•Heavy metals were determined in corn oil based on nano-chemo selective response dye combined with NIR spectroscopy.•Rapid detection of heavy metals in corn oil without pretreatment steps was ...achieved.•Porous silica nanospheres were synthesized to optimize dyes to prepare CSA for trace heavy metal detection.•The color sensitive dyes sensitive to heavy metals were acquired.
Heavy metal concentrations are one of the major problems bedeviling the market and consumption of edible oil. This study attempts to use near-infrared spectroscopy (NIRS) combined with chemoselective responsive dyes, as capture probes for the quantification of lead (Pb) and mercury (Hg) heavy metals in oils. Olfactory visualization system was used to screen chemoselective responsive dyes. The synthesized porous silica nanospheres (PSNs) were used to further optimize the color sensor and applied based on selected dyes. The spectral data were preprocessed by standard normal variation (SNV), which follows the application of chemometrics like partial least squares (PLS), ant colony optimization-PLS (ACO-PLS), synergy interval partial least squares (SiPLS), genetic algorithm-PLS (GA-PLS), and competitive adaptive reweighted sampling-PLS (CARS-PLS) were applied to construct the regression model. ACO-PLS achieved optimum result, with the Rp2 value of 0.9612 in the linear range of 0.001 ∼ 100ppm, and LOD of ≤ 1ppb recorded. Verified by the National Standard Detection Method, the effectiveness of this strategy has proven to be satisfactorily accurate. Therefore, the developed method could be used for non-destructive detection of lead and mercury in edible oil.
Abstract
Multi-hole Drilling with Multiple Tool dimensions (MDMT) is a crucial technique in today’s industry, allowing manufacturers to satisfy the increasing demand for precise and high-quality ...components while adopting the latest technological advancements and environmental standards. This paper introduces and validates a computational model for MDMT, offering numerous advantages over conventional drilling methods, including enhanced efficiency, accuracy, cost-effectiveness, and flexibility. The computational model was developed for the MDMT problem using the Travelling Salesman Problem (TSP) concept to measure the total toolpath distance. The Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) are applied to solving 12 cases of MDMT problems with varying numbers of holes, classified as small, medium, and large, using MATLAB software R2022b. Note that the algorithms were evaluated based on their solution quality, with lower fitness values indicating better performance. Overall, GA performed the best across most hole configurations, achieving the optimal fitness value in 5 out of 12 cases (small, medium, and large), ACO performed better in 4 out of 12 cases (small and medium) and PSO performed better in 3 out of 12 cases (medium and large). The research emphasizes the potential of multi-dimensional tools for accomplishing intricate drilling tasks. Other than that, this paper contributes to the existing literature on MDMT and highlights the importance of multi-dimensional tools in modern manufacturing. Future research could optimize the proposed computational model for various materials and drilling scenarios in MDMT.
Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and ...better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.
Abstract
In the test of cross span distance of transmission line, the estimation of cross span distance is not accurate, which leads to the increase of subsequent test error. Therefore, a test method ...of transmission line cross span distance based on a multi-objective ant colony algorithm is proposed. Statistics of the actual information of different crossovers including trees and forest areas. Considering the transmission line as a flexible cable, the objective function for estimating the cross span distance of the transmission line is constructed according to the catenary model of the flexible cable model. On this basis, the multi-objective ant colony algorithm is used to solve the function, and the optimization results of transmission line crossing distance are output. The experimental results show that the method has high accuracy, small error, short time and reliability.
Hybrid flow shop (HFS) scheduling has been extensively examined and the main objective has been to improve production efficiency. However, limited attention has been paid to the consideration of ...energy consumption with the advent of green manufacturing. This paper proposes a new ant colony optimization (MOACO) meta-heuristic considering not only production efficiency but also electric power cost (EPC) with the presence of time-of-use (TOU) electricity prices. The solution is encoded as a permutation of jobs. A list schedule algorithm is applied to construct the sequence by artificial ants and generate a complete schedule. A right-shift procedure is then used to adjust the start time of operations aiming to minimize the EPC for the schedule. In terms of theoretical research aspect, the results from computational experiments indicate that the efficiency and effectiveness of the proposed MOACO are comparable to NSGA-II and SPEA2. In terms of practical application aspect, the guideline about how to set preference over multiple objectives has been studied. This result has significant managerial implications in real life production. The parameter analysis also shows that durations of TOU periods and processing speed of machines have great influence on scheduling results as longer off-peak period and use of faster machines provide more flexibility for shifting high-energy operations to off-peak periods.
► This paper proposes a new ant colony optimization (MOACO) meta-heuristic considering not only production efficiency but also electric power cost (EPC) with the presence of time-of-use (TOU) electricity prices. ► The results from computational experiments indicate that the efficiency and effectiveness of the proposed MOACO is comparable to NSGA-II and SPEA2. ► The guideline about how to set preference over multiple objectives has been studied. ► This result has significant managerial implications in real life production. ► The parameter analysis shows that durations of TOU periods and processing speed of machines have great influence on scheduling results as longer off-peak period and use of faster machines provide more flexibility for shifting high-energy operations to off-peak periods.
Hazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an ...increasing number of scholars have begun to study this field. In this study, a multi-objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with three goals (transportation cost, risk, and carbon emission). This model fills the gap in the research on hazardous material transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compared the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and space, which verifies the validity and reliability of the improved ACO.
Drones, with the potential to significantly increase the efficiency of the delivery, have received much attention in recent years. Still, there are some bottleneck problems in the application of the ...long-distance drone delivery, such as the limited flight range and flight safety. Therefore, the article proposes a novel service system, including the battery exchange stations and maintenance checkpoints, to provide long-distance delivery services. Then, with respect to the service system, we construct a drone path programming model, where a special penalty value is proposed as the objective function to simultaneously minimize the path length and number of landing depots for the delivery service. Thereafter, to efficiently find the optimal flight path among huge solution space, we improve the ant colony optimization with the A * algorithm embedded to avoid the nondirectional searching of ants. Finally, we use a case of Shanghai city to study the feasibility and effectiveness of our approaches, which includes the comparison of our algorithm and the other three heuristics on ten random delivery cases, the verification of the effectiveness of our algorithm on the long-distance delivery service, and a sensitive analysis of the effect of the depot number on the optimal solution.
Noise pollution from construction activities is a major factor jeopardizing occupational health as well as human living environments. However, previous research mainly focused on reducing noise ...pollution surrounding sensitive buildings off site rather than for on-site workers. Moreover, currently available noise mitigating methods tend to be passive and accompanied by significant additional expenditures. As the location of facilities is a critical factor of noise pollution, this study attempts to analyze how noise pollution for workers can be reduced by optimizing construction site layout planning in the pre-construction stage. Considering that mitigating noise by optimizing site layout may generate negative impacts on safety and cost, the desired site layout should establish a balance among noise reduction, safety improvement and cost control. Therefore, functions addressing potential safety risk and transportation cost arising from interactions between onsite facilities are designed. For solving this multi-objective optimization problem, a hybrid genetic algorithm-ant colony optimization model is applied to obtain trade-off solutions. Feasibility and effectiveness of the optimization model is verified by a case study on a residential building project. This study incorporates noise pollution reduction into site layout optimization problems in the pre-construction stage without additional expenditure and in safe manners. It aids future researchers in improving construction sustainability from as early as the planning stage. It also helps site managers enhance on-site sustainability incorporating environmental protection, economic efficiency and occupational safety together. The proposed research framework can be used as a reference to balance conflicting sustainability objectives in other industrial layout facilities as well.
•Construction noise pollution levels can be reduced by optimizing the site layout.•A multi-objective model is designed to optimize noise-cost-safety simultaneously.•The hybrid GA-ACO algorithm is used to balance conflicting objectives.•It aids to improve construction sustainability from as early as the planning stage.•It is suggested to arrange temporary facilities around the specific fixed facilities.
Mobile-edge nodes, as an efficient approach to the performance improvement of wireless sensor networks (WSNs), play an important role in edge computing. However, existing works only focus on ...connected networks and suffer from high calculational costs. In this article, we propose a rendezvous selection strategy for data collection of disjoint WSNs with mobile-edge nodes. The goal is to achieve full network connectivity and minimize path length. From the perspective of the application scenario, this article is distinctive in two aspects. On the one hand, it is specially designed for partitioned networks which are much more complex than conventional connected scenarios. On the other hand, this article is specially designed for delay-harsh applications rather than usual energy-oriented scenarios. From the viewpoint of the implementation method, a simplified ant colony optimization (ACO) algorithm is performed and displays two characteristics. The first one is the path segmenting mechanism, simplifying the path construction of each part and consequently reducing the computational cost. The second one is the candidate grouping mechanism, reducing the search space and accordingly speeding up the convergence speed. Simulation results demonstrate the feasibility and advantages of this approach.
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