Optimization Goal Research Articles

Three-point support analysis and optimization design under the change of gravity center of diamond lathe

The position of three-point support has an important impact on the stiffness and accuracy of the diamond lathe. To determine the position of the support, this paper commences by conducting static and dynamic analysis on the large-size platform machine tool whose base is 2340×1540 mm under the two support schemes. It determines the basic layout scheme of the support points of the machine tool under the change of gravity center. Subsequently, by combining the least squares fitting and particle swarm optimization algorithm with the optimization goal of minimum deformation of the machine tool base working surface, the position of the support points is optimized under the influence of the Change of Gravity center. The results indicate that the optimal solution of the design variable within the design interval can be obtained through limited sampling points and optimization iterations. The average and maximum deformation of the base working surface are 0.867 μm and 1.45 μm. They decreased by 11.17% and 26.02% compared with before optimization.

D-shaped photonic crystal fiber based on surface plasmon resonance for low refractive index applications

Here, a fresh D-type photonic crystal fiber has been developed by us, relying on the surface plasmon resonance effect. Differing from conventional D-type photonic crystal fiber, this model coats gold film on an open-loop channel for excitation. Through the finite element method analysis, this sensor exhibits an effective scope of 1.20–1.30 for the refractive index of analyte. As the RI of the analyte rises, the loss peak within the spectrum gradually intensifies and then remains stable. At an RI value of 1.30, achieving a resolution of 9.52 × 10−6 RIU, this sensor exhibits excellent performance with a maximum sensitivity of 10,500 nm/RIU. Its simple structure adds to its overall appeal. The outstanding sensing performance demonstrates enhanced competitiveness in sectors with lower refractive indices. Farther, to enhance the sensor's sensing capabilities, we conducted an analysis of how structural parameters impact the resonance spectrum with the goal of optimization.

Multi-stage manufacturing process parameter optimization method based on improved marine predator algorithm

Product quality is a critical factor in manufacturing industry competition, and mechanical processing technology has been widely applied in manufacturing, directly affecting product quality. Therefore, it is very important to find the appropriate optimal parameters to improve the impact of processing on product quality. However, modern production processes are characterized by complex mechanisms and the mutual influence of multiple processes, which poses higher challenges for optimizing processing technology parameters. In this regard, the thesis proposes a method for optimizing process parameters in multi-process manufacturing based on an improved marine predator algorithm, aiming to optimize and improve process parameters in multi-process manufacturing processes. Firstly, a multi-process modeling strategy is adopted to explore the nonlinear relationship between process parameters and quality indicators based on multi-gene genetic planning, establishing a multi-process parameter optimization objective model. This effectively solves the problem of modeling difficulty caused by severe coupling of multiple processes. Then, to improve the efficiency of solving the optimization objective model, an improved marine predator algorithm is proposed, utilizing reverse learning strategies and mixed control parameters to enhance optimization capability, thereby obtaining the global optimal solution. Finally, using production process data from a certain factory as an example, the feasibility of the proposed method is verified, achieving the goal of multi-process process parameter optimization and ensuring the stability of product quality.

A mixture design approach to develop new starter formulation for Lben: Key aroma compounds, physical, chemical and sensory properties of the product

The study investigated the evolution of the physicochemical and sensory properties of Lben fermented milk inoculated with different starter formulations. The aim was to determine the optimal starter culture formula for Lben production using different combinations of four strains, including three strains of Lactococcus lactis subsp. lactis with strain codes A, E and F and one strain of Leuconostoc mesenteroides subsp. mesenteroides with strain code C. While individual strains had previously demonstrated significant technological and organoleptic properties their interaction in mixtures had not been explored. In the study, a response surface methodology (mixture design approach) was applied to evaluate the effects of the interactions between strains on the physicochemical, antioxidant, volatile, and sensory properties of Lben, a fermented milk. In addition, product optimization using ridge analysis was performed to determine the ideal strain mixture formulation based on the sensory properties of Lben. The optimization goal for odor, taste, consistency, and overall preference was set to maximize their values. A high desirability score of 0.832 was achieved with the following percentage distribution: 34.39% for L. lactis E, 42.38% for L. lactis A, 10% for L. lactis F, and 13.23% for Leuc. mesenteroides C.

Optimizing plastic shredder rotor design through structural finite element analysis: A comprehensive approach using experimental design and response surface methodology

Shredder machines are essential for recycling plastic waste. They are used to shred plastic materials into smaller pieces, which can then be processed into new products. The shaft and rotary blades of the plastic shredder are its most crucial parts since they directly influence how effectively the machine shreds or recycles material. The shredder’s rotor is optimized based on the design of experiments and the response surface method. The natural frequencies and mode shapes are identified by modal analysis, and the critical natural frequencies that must be avoided during operation are identified through harmonic response analysis. The shredder’s rotor is optimized by determining its critical areas through a transient structural analysis. The optimization goals are attained by using the response surface method and experiment design in the ANSYS WORKBENCH DESIGN EXPLORATION module. The rotor of the optimized shredder is investigated, and its results are contrasted with the nominal design. The study of the optimum structure leads to a slight increase in mass of 2.43% in addition to reductions in equivalent stress and total deformation by about 9.84% and 42.86% respectively, as well as a 21.4%, 4.35%, and 4.48% increase in the first three natural frequencies, respectively.

Self‐triggered control for linear systems based on hierarchical reinforcement learning

AbstractIn this article, we propose a model‐free self‐triggered control approach for linear systems using a hierarchical policy framework. This framework splits the self‐triggered control approach into top and bottom‐level policies. The top‐level policy generates a triggering time interval based on an initial control strategy, while the bottom‐level policy creates a control inputs guided by the top‐level sub‐goal. This division ensures that both strategies have their own independent tasks and optimization goals, facilitating a model‐free iterative design process for self‐triggered control. The present structure integrates with a dual‐Actor Critic algorithm, utilizing two interconnected neural networks to approximate control and trigger policies. It reflects the framework of hierarchical reinforcement learning, wherein top‐level policies guide bottom‐level decision‐making. It fosters the model‐free design of self‐triggered controller, thereby enhancing the efficiency of the learning process. To validate the effectiveness of our proposed method, we conduct a series of numerical simulations.

Green Logistics Sustainable and Eco- friendly Transportation Practices

The goal of this study is to better understand the factors implementing eco-friendly practices in transportation to reduce environmental impact. Well, understanding of factors may not lead to the implementation of eco-friendly practices. The COP26 resolution to achieve carbon neutrality till 2050. The logistics and transportation industry contributes more to global greenhouse gas emission. Government and organization strategies analyze if the problem is genuine or not for the public. This study finds and investigates some elements that have a substantial impact on the green logistics and eco-friendly business practices. Making aware to the public as well as logistics industry about green logistics and eco-friendly technology is the future. Electric vehicles and other fuels option should be the problemsolving of logistics industry as well as for the people which they are facing. Make electric vehicles and other green packing method or fuels on the trial stage and gather feedback from the pilot audience that the green logistics is solving the problem you identified. Data was collected through the customer survey. Using technology that is easily operated and that focuses on environmental impact of logistics transportation. green and sustainable eco-friendly transports help reducing cost and dependency on fuels like petrol diesel etc. green logistics helps in the economic growth that will help the logistics provider and people of that area to be developed. it presents the split routing optimization problem of lowcarbon distribution with the optimization goal of reducing carbon emissions. The study shows how to tackle the problem, and end with a conclusion to appeal the industry to start the green journey now.

Plateau-Adapted Single-Pump, Single-Bed Vacuum Pressure Swing Adsorption Oxygen Generation Process Simulation and Optimization

To enhance the oxygen guarantee capacity in high altitude areas and address the challenges of traditional pressure swing adsorption oxygen generation fixed equipment with large volume and multiple device modules, a novel single-reversible-pump single-bed vacuum pressure swing adsorption (VPSA) oxygen generation process was proposed and simulated. This study investigated the effects of purge on oxygen productivity, purity, recovery, and energy consumption, determining that the optimum ratio of total oxygen in the purge gas to the total oxygen in the feed gas (P/F) was 0.176. A set of principle prototypes was developed and validated in plains. The process performance was then simulated and studied at altitudes of 3000 m, 4000 m, and 5000 m. Finally, the optimization was carried out by adjusting the product flow rate and feed flow rate, revealing that the best performance can be achieved when the oxygen purity exceeded 90% with lower energy consumption or larger productivity than the optimization goal. This study serves as a valuable reference for the optimization of the VPSA oxygen generation process in a plateau environment.

Multi-objective optimization of membrane cavity curve of diaphragm compressor and its verification

According to the trend of compressor upgrades in hydrogen refueling stations, this article optimizes the single exponential curve of membrane chamber volume and diaphragm stress that performs poorly. First, set the optimization goals as the membrane cavity volume and the arc length of the curve, use the MOPSO algorithm to optimize the curve, and fit the optimization results to obtain a new curve mathematical model. Then, select three curves for comparison. Theoretical calculations and finite element simulations were performed on the membrane cavity volume and diaphragm stress, and the theoretical calculation methods and results were verified with the help of existing experiments. Finally, it was found that the new curve can increase the membrane cavity volume and reduce the diaphragm stress requirement by changing its own parameters, and the new curve has a small amount of cavity volume near the transition point, which has little impact on the membrane cavity volume. Under the same maximum radial stress, the membrane cavity volume of the new curve can be increased by 5.56% compared to the single exponential curve, while under the same membrane cavity volume, the new curve can reduce the maximum stress on the gas side by 8.9% compared with the single exponential curve, and the maximum stress on the oil sides is reduced by 9.8%.

An Extensive Assessment of the Energy Management and Design of Battery Energy Storage in Renewable Energy Systems

Many benefits are derivable when renewable energy systems (RES) are integrated with battery energy storage systems (BESS). However, appropriate energy management techniques should be adopted to realize optimal benefits. Many BESS operations’ optimization approaches are available in RES with various techno-economic, environmental, and dispatch-related outputs. BESS operations are optimized using different methods. Past studies have mainly concentrated on certain renewable energy systems designed for specific purposes, such as distributed generation or large-scale. This paper thoroughly examines and analyzes various battery management systems by considering the relationship between the optimization methodology and the intended application. This strategy enables the identification of connections between favored optimization approaches and specific optimization goals. Some approaches are more effective in solving economic goal optimizations, whereas others are commonly used for technical goal optimizations. The selection of the solution methodology is also demonstrated to be highly contingent upon the degree of mathematical formulation of the problem. An analysis is conducted to assess the strengths and limitations of the described optimization techniques. The conclusion is that hybrid approaches, which combine the benefits of multiple techniques, will significantly impact the creation of future operating strategies. This paper provides a comprehensive analysis of optimization approaches and battery applications, aiming to assist researchers in efficiently identifying appropriate optimization strategies for emerging applications in the new generation.

Blast-Resistant Performance of Steel Petrochemical Control Room with 3D-Kagome Sandwich Wall

As the control brain of the petrochemical plant, blast-resistant performance requirements are important for the sustainability of the petrochemical control room and should be guaranteed when the vapor cloud explosion occurs in the petrochemical production process. The 3D-Kagome truss core sandwich structure is a kind of blast-resistant material with high energy absorption and recycling. Considering the influential factors of the radius of the truss core rod and thickness of the upper and lower panels, in this paper, the blast-resistant performance of a real steel petrochemical control room with a 3D-Kagome truss core sandwich wall was analyzed. With the optimization goal of plastic deformation energy and panel displacement, the optimal wall thickness and radius of the truss core rod were obtained. The optimized blast-resistant walls were assembled, and the dynamic response of the steel petrochemical control room with the 3D-Kagome truss core sandwich blast-resistant wall was analyzed. The simulation results indicate that the truss core layer is ineffective in dissipating blast energy when the radius ratio of the truss core rod exceeds 2.7% of the total wall thickness. Moreover, as the thickness of the upper and lower panels increases from 0.5 cm to 3 cm, the proportion of plastic deformation energy in the truss core layer gradually rises from 55% to 95%, stabilizing at around 90%. The optimal configuration for blast resistance is achieved when the panel thickness ratio is 6.7% of the total wall thickness; the truss core rod radius ratio is 2.7% of the total thickness. This study establishes the effectiveness of the optimized 3D-Kagome sandwich wall as a blast-resistant solution for steel petrochemical control rooms.

Integration of Machine Learning Algorithms with Cloud Computing for Real-Time Data Analysis

As part of this study though, real-time data analysis is examined by exploring the combination of machine learning algorithms with cloud computing. It does so by defining optimization problems and solutions, as well as outlining optimization goals and directions for development. The efficiency of real time data processing ability is also amplified with the use of an amalgamation of machine learning and cloud computing though traditional systems are often associated with high failure rates and high costs of maintenance. Enhanced indexing, systematic control of information storage and retrieval, query optimization are the main benefits obtainable from this. This is because despite the challenges such as limited resources, integration has never been a problem even with challenges of data privacy. Peculiar trends such as Explainable AI, Automated ML, and Continuous Intelligence present the ability to substantially enhance operational proficiency and decision-making.

Engineering, Emulators, Digital Twins, and Performance Engineering

Developments in digital twins are driven by the availability of sensor technologies, big data, first principles knowledge, and advanced analytics. In this paper, we discuss these changes at a conceptual level, presenting a shift from nominal engineering, aiming at design optimisation, to performance engineering, aiming at adaptable monitoring diagnostic, prognostic, and prescriptive capabilities. A key element introduced here is the role of emulators in this transformation. Emulators, also called surrogate models or metamodels, provide monitoring and diagnostic capabilities. In particular, we focus on an optimisation goal combining optimised and robust performance derived from stochastic emulators. We demonstrate the methodology using two open-source examples and show how emulators can be used to complement finite element and computational fluid dynamic models in digital twin frameworks. The case studies consist of a mechanical system and a biological production process.

Investment Strategy and Benefit Analysis of Power and Heat Hybrid Energy Storage in Industrial Parks Based on Energy Performance Contracting

To solve the problems of a single mode of energy supply and high energy cost in the park, the investment strategy of power and heat hybrid energy storage in the park based on contract energy management is proposed. Firstly, the concept of energy performance contracting (EPC) and the advantages and disadvantages of its main modes are analyzed, and the basic scheme of EPC for parks is proposed combined with the actual demand. Furthermore, the multiple energy storage model for power and heat storage in parks is established, which includes lithium batteries and heat storage tanks. Based on this, minimizing the annual operation cost of parks is taken as the optimization goal, and the capacity optimization model for power and heat storage is constructed, which considers the investment costs, operation and maintenance costs, purchased energy costs, peak-shaving subsidy, and environmental subsidy. Finally, an industrial park is selected as an example of EPC to verify the effectiveness of our proposed investment strategy. The results show that compared with the situation before the energy-saving renovation, the park can save 35.14 ten thousand CNY in annual cost expenses. When the unit power price of the lithium battery exceeds 3900 CNY/kW, the unit capacity price exceeds 5460 CNY/kWh, the unit power price of the heat storage tank (HST) exceeds 6000 CNY/kW, and the unit capacity price exceeds 1000 CNY/kWh, the configuration of the lithium battery and HST in the park is no longer the optimal choice to perform the energy-saving renovation.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm has been a good approach for tasks where latency is not critical, but it is not useful when latency matters, so Multi-access Edge Computing (MEC) can be of use. In this work, we propose a distributed Deep Reinforcement Learning (DRL) tool to optimize the binary task offloading decision, this is, the independent decision of where to execute each computing task, depending on many factors. The optimization goal in this work is to maximize the Quality-of-Experience (QoE) when performing tasks, which is defined as a metric related to the battery level of the UE, but subject to satisfying tasks’ latency requirements. This distributed DRL approach, specifically an Actor-Critic (AC) algorithm running on each User Equipment (UE), is evaluated through the simulation of two distinct scenarios and outperforms other analyzed baselines in terms of QoE values and/or energy consumption in dynamic environments, also demonstrating that decisions need to be adapted to the environment’s evolution.

A synthetic indicator for structuring resilient public transport operation

ABSTRACT This study introduces a synthetic resilience indicator as a unified analytical tool to evaluate public transport resilience, assessing performance changes in bus networks against random disruptions. This methodology measures the cumulative impact of disruptions, considering the network’s capacity for maintaining planned functionality (robustness and reliability) and recoverability (i.e., route diversity), factoring in network topology, passenger flow, and resource diversity. This paper analyzes the impact of disruptions at network and station levels by applying an index to six benchmark networks. Our findings reveal: (1) networks with high station connectivity maintain robustness and flow; (2) a clear link between flow reduction and alternate routes highlights route diversity’s value; (3) interplay between topological and flow-related network attributes underlines the need for an aggregated analytical approach. The proposed index can be used as an optimization goal during route network design; informing operators regarding the impact of design choices on future network properties.

Optimization of a non-pneumatic tire using design of experiments and machine learning techniques

In this paper, the spoke structure of a non-pneumatic tire is optimized. The finite element model of the spoke structure is created and structural dynamic analysis is made using ABAQUS. The optimization goal is to simultaneously reduce three characteristics of the spoke structure, the mass, vertical displacement and acceleration. Simulations are performed according to the center composite design table, and the objective function is estimated using response surface analysis (RSA), random forest regression (RFR), gradient boosting regression (GBR) and artificial neural network (ANN). The genetic algorithm is used for minimization, and the optimization results of each objective function are analyzed through verification simulation. As a result of the optimization, the error between the predicted value and the actual value is 18.6% for RSA 3.1% for RFR, 1.7% for GBR, and 15.0% for the ANN. The ANN proposes the optimum design variables which derive output values smaller than the current minimum value because the ANN properly represents the nonlinear characteristics of the spoke model generated in this study. Highlights Simulation based optimization of the spoke structure of a non-pneumatic tire Various regression models are used and their accuracy is compared A two-step genetic algorithm is used for optimization

A novel multi-task learning network for skin lesion classification based on multi-modal clues and label-level fusion

In this paper, we propose a multi-task learning (MTL) network based on the label-level fusion of metadata and hand-crafted features by unsupervised clustering to generate new clustering labels as an optimization goal. We propose a MTL module (MTLM) that incorporates an attention mechanism to enable the model to learn more integrated, variable information. We propose a dynamic strategy to adjust the loss weights of different tasks, and trade off the contributions of multiple branches. Instead of feature-level fusion, we propose label-level fusion and combine the results of our proposed MTLM with the results of the image classification network to achieve better lesion prediction on multiple dermatological datasets. We verify the effectiveness of the proposed model by quantitative and qualitative measures. The MTL network using multi-modal clues and label-level fusion can yield the significant performance improvement for skin lesion classification.

Meningkatkan kemandirian siswa SD melalui pembelajaran metode montesorri

This study is to further illustrate how to increase student independence using Montessori learning modules. This study applies a type of literary study research. This study was conducted by examining other research related to how to increase the independence of elementary school students by describing, explaining and presenting related research and then drawing conclusions. The conclusions of this research are 1) the need for learning that is specifically designed to increase student independence, 2) teachers need to provide learning resources, 3) the need for collaboration between students, teachers and parents in order to achieve optimal educational goals.