrainers for pressure swing extractive distillation refining gasoline additives based on multi-objective optimization and process intensification

As a new gasoline additive, methyl tertiary amyl ether (TAME) can improve the antiknock performance and quality of gasoline. This study uses an energy-saving process for separating TAME/methanol (MeOH)/H2O by pressure-swing extractive distillation with different entrainers based on multi-objective optimization and 4E analysis. The 1entrainers are screened by the difference in relative volatility of components under different entrainer conditions, and the electrostatic potential of the molecular surface of the system and entrainers are analyzed. Multi-objective optimization of the pressure swing extractive distillation process with 5 different entrainers is carried out based on the generation non-dominated sorting genetic algorithm (NSGA-Ⅱ) optimization scheme, and the Pareto frontier is optimized by the minimum distance method. The results indicate that the extraction schemes of 1,3-propanediol (PDO) and dimethyl sulfoxide (DMSO) have excellent economic and environmental benefits through comprehensive analysis. As an effective means of process intensification, the application of heat pump technology can further reduce process costs and environmental pollution. The coefficient of heating performance (COP) is used to find the most suitable pairing route for heat pump-assisted processes, achieving the goal of reducing gas emissions and energy use. Then, different processes are evaluated in terms of economy, energy consumption, environment, and exergy loss. The results show that the process intensification scheme based on heat pump technology can effectively improve the economic and environmental benefits of the process, reducing the annual total cost (TAC) and gas emissions by 9.19% and 23.55%, respectively. This study provides the selection and reference for azeotrope separation of TAME and recycling of wastewater.

A multi-objective configuration optimization method of passive hybrid energy storage system for pulse loads operating under very low temperatures

The lithium-ion battery energy storage system currently widely used faces a problem of rapid degradation of electrical performance at very low temperatures (such as −40 °C), making it difficult to meet the power supply requirements of high-power pulse loads in low-temperature environments. To address this issue, this paper proposes a multi-objective configuration optimization method for passive lithium-ion battery-supercapacitor hybrid energy storage systems (HESS) based on an electro-thermal-aging coupling model, in order to achieve non-preheating power supply for pulse loads under low temperatures. Firstly, an electro-thermal-aging coupling model for the passive HESS is established to accurately describe the dynamic characteristics during discharge. Subsequently, aiming at the low-temperature application requirements of high-power pulse loads, a multi-objective configuration optimization model is established based on the coupling model with the objectives of minimizing the mass and the minimum operating ambient temperature of the HESS; a solving method of the optimization model is designed based on the non-dominated sorting genetic algorithm with elite strategy. Finally, a case study is conducted on configuration optimization for a certain type of pulse load. The optimization results show that when the minimum operating temperature is consistent and below 0 °C, the passive HESS compared with the lithium-ion battery energy storage system can reduce the system mass by more than 23% and the acquisition cost by more than 18% while maintaining basically consistent single-pulse costs. When the minimum operating temperature is lower, the advantages of the HESS are even more significant.