In the present study, an artificial neural network (ANN) was developed to predict the thermal and hydrodynamic behavior of two types of Newtonian nanofluids used as coolants in a shell and tube heat ...exchanger (STHE). Inputs of the ANN model are nanoparticle volume concentration, Reynolds number, nanoparticle thermal conductivity, and Prandtl number. Results indicate that the ANN model predicts the experimental data with very high accuracy. Values of Nusselt number resulted from experiments and those obtained from the ANN have at most 9% difference, this value is 9.6% for the pressure drop. Multi-objective optimization was implemented with the aim of minimizing the total pressure drop and maximizing the nanofluids Nusselt number in the STHE according to NSGA-II algorithm. In optimization procedure nanofluids pressure drop and the Nusselt number (tube-side) was evaluated by the ANN model. To find the shell-side pressure drop method of Delaware was employed. Nanofluids concentration and Reynolds number were selected as decision parameters. The Pareto front was obtained. The best solution adopted from points on the Pareto front by two well-known decision-making methods LINMAP and TOPSIS. The Nusselt number of optimal solutions are about 30% greater than the base fluid and pressure drop of optimal solutions are about 10% lower than the base fluid.
Optimization of thermal conductivity (TC) and viscosity of Al2O3-MWCNT/thermal oil hybrid nanofluid with NSGA-II and RSM was investigated. Effect of temperature and volume fraction (VF) on heat ...conduction and viscosity of the nanofluid was studied. Modeling of nanofluid properties of heat conduction and viscosity were done with RSM and MLP methods. Nanofluid TC and viscosity models of RSM have the regression coefficient of R 2=0.9959 and R 2=0.9989, respectively and adjusted regression coefficient of these models are Radj2=0.9947 and Radj2=0.9984, respectively. Based on these values, it can be concluded that this model is suitable for nanofluid TC and viscosity prediction. In MLP modeling the best topology and structure selected between more than 100 investigated topologies and optimal number of neurons determined for each hidden layer. Obtained results from MLP modeling, maximum residual values of nanofluid TC are +0.009, and -0.006 and maximum residual values of nanofluid viscosity are +0.016 and -0.02. Considering result of MLP, it may be concluded that the designed model is highly capable of predicting heat conduction and viscosity of the nanofluid. In optimization with NSGA-II, optimum viscosity and heat conduction were reported in maximum operating temperature. Furthermore, in RSM optimization, the optimum condition using this nanofluid was achieved in 49.99 °C and VF of 1.49% with TC of 0.1820 (W/mK), viscosity of 0.1174 (Pa.sec) and total desirability function of 0.9725. Desirability is a criterion for evaluation of optimization process accuracy. Experimental results revealed that temperature enhancement has a positive effect on both heat conductivity and viscosity of the nanofluid to reach the best nanofluid efficiency. Also, it was concluded that temperature and VF have direct effects on heat conductivity.
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•MLP and RSM models of viscosity and heat conduction of the nanofluid are presented.•The NSGA-II was coupled with RSM and MLP to optimization .•Utilization of RSM in optimization of nanofluid heat conduction and viscosity.•Temperature enhancement has positive effect on heat conduction and viscosity.•Optimum value of viscosity and heat conduction are in maximum operating temperature.
•A methodology for obtaining optimal time-jerk trajectory of robot manipulator is proposed. The methodology applies 5th-order B-spline interpolation method to construct the trajectory and optimizes ...the trajectory with NSGA-II algorithm.•Two virtual points are introduced in the process of B-spline modeling so that the initial and final conditions for jerk can be respected.•A number of solutions lying on or near the Pareto-optimal front are obtained using NSGA-II algorithm.•Improved performance measures are proposed to evaluate the diversity of the Pareto front and the fitness of the Pareto solutions.•Simulated and experimental results validate the proposed methodology together.
A methodology for time-jerk synthetic optimal trajectory planning of robotic manipulators is described in this paper. The trajectory is interpolated in the joint space by means of 5th-order B-spline and then optimized by the elitist non-dominated sorting genetic algorithm (NSGA-II) for two objectives, namely, traveling time and mean jerk along the whole trajectory. 5th-order B-spline interpolation technique enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk while satisfying the continuity of jerk. NSGA-II as a multi-objective optimization technique is used to address the time-jerk optimal trajectory planning problem. The obtained Pareto optimal front provides decision-makers flexible selections on non-dominated solutions for industrial applications. Two performance measures are presented to evaluate the strength of the Pareto optimal front and to select the best optimal solution respectively. Simulations and experiments validate the effectiveness and practicability of the proposed methodology in comparison with those provided by another important trajectory planning methodology.
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•Assembled Vulnerability (AV) is accumulated from optimized DRASTICA and GALDIT.•AVs are mapped at two strategies by NSGA-II multi-objective optimization approach.•Strategy 1 ...maximizes the correlation between NO3 and AV, and between TDS and AV.•Strategy 2 uses a data fusion model and divides data to optimize the weights in the AV.•The correlation coefficient (r) criteria for AV map using the strategy 2 is higher than other vulnerability maps.•The results reveal that the proposed methodology is appropriate for management planning.
Developing Assembled Vulnerability (AV) maps is essential for preventing environmental issues corresponding to the groundwater conditions in aquifers that are exposed to multiple contaminants from multiple origins. There is a research gap in the application of multiple models to produce the AV map of aquifers with two or more origins of contaminant. This study fills this knowledge gap by considering two objectives using the non-dominated sorting genetic algorithm-II (NSGA-II) multi-objective optimization approach to evaluate the vulnerability of coastal urban aquifers to wastewater and seawater intrusion contamination for the first time in the literature. For this purpose, the DRASTICA-GA and GALDIT-GA groundwater vulnerability models as individual models have been combined using NSGA-II to produce the AV maps as multiple models. In this step, two states are considered to produce the AV map: (1) The NSGA-II multi-objective optimization model aims to maximize the correlation coefficients between nitrate (NO3) and AV as well as total dissolved solids (TDS) and AV, and (2) A qualitative index obtained by data fusion (DF) is used in the NSGA-II model to produce the AV map. Finally, to prove the efficiency and applicability of the proposed framework, it is applied to an important aquifer in Oman, the Al Khoudh aquifer, which shows that the north of the Al Khoudh aquifer is more vulnerable than other parts exposed to wastewater and seawater intrusion contamination. In addition, results indicate that the presented methodology can accurately define the AV indices to identify the vulnerable areas corresponding to the different contamination origins. Moreover, the ROC/AUC criteria for the AV-NSGA-II-DF are more than the AV-NSGA-II, which are 0.962 and 0.956, respectively.
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.
In recent years, the demand for dry bulk cargo trade has increased, and the scheduling pressure on bulk cargo ports is increasing daily. The most effective and cost-effective way to alleviate the ...pressure of port scheduling management rapidly is to optimize the overall scheduling of different port resources. Channel vessel scheduling and berth–yard loading and unloading operations are usually regarded as independent operations; however, plans that meet the needs of comprehensive port management can only be effectively developed through the integrated scheduling of multiple resources. Therefore, from the perspective of overall port management and operation, considering practical factors in scheduling, this article construct a multiobjective optimization model for the overall scheduling of restricted channels, berths, and yards in bulk cargo ports. Based on actual allocation principles, corresponding allocation algorithms for storage yards and stacker–reclaimer machines are designed, and these algorithms are combined with a multiobjective genetic algorithm to construct the NSGA-II-DPGR algorithm for model calculation. Results show that the proposed model and algorithm can provide vessel scheduling and cargo loading and unloading plans. This article experimentally verify the rationality of the results, the superiority of the strategy compared to the traditional first-come, first-serve strategy, and the feasibility of combining the proposed model and algorithm with onshore scheduling in the future.
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•Introducing a cost-benefit optimisation framework for Blue-Green Infrastructure.•The use of a detailed flood model provides exact locations for interventions.•Smaller-sized permeable ...interventions in many places offer better cost-benefits.
Designing city-scale Blue-Green Infrastructure (BGI) for flood risk management requires detailed and robust methods. This is due to the complex interaction of flow pathways and the need to assess cost-benefit trade-offs for various BGI options. This study aims to find a cost-effective BGI placement scheme by developing an improved approach called the Cost OptimisatioN Framework for Implementing blue-Green infrastructURE (CONFIGURE). The optimisation framework integrates a detailed hydrodynamic flood simulation model with a multi-objective optimisation algorithm (Non-dominated Sorting Genetic Algorithm II). The use of a high-resolution flood simulation model ensures the explicit representation of BGI and other land use features to simulate flow pathways and surface flood risk accurately, while the optimisation algorithm guarantees achieving the best cost-benefit trade-offs for given BGI options. The current study uses the advanced CityCAT hydrodynamic flood model to evaluate the efficiency of the optimisation framework and the impact of location and size of permeable interventions on the optimisation process and subsequent cost-benefit trade-offs. This is achieved by dividing permeable surface areas into intervention zones of varying size and quantity. Furthermore, rainstorm events with 100-year and 30-year return periods are analysed to identify any common optimal solutions for different rainfall intensities. Depending on the number of intervention locations, the automated framework reliably achieves optimal BGI implementation solutions in a fraction of the time required to find the best solutions by trialling all possible options. Designing and optimising interventions with smaller sizes but many permeable zones save a good fraction of investment. However, such a design scheme requires more computational time to find optimal options. Furthermore, the optimal spatial configuration of BGI varies with different rainstorm severities, suggesting a need for careful selection of the rainstorm return period. Based on the results, CONFIGURE shows promise in devising sustainable urban flood risk management designs.
The development of cost-effective high-performance concrete (HPC) has long been a focus of concrete research. Multiple objectives are required for the design of the HPC mix proportion. This paper ...develops a hybrid intelligent framework based on the random forest (RF) algorithm, the least-squares support vector machine (LSSVM) algorithm and the nondominated sorting genetic algorithm with an elite strategy (NSGA-II) to realize the efficient optimization of concrete mixture. The developed framework can identify the key influencing factors in terms of the concrete mix proportion, predict concrete performance, and obtain a series of optimized solutions through multi-objective optimization. The optimal solution is then determined according to the engineer's preference as the recommended mix proportion. An actual engineering case is studied to verify the feasibility of the developed framework, in which the material proportions in the concrete composition are taken as decision variables and the frost resistance, impermeability and cost of the concrete are taken as objectives. The results are as follows: (1) In the RF-based importance ranking, the water–binder ratio, cement content, fly ash content, fine aggregate content, coarse aggregate content and compound superplasticizer content are found to be key factors influencing concrete durability, indicating that predictions based on these factors will yield more accurate results. (2) LSSVM models show excellent predictive fitting capabilities, with the goodness of fit for predicting the frost resistance and impermeability of concrete reaching 0.94084 and 0.9443, respectively. The obtained surrogate model can be used to establish the fitness function for the optimization algorithm to improve efficiency. (3) The LSSVM–NSGA-II algorithm can determine the optimal mix proportion for concrete considering durability and cost. Compared with the average performance of the original mixture, the permeability and frost resistance of the obtained mixture are increased by 30.71% and 3.17%, respectively, and the cost is lower by 1.84%. In practice, the proposed approach can provide guidance for realizing multi-objective optimization of concrete and improve the efficiency of concrete mix proportion design. Notably, the current algorithm needs to be trained on a large amount of data to obtain accurate results; in the future, either a large amount of data should be collected or the algorithm should be improved to enhance its universality.
Metamaterials with honeycomb structures have garnered significant attention in energy-harvesting applications. In this study, we present three conventional cantilever resonators with honeycomb ...substrates featuring positive, zero, and negative Poisson’s ratios (PPR, ZPR, and NPR). Polyvinylidene fluoride consisting electrode layers is attached to the substrate surfaces. A process parameter optimization method of honeycomb structure design was proposed, in which finite element simulation for data generation, backpropagation neural network for data prediction, and nondominated sorting genetic algorithm II (NSGA-II) for data optimization. This method aims to determine the optimum geometric parameters of the honeycomb structures, which addresses general cantilever resonators, where the first structural vibration modes are typically of interest and have to match specific target eigenfrequencies for engineering applications. Finite element simulations show that the peak voltage of PPR, ZPR, and NPR resonators at 75 Hz is increased by 12 %, 22 %, and 32 %, respectively, due to optimization. The optimized structures are fabricated and measured to validate the numerical model. The performance of the resonators with cellular materials featuring a full characteristic range of Poisson’s ratios is then compared systematically to explore their energy-harvesting potential. The results revealed that the NPR structure exhibits superior performance for energy-harvesting. Moreover, stress distribution and displacement have also been highlighted in this paper.
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•A backpropagation neural network is trained to predicting the performance of piezoelectric energy harvester.•An optimization method combining backpropagation neural network and evolutionary algorithm is developed.•The performance of piezoelectric energy harvesters with different cellular substrates are systematically compared.•The optimization results are validated by finite element analysis and experiments.
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•Introducing novel hybrid approaches for optimizing intricate façade designs.•Significantly reducing computational cost and time by adopting the hybrid approach.•Justifying ...evolutionary algorithm settings by conducting approximate models.•Optimizing façade patterns regarding visual comfort and energy performance.
Parametric tools in architecture allow for the design of complex and multi-dimensional forms on building facades. Among these, geometric patterns can mitigate direct sunlight and enhance energy efficiency. However, conventional simulation methods and available optimization tools are prohibitively expensive for optimizing such complex forms. To address this challenge, this study proposes two innovative hybrid workflows that integrate parametric modeling, evolutionary approximate and accurate models (NSGA-III), clustering through k-means algorithm, and local search (Tabu search) technique. The outcomes obtained from employing these hybrid approaches demonstrate a substantial reduction in computational time and costs while simultaneously achieving optimal results. Additionally, an extensive comparison between the two proposed methodologies is presented encompassing factors such as performance metrics and computational expenses incurred during implementation. The findings derived from pattern optimization reveal several key insights: increasing pattern counts; dispersing them across the facade; minimizing the distance between the pattern wall from windows; adopting a south-facing orientation with positive vertical rotation – all contribute towards diminishing energy consumption (measured by Energy Use Intensity or EUI) within cold climates. However, material selection for these patterns primarily affects visual comfort levels.
