Multi-objective Optimization Strategy Research Articles

Energy saving technology design and multiple performance evaluation for the extractive distillation process to recover benzene and isopropanol from wastewater by mixed entrainer

In the industrial production of chloramphenicol, a ternary azeotrope consisting of isopropanol, benzene and water exists. A new process for the extractive distillation of recycling benzene and isopropanol from wastewater by mixed entrainer was proposed. Quantum chemical analysis and relative volatility analysis were employed to screen suitable entrainers. The multi-objective optimization scheme was established to determine the basic process parameters such as such as composition and ratio. Thermal coupled extractive distillation process and heat pump with an assisted extractive distillation process were used to improve energy saving. Subsequently, the interaction energy between the entrainer and the system was computed via molecular dynamics, and different processes are evaluated by economic analysis, environmental analysis, energy consumption analysis and exergy analysis. The results indicated that the optimal mixed entrainer under decompression pressure, it was 40.37 % dimethyl sulfoxide +59.63 % glycerol. The thermally coupled extractive distillation process 1 has the best economic benefits and has good thermodynamic efficiency and environmental advantages. Compared with ethylene glycol extractive distillation process, total annual cost is reduced by 16.93 % and gas emissions is reduced by 43.19 %.

A greedy post-processing strategy for multi-objective performance optimization of general single-server finite queueing networks

A greedy post-processing strategy for multi-objective performance optimization of general single-server finite queueing networks

Adaptive projection plane and reference point strategy for multi-objective particle swarm optimization

Achieving a balance between convergence and diversity and their mutual enhancement is a complex task in the process of algorithm improvement. This is crucial because it is directly related to the effectiveness of the algorithm in obtaining accurate and uniformly distributed Pareto frontiers. Although significant progress has been made in particle swarm algorithms, exploring new approaches is necessary. In this paper, we construct a projection plane (projection line in 2D) based on the extreme values of the non-dominated solutions, select a set of uniform reference points on the projection plane, and then project the non-dominated solutions onto the constructed projection plane to form projection points. The reference points and projection points on the projection plane are thus utilized to guide the updating of the population as well as the maintenance of the external archive, a strategy that enhances the algorithm's global exploration and local exploitation capabilities. Secondly, we aggregate the target values of particles into a single scalar value and combine the idea of particle fusion to design a scheme for the particle selection of individual optimal particles. This paper further improves the algorithm's overall performance by using the information between populations to select individual optimal particles. Lastly, it is evaluated against a number of multi-objective algorithms that are currently in use and perform well on 22 test problems. The findings demonstrate that the algorithm this paper proposes performs better when solving multi-objective problems.

Multi- objective modeling and optimization of dissimilar laser welding by integrating an artificial intelligence predictive model with NSGA-II algorithm

This study presents a pioneering approach in laser welding of two dissimilar materials by integrating an Artificial Intelligence (AI)-based on predictive model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing key performance characteristics of Nickel-based alloy and duplex 2205 stainless steel. Focusing on four primary input variables including laser power, welding speed, focal distance and deviation, the research aims to predict and optimize the responses including temperature field adjacent to the melt pool, penetration depth, and tensile strength of the joint according to the experimental results. The developed AI model first accurately forecasts these characteristics based on the inputs. This predictive accuracy is critical in defining the optimal target values. Considering the multidimensional nature of the problem, where enhancing one characteristic could compromise another, the study employs a Multi-Objective Optimization (MOO) strategy. This is where the innovative integration with NSGA-II becomes pivotal. Renowned for its efficiency in navigating multiple, potentially conflicting objectives, NSGA-II assists in achieving a balanced optimization of all target parameters. This method is adept at considering the complex interdependencies among various characteristics. The novelty of this work lies in its unique combination of AI for prediction and MOO for optimization and it is for the first time that machine learning is applied on this kind of alloys with four features and four targets.

Multi-objective optimization strategy for the distribution network with distributed photovoltaic and energy storage

The randomness and fluctuation of large-scale distributed photovoltaic (PV) power will affect the stable operation of the distribution network. The energy storage system (ESS) can effectively suppress the power output fluctuation of the PV system and reduce the PV curtailment rate through charging/discharging states. In order to improve the operation capability of the distribution network and PV consumption rate, an optimal multi-objective strategy is proposed based on PV power prediction. First, the back propagation (BP) neural network with an improved genetic algorithm (GA) is used to predict PV power output. Furthermore, an adaptive variability function is added to the GA to improve the prediction accuracy. Then, the distribution network model containing distributed PV and the ESS is constructed. The optimal object contains network power loss, voltage deviation, and PV consumption. The model is solved based on the improved multi-objective particle swarm optimization (MOPSO) algorithm of Pareto optimality. The probabilistic amplitude is adopted to encode the particles for avoiding local optimal. Finally, the proposed optimal strategy is verified by the IEEE 33-bus distribution network. The results show that the proposed strategy has an obvious effect on reducing the network power loss and voltage deviation, as well as improving the PV consumption rate.

Digital twin-driven architecture for AIoT-based energy service provision and optimal energy trading between smart nanogrids

This article explores integrating digital twin technology and blockchain within smart grids to optimize energy trading among prosumers and consumers in smart nanogrids. Our platform employs a multi-objective optimization strategy, including Particle Swarm Optimization (PSO), to delineate energy trading routes between nanogrids, optimizing parameters such as route distance, surplus renewable energy, and energy power loss. Our platform ensures efficient and effective energy trading services by meticulously considering factors such as surplus energy amount, energy price, route distance, and time. The proposed digital twin-based architecture comprises seven layers, each tailored to address specific functionalities and services for energy management within smart nanogrids. At the apex lies the application layer (digital twin services), leveraging the digital twin's capabilities to optimize energy trading, manage surplus energy, and efficiently meet energy demand. This layer facilitates informed decision-making and resource optimization. Integrating a digital twin-driven architecture with a blockchain-based platform tackles challenges inherent in decentralized energy trading. The digital twin offers real-time energy resource monitoring and optimisation, ensuring efficient utilisation and autonomous decision-making. Concurrently, leveraging blockchain technology ensures secure and transparent transactions, fostering trust among participants and facilitating peer-to-peer energy exchange. Task generation, device virtualization, task mapping, scheduling on edge devices, and task assignment layers further streamline task execution and resource utilization, enhancing the efficiency of energy management processes. The predictive optimal energy control layer also orchestrates the entire architecture, enabling predictive and optimized energy control within smart nanogrids. Furthermore, the Security as a Service (SECaaS) layer enhances security and trustworthiness using blockchain technology, incorporating components such as consensus management, real-time distributed ledgers, and identity management. This layer enhances the security and transparency of energy-related transactions and data within the digital twin framework. The results showcase a remarkable 53% reduction in peak load, emphasizing the optimized energy consumption and demand achieved. Furthermore, our platform has significantly increased the utilization of renewable energy resources by 24%, highlighting its contribution to sustainable energy resource management. Rigorous assessment of the prediction and optimization modules reveals their high accuracy and precision, with mean absolute percentage error (MAPE) values of 15.125 and 14.369, respectively. These findings underscore the efficacy and reliability of our digital twin-based approach, surpassing existing solutions and benchmarks.

Global Path Planning Algorithm for Unmanned Vehicles Based on Multi-Objective Particle Swarm Optimization Strategy

To overcome the limitations of unmanned vehicle global path planning with the sole objective of distance, a multi-objective particle swarm optimization strategy is proposed for indoor unmanned vehicle path planning. This strategy integrates objectives related to travel distance and cumulative turning angle. The traditional distance function is enhanced to accelerate algorithm convergence, and cumulative turning angle is introduced to construct a comprehensive function, meeting the demands of multi-objective navigation. The Pareto solution set concept is incorporated, and through the multi-objective particle swarm optimization algorithm, optimal paths for different travel objectives are identified, enhancing solution comprehensiveness. Experimental results validate the feasibility and effectiveness of the improved algorithm.

Statistical modeling and multi-objective optimization of a flat tubular indirect evaporative cooler for enhanced performance

The flat tubular indirect evaporative cooler complements the conventional round tubular indirect evaporative cooler owing to its superior wetting surface area and enhanced heat transfer capacity. Given the multitude of influencing parameters, achieving a comprehensive analysis and optimization of the flat tubular indirect evaporative cooler demands a significant number of experimental tests or numerical simulations. Consequently, this study seeks to employ statistical techniques for constructing a precise and expeditious performance prediction model for the flat tubular indirect evaporative cooler. Nine pivotal parameters were selected to evaluate their impact on the cooling performance of the flat tubular indirect evaporative cooler. A numerical model was developed and verified experimentally. The response surface method was applied to establish polynomial regression equations for the cooling performance prediction of flat tubular indirect evaporative coolers. In addition, this work proposed a multi-objective optimization strategy for the proposed cooler. By innovatively using weighting factors to assign different weights to response values, multi-objective optimization with preferences was performed for the flat tubular indirect evaporative cooler with different demands to increase the applicability of the proposed cooler. The optimization results showed that when only the optimized wet-bulb efficiency was considered, the wet-bulb efficiency can reach up to 81.1%. When optimizing both the wet-bulb efficiency and the coefficient of performance simultaneously, the coefficient of performance can be up to 58.0, and the wet-bulb efficiency was 64.7%.

Bi-Objective Battery Electric Truck Dispatching Problem with Backhauls and Time Windows

The battery electric truck (BET) has emerged as a promising solution to reduce greenhouse gas emissions in urban logistics, given the current strict environmental regulations. This research explores the formulation and solution of the bi-objective BET dispatching problem with backhauls and time windows, aiming to simultaneously reduce environmental impacts and enhance the efficiency of urban logistics. From the sustainability perspective, one of the objectives is to minimize total energy costs, which include energy consumption and battery replacement expenses. On the other hand, from an economic perspective, the other objective is the minimization of labor costs. To solve this bi-objective BET dispatching problem, we propose an innovative approach, integrating an adaptive large neighborhood search-based metaheuristics algorithm with a multi-objective optimization strategy. This integration enables the exploration of the trade-off between fleet energy expenses and labor costs, optimizing the dispatching decisions for BETs. To validate the proposed dispatching strategy, extensive experiments were conducted using real-world fleet operations data from a logistics fleet in Southern California. The results demonstrated that the proposed approach yields a set of Pareto solutions, showcasing its effectiveness in finding a balance between energy efficiency and labor costs in urban logistics systems. The findings of this research contribute to advancing sustainable urban logistics practices and provide valuable insights for fleet operators in effectively managing BET fleets to reduce environmental impacts while maintaining economic efficiency.

Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization.

The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design's performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.

Multi-objective optimization of hot embossing process for high-quality glass micro gratings

This study proposed a multi-objective optimization strategy to produce high-quality glass micro gratings. The applied methods include factor screening, surface response methodology (RSM), non-dominated sorting genetic algorithm (NSGA-II) and decision-making. Specifically, the significant factors were selected among several process parameters in hot embossing through screening experiments. Afterward, the regression models for the transferred ratio, uniformity of glass microstructures and warpage concerning those significant process parameters were established. Meanwhile, the significance of selected factors and their interactions was analyzed. Based on the regression models, the Pareto front was obtained by using NSGA-II program, where the top 10 solutions were applied in glass hot embossing. Finally, a decision-making strategy was employed to further choose a feasible solution that allows the fabrication of glass micro gratings with a high transfer ratio, high uniformity, low warpage, low surface roughness, and no defect. The experimental results indicated that embossing temperature has the most significant effect on the replication accuracy of glass micro gratings, followed by embossing force and embossing time. However, warpage was not significantly sensitive to any process parameters in this hot embossing process. The defect-free glass micro gratings could be eventually fabricated under the optimal process parameters, achieving a transfer ratio and uniformity of higher than 95 % and 83 %, respectively. Moreover, their Ra surface roughness and warpage can be lower than 13 nm and 1.5 μm. The satisfactory quality of embossed micro gratings revealed that the proposed multi-objective optimization strategy is feasible in glass hot embossing.

Multi-objective optimization strategy for regional multi-energy systems integrated with medium-high temperature solar thermal technology

The high proportion and volatility of renewable energy pose a significant challenge to efficient collaboration between photovoltaic/thermal and wind power in multi-energy systems. Therefore, this paper proposes a Regional Multi-Energy System (RMES) based on Medium-High Temperature Solar Thermal (MHTST) technology. The novelty of the solution integrates dispatchable photovoltaic and photothermal conversion pathways, facilitating flexible and controllable utilization of solar energy via installed Thermal Energy Storage (TES). A multi-objective optimization model is developed to ensure the optimal operation of the proposed system under typical seasonal scenarios. The method that combines Branch-and-Reduce with Multi-Learning-Task is used to handle the formulated Mixed Integer Nonlinear Programming (MINLP) and swiftly identify optimal Pareto solutions. Case studies are performed over the improved united communities of the Test Region for Energy Transition and Comprehensive Reform in Taiyuan, China. Compared with the conventional photovoltaic technology, the simulation results show that the proposed solution has superior performance in both winter-heating and summer-cooling operations across 24-hour scheduling periods: operating costs reduced by 12.5% and 18.7%, carbon emissions decreased by 7.4% and 21.4%, with local solar power accommodation increased by 263.89kW and 487.47kW, respectively.

Optimized design of composition and brazing process for Cu-Ag-Zn-Mn-Ni-Si-B-P alloy brazing material based on machine learning strategy to improve brazing properties

As brazing devices become more sophisticated and service environments become more demanding, Cu-Ag-Zn-Mn-Ni-Si-B-P brazing material are subjected to higher wettability and brazed joint strength requirements. In this paper, a machine learning strategy is proposed. Firstly, the composition-process-properties dataset is obtained based on high-throughput experimental techniques. Then, the composition features are constructed by elemental chemical and physical parameters instead of the direct input of alloy composition, and the proposed novel feature selection method is used to effectively identify the key composition feature combinations that affect brazing properties. Then, the support vector regression algorithm was used to establish a joint strength model with an error of less than 3.47 % and a spreading area model with an error of less than 8.37 % using the key alloy composition features and brazing process features as inputs, respectively. Finally, two multi-objective optimization strategies, Bayesian optimization and NSGA-II genetic algorithm, were used for the integrated optimization design of composition and brazing process. The experimental results show that the Cu-14Ag-4Zn-24Mn-13.5Ni-0.4Si-0.3B-0.3 P alloy and the matched brazing process (945 °C and 12 min) were optimally designed using NSGA-II genetic algorithm, corresponding to the measured joint strength and spread area of 356 MPa and 412 mm2, respectively, which outperforms the high-throughput experimentally optimized data as well as commercialized data. The machine learning strategy established in this work provides a new research idea for the comprehensive improvement of brazing properties of brazing alloys with small data sets.

Adaptive Sampling Offspring Generation Strategy for Multi-objective Optimization

A covariance adaptive sampling offspring generation strategy (CASS) based on fuzzy clustering is proposed, and a multi-objective distribution estimation algorithm (MEDCA) based on this strategy is introduced. The GK-FCM clustering partitioning strategy is designed to build a Gaussian model for each individual, collectively approximating the manifold of the Pareto solution set and generating offspring through sampling. The introduction of an individual’s survival generation adapts the individual’s preference for exploration and exploitation. This is achieved by incorporating it as a scaling factor of the covariance matrix in the sampling model, in order to satisfy the individual’s preferences for development and exploration in different evolutionary stages. This method significantly improves the performance of MEDCA in solving complex multi-objective optimization problems through covariance matrix adaptation sampling strategy and scaling factor adaptation strategy. The experimental results demonstrate the advantages of MEDCA in the application of offspring generation strategies during model sampling.

Deterministic mathematical modeling, sensitivity analysis, and dynamic optimization of cross-flow ultrafiltration systems for concentration of monoclonal antibody solutions

This manuscript proposes a general new framework for mathematical modeling, extended sensitivity analysis and dynamic optimization of tangential flow filtration (TFF) systems for concentration of monoclonal antibody (mAb) products and, potentially, other biologics. This framework is comprised of four major components: (I) a new first-principles-inspired TFF model; (II) dedicated parameter estimation strategies for automated model training; (III) new extended sensitivity analysis techniques for enhancing TFF phenomenological understanding and providing general guidance on TFF process development; and (IV) novel mono-objective and multi-objective dynamic optimization strategies for optimal TFF design and operation. The application of this framework to Bovine immunoglobulin γ (IgG) – a mAb analog in terms of physicochemical properties – shows the potential benefits it may offer in terms of overall TFF performance and rapid TFF development for new mAb candidates, compared to the current state of the art.

Multi-objective optimization strategy for multipolar magnesium electrolysis cell based on thermal-electric model

In the operation of industrial multipolar magnesium electrolysis cell, cell voltage and current density are important indicators of the production performance of the cell, while thermal balance is a key factor for normal operation. This paper utilizes the finite element method to establish a three-dimensional full cell thermal-electric model, studying the thermal field distribution and current density distribution in a 165kA multipolar magnesium electrolysis cell. Based on the heat balance of the multipolar cell, the influence of structural parameters on current intensity, average current density at the anode surface and resistance voltage is assessed. Dimensionless analysis yielded optimization criteria, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for multi-objective optimization problems. When stringent optimization is executed, significant performance improvements can be achieved in terms of the electrolysis cell's resistance voltage and current density. Moreover, under the optimization criteria, the relative error of the 265kA multipolar magnesium electrolysis cell model is less than 1 %. The results indicate that the workflow combining the finite element method with multi-objective optimization algorithms can be applied to the optimization and scale-up design of high-current multipolar magnesium electrolysis cell.

Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm

To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.

Thermodynamic performance of an innovative space nuclear Brayton cycle with S–N2O

The supercritical Brayton cycle system is renowned for its characteristics of elevated high-power density, compact structure, lightweight design, and extended operational lifespan, rendering it as a preeminent energy conversion approach for space nuclear power systems. Using nitrous oxide (N2O) as the working fluid in a supercritical Brayton cycle system enables heightened efficiencies at comparatively lower cycle temperatures. This study examined four distinct layout configurations, culminating in an in-depth analysis of their respective thermodynamic performances. Among the configurations analyzed, the newly proposed recompression Brayton cycle with partial cooling and heat recovery (RBC-PCHR) layout exhibited the best thermodynamic performance compared to other cycle configurations. This superiority can be principally attributed to augmented heat recovery procedures and reduced compression workloads. A detailed multi-objective optimization strategy is subsequently embarked upon, building upon the insights gained from the sensitivity analysis pertaining to the key operating parameters. The optimization outcomes show significant enhancements, with an increase of 1.37% in ηth and 32.18% in MBRU, along with a marginal decrease of 0.70% in ηex.

Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations

Radio-frequency quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to repeatedly perform high-fidelity simulations. Several recent papers have demonstrated that machine learning can be used to build surrogate models (fast-executing replacements of computationally costly beam simulations) for order-of-magnitude computing time speedups. However, while these pilot studies are encouraging, there is room to improve their predictive accuracy. Particularly, beam summary statistics such as emittances (an important figure of merit in particle accelerator physics) have historically been challenging to predict. For the first time, we present a surrogate model trained on 200 000 samples that yields < 6% mean average percent error for the predictions of all relevant beam output parameters from defining RFQ design parameters, solving the problem of poor emittance predictions by identifying and including hidden variables which were not accounted for previously. These surrogate models were made possible by using the Julia language and GPU computing; we briefly discuss both. We demonstrate the utility of surrogate modeling by performing a multi-objective optimization using our best model as a callback in the objective function to select an optimal RFQ design. We consider trade-offs in RFQ performance for various choices of Pareto-optimal design variables—common issues for any multi-objective optimization scheme. Lastly, we make recommendations for input data preparation, selection, and neural network architectures that pave the way for future development of production-capable surrogate models for RFQs and other particle accelerators.

Research on the optimization of the clean heating system for regional residential buildings based on the multi-objective optimization strategy

For the residential building district heating (RBDH) system, choosing the appropriate combination of heat sources according to local conditions is the key to improving economic efficiency. In this study, three climatic regions in China were selected, namely, a hot summer and cold winter region, cold region, and severe cold region. Among them, Nanjing, Tianjin, and Shenyang were selected as typical representative cities in the hot summer and cold winter region, cold region, and severe cold region, respectively. Taking the levelized cost of heat (LCOH) as the economic evaluation index and considering the carbon emission cost of the system operation, the energy consumption and the CO2 emissions were analyzed. TRNSYS software was used to simulate and analyze the system performance. The multi-objective optimization based on a genetic algorithm was proposed to optimize system parameters. From an economic point of view, the SA system was suitable for the hot summer and cold winter region, the SAS system was suitable for the cold region, and the SE system was suitable for the severe cold region. The operation control strategy based on quality adjustment can reduce heating energy consumption and maintain indoor temperatures at approximately 20°C. The proportion of clean energy heating in the optimized heating system decreased after the multi-objective optimization strategy. However, the initial investment and maintenance costs of the system were reduced, which reduced the LOCH of the system. Therefore, the multi-objective optimization strategy can effectively reduce the heating costs.