Optimal System Research Articles

Triple-Level Full-Stage Adaptive Restoration Optimization for Coupled Transmission and Distribution Systems Considering Restoration Security Risks

Triple-Level Full-Stage Adaptive Restoration Optimization for Coupled Transmission and Distribution Systems Considering Restoration Security Risks

Experimental and simulation study on hygroscopic hydrogel-based thermal management of electronic device

With the increasing power of electronic devices, thermal management has become crucial for their operational efficiency and lifespan. In this work, we have demonstrated a sorption-based thermal management strategy enabled by hygroscopic hydrogel. The polyacrylamide (PAM) hydrogel impregnated with LiCl (PAM-LiCl) was attached to a silicon wafer. And Pt metal circuits were coated to the silicon wafer to measure the temperature variations under different heat flux densities. The results show that the hydrogel can effectively reduce the surface temperature. At a heating power of 0.3 W/cm2, the temperature control duration reached 30 min, with an average effective heat transfer coefficient of 65.5 W/(m2·K) and an effective heat storage density of 171 J/cm2. Additionally, we developed and validated a multiphysics model considering desorption and vapor diffusion within the hydrogel. Numerical results indicate that during the desorption cooling process, the surface heat transfer resistance and the internal diffusion resistance play a dominant role. Additionally, we analyzed the effects of material properties, structure parameters, and operating conditions on the thermal management performance. This study helps to understand the key points of sorption-based thermal management technology and guide the design and optimization of sorption-based thermal management system.

Dual-objective Optimization of Solar Driven Alkaline Electrolyzer System for On-Site Hydrogen Production and Storage: Current and Future Scenarios

Dual-objective Optimization of Solar Driven Alkaline Electrolyzer System for On-Site Hydrogen Production and Storage: Current and Future Scenarios

Olive mill wastewater treatment using coagulation/flocculation and filtration processes

Olive mill wastewater (OMWW), a pollutant resulting from the olive oil industry, poses a serious ecological challenge due to its high pollution load. This effluent is highly concentrated in chemical oxygen demand (COD), which is 200 times higher than that of sewage wastewater. Moreover, OMWW is characterized by a strong acidity, high content of fatty matter, and high concentration of phenolic compounds.In this study, coagulation/flocculation and filtration processes were investigated in order to decrease the pollution load of OMWW for potential reuse in olive orchard irrigation. First, two successive coagulation/flocculation steps were applied to a centrifuged OMWW. Lime and aluminum sulfate (alum) were used as coagulants by testing different concentrations in order to select optimal conditions. Then, the efficiency of various filtration systems using activated carbon and/or natural materials (olive stones, olive leaves, sand, celite, and gravel) was tested. pH, electrical conductivity (EC), total solid (TS), and COD were measured before and after each treatment step (coagulation or filtration). Five phenolic compounds were monitored before and after applying the selected treatment steps under optimal conditions. The quantification and valorization of the sludge derived from coagulation/flocculation were also performed.During the first coagulation/flocculation step, the results showed that the application of 8 g/L of lime combined with 7 g/L of alum allows the removal rates for EC, TS, and COD of approximately 10%, 41%, and 48%, respectively. While the application of 5 g/L of lime and 4 g/L of alum during the second coagulation/flocculation step allows for lower reductions rates for TS (37%) and higher reduction rates for COD (67%). In addition, the resulting sludge showed its potential usage as a solid alternative fuel with a calorific value of 3295.79 cal/g. Moreover, filtration using activated carbon and gravel was found to be the optimum filtration system. The reduction rates were 51%, 37%, and 26% for EC, TS, and COD, respectively.Finally, the combination of coagulation/flocculation and filtration allows the substantial elimination of the studied phenolic compounds with global reduction yields of 97% for vanillyl alcohol, 92% for tyrosol, and 91% for vanillic acid and p-coumaric acid.Besides, Mediterranean countries are suffering from water shortages and the majority of olive mill trituration units are known for their artisanal types thus facing economic challenges. This research suggests a practical treatment process and the final effluent can be used to irrigate olive orchards at a rate of 170 m3 per hectare.

A composite structure device: vibration control and energy harvesting of tool system in aviation thin-walled parts milling process

Abstract The intense vibration generated by the high-speed milling cutter in the processing process not only seriously affects the surface roughness but also may damage important components of the machine tool, resulting in significant economic losses. Therefore, a composite structure of piezoelectric energy-collector (PEC) and nonlinear energy sink (NES) is innovatively constructed by combining theoretical research and numerical analysis to achieve more efficient vibration suppression and energy recovery in milling. Using the Hamilton principle, the electromechanical model of the composite structure is developed. Then, based on the Galerkin truncation to discretized displacement functions, we solve the amplitude-frequency response of the tool structure by using the harmonic balance method. The validity analysis of the proposed method can be verified by rigorous comparison with the results of the Runge-Kutta method. A novel NES-PEC composite structure presented in this work shows excellent vibration suppression capability in the milling process. More importantly, the structure also enables self-powered sensing of the tool system, significantly reducing dependence on external power sources. This innovative design substantially improves the stability and reliability of milling processes and opens up new ways to optimize the energy efficiency of tool systems. In summary, this paper’s modeling and solving methods, vibration reduction techniques, and energy harvesting strategies have laid a solid foundation for applying a high-speed milling cutter system. More relevant literature on applying vibration suppression and energy harvesting devices in milling must be reviewed. This paper fills in the research gap and provides a valuable reference for future tool systems design and optimization.

Advanced Machine Learning and Experimental Studies of Polypropylene Based Polyesters Tribological Composite Systems for Sustainable Recycling Automation and Digitalization

Digitalization and automation are emerging solutions to the complex problems of recycling. In this research work, the experimental and Python based Archard deep learning wear rate models are introduced regarding recycling automation and composite tribological systems optimization. The optimum polyester fibers (PESF) of length of 3-3.5 mm were used for fabrication of polypropylene (PP)-PESF composite systems. The deformation, high texture, asperities, and micro-cracks were observed during scanning electron microscope and machine-learning studies. The lowest experimental value of abrasive wear of 3.0 × 10-6 mm3/Nm was observed for PP. Comparatively, higher experimental values of abrasive wear of the PP-PESF composites are found in the range of 4.35 × 10-6 to 4.7 × 10-6 mm3/Nm due to presence micro-defects on the surface of composites. The experimental values of Coefficient of friction (COF) of PP and PP-PESF are found in the range of 0.70 - 0.8 and 1.1 - 1.3, respectively. The experimental values of abrasive wear and COF are found compatible with literature. Similarly, the simulated values of abrasive wear of PP and PP-PESF composites are predicted in the range of 4.8×10-7 to 3.75×10-7 mm3/Nm, respectively. The predicted values of PP and PP-PESF composite show better resistance towards abrasive wear. The proposed experimental and simulated (in terms of Python coding, machine learning, image processing, artificial intelligence, and deep learning studies) research work can be introduced industrially for automation as well as digitalization of grinding of PES waste, processing, tribological testing, and SEM characterization evaluations.

Network Attack Simulation and Defense System Optimization based on Big Data

Network Attack Simulation and Defense System Optimization based on Big Data

AI-Driven Optimization of Renewable Energy Storage Systems in Smart Cities Using Improved Binary Genetic Algorithm

AI-Driven Optimization of Renewable Energy Storage Systems in Smart Cities Using Improved Binary Genetic Algorithm

Development of methods for monitoring and optimization of underground drainage systems using wireless sensor networks and ultra-wideband antennas

This research focuses on optimizing ultra-wideband (UWB) antennas, which are critical in modern communication systems due to their wide frequency range (3.1–10.6 GHz) and high data transmission capabilities. The study addresses the challenge of optimizing key antenna parameters – such as return loss, peak gain, and radiation efficiency – while also ensuring energy efficiency and network longevity. Traditional optimization methods, such as LEACH-C, often fail to balance these factors, leading to suboptimal performance. To solve this problem, the researchers developed the Generalized Position-based Optimization Neural Network (GPON) for UWB antenna optimization. They also evaluated the Position-based Hybrid Neural Network (PAN) method, comparing its performance with existing algorithms including LEACH-C, Firefly Algorithm (FA), HFAPSO, FA-ANN, and HWOABCA. The GPON model reduced return loss to 25.5 dB at 3.5 GHz and improved peak gain to 4.2 dB i, while maintaining 92 % radiation efficiency. In contrast, PAN demonstrated a 15–25 % improvement in residual energy and extended network lifetime by 20 % compared to LEACH-C. These improvements were due to the integration of advanced neural network techniques in GPON and the effective use of positional data in PAN, enabling more precise and adaptive optimization. The ability to balance multiple performance metrics simultaneously – a challenge previous models struggled with – is a key feature. This balance is crucial for UWB antennas in communication systems where both performance and energy efficiency are vital. The findings are especially relevant for practical applications in wireless sensor networks, mobile communications, and radar systems, requiring long-term network reliability and optimal antenna performance

Numerical Treatment for the Distributed Order Fractional Optimal Control Coronavirus (2019-nCov) Mathematical Model

In this paper, we presented the distributed order fractional optimal control of the Coronavirus (2019-nCov) mathematical model. The distributed order fractional operator is defined in the Caputo sense. Control variables are considered to reduce the transmission of infection to healthy people. The discretization of the composite Simpson's rule and Grünwald-Letnikov nonstandard finite difference method is constructed to solve the obtained optimality system numerically. The stability analysis of the proposed method is studied. Numerical examples and comparative studies for testing the applicability of the utilized method and to show the simplicity of this approximation approach are presented. Moreover, by using the proposed method we can conclude that the model given in this paper describes well the confirmed real data given in Spain and Wuhan.

AI-driven predictive maintenance and optimization of renewable energy systems for enhanced operational efficiency and longevity

The rapid growth of renewable energy systems necessitates advanced strategies for maintenance and optimization to ensure long-term operational efficiency and sustainability. Traditional approaches often fall short in predicting failures and optimizing performance across diverse and dynamic renewable energy infrastructures. This study investigates the application of artificial intelligence (AI) techniques for predictive maintenance and optimization of renewable energy systems, with the aim of enhancing operational efficiency and extending system longevity. We employ a combination of machine learning algorithms, including deep neural networks and reinforcement learning, to develop predictive models and optimization strategies. These models are trained on large-scale datasets collected from operational wind farms, solar installations, and hydroelectric plants. Our results demonstrate that AI-driven approaches can predict equipment failures with 92% accuracy, reducing unplanned downtime by 35% compared to traditional methods. Moreover, AI-optimized operational parameters improved overall energy output by 8.5% across the studied systems. The proposed framework also showed adaptability to various environmental conditions and system configurations, suggesting broad applicability across the renewable energy sector. This research underscores the significant potential of AI in revolutionizing maintenance practices and operational strategies in renewable energy systems, paving the way for more reliable, efficient, and sustainable clean energy production.

Influence and Optimization of Diverse Culture Systems on Chicken Embryonic Stem Cell Culture

Background: The importance of embryonic stem cells (ESCs) in chickens is undeniable, as they can be applied across various fields, including animal modeling, developmental biology, cell fate research, drug screening, toxicity testing, and gene function studies. However, a widely applicable culture system for chicken ESCs has yet to be developed. Objectives: This study aimed to investigate the effects of different culture systems on the derivation and maintenance of chicken ESCs, with a focus on optimizing the selected culture conditions. Methods: To achieve this, we tested the effectiveness of various species-specific ESC media in the derivation and culture of chicken PGCs, while incorporating different small molecule compounds to optimize the process. The pluripotency and differentiation potential of the resulting ESC-like cells were also evaluated. Results: The combination of PD0325901, SB431542, and LIF (R2i+LIF system) was found to be effective in generating chicken ESC-like clones. Further experiments showed that enhancing the R2i+LIF system with cytokines such as SCF and FGF2 significantly extended the culture period and increased the passage number of chicken ESC-like cells. These ESC-like cells were characterized through positive alkaline phosphatase staining and the expression of pluripotency markers POUV, NANOG, and SOX2. Additionally, differentiation assays confirmed their ability to form the three germ layers. Conclusions: The newly developed culture system provides suitable conditions for the short-term culture of chicken ESCs. However, further optimization is required to establish a system that can sustain long-term maintenance.

Optimization of TiO2-based UV-LED Photocatalytic System for Mixed Dyes and Pharmaceutical Contaminants

To optimize the working efficiency of the novel UV-LED system based on TiO2 photocatalyst, the influence mechanism of LED lamp arrangement, light source wavelength and working voltage on photocatalytic efficiency was investigated. Acid red 26 (AR 26), acetaminophen (ACT) and diclofenac (DCF) were used as contaminant targets of the photocatalytic system. LED lamp arrangement had almost no effect on the degradation of AR26. However, the degradation efficiency of ACT and DCF was improved under a higher light uniformity. The ACT concentration and DCF concentration at 360 min decreased by 14% and 15%, respectively, with increasing light distribution from 45% to 66.5%. The main reason for this discrepancy in effect was whether the rate-determining step of the degradation mechanism was affected by the light uniformity. The short wavelength and high working voltage of LEDs were conducive to the photocatalytic degradation of contaminants to a different degree. When the wavelength was reduced from 405nm to 365nm, the conversion of AR26, ACT, and DCF increased by 77%, 227%, and 106%, respectively. The conversion rates of AR26, ACT, and DCF increased by 28%, 54%, and 32%, respectively, with voltage increasing from 3 V to 4 V. The data of this work will provide support for optimizing the working efficiency of UV-LED systems based on TiO2 photocatalysts. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant

This paper presents optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant. The power plant is equipped with four steam boilers using a damper-louvers airflow control system. Analysis has revealed significant energy wastage due to inefficiency and over-air supply, with the system requiring 59,904,436 kWh annually at a financial cost of N1, 093,255,884. The existing system's inefficiency results in an annual electrical energy waste of 8,502,302 kWh. This inefficiency is attributed to the Force Draft Fan (FDF) motor operating at maximum speed continuously, regardless of the steam profile or airflow requirements. This study aimed at optimizing the steam boiler airflow control system in a typical oil refinery power plant for improved power efficiency, energy conservation, and cost savings citing Port Harcourt Refinery Company (PHRC) at Alesa-Eleme, Rivers State, Nigeria. To address this, the damper-louvers system was replaced with a Variable Frequency Drive (VFD), which would adjust the motor speed to match the required airflow, thereby reducing energy wastage. The process was simulated and VFD/FDF motor analyzed in MATLAB Simulink, determining the optimal speed, airflow, and power requirements. Results obtained show that the VFD control system improves the FDF motor's power efficiency from 47.7% to 85%, with an annual energy saving of 8,502,302 kWh (8.5 GWh). This results in a total cost benefit of N478.18 million annually, representing a 43.8% cost saving. The implementation cost of the VFD infrastructure was estimated at N111, 440,000, with a payback period of approximately eleven months. The findings suggest deployment of higher FDF motor speed ratings to enhance speed control and energy efficiency in Oil refinery power plant.

Stochastic optimization of a hybrid photovoltaic/fuel cell/parking lot system incorporating cloud model

The interaction and optimization of electric parking lots (EPLs) with hybrid renewable energy to exchange power and improve the system load reliability has been welcomed systems in energy industry, but it is accompanied by challenges such as uncertain parameters, and also implementing the optimal energy management. This paper proposes a novel stochastic optimization framework for a hybrid energy system including photovoltaic resources, hydrogen storage based-fuel cell integrated with an EPL (PV/FC/EPL) aiming for minimization of the total net present cost (TNPC) while ensuring the maximum probability of power shortage (PPSHmax) to meet a commercial load in Tianjin, China using the real meteorological data. A new improved kepler optimization algorithm (IKOA) utilizing the golden sine strategy is applied to optimize system component sizes and EPL charge/discharge. In a deterministic scenario, the optimal system configuration ensures cost-efficiency and reliability for load supply. Also, a new stochastic modeling framework is proposed using the Cloud Model (CM) to evaluate the effect of uncertainties in irradiance, temperature, and system demand on TNPC and PPSH. Deterministic findings reveal enhanced reliability and reduced TNPC through FC and EPL overlap during power shortages, compared to systems lacking EPL. Specifically, TNPC and PPSH are $8,632,643 and 1.45 %, respectively, for PPSHmax = 2 % over 20 years of operation. However, stochastic optimization demonstrates a 10.82 % increase in TNPC and an 8.67 % decrease in PPSH compared to the deterministic model under PPSHmax = 2 %, indicating heightened cost and reduced reliability when uncertainties are considered. This underscores the efficacy of the cloud model-based stochastic approach in addressing uncertainty in hybrid system optimization. Also, it reveals fluctuations in optimal cost and reliability values with changes in cloud droplet distribution around the expected value, emphasizing the significance of stochastic modeling for robust decision-making in hybrid energy systems. Moreover, evaluating the incorporating the penalty for the system's inability to supply the load, changing the storage investment cost, and interest rate variations on the system optimization cleared considerable effect of the system cost and reliability.

Study of complex instability in a Thulium-doped figure-eight fiber laser including a polarization-imbalanced NOLM with no polarization control.

This study proposes a Thulium-doped figure-eight (F8) fiber laser design to be used as a platform to explore complex dynamics in passively mode-locked fiber laser systems. The proposed scheme incorporates a polarization-imbalanced nonlinear optical loop mirror (PI-NOLM) without a polarizer in the ring section. The absence of a polarizer prevents controlling the polarization state at the PI-NOLM input, which in turn allows the PI-NOLM switching power to vary dynamically during the laser operation. This leads to an intermittent noise-like pulse (NLP) operation, which is affected by complex instability in the form of non-periodic Q-switched-like behavior. Using real-time time-domain mapping techniques, we get a deep insight into the laser intricate dynamics, which involves not only NLPs, but also solitons and quasi-continuous-wave components with their subtle interactions. This study highlights the importance of the PI-NOLM in F8 laser schemes for the study and understanding of complex dynamics in Thulium-doped fiber lasers, which have been less extensively studied compared to other rare-earth-doped lasers. Our findings will contribute to the optimization and design of advanced laser systems for various scientific and industrial applications.

Estimation of hydrogen solubility in aqueous solutions using machine learning techniques for hydrogen storage in deep saline aquifers

The porous underground structures have recently attracted researchers’ attention for hydrogen gas storage due to their high storage capacity. One of the challenges in storing hydrogen gas in aqueous solutions is estimating its solubility in water. In this study, after collecting experimental data from previous research and eliminating four outliers, nine machine learning methods were developed to estimate the solubility of hydrogen in water. To optimize the parameters used in model construction, a Bayesian optimization algorithm was employed. By examining error functions and plots, the LSBoost method with R² = 0.9997 and RMSE = 4.18E-03 was identified as the most accurate method. Additionally, artificial neural network, CatBoost, Extra trees, Gaussian process regression, bagged trees, regression trees, support vector machines, and linear regression methods had R² values of 0.9925, 0.9907, 0.9906, 0.9867, 0.9866, 0.9808, 0.9464, and 0.7682 and RMSE values of 2.13E-02, 2.43E-02, 2.44E-02, 2.83E-02, 2.85E-02, 3.40E-02, 5.68E-02, and 1.18E-01, respectively. Subsequently, residual error plots were generated, indicating the accurate performance of the LSBoost model across all ranges. The maximum residual error was − 0.0252, and only 4 data points were estimated with an error greater than ± 0.01. A kernel density estimation (KDE) plot for residual errors showed no specific bias in the models except for the linear regression model. To investigate the impact of temperature, pressure, and salinity parameters on the model outputs, the Pearson correlation coefficients for the LSBoost model were calculated, showing that pressure, temperature, and salinity had values of 0.8188, 0.1008, and − 0.5506, respectively, indicating that pressure had the strongest direct relationship, while salinity had an inverse relationship with hydrogen solubility. Considering the results of this research, the LSBoost method, alongside approaches like state equations, can be applied in real-world scenarios for underground hydrogen storage. The findings of this study can help in a better understanding of hydrogen solubility in aqueous solutions, aiding in the optimization of underground hydrogen storage systems.

Harnessing Oxygenic Photosynthetic Microorganisms for Sustainable Wastewater Treatment Systems: A Comprehensive Review

Aquaculture systems globally face significant environmental challenges, particularly concerning wastewater management. This review explores the innovative application of oxygenic photosynthetic microorganisms (OPhMs), specifically microalgae and cyanobacteria, as a sustainable solution for wastewater treatment within these systems. OPhMs offer a dual role in wastewater treatment by removing harmful pollutants such as nitrogen, phosphorus, and heavy metals, while simultaneously improving water quality through oxygenation. We evaluate the integration of OPhMs into existing aquaculture operations, considering key factors such as system design, operational conditions, and economic viability. Additionally, we discuss the potential of algal biomass as a secondary resource for producing biofuels, animal feed, and other bio-products, contributing to the circular economy model. Our findings highlight the ability of OPhM systems to significantly reduce the ecological footprint of aquaculture while recovering valuable resources. However, challenges such as process stability, especially under low-temperature conditions, and the efficiency of biomass harvesting, require further research. This study provides a comprehensive framework for future development and optimization of OPhM-based wastewater treatment systems to improve the sustainability of aquaculture operations.

Kalman Filtering-based Decision Threshold Dynamic Optimization in Photonics-Aided Millimeter-Wave Systems

Kalman Filtering-based Decision Threshold Dynamic Optimization in Photonics-Aided Millimeter-Wave Systems

Optimization and Application of Comprehensive Environmental-Economic Evaluation Indicator System in Sustainable Development Policies of Chinese Manufacturing Industry

This paper focuses on optimizing the comprehensive environmental-economic evaluation index system to enhance the sustainable development policy of China’s manufacturing industry. The current indicator system, while useful, has notable shortcomings that limit its effectiveness. To address these issues, the paper proposes the inclusion of crucial indicators such as green productivity, carbon footprint, and water footprint, which are essential for providing a more holistic evaluation. These additions aim to capture better the environmental impacts and resource efficiency within the manufacturing sector. The paper also discusses the challenges in data acquisition, emphasizing the need for a standardized national environmental data platform. Such a platform would facilitate the collection of accurate, reliable, and accessible data, thereby improving the precision of evaluations. To further refine the assessment process, dynamic evaluation models, like the system dynamics model, are introduced. These models are designed to provide a more nuanced understanding of the environmental impacts over time, allowing for more responsive and adaptive policy-making. The optimized indicator system is expected to have significant applications in policy formulation, enterprise management, and social oversight. By providing a scientific basis for decision-making, it will play a critical role in advancing the sustainable development of China’s manufacturing industry, ensuring that environmental considerations are fully integrated into economic planning and industrial practices.