Optimal System Research Articles

Modelling and optimization of a hybrid renewable energy systems for rural electrification in Nigeria: A review

Modelling and optimization of a hybrid renewable energy systems for rural electrification in Nigeria: A review

Systemic Multi-Objective Optimization of Induction Motor-Driven Electromechanical Systems

The optimization of electromechanical systems, such as those involving induction motors and gearboxes, is crucial for improving energy efficiency, system performance, and reliability in industrial applications. This paper presents an advanced methodology for optimizing the energy efficiency of electromechanical systems, integrating both mechanical and electrical subsystems to minimize the system's overall weight, energy losses, and transient response time. The optimization problem is approached holistically, considering the interdependence of various system parameters and applying multi-objective optimization techniques to address conflicting objectives. The analysis focuses on optimizing the gearbox speed ratio to minimize the relative weight of the motor-gearbox system while maintaining operational efficiency. A systemic approach, utilizing convex surrogate modeling and multi-stage gearboxes, is proposed to improve the scalability of the solution. The results demonstrate the existence of optimal gearbox speed ratios for various motor sizes and configurations, offering insights into the best design choices for minimizing system weight and optimizing performance. These findings apply to a range of real-world systems, including electric vehicles and industrial machinery, where minimizing weight and optimizing energy efficiency are critical for improving overall system performance and reducing operational costs.

Continuous Electrochemical Carbon Capture via Redox-Mediated pH Swing─Experimental Performance and Process Modeling.

We investigate a continuous electrochemical pH-swing method to capture CO2 from a gas phase. The electrochemical cell consists of a single cation-exchange membrane (CEM) and a recirculation of a mixture of salt and phenazine-based redox-active molecules. In the absorption compartment, this solution is saturated by CO2 from a mixed gas phase at high pH. In the electrochemical cell, pH is reduced, and CO2 is selectively released in a desorption step. We investigate the influence of redox molecule concentration on the charge storage capacity of the solution, as well as the impact of current density and solution recirculation rate on process performance. A theoretical framework, based on a minimal set of assumptions, is established. This framework describes the data very accurately and can be used for system design and optimization. We evaluate the trade-off between energy consumption and CO2 capture rate and compare with published reports. We report a low energy consumption of 32 kJ/mol of CO2 at a capture rate of 39 mmol/m2/min.

Harmonic analysis and optimization for closed-loop superconducting shim coils of 7 T MRI magnet.

High-resolution brain imaging is crucial in clinical diagnosis and neuroscience, with ultra-high field strength MRI systems ( ) offering significant advantages for imaging neuronal microstructures. However, achieving magnetic field homogeneity is challenging due to engineering faults during the installation of superconducting strip windings and the primary magnet. This study aims to design and optimize active superconducting shim coils for a 7 T animal MRI system, focusing on the impact of safety margin, size, and adjustability of the second-order shim coils on the MRI system's optimization. The study employs a nonlinear optimization method to determine the parameters of the shim coils, considering the size of the coil, the level of undesired harmonics, and the whole number approximation of the turns in each coil. The study also conducts a thorough robustness analysis, examining the effects of coil winding accuracy, former processing accuracy, and assembly angle accuracy on the harmonic intensity of each coil. The optimization design results for the 7 T MRI system's shim coils show that the magnetic field changes are less than 0.5 %. After second-order shimming and the harmonic coupling an, the low-order harmonics are minimized, resulting in an improved magnetic field peak-to-peak uniformity from 254.47 to 8.970ppm. The study successfully demonstrates the creation of a set of second-order shim coils for a 7 T animal MRI system through numerical optimization. The design outputs provide essential technological support for the development of a human whole-body 7 T MRI system, ensuring high-quality imaging at the neuronal level. The project also highlights the importance of considering manufacturing and assembly flaws in the shim coil design process to achieve effective shimming in practical engineering scenarios.

Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.

The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia--seeing faces in inanimate objects. Despite extensive research, it remains unclear why the visual system employs such broadly tuned face detection capabilities. We hypothesized that face pareidolia results from the visual system's optimization for recognizing both faces and objects. To test this hypothesis, we used task-optimized deep convolutional neural networks (CNNs) and evaluated their alignment with human behavioral signatures and neural responses, measured via magnetoencephalography (MEG), related to pareidolia processing. Specifically, we trained CNNs on tasks involving combinations of face identification, face detection, object categorization, and object detection. Using representational similarity analysis, we found that CNNs that included object categorization in their training tasks represented pareidolia faces, real faces, and matched objects more similarly to neural responses than those that did not. Although these CNNs showed similar overall alignment with neural data, a closer examination of their internal representations revealed that specific training tasks had distinct effects on how pareidolia faces were represented across layers. Finally, interpretability methods revealed that only a CNN trained for both face identification and object categorization relied on face-like features-such as 'eyes'-to classify pareidolia stimuli as faces, mirroring findings in human perception. Our results suggest that human-like face pareidolia may emerge from the visual system's optimization for face identification within the context of generalized object categorization.

Review of Design Schemes and AI Optimization Algorithms for High-Efficiency Offshore Wind Farm Collection Systems

The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the collector system constitutes a substantial part of the investment cost in wind farms, the design and optimization of this system are pivotal to enhancing the economic viability of offshore wind farms. A thorough examination of collector system design and optimization methodologies is essential to elucidate the critical aspects of collector system design and to assess the comparative merits and drawbacks of various optimization techniques, thereby facilitating the development of collector systems that offer superior economic performance and heightened reliability. This paper conducts a review of the evolving trends in collector system research, with a particular emphasis on topology optimization models and algorithms. It juxtaposes the economic and reliability aspects of collector systems with varying topologies and voltage levels. Building on this foundation, the paper delves into the optimization objectives and variables within optimization models. Furthermore, it provides a comprehensive overview and synthesis of AI-driven optimization algorithms employed to address the optimization challenges inherent in offshore wind farm collector systems. The paper concludes by summarizing the existing research limitations pertaining to offshore wind farm collector systems and proposes innovative directions for future investigative endeavors. The overarching goal of this paper is to enhance the comprehension of offshore wind farm collector system design and optimization through a systematic analysis, thereby fostering the continued advancement of offshore wind power technology.

Research on Optimized Multi‐Objective FCS‐MPC Without Weighting Factors of NPC Three‐Level Inverter

ABSTRACTTo address a series of issues in the application of finite control set model predictive control (FCS‐MPC) strategy for neutral point clamped (NPC) three‐level inverters, such as large amount of rolling optimization calculation, poor midpoint voltage balance effect, high common mode voltage, and complex design of weighting coefficients, this paper proposes an optimized multi‐objective FCS‐MPC strategy without weighting factors. First, the method applies the idea of space vector region division, to obtain the optimal region based on Lyapunov function, and combines with delay compensation, reducing the system's optimization time and enhancing the control precision of the system. Second, aiming at the issue of multi‐objective control for inverters, by constructing the objective function to control tracking reference current, midpoint voltage balance, and reduce common mode voltage, a hierarchical optimization control method is adopted. This approach eliminates the need to design complex weighting coefficients constrained in the objective function as constraints, thereby improving the accuracy of predicted currents. Finally, the optimized multi‐objective FCS‐MPC control strategy is validated through simulation and experimentation to achieve a better balance of midpoint voltage and reduce common mode voltage and harmonic content, thereby enhancing the control precision and dynamic/static performance of the system.

Analytical simulation of mixed convective Williamson nanofluid flow above a stretched Riga plate considering viscous dissipation effects

This work examines, accounting for viscous dissipation, an analytical simulation of mixed convection heat transfers in a Williamson blood base nanofluid flow on a stretched Riga plate (RP). The Lorentz force, which influences fluid dynamics, is produced by the alternating magnetic fields that comprise the RP. The flow characteristics are further complicated by the blood base Williamson nanofluid’s (WN) non-Newtonian behavior. The governing partial differential equations (PDEs) for momentum and energy are transformed into a set of nonlinear ordinary differential equations (NODEs) by similarity transformations. We solve these equations analytically using the homotopy analysis method (HAM). We investigate in detail how the temperature and velocity profiles (VPs) are affected by significant parameters as the Williamson parameter (WP), viscous dissipation, volume friction of nanoparticles, modified Hartmann number (MHN), heat generation parameter and Biot number (BN). The findings show that the thermal conductivity (TC) of nanoparticles is increased, improving the efficiency of heat transfer. Moreover, a Lorentz force generated by the RP considerably alters the temperature and flow fields. With regard to the design and optimization of thermal systems utilizing complex boundary conditions (BCs) and non-Newtonian nanofluids, this work offers significant new insights.

A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model

Air conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems. The model is based on using an optimized design of Gated recurrent unit (GRU) network and an enhanced metaheuristic algorithm, named Improved Alpine Skiing Optimizer (IASO). GRU is a recurrent neural network that has the ability to comprehend intricate temporal relationships within the input data. On the other hand, the IASO technique has been considered to be a population-based optimization technique emulating the downhill skiing behavior of skiers. The proposed GRU/IASO model is trained and tested utilizing data of real-world obtained through a commercial complex situated within an area characterized by high humidity and hot climate. By comparing the proposed method with some other commonly used techniques, including ---, the advantage of the suggested model regarding accuracy and robustness has been defined.

Energy Consumption Reduction in Underground Mine Ventilation System: An Integrated Approach Using Mathematical and Machine Learning Models Toward Sustainable Mining

This study presents an integrated approach combining the Hardy Cross method and a gradient boosting (GB) optimization model to enhance ventilation systems in underground mines, with a specific application at the Jabal Sayid mine in Saudi Arabia. The Hardy Cross method addresses variations in airflow resistance caused by obstacles within ventilation pathways, enabling accurate predictions of the flow distribution across the network. The GB model complements this by optimizing fan placement, pressure control, and airflow intensity to achieve reduced energy consumption and improved efficiency. The results demonstrate significant improvements in fan efficiency, optimized energy usage, and enhanced ventilation effectiveness, achieving a 31.24% reduction in electricity consumption. This study bridges deterministic and machine learning methodologies, offering a novel framework for the real-time optimization of underground mine ventilation systems. By combining the Hardy Cross method with GB, the proposed approach outperforms traditional techniques in predicting and optimizing airflow distribution under dynamic conditions.

Depolarized Forward Light Scattering for Subnanometer Precision in Biomolecular Layer Analysis on Gold Nanorods.

Functional gold nanoparticles have emerged as a cornerstone in targeted drug delivery, imaging, and biosensing. Their stability, distribution, and overall performance in biological systems are largely determined by their interactions with molecules in biological fluids as well as the biomolecular layers they acquire in complex environments. However, real-time tracking of how biomolecules attach to colloidal nanoparticles, a critical aspect for optimizing nanoparticle function, has proven to be experimentally challenging. To address this issue, we present a depolarized forward light scattering (DFLS) method that measures rotational relaxation constants. In DFLS, optically anisotropic nanoparticles are illuminated with linearly polarized light and the forward light scattering is analyzed in a cross-polarized configuration. We demonstrate the application of DFLS to characterize various functional coatings, analyze biomolecular binding kinetics to gold nanoparticles, and determine specific protein adsorption affinity constants. Our results indicate that DFLS offers a powerful approach to studying nanoparticle-biomolecule interactions in complex environments such as bodily fluids, thereby opening new pathways for advancements in nanomedicine and the optimization of nanoparticle-based drug delivery systems.

System Optimization Scheduling Considering the Full Process of Electrolytic Aluminum Production and the Integration of Thermal Power and Energy Storage

To address the curtailment phenomenon caused by the high penetration of renewable energy in the system, an optimization scheduling strategy is proposed, considering the full process of electrolytic aluminum production and the integration of thermal power and energy storage. Firstly, to explore the differentiated response capabilities of various devices such as high-energy-consuming electrolytic aluminum units, thermal power units, and energy storage devices to effectively address uncertain variables in the power system, a Variational Mode Decomposition method is introduced to construct differentiated response methods for its low-frequency, medium-frequency, and high-frequency components. Secondly, based on the real production regulation characteristics of the high-energy-consuming electrolytic aluminum load, and considering various influencing factors such as current, temperature, and output, a scheduling model involving electrolytic aluminum load is established. Then, the power generation characteristics in other processes of electrolytic aluminum production are fully exploited to achieve energy storage conversion, replacing the energy storage batteries that respond to high-frequency components. Finally, by combining the deep peak-shaving model of thermal power units, an optimization scheduling model is established for the joint operation of the full electrolytic aluminum production load and thermal-power-storage systems, with the goal of minimizing system operating costs. The case study results show that the proposed model can significantly enhance the system’s renewable energy absorption capacity, reduce energy storage installations, and enhance the economic efficiency of the system’s peak-shaving operation.

Inpatient Audiograms: A Mixed Methods Study Examining Clinical Decision Making, Financial Analyses, and Audiologist Perspectives.

Evaluate inpatient audiometry on clinical decision-making. Assess stakeholder perspectives on the practice of inpatient audiometry and financial impact. This is a mixed methods study utilizing retrospective chart review, a focus group, and financial analyses. Academic tertiary referral center. Included subjects were adults (18+) admitted from 2015 to 2022 who received an inpatient audiogram (n = 302). A binary assessment of whether audiograms impacted inpatient clinical management was determined. Financial analyses estimated the cost of audiograms. An audiology focus group was conducted. The average age was 51 years. Thirty-nine percent were female. Seventy percent of inpatient audiograms were associated with Otolaryngology consultation. Inpatient audiograms were performed an average of 3.6 days after request (0-47 days, 90th percentile 8.2 days). Forty-nine percent were performed within 1 day. Twenty-three percent were performed for acute hearing loss. Ototoxic monitoring was the most frequent indication (15%). Sixty-two percent of audiograms did not impact the initiation or withholding of treatment. Inpatient testing results in a 63% increase in cost over outpatient. Audiologists endorsed challenges with equipment, patient-level factors, and system-level challenges. Inpatient audiometry is resource-intensive without significant data examining the impact and clinical utility. In most cases, inpatient audiometry is not used in the decision to withhold or deliver treatment and may cost 63% more than outpatient audiograms. While inpatient audiometry has a critical role in appropriate settings, system optimization and guidelines are necessary. Outpatient audiograms may suffice for the majority of otologic consults in combination with a thorough history and physical exam. Additional study across institutions with variable practice would be beneficial to establish broader recommendations.

Strategies for Multigeneration in Residential Energy Systems: An Optimization Approach

With the energy transition, energy supply trends indicate more autonomy for the final consumer, with a more decentralized, intelligent, and low-carbon scenario. Multigeneration technologies offer substantial socioeconomic and environmental advantages by enhancing the efficient utilization of energy resources. The main objective of this study is to develop a flexible, easy-to-use tool for the optimization of multigeneration systems (configuration and operation), focused on obtaining minimal annual costs. C++ was used for the implementation of the optimization problem, which was solved using IBM’s ILOG CPLEX Optimization Studio solver. The case study is a residential consumer center, with energy demands encompassing electricity (including electric vehicles), sanitary hot water, and coolth (air conditioning). The optimal economic solution indicates the installation of 102 photovoltaic modules and the use of biomass to produce hot water. When compared with a conventional solution, where all energy demands are met conventionally (no renewables nor cogeneration), the optimal economic solution reduced annual costs by 27% despite presenting capital costs 42% higher.

Experimental measurements of particle deposition in the human nasal airway.

Intranasal drug delivery is a promising non-invasive method for administering both local and systemic medications. While previous studies have extensively investigated the effects of particle size, airflow dynamics, and deposition locations on deposition efficiency, they have not focused on the thickness of deposited particles, which can significantly affect drug dissolution, absorption and therapeutic efficacy. This study investigates the deposition patterns of dry powder particles within the nasal airway, specifically examining how factors such as flow rates, particle size, and particle cohesiveness influence deposition patterns and their thickness. Using optical coherence tomography (OCT), this study assessed the deposition behaviour of three different lactose powders in a reconstructed nasal airway model at three key anatomical locations under varying flow rates (15, 35 and 55 L/min). Computational fluid dynamics (CFD) simulations were conducted to complement the experimental data, demonstrating the airflow dynamics in the nasal airway and highlighting recirculation zones that impact deposition patterns. The results revealed that the anterior section of the nasal airway is particularly effective at capturing particles, with localised flow patterns playing a critical role in particle accumulation. These flow patterns, combined with particle size and cohesiveness, are key factors in determining where and how particles cluster, leading to thicker deposition in specific areas of the nasal airway. This study addresses the gap in understanding how these factors influence deposition thickness and spatial distribution, ultimately contributing to the optimisation of nasal drug delivery systems.

Timing of Preventive Highway Maintenance: A Study from the Whole Life Cycle Perspective

As the mileage of China’s highways increases, the focus of highway development has shifted from construction to maintenance and management. With the rapid increase in highway traffic, the overuse of highways, combined with the effects of climate change, has accelerated the occurrence of highway problems and poses new challenges to maintenance strategies. From a system optimization perspective, this paper examines the problem of determining the optimal timing of preventive maintenance on highways. To overcome the shortcomings of this method, which cannot reflect the economic benefits and costs, the total benefits and costs were calculated for different pavement ages within a given analysis period, based on the framework of life cycle analysis. The maintenance timing program with the highest cost-benefit ratio was taken as the optimum maintenance timing program and an example analysis was adopted to validate the feasibility of this method. When the timing of preventive maintenance is later in a pavement’s life cycle, the total life cycle benefit decreases while the total life cycle cost increases. Compared to previous studies, this paper considers both direct and indirect influences simultaneously, showing that indirect influence contributes to shortening the timing of pavement preventive maintenance.

Some Critical Thinking on Electric Vehicle Battery Reliability: From Enhancement to Optimization

Electric vehicle (EV) batteries play a crucial role in sustainable transportation, with reliability being pivotal to their performance, longevity, and environmental impact. This study explores battery reliability from micro (individual user), meso (industry), and macro (societal) perspectives, emphasizing interconnected factors and challenges across the lifecycle. A novel lifecycle framework is proposed, introducing the concept of “Zero-Life” reliability to expand traditional evaluation methods. By integrating the reliability ecosystem with a dynamic system approach, this research offers comprehensive insights into the optimization of EV battery systems. Furthermore, an expansive Social–Industrial Large Knowledge Model (S-ILKM) is presented, bridging micro- and macro-level insights to enhance reliability across lifecycle stages. The findings provide a systematic pathway to advance EV battery reliability, aligning with global sustainability objectives and fostering innovation in sustainable mobility.

Experimental Investigation of Dual-Path Inline Mixing System for Sprayers in Corn-Soybean Strip Intercropping Mode

Corn-soybean strip intercropping, which fully utilizes land resources and has high total yield and soil fertility, has become a modern agricultural cultivation mode that is actively promoted. In order to solve the weed problem in corn-soybean strip intercropping, the agricultural technology requirements cannot be met by traditional pre-mixed spraying machines, so a direct injection dual-path inline mixing system was designed for the corn-soybean strip intercropping mode. The system was integrated to improve its installation convenience and universality, and was capable of fulfilling the requirements for the simultaneous application of two types of pesticides at varying mixing ratios. The system mainly consists of a water solvent injection module, glyphosate (pesticide for corn) inline mixing module, and a fomesafen (soybean pesticide) inline mixing module. First, the detection rules of the mixing ratio of related pesticides based on the electrical conductivity measurement principle were studied. Then, the working characteristics of the designed direct injection dual-path inline mixing system were studied through experiments using different pesticides and mixing ratio adjustment ranges. The mixing uniformity test showed that the designed direct injection dual-path inline mixing system had good mixing uniformity, and the maximum uniformity coefficient of the mixing ratio was 9.7%. The stability test showed that the mixing ratio of the designed dual-path inline mixing system was relatively stable, with the maximum standard deviation of the mixing ratio accounting for about 2.2% of its average value, and the maximum average deviation was less than 1.5%. The precision and response time test showed that the designed dual-path inline mixing system had an average deviation of the mixing ratio of less than 2.7% under the condition of a step signal target mixing ratio, and the response time was a maximum of 3.4 s. The results show that the designed dual-path inline mixing system has good performance, and the research findings provide a reference for the design and optimization of inline mixing systems.

Dynamic Characteristics and Periodic Stability Analysis of Rotor System with Squeeze Film Damper Under Base Motions

Inertial loads induced by base motion excitation introduce significant complexities in equilibrium point determination and linearization of systems incorporating squeeze film dampers (SFDs). The coupled effects of base motion excitation and SFD characteristics on periodic stability have received limited attention in previous investigations. This study investigates the dynamic characteristics and periodic stability of a rotor system with SFD subjected to base motion excitation. A finite element model of the rotor-SFD-support system under non-inertial motion is established. The periodic responses are solved using the harmonic balance method with alternating frequency/time technique (HB-AFT) and the arc-length continuation algorithm incorporating the predictor–corrector method, while system stability is analyzed using Floquet theory and the Newmark method. A systematic parametric study is conducted to investigate the effects of base motion parameters, mass unbalance, and SFD parameters on the system’s periodic response. Results demonstrate that base pitching motion enhances system stability, suppresses bistable responses and jump phenomena, and reduces unstable vibration regions. However, under specific parameter combinations, pitching motion can trigger secondary Hopf bifurcations, leading to quasi-periodic and chaotic motions, among other complex nonlinear behaviors. This research provides theoretical foundations for stability-oriented design optimization of rotor systems with SFDs under base motion excitation.

Towards More Efficient PEM Fuel Cells Through Advanced Thermal Management: From Mechanisms to Applications

Proton membrane exchange fuel cells (PEMFCs) provide an important energy solution to decarbonizing transport sectors and electric systems due to zero carbon emission during the operating process, and how to enhance the system efficiency of PEMFCs is one of the most challengeable issues to hinder the large-scale commercial application of PEMFCs. In recent years, numerous studies have been conducted to explore the feasibility and techno-economic performance of advanced thermal management to promote the efficiency of PEMFC systems. The thermal management of PEMFCs can be implemented from two aspects: one is efficient cooling methods to maintain the PEMFC under proper working temperature range, and the other one is waste heat recovery from PEMFCs to improve the overall system efficiency. Concentrated on these topics, many achievements have been gained by academic and industrial communities, and it is imperative to analyze and conclude these experienced studies from mechanism, technology, and application aspects. Therefore, this review summarized the great advances of thermal management of PEMFCs with efficient cooling and waste heat recovery for the sake of improving the overall efficiency of PEMFC systems, providing guidelines for the future design and optimization of PEMFC systems.