Multi-objective Genetic Optimization Research Articles

Enhanced reaction extraction distillation via multi-objective optimization and 4E analysis for sustainable recovery of organic compounds from wastewater

Resource utilization and sustainable treatment of wastewater, which can achieve efficient energy utilization and environmental protection, have received hot social attention. Tetrahydrofuran and methanol are the two most common substances in pharmaceutical wastewater. It is very necessary to explore the wastewater treatment process containing these two substances. In this study, extractive distillation and reactive distillation were proposed to effectively separate the tetrahydrofuran-methanol-water component of pharmaceutical wastewater. To further reduce energy consumption, two strengthening processes, double-column reactive-extractive distillation and reactive-extractive dividing wall column distillation were designed. Taking the minimum operating and equipment cost as the objective function, the processes were optimized by using the multi-objective genetic optimization algorithm to obtain the best process parameters. Four processes were evaluated in terms of total annual cost, energy consumption, gas emissions and thermodynamic efficiency. Compared to the traditional three-column extractive distillation process, the above-mentioned four aspects of the reactive-extractive dividing wall column process was reduced by 40.63%, 43.13%, 43.14% and 17.71%, respectively. The reactive-extractive dividing wall column distillation process is of great significance for the efficient purification of valuable organic matter in wastewater and environmental protection.

Multi-Objective Optimization of a Multi-Cavity, Significant Wall Thickness Difference Extrusion Profile Mold Design for New Energy Vehicles.

With the rapid development of the new energy vehicle market, the demand for extruded profiles for battery trays, mainly characterized by significant wall thickness differences in multiple chambers, is increasing, posing new challenges to production and quality control. This study examines the multi-objective optimization problem in the design process of aluminum profile dies with multi-cavity profiles and significant wall thickness differences. Using QFORM-extrusion professional aluminum extrusion finite element analysis software and the response surface analysis method, the standard deviation of the velocity (SDV), standard deviation of the pressure (SDP), and thick wall hydrostatic pressure (TWHP) on the profile section at the die exit are optimized. By analyzing the functional relationship between the key die structure parameters (the height of the baffle plates, the length of the bearing, and the height of the false mandrel) and the optimization objective, the optimal combination scheme of die structure parameters was obtained using the NSGA2 (non-dominated sorting genetic algorithm-2) multi-objective genetic optimization algorithm. The results show that, compared with the initial design scheme, the standard deviation of profile section velocity was reduced by 5.33%, the standard deviation of pressure was reduced by 11.16%, and the thick wall hydrostatic pressure was increased by 26.47%. The die designed and manufactured using this scheme successfully completed the hot extrusion production task, and the profile quality met the predetermined requirements, thus verifying the effectiveness of this study in optimizing the design of a multi-cavity aluminum profile die with significant differences in wall thickness for complex structures.

Investigation of side-stream extractive distillation for separating multi-azeotropes mixture based on multi-objective optimization

In this study, a conventional triple-column extractive distillation process and three side-stream extractive distillation processes are investigated for separating a ternary mixture of n-hexane/ethyl acetate/acetonitrile. A multi-objective genetic optimization method is applied to optimize the four processes with the minimum total annual cost (TAC) and entropy production as two objective functions. The results indicate that the three side-stream extractive distillation processes can effectively reduce the remixing phenomenon, achieving a reduction in energy consumption. In addition, the three side-stream extractive distillation processes exhibit better economic advantages and significantly decrease entropy production compared to the conventional triple-column extractive distillation process. Among the processes, the single side-stream extractive distillation process presents the optimal performance considering economy, energy-efficient and entropy production. Specifically, it can reduce 6.17% of TAC, 7.85% of energy consumption and 15.54% of entropy production in comparison to the conventional triple-column extractive distillation process, respectively.

Energy and economic analysis of building integrated photovoltaic thermal system: Seasonal dynamic modeling assisted with machine learning-aided method and multi-objective genetic optimization

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

Research on Vehicle Frame Optimization Methods Based on the Combination of Size Optimization and Topology Optimization

The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design.

Research on Microclimate Performance Simulation Application and Scheme Optimization in Traditional Neighborhood Renewal—A Case Study of Donghuali District, Foshan City

With global warming and rapid urbanization, the microclimate in the Lingnan region is prone to health problems, such as pyrexia and infectious diseases, and the average annual number of heatwave-related deaths is rising rapidly. The large-scale regeneration of traditional neighborhoods in Lingnan under high-quality development is underway, which has implications for the thermal comfort of microclimatic environments. This study focused on the impact of different building unit types and spatial patterns on thermal comfort in the Donghuali traditional neighborhood of Foshan City as an example. We extracted eight basic morphological units and designed a prototype block of 400 m × 400 m. In the Rhinoceros & Grasshopper parametric software 6.7, a variety of plug-ins were integrated to establish a platform with parametric modeling, microclimate simulation and evaluation, and optimal design for thermal comfort. Through experiments, the effects of new single-type and new composite building units on thermal comfort were investigated, and the correlation equations between spatial morphology and microclimate comfort in Lingnan traditional neighborhoods were established. Finally, the multi-objective genetic optimization of thermal comfort was carried out as an example of real block renewal, which provides a practical reference for the planning and design of traditional blocks.

Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation.

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as "shape sensing". The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

Genetic Multi-Objective Optimization of Sensor Placement for SHM of Composite Structures

Increasingly often, due to the high sensitivity level of diagnostic systems, they are also sensitive to the occurrence of a significant number of false alarms. In particular, in structural health monitoring (SHM), the problem of optimal sensor placement (OSP) is appearing due to the need to reach a balance between performance and cost of the diagnostic system. The applied approach of considering nondominated solutions allows for adaption of the system parameters to the user’s expectations, treating this optimization problem as multi-objective. For this purpose, the NSGA-II algorithm was selected for the determination of an optimal set of parameters in the OSP problem for the detection of delamination in composite structures. The objectives comprise minimization of type-I and type-II errors, and number of sensors to be placed. The advantage of the proposed approach is that it is based on experimental data from the healthy structure, whereas all cases with a presence of delamination were acquired from numerical experiments. This makes it possible to develop a customized SHM system for the arbitrary location of damage.

Analysis of Taxiing Performance of Single Strut Landing Gear Based on ISD

In this paper, based on the “spring-damper” landing gear (SDLG), the inerter is introduced to form the “inerter-spring-damper” landing gear (ISDLG), and the taxiing dynamic models of both are established. Taking fuselage acceleration, buffer stroke and dynamic tire load as performance indexes, the parameters of ISDLG are optimized by using multi-objective genetic optimization algorithm. In MATLAB/Simulink environment, SDLG and ISDLG are simulated in time-domain and frequency-domain. The results show that compared with SDLG, the values of performance indexes of ISDLG are all reduced, especially at low frequency, which can effectively suppress the low frequency vibration of the fuselage and improve the ride comfort and handling stability of the aircraft when it is taxiing on the ground. In addition, through comparison, it is found that speed and road grade have different influences on aircraft taxiing performance, and road grade has the greatest influence, followed by speed.

Machine Learning Assisted Microchannel Geometric Optimization—A Case Study of Channel Designs

At present, microchannel heat exchangers are widely applied in the fields of air-conditioning and heat pumping applications given their high heat transfer performance, compact size, and low material cost. However, designing and optimizing the channel geometries remain challenging, as they require balancing multiple competing objectives to achieve the optimal performance. This study investigates various parameters, including the channel count, wetted perimeter, cross-sectional area, and mass flow rate for each channel, to achieve the optimal efficiency. The optimization objectives include maximizing the heat transfer rate, minimizing the refrigerant convective thermal resistance, maximizing the refrigerant heat transfer coefficient, and minimizing the pressure drop. A multi-objective genetic optimization algorithm, in conjunction with artificial neural network (ANN)-based machine learning models, was used to predict the heat transfer rate to speed up the calculation process during the optimization. We identified that a gradient reduction in the wetted perimeter from the air inlet along the airflow direction could enhance the heat transfer rate. Additionally, the results indicate that an increase in the number of channels leads to an enhanced heat transfer efficiency rate. However, with the increase in the number of channels, the cross-sectional area of each channel is correspondingly reduced to maintain a consistent overall cross-sectional area. This reduction increases the fluid resistance, leading to an increased pressure drop across the system. This observation is critical for a microchannel design optimization, highlighting the importance of attaining a balance between achieving a higher heat transfer efficiency and maintaining a favorable fluid dynamic performance.

Quadrupole magnet design based on genetic multi-objective optimization

This work suggests to optimize the geometry of a quadrupole magnet by means of a genetic algorithm adapted to solve multi-objective optimization problems. To that end, a non-domination sorting genetic algorithm known as NSGA-III is used. The optimization objectives are chosen such that a high magnetic field quality in the aperture of the magnet is guaranteed, while simultaneously the magnet design remains cost-efficient. The field quality is computed using a magnetostatic finite element model of the quadrupole, the results of which are post-processed and integrated into the optimization algorithm. An extensive analysis of the optimization results is performed, including Pareto front movements and identification of best designs.

Optimal design and analysis of lubricating oil system in marine nuclear power based on adaptive genetic algorithm

Lubricating oil system is one of the important auxiliary systems of marine nuclear power plants. It can play the role of reducing wear, cooling, cleaning, and sealing, to ensure the normal operation of the lubricating oil users. But the traditional design process of the lubricating oil system is complex and inefficient. Aiming at this shortcoming, a new design method for lubricating oil systems is proposed in this study based on the new hybrid genetic multi-objective optimization algorithm. The mathematical model of the lubricating oil system is developed on the basis of the model base established by SQL Server according to the actual equipment. The optimization algorithm is integrated into the design process so that the economic index and reliability index can be considered comprehensively. Through sensitivity analysis, the optimization parameters that have a great impact on the optimization objective are selected as optimization variables. Finally, two sets of optimization schemes are obtained. Scheme 1 is the optimal scheme of system weight and volume, and Scheme 2 is the optimal scheme of reliability. For the former, the weight and volume are optimized by 35.25% and 35.16%, respectively; for the latter, the reliability is optimized by 2.04%.

Multi-objective niching quantum genetic algorithm-based optimization method for pneumatic hammer structure

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in various drilling applications owing to its high drilling efficiency, low gas consumption, non-polluting nature, and safety. Optimizing the hammer structure using empirical approaches is challenging due to the highly complex nonlinear problem caused by the piston movement. To address it, the intelligent algorithm is introduced for the first time and a multi-objective niching quantum genetic algorithm-based (MNQGA) optimization method for pneumatic hammer structure design is proposed in this study, to simultaneously maximize impact energy and minimize air consumption or optimize these objectives individually. Additionally, a novel niching strategy, corresponding adaptive quantum rotation gate, and mutation strategy are designed for the proposed method. The radial structures of a GQ127-type pneumatic hammer are optimized to maximize impact energy, followed by the optimization of the axial structures to satisfy four working conditions. The results show that different optimization objectives and constraints lead to different axial structural parameter values. After optimization, the hammer air consumption is reduced by at least 50.80 % at a constant impact energy of 350 J. The hammer impact energy is improved by more than 15.93 % if the volumetric flow rate of the supplied air is maintained at 10 m3/min. In addition, the effects of the hammer’s axial structure on its impact energy are investigated in detail based on an analytical model to validate the results of the proposed method. Five axial structure parameters are analyzed that have different effects on the impact energy. Within the given constraints, these parameters have their own optimal values that can be easily obtained using the proposed method. That is, the optimizations using the proposed method are consistent with those of the analytical models published in the literature. Therefore, the proposed method is efficient and accurate and can be used confidently when designing pneumatic hammers.

Design and off-design performance analysis of supercritical carbon dioxide Brayton cycles for gas turbine waste heat recovery

The utilization of waste heat from marine gas turbines is an effective means of meeting future electric demands and achieving energy conservation and emission reduction. Meanwhile, the supercritical CO2 Brayton cycle, benefiting from its compact structure, is well-suited for power generation in limited spaces. Based on the reality that gas temperature and flow rate deviate from the design point with the variation of gas turbine operating conditions, this paper proposes a method for analyzing the off-design performance of supercritical CO2 cycles based on thermal source fluctuations rather than simply providing prescribed temperatures on the cycle side. This method allows for a clear understanding of the impact on the cycle when there are fluctuations in the thermal source. Firstly, the thermodynamic performance and economic viability of four types of supercritical CO2 cycles, namely simple regenerative, recompression, intercooling, and reheat, were optimized using a multi-objective genetic optimization algorithm. Subsequently, the off-design performance of these four cycles was investigated when the gas temperature and flow rate deviate from the design point. The results indicate that, compared to the decrease in gas temperature, the reduction in gas flow rate has a greater impact on the decline in cycle performance. Decreasing gas temperature enhances the exergy efficiency of all four cycles, while reducing gas flow rate initially increases and then decreases the exergy efficiency. The decrease in turbomachinery efficiency significantly contributes to the decline in cycle performance. When the gas flow rate exceeds 55% of the design value, it is recommended to adopt an intercooling cycle arrangement. Otherwise, a recompression cycle layout should be used.

Multi-objective rapid optimization method for a variable-stroke valve train device

Raising the maximum valve lift in two-stroke drive mode is the key to increasing the power output of the variable-stroke engine, so the limit design of the variable-stroke valve train device is critical. The multi-objective genetic optimization algorithm NSGA-2 was coupled with the single-freedom dynamics model of the variable-stroke valve train device. A multi-objective rapid optimization program for the valve train device was constructed in Matlab. Two optimization methods explored the maximum valve lift limit in two-stroke drive mode. The first was to optimize the rocker structure, and the second was to optimize the cam position and the rocker structure together. The results show that the single-freedom dynamics model has sufficient computational accuracy. The collaborative optimization results of the cam position-rocker arm structure are more optimal. Under the same maximum lift of the valve, compared to the results of individual optimization of the rocker arm structure, the maximum contact force is reduced by up to 285 N. The maximum contact stress is reduced by up to 63 MPa through collaborative optimization. Compared with the original case, the maximum increase rate of the maximum valve lift obtained through optimizing the rocker structure is 12.45%, while that obtained through collaborative optimization is 12.85%.

Multi-flow channel proton exchange membrane fuel cell mass transfer and performance analysis

This paper presents a study on the proton exchange membrane fuel cell (PEMFC) with a high humidity three-dimensional two-phase multi-channel model using COMSOL. The effects of gradient porosity in the gas diffusion layer (GDL) and gradient electrolyte volume fraction in the catalyst layer (CL) in the Y-direction on oxygen distribution, liquid water removal, current density surface area reaction rate (SCS), and current density magnitude of PEMFC are investigated. The results indicate that an appropriate gradient porosity in the GDL can improve the performance of PEMFC, especially in terms of oxygen distribution. Similarly, an appropriate gradient electrolyte volume fraction in the CL can enhance the current density uniformity. In addition, an improved multi-objective genetic optimization algorithm (RONSGA-II) is proposed in Chapter 4, along with an optimization method based on neural networks and RONSGA-II. As an example, the gradient porosity in the GDL in the Y-direction is optimized for current density and water removal. The optimized current density is increased by 1.22%, and water removal performance is improved by 16.67%.

Optimization of crashworthiness of CFRP thin-walled beams filled with aluminium honeycomb based on surrogate model

The CFRP thin-walled beam filled with aluminum honeycomb is a new design form in the crashworthiness design of automobile energy-absorbing component. It not only meets the requirements of strength, rigidity and lightweight under the normal load, but also ensures absorption of crash energy in a stable failure mode when a crash occurs. In this paper, under two typical conditions of axial impact and lateral impact, structural sample points are established by orthogonal experiment; a highly feasible surrogate model is constructed based on the radial basis function; the initial load peak Fmax and specific energy absorption (SEA) are taken as the main criteria to evaluate the energy absorption performance of the structure; the multi-objective genetic optimization algorithm NSGA-II is used to optimize the energy absorption design of CFRP thin-wall beams filled with aluminum honeycomb. The structure and material parameters of the minibus frame are optimized according to the crash condition to improve the energy absorption performance of the minibus in frontal and side crashes, and on this basis, an effective method to improve the crashworthiness of minibuses is explored.

Multi-objective optimization of fin shape in a cylindrical encapsulated phase change material for thermal energy storage applications

This study presents a novel fin design for a latent heat storage unit that improves its thermal performance through a methodology that integrates numerical simulation, the response surface method, and a multi-objective genetic optimization algorithm. The stored latent energy per mass (Em) and mean power (Pt) are chosen as the objectives for the optimization process. The fin parameters are optimized using the NSGA-II (non-dominated sorting genetic algorithm) to achieve the objectives of maximizing both the stored latent energy per mass (Em) and mean power (Pt). The proposed design is evaluated through simulations and the results are analyzed to determine the optimal fin configuration and the effect of the fin design parameters on the stored energy and power of the unit. The results show that the implementation of full-fin in the latent heat storage unit leads to a 7.2% reduction in total stored energy, but a 34% increase in power in comparison to the finless case. However, the use of an optimization approach results in a substantial increase in power output while maintaining minimal compromise to the stored latent energy. In particular, the optimal fin configuration, Design B, is recommended when power and stored energy are given equal importance, as it results in a 90.1% increase in power and a negligible 4.3% decrease in stored energy per mass.

Optimizing the Planning of Airport Airside Expansion Projects to Minimize Air Traffic Disruptions and Construction Cost

This paper presents the development of a novel multiobjective model for optimizing the planning of airport airside expansion projects that provides the capability of minimizing both airport operations disruption cost and total construction cost. The model computations are performed in four main modules: (1) a simulation module that calculates and quantifies the impact of airport construction activities on airport operations, (2) a multiobjective genetic algorithms optimization module that identifies an optimal schedule for airport airside expansion projects, (3) a scheduling module that calculates the start and finish times of each construction activity, and (4) a cost module that computes the total cost of construction-related disruptions in airport operations and the total construction cost. A real-life case study of an airport airside expansion project is analyzed to illustrate the use of the model and highlight its original contributions to the body of knowledge including its novel methodologies for (1) quantifying and minimizing the impact of construction activities on airport operations, (2) analyzing and minimizing the impact of air traffic data and airport operations on total construction cost, and (3) generating a set of optimal construction plans including optimal project start time, activities start time, and weekly and daily work plans for all activities. The results of the case study clearly illustrate the original contributions of the developed model and its novel capabilities that are expected to support airport and construction planners in their efforts to improve the cost-effectiveness and functional performance of airports during airside expansion projects.

Novel Partitioned Stator Flux-Switching Permanent Magnet Linear Machine: Design, Analysis, and Optimization

Over the past few decades, flux-switching permanent magnet (FSPM) machines have gained more attention. A novel flux-switching permanent magnet linear machine with a partitioned stator (FSPMLM-PS), which has the advantages of high thrust force density and high cost efficiency for short-stroke applications, is presented and analyzed in this article. Firstly, the twelve mover slots and seven stator poles (12s/7p) structure of FSPMLM-PS is introduced, and the fundamental principle of operation is investigated. The partitioned stator helps with the reduction in iron losses and the overall cost of the proposed FSPMLM-PS. One of the frequent issues in linear machines is the end effect, which is compensated for by setting assistant teeth at both ends of the mover. The proposed machine’s main design specifications are globally optimized through a multiobjective genetic optimization algorithm using JMAG software ver. 16.1, although the volumes of the PM and the magnetic and electric loadings are kept the same. The peak-to-peak flux linkage, thrust force, thrust ripples, and detent force are improved by 26.98%, 27.98%, 22.03%, and 68.33%, respectively, after optimization. The comparison results show that the proposed FSPMLM-PS is preferable to the conventional FSPMLM given in the literature. Under the same PM volume, the proposed machine provides 27.95% higher thrust force density.