Non-dominated Sorting Genetic Algorithm II Research Articles

Three-dimensional numerical investigation on flow and heat transfer characteristics and multi-objective optimization of interconnected microchannels

The interconnected microchannel demonstrates excellent performance in enhancing flow heat transfer. However, the optimal width of the interconnecting slot has not been thoroughly investigated. To address this gap, this paper numerically simulates the three-dimensional flow and heat transfer of interconnected microchannels featuring varied slot widths and channel depths. The investigated slot widths range from 50 to 1000 μm and depths of 100, 200, and 300 μm. Evaluation of overall performance encompasses heat transfer and flow characteristics such as average wall temperature, heat transfer coefficient, thermal resistance as well as pressure drop. It is the interconnected slots that interrupt the velocity and thermal boundary layers, contributing to enhanced heat transfer. Furthermore, the back propagation neural network and non-dominated sorting genetic algorithm-II are utilized for multi-objective optimization of thermal resistance and pressure drop. The technique for order preference by similarity to an ideal solution (TOPSIS) method combined with the entropy weight method is employed to select the compromise optimal solution from the Pareto optimal solutions. The optimized slot widths are 369, 424, and 167 μm for channel depths of 100, 200, and 300 μm, respectively. This research provides data support for the optimization design and engineering manufacturing of interconnected microchannels.

Delivery Route Scheduling of Heterogeneous Robotic System with Customers Satisfaction by Using Multi-Objective Artificial Bee Colony Algorithm

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method.

Anti-Vibration Method for the Near-Bit Measurement While Drilling of Pneumatic Down-the-Hole Hammer Drilling

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in the fields of heat reservoir exploitation and geological exploration owing to its advantages of high efficiency and low pollution. However, the vibration near the bit is up to 40 g while DTH hammer drilling, which significantly affects the performance and longevity of the near-bit measurement while drilling (MWD). To enhance the environmental adaptability of the near-bit MWD in pneumatic DTH operations, a design method for a vibration-damping system based on the parameter optimization of a non-dominated sorting genetic algorithm II (NSGA-II) is proposed in this study. First, the whole structure of the near-bit MWD is designed, including the MWD sub-shell, sensors, measurement circuits, batteries, and connecting structures (the circuit unit). Secondly, this study analyzes the vibration characteristics of the pneumatic DTH hammer near the bit. According to the damping structure, the vibration response model for the circuit unit and the damping model are established. Thirdly, NSGA-II is employed to optimize the parameters of the damping model in terms of the low-frequency, high-intensity vibration characteristics near the bit in pneumatic DTH operations, thereby devising a damping scheme tailored to the unique conditions of DTH hammer drilling. Finally, vibration experiments were conducted to verify the effectiveness of the vibration-damping device. The experimental results indicate that within the vibration frequency range of 5–20 Hz and vibration level of 10–40 g, the peak attenuation rate of the circuit unit is more than 86.446%, and the improvement rate of the vibration stability of the system is more than 75.214%; the anti-vibration performance of the near-bit MWD system in DTH hammer drilling is improved remarkably. This study provides strong technical support for the stability of MWD equipment under such special working conditions. It has broad engineering application prospects.

Optimizing High-Performance Predictive Modeling of the Medium-Speed WEDM Processing of Inconel 718

The purpose of this research was to create a predictive model for a medium-speed wire electrical discharge machine (WEDM) utilizing an artificial neural network (ANN). Medium-speed WEDM experiments were developed based on the I-optimal mixture design for machining, the Inconel 718 superalloy. During the experiment, the input parameters were the spark ontime, spark offtime, wire feed, and current, with the material removal rate (MRR) and surface roughness (Ra) selected as performance indicators. The ANN model was trained on experimental data and built using a feed-forward backpropagation neural network with a (4-8-2) structure and the Bayesian regularization (BR) learning approach. The model correctly predicted the relationship between the medium-speed WEDM’s primary process parameters and machining performance. An integrated ANN model and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) were used to determine the ideal parameters for the MRR and Ra, resulting in a set of Pareto-optimal solutions. The confirmation experiment revealed that the mean prediction error between the experimental and ideal solutions had a maximum error percentage of 1% for the MRR and 2% for the Ra, which are within acceptable ranges. This showed that the best process–parameter combinations were better for the MRR and Ra.

Balancing Economic Growth, Carbon Emissions, and Sequestration: A Multi-Objective Spatial Optimization in Zhengzhou Metropolitan Area in China

As China’s “Dual Carbon” strategy is implemented and the new urbanization advances, balancing economic development, emission reduction, and carbon sequestration has become an important issue during the growth of emerging metropolitan areas, and it is also important for achieving high-quality urban development. Therefore, this study had three major objective functions: economic growth, carbon emission reduction, and increased carbon storage. The multi-objective land use quantity structure was solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the best solution in the solution set was introduced using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for evaluation. Finally, combined with the Future Land Use Simulation (FLUS) model, the low-carbon evolution of the metropolitan area was predicted on a spatial scale. The trade-off plan results show that by 2035, the economic benefits will reach CNY 7.65 trillion, carbon emissions will be kept under 99.24 million tons, and carbon storage will steadily increase by 15.2 million tons. Therefore, optimizing land use from the perspective of balancing carbon emissions, carbon sequestration, and economic development can provide valuable references for planning low-carbon development and the rational use of spatial resources in future metropolitan areas.

Optimization of Secondary Chlorination in Water Distribution Systems for Enhanced Disinfection and Reduced Chlorine Odor Using Deep Belief Network and NSGA-II

This research explores the strategic optimization of secondary chlorination in water distribution systems (WDSs), in order to enhance the efficiency of disinfection while mitigating odor and operational costs and promoting sustainability in water quality management. The methodology integrates EPANET simulations for water hydraulic and quality modeling with a deep belief network (DBN) within the deep learning framework for accurate chloric odor prediction. Utilizing the non-dominated sorting genetic algorithm-II (NSGA-II), this methodology systematically balances the objectives of chloride dosage and chloramine formation. It combines a chloric odor intensity assessment, a multi-component kinetic model, and dual-objective optimization to conduct a comparative analysis of case studies on secondary chlorination strategies. The optimal configuration with five secondary chlorination stations reduced chloric odor intensity to 1.20 at a cost of USD 40,020.77 per year in Network A while, with eight stations, chloric odor intensity was reduced to 0.88 at a cost of USD 71,405.38 per year in Network B. The results demonstrate a balanced trade-off between odor intensity and operational cost on one hand and sustainability on the other hand, highlighting the importance of precise chlorine management to improve both the sensory and safety qualities of drinking water while ensuring the sustainable use and management of water resources.

Failure Probability-Based Optimal Seismic Design of Reinforced Concrete Structures Using Genetic Algorithms

Artificial intelligence (AI) has enabled several optimization techniques for structural design, including machine learning, evolutionary algorithms, as in the case of genetic algorithms, reinforced learning, deep learning, etc. Although the use of AI for weight optimization in steel and concrete buildings has been extensively studied in recent decades, multi-objective optimization for reinforced concrete (RC) and steel buildings remains challenging due to the difficulty in establishing independent objective functions and obtaining Pareto fronts. The well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is an efficient genetic algorithm approach for multi-objective optimization. In this work, the NSGA-II approach is considered for the multi-objective structural optimization of three-dimensional RC buildings subjected to earthquakes. For the objective of this study, two function objectives are considered: minimizing total cost and the probability of structural failure, which are obtained via several nonlinear seismic analyses of the RC buildings. Beams and columns’ cross-sectional dimensions are selected as design variables, and the Mexican Building Code (MBC) specifications are imposed as design constraints. Pareto fronts are obtained for two RC-framed buildings located in Mexico City (soft soil sites), which demonstrate the efficiency and accuracy of NSGA-II for structural optimization.

Intelligent design of nerve guidance conduits: An artificial intelligence‐driven fluid structure interaction study on modelling and optimization of nerve growth

AbstractNerve guidance conduits (NGCs) have been shown to be effective in promoting nerve regeneration in a variety of clinical applications, including nerve defects resulting from a trauma or surgery. By providing a conducive environment for nerve growth, NGCs can help to restore function in nerve‐damaged patients. Challenges include limited repair length, difficulty replicating natural nerve, and rapid substance degradation affecting neurotrophic factor delivery. Considering these issues with mass transfer and fluid structure interaction (FSI) emphasizes the need for enhancing nerve regeneration efficiency. To facilitate nerve growth and deliver appropriate amount of growth factors, these conduits need to be designed with specific topological, mechanical, and biological properties. Additionally, considerations must be given to functional mass transfer FSI design. An intelligent NGC design is proposed as an evaluation‐optimization and AI‐based method. It is found that design parameters significantly impact the physical properties being optimized, including hydraulic pressure, porosity, diffusivity, water absorption, and maximum stress. The mathematical surrogate model obtained from data‐based modelling is used for artificial intelligence (AI) optimization algorithms, differential evolution (DE), and non‐dominated sorting genetic algorithm II (NSGA‐II). It is revealed that both DE and NSGA algorithms generate nearly identical solutions, ensuring the robustness of ML optimization. Our results show that NGC with the thickness of 750 μm results in more than 170% augmentation of porosity. Moreover, at a constant ovality, increasing the channel thickness results in more than 39.2% augmentation of the maximum stress. The accurate forecasting of physical characteristics on NGC regarding nerve growth factors enables a hopeful outlook for the future clinical treatment of nerve injuries and advanced tissue engineering.

A multi‐objective optimization model for RSU deployment in intelligent expressways based on traffic adaptability

AbstractThe intelligent expressway exemplifies a prominent application of intelligent transportation systems. Roadside units (RSUs), strategically deployed alongside roadways, serve as pivotal infrastructure in facilitating interactions within intelligent expressways. A well‐planned RSU deployment strategy is crucial for enhancing service quality, it necessitates balancing performance improvements with significant financial costs due to the limited transmission range and high deployment expenses of RSUs. To tackle these challenges, an adaptive approach for RSU deployment is proposed, which takes into account economic feasibility, service requirements, and dynamic traffic demands. A traffic adaptability‐based RSU deployment (TARD) model, which integrates factors such as deployment cost, the effectiveness of information coverage, road network topology, and traffic flow characteristics have been devised. The TARD aims to minimize deployment expenses while maximizing the benefits of information coverage and alignment with road traffic demands. The Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) is employed to solve this optimization model. To validate its efficacy, simulations are conducted on the G2 expressway in Shandong Province, China, demonstrating the superior performance of the TARD compared to three other deployment strategies. Ablation experiments further underscore the critical role of tunnel deployments and comprehensive coverage along long sections in bolstering network connectivity and elevating service quality.

Performance-constrained multi-objective optimization of antennas for miniaturization design

The miniaturization of antennas is crucial as it improves the integration of wireless communication system. In order to achieve miniaturization of antennas, a performance-constrained multi-objective optimization method (PCMOM) considering the size and return loss is proposed. In the PCMOM method, an optimization strategy based on a multi-port network model is introduced, enabling the formation of antennas with various structures. Furthermore, we integrate the constraint of return loss performance into the non-dominated sorting genetic algorithm II (NSGA-II), eliminating solutions that do not meet performance requirements. Three pixel antennas are designed using the PCMOM method and two of them are fabricated. Experimental results demonstrate that the proposed PCMOM method can effectively address the complex trade-off issues in antenna miniaturization design.

Time-based redeployment of multi-class nodes for reliable wireless sensor network coverage

We consider the problem of redeploying nodes into a wireless sensor network (WSN) to maintain reliable area coverage over time as nodes fail. Specifically, we consider the class of time-based node redeployment policies in which the WSN is inspected after a fixed amount of time, after which new sensor nodes are redeployed to bring the number of functioning nodes to a desired level. Whereas previous research on time-based node redeployment assumes nodes are identical with respect to time to failure, we use multiple classes of sensor nodes to represent a scenario where nodes’ times to failure are dependent on positioning in the network. We propose a partial survival signature (PSS) approach for estimating area coverage reliability under a given time-based redeployment policy, where the PSS is estimated by Monte Carlo simulation. This PSS representation enables efficient re-evaluation of coverage reliability under different redeployment policies, thus allowing the use of metaheuristics to obtain a set of redeployment policies that are near-efficient with respect to cost and coverage reliability. We present a numerical example to demonstrate that the PSS approach yields accurate estimates of coverage reliability within a reasonable amount of computation time. Furthermore, we apply non-dominated sorting genetic algorithm II (NSGA-II) to optimize the numerical example with respect to cost and coverage reliability.

Integration of the wind and solar power for the dynamic economic emission dispatch with the charging and discharging of plug-in electric vehicles

Wind and solar power are incorporated into the dynamic economic emission dispatch with the plug-in electric vehicles (DEED-PEV) in this paper, seeking to reduce the generation cost and pollutant emission significantly. A Laplacian non-dominated sorting genetic algorithm-II (LNSGA-II) and a constraint handling method (CHM) are proposed for the DEED-PEV. The LNSGA-II applies the Laplace distribution to both the crossover and mutation. The Laplace-based crossover makes each individual carry out unidirectional search towards its partner and enhances the information exchange between each pair of individuals. The Laplace-based mutation makes each individual carry out bi-directional search surrounding itself and adjust the searching range adaptively. Therefore, the LNSGA-II is able to explore and exploit the decision space of the DEED-PEV sufficiently, contributing to the reductions of the generation cost and pollutant emission in the objective space. For the variables of every individual, the CHM limits them within the feasible operating zones. The CHM also limits them within the dynamic boundaries. It further adjusts them according to the power balances. The power generation constraints and up/down ramp rate constraints can be always satisfied while the elimination of the total violation of the other constraints can be expedited. Therefore, the CHM can transform infeasible individuals to feasible ones rapidly. Four strategies are investigated for six PEV charging/discharging scenarios. The first three strategies consider no more than one kind of renewable energy generation while the fourth strategy involves both the wind power and solar power. Experimental results suggest that the fourth strategy is more economical and environment-friendly than the other three strategies. Furthermore, the LNSGA-II outperforms the other three competitor algorithms for six DEED-PEV problems in the light of hypervolume, coverage rate and spacing.

Performance Analysis and Optimization of Supercritical CO2 Recompression Brayton Cycle Coupled With Organic Flash Cycle With a Two-Phase Expander

Abstract In this work, a combined supercritical CO2 recompression Brayton cycle (SCRBC)/organic flash cycle with a two-phase expander (OFCT) system is proposed to improve the thermal efficiency of the SCRBC, which utilizes a two-phase expander to replace the high-pressure throttling valve of a basic organic flash cycle (OFC). In addition, the OFCT is coupled at the waste heat end of the SCRBC as the bottom cycle for the use of waste heat at low temperatures. A comprehensive comparison is carried out for different organic working fluids, including the R123, R245fa, R142B, R236ea, and R600, regarding the thermal performance, environmental effect, and safety levels. Furthermore, influences of various factors on the thermal performance of the combined SCRBC/OFCT cycle are also examined, including the top cycle pressure ratio, top cycle turbine inlet temperature, mass flowrate ratio, evaporation temperature, and the condenser's pinch point temperature difference. A multi-objective optimization approach is employed on the combined SCRBC/OFCT system, which considers both the thermal efficiency and the specific investment cost as the objective function, and the optimization procedure is implemented through the nondominated sorting genetic algorithm II (NSGA-II) algorithm. The Pareto solution set and the compromise solution are finally obtained. The results indicate that the optimized combined SCRBC/OFCT system can improve the thermal efficiency by 11.75% and 9.70% when compared with the SCRBC and SCRBC/OFC, respectively.

Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which has been widely used in building structures. Due to the coupling effect of many factors such as wall section shape, shear span ratio, so the shear performance evaluation of flanged wall is still very limited. This paper proposed a prediction framework for the shear capacity of RC flanged shear walls. A database containing 14 input variables, 1 output variable and 153 samples was constructed to evaluate the prediction accuracy of 11 existing design methods. The Pearson coefficient was used to preliminarily analyze the correlation between variables. The grid search was used to optimize the hyperparameters of 4 machine learning models, and six statistical indicators ( R2, R, RMSE, SD, MAE, and MAPE) were used to comprehensively compare the prediction results of the ML models to determine the best model. On this basis, SHapley Additive exPlanations (SHAP) was used to enhance the interpretability of the prediction models, and the mechanism of the input variables on the shear capacity was quantified. A graphical user interface (GUI) was proposed to guide the engineering design. A multi-objective model (MOO) was established to analyze the trade-off between shear performance and cost, thereby determining the best optimal scheme. The results show that the prediction accuracy of the ML models is better than the existing design methods. The XGB model has the best prediction performance, with R2, R, RMSE are 0.99, 0.99, 118.96, respectively. The SHAP method can effectively enhance the interpretability of the ML models, and tw, lw and f ′c are the key parameters affecting the shear capacity of the flanged shear wall.

Investigation of sustainable operation oriented- economic, process and environment based multi-criteria optimization of large scale methanol production plant

Methanol is one of the main raw materials and a building block for many essential chemicals in the industry, and it is widely produced from the synthesis gas. It is used as a fuel, solvent, inhibitor, antifreeze agent, and in producing a variety of chemicals including olefins and paraffin. The global demand for methanol is 70 million tons per year and it is estimated to grow by a factor of 6–8% per year so it is necessary to develop a methanol production process, which is economical, and environment friendly. The multi-objective optimization (MOO) approach is used to optimize the large scale Lurgi two-step methanol synthesis process using the non-dominated sorting genetic algorithm-II (NSGA-II) to find the Pareto front solutions involving the process, economic, and environmental-based objectives. Methanol production rate, total energy consumption, total annual CO2 emissions, and profit are considered as objectives. The five decision variables include syngas molar flow, reactor feed pressure, reactor 1 temperature, reactor 2 temperature, and by-product mass flow in the methanol distillation column. Two constraints are set on ethanol mole fraction in the methanol stream, and methanol mole flow in water to the treatment stream. Three optimization cases (two bi-objective and one tri-objective) are developed. In the Case 1 study, CO2 emissions decreased by 65.83%, the methanol production rate increased by 10.11%, and total energy was reduced by 57.55%. The Pareto ranking is carried out using the Preference Ranking On the Basis of Ideal-average Distance (PROBID) method and the best optimal solution is reported for all cases.

Towards Green and Low-Carbon Transformation via Optimized Polygeneration System: A Case Study of the Iron and Steel Industry

Polygeneration systems have significant potential for energy conservation and emission reduction and can effectively promote green and low-carbon development in energy-intensive industries, such as the iron and steel industry. However, its application faces the difficulty in technology selection under multiple objectives simultaneously, which is to determine the technology portfolio to achieve the synergy of energy conservation goals and air pollutant emission reduction goals, as well as ensure the economic benefits of the enterprises. This study investigated a case polygeneration system where the iron and steel plant are the core with four polygeneration paths and twenty polygeneration technologies. A multi-objective optimization model is developed to select the optimal technology combination of each polygeneration path under energy conservation, emission reduction, and cost control objectives, which is solved by the non-dominated sorting genetic algorithm-II (NSGA-II). The optimal results can reach significant energy conservation and emission reduction effects while obtaining economic benefits. However, synergistic and conflicting relationships among the objectives exist in both scales of iron and steel plants. The final decision scheme can achieve the mitigations equivalent to 15.9–27.1% and 16.3–42.6% of the energy consumption and air pollutant emissions of the steel enterprises with annual production of 3 Mt/a and 9 Mt/a, respectively. There are thirteen and twelve technologies that are selected as the final decision scheme in the polygeneration system in these two case enterprises. These findings demonstrate the significant roles the polygeneration system plays and provide critical insights and methodology in the technical selection of the polygeneration system.

Crashworthiness and optimization design of additive manufacturing double gradient lattice enhanced thin-walled tubes under dynamic impact loading

It is of paramount importance to ensure the protection of both infrastructure and personnel in scenarios characterized by a high strain rate dynamic impact. In order to develop lighter weight and higher crashworthiness energy-absorbing structures, this paper incorporates the bionic design of lattice reinforced thin-walled tube structures in the form of double gradient, as a means of achieving the aforementioned objectives. Material mechanical properties and the designed structures at high strain rate were researched using Split Hopkinson Pressure Bar (SHPB) experiment. The effects of gradient form and gradient parameters on the deformation pattern and crashworthiness of lattice enhanced thin-walled tubes are researched by means of the validated finite element model and the experimentally fitted Johnson-Cook material model. The results indicate that the specific energy absorption of the double gradient lattice enhanced thin-walled tube (T3L3) is increased by 36.19 % and the peak crush force is reduced by 21.19 % compared to the lattice enhanced thin-walled tube without gradient. Finally, the multi-objective optimization analysis of the T3L3 with the objective of achieving the maximum SEA and minimum PCF is carried out by Response Surface Methodology (RSM) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to find the Pareto optimal solution set. Double-gradient bionic structure offers a new idea for designing energy-absorbing structures under high-speed impact conditions.

Thermo-enviro-economic analyses of a landfill biogas-fed polygeneration process combined with a liquefied natural gas cold energy utilization unit

Landfill biogas offers a promising opportunity to supply society’s energy requirements, following the waste-to-energy approach, which issuitable for sustainable production. However, structural modification for its utilization is the main gap, requiring additional research efforts. Considering this issue, the current study aims to design an eco-friendly polygeneration method for landfill biogas utilization for a cutting-edge heat recovery system that integrates a cold energy recovery and utilization unit for liquefied natural gas. The liquefied natural gas recovery section, the organic Rankine cycle with a zeotropic mixture, and the Kalina cycle are all started using the flue gas from the combustion of biogas in a cascade process. In addition, the waste heat of the Kalina cycle is recovered by a single-effect desalination cycle and liquefied natural gas recovery section. Also, a water electrolysis process is integrated into the whole system. As a result, the products include hydrogen, electricity, natural gas, hot and cold water, and freshwater. The Aspen HYSYS software is utilized to design the system and investigate its thermodynamic, environmental, and economic aspects. The results demonstrate an energy efficiency of 57.54 % and an exergy efficiency of 18.78 %. Also, the carbon dioxide footprint associated with the system equals 0.34 kg/kWh, and the specific cost of products is 73.96 $/GJ. An optimal exergy efficiency of 25.8 % and a specific cost of products of 69.7 $/GJ are obtained by means of an intelligent optimization approach that ultimately makes use of non-dominated sorting genetic algorithm-II and artificial neural networks.

Continuous evolution for efficient quantum architecture search

Variational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling, and quantum machine learning models. The performance of VQAs is significantly influenced by the architecture of parameterized quantum circuits (PQCs). Quantum architecture search aims to automatically discover high-performance quantum circuits for specific VQA tasks. Quantum architecture search algorithms that utilize both SuperCircuit training and a parameter-sharing approach can save computational resources. If we directly follow the parameter-sharing approach, the SuperCircuit has to be trained to compensate for the worse search space. To address the challenges posed by the worse search space, we introduce an optimization strategy known as the efficient continuous evolutionary approach using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Then, we leverage prior information (symmetric property) designing Structure Symmetric Pruning for removing redundant gates of the searched ansatz. Experiments show that the efficient continuous evolutionary approach can search for more quantum architectures with better performance; the number of high-performance ansatzes obtained by our method is 10% higher than that in the literature (Du et al. in npj Quantum Inf. 8:62, 2022). The application of Structure Symmetric Pruning effectively reduces the number of parameters in quantum circuits without compromising their performance significantly. In binary classification tasks, the pruned quantum circuits exhibit an average accuracy reduction of 0.044 compared to their unpruned counterparts.

Chemical plant optimization layout based on the domino hazard index considering the fixed hazard unit outside the available area

The layout of chemical plants is a significant step to promote the safe and efficient operation of a chemical factory. In the actual production process, the chemical process plant layout may be affected by immovable hazards outside the available layout area. In this paper, the external hazard unit is arranged as one of the chemical process plants. The domino effect potential of all chemical process plants is quantified by using the classical domino hazard index (DHI). A layout optimization model considering the effects of external hazard units has been proposed to obtain the optimal plant layout from economic and safety aspects. The feasible layout solutions are obtained under the orientation, boundary and non-overlapping constraints by the non-dominated sorting genetic algorithm II (NSGA-Ⅱ). The proposed model is further analyzed through a hypothetical case of three storage tanks outside the acrylic acid (AA) production plant layout area. The results show that the total cost range for the optimal solution is 650348–1365443 $, with corresponding DHI values ranging from 29 to 129. The plant with larger DHI value will be arranged to far away from the external hazard units to obtain a safer layout. This work can provide rational layout solutions for the layout designers to make different decisions according to the actual situation.