Optimal System Research Articles

Probing the influence of composition and cross-linking degree on single-chain nanoparticles from poly(tetrahydrofuran-ran-epichlorohydrin) copolymers: Insights from neutron scattering, calorimetry, and dielectric spectroscopy

The industrial sector has made significant strides in the development of multicomponent and multiphasic polymer materials, including polymer blends, composites (such as nanocomposites), and various copolymers. Random copolymers, characterized by their statistical arrangement of repeating units, are particularly noteworthy due to their tunability from amorphous to semicrystalline states. In this study, we focus on poly(tetrahydrofuran-ran-epichlorohydrin) (P(THF-ran-ECH)) copolymers, which serve as precursors for single-chain nanoparticles (SCNPs). These SCNP-based materials are of particular interest as they bridge the gap between traditional polymers and colloids. This research comprehensively investigates how the type and degree of internal cross-linking influence the structure and dynamics of P(THF-ran-ECH) copolymers and their SCNPs. Techniques such as quasielastic neutron scattering (QENS), differential scanning calorimetry (DSC), and broadband dielectric spectroscopy (BDS) were employed to study copolymers with varying compositions and levels of cross-linking. By analyzing two samples with different epichlorohydrin (ECH) contents (13 mol% and 27 mol%), we aim to control crystallization and explore its effects on dynamic behavior. Our results show that both the composition and the degree of cross-linking significantly impact the dynamics of the SCNPs, with SCNPs exhibiting slower dynamics compared to their precursor copolymers. Furthermore, semicrystalline samples display faster dynamics in SCNPs than amorphous samples. These findings provide valuable insights for the design and optimization of advanced multicomponent polymer systems.

Optimizing greywater treatment in green walls: A comparative analysis of recycled substrate materials

The utilization of green walls for on-site greywater treatment in urban buildings has garnered significant attention in recent research, with substrates playing a vital role in treatment performance. The incorporation of recycled materials as substrates not only offers a cost-effective solution for greywater treatment, but also enhances sustainability by repurposing wastes. This study investigates the efficacy of three recycled substrates: alum sludge, garden compost, and a mixture of perlite-coconut fiber in three green wall systems for greywater treatment. Each substrate was allocated to a three-pot column. Comparative analysis revealed alum sludge (C3) and the coconut fiber-perlite mix (C1) achieving the highest chemical oxygen demand (COD) removal at 89.45 ± 2.92 % and 89.34 ± 2.47 %, respectively, with garden compost (C2) recording 67.98 ± 7.45 %. For total nitrogen (TN) removal, C2 achieved the lowest efficiency (49.27 ± 9.39 %), while C1 demonstrated 83.79 ± 10.45 %. Notably, C3 exhibited superior total phosphorus (TP) removal at 94.86 ± 1.87 %, compared to 52.07 ± 17.88 % for C1 and 24.90 ± 30.95 % for C2. Our findings demonstrate that substrate selection is crucial for optimizing both pollutant removal and plant health in green walls. The coconut fiber mix emerged as the most effective substrate, supporting robust plant growth and superior pollutant removal, particularly for nitrogen removal. In contrast, garden compost proved unsuitable, with minimal plant survival post-greywater introduction. Alum sludge showed significant promise, particularly for TP removal, reinforcing its effectiveness in green walls. These insights underscore the importance of substrate optimization in greywater treatment systems in urban environments.

Distributed Fixed-Time Optimal Flocking for Second-Order Multi-Agent Systems

In this paper, we investigate the problem of distributed optimization for second-order multi-agent systems with fixed-time flocking. The objective is to concurrently steer the agents towards a common velocity while optimizing the global objective function. To solve the problem, we first design a fixed-time estimator to predict the global gradient of the objective functions, then based on which, each agent is endowed with fixed-time tracking controller to track the optimal solution. Motivated by the fact that the relative velocity information is difficult to obtain accurately, the proposed algorithm is designed without using neighbors’ velocity. The upper bounds of the settling time are provided without relying on the initial states of the agents, only requiring the adjustment of parameters. The effectiveness of the proposed control protocol is also demonstrated through numerical simulations.

Flexible design of renewable hydrogen production systems through identifying bottlenecks under uncertainty

Renewable hydrogen production technology within the energy sector stands as a vital avenue towards decarbonization in process industries. However, for systems characterized by high penetration of renewable energy, the challenges posed by uncertain parameters impacting stable production present significant obstacles to the large-scale application of hydrogen production from renewable energy. To address the issues, this work proposed a comprehensive four-step strategy for the bottleneck identification and optimization of the renewable hydrogen production systems (RHPS), which encompasses four models including system configuration optimization, flexibility analysis, bottleneck identification, and debottlenecking models. The effectiveness of the proposed strategy is substantiated through a case study. The results indicate that positive fluctuations on supply side and negative fluctuations on demand side contribute to bolstering the flexibility of RHPS. When the RHPS possesses sufficient flexibility, the critical fluctuation amplitude for photovoltaic power is −36 %, whereas for wind turbine power, it stands at −32 %. It highlights that when the photovoltaic power fluctuates, the ability of RHPS to maintain its own stability is stronger than when the wind turbine power fluctuates. Furthermore, when the fluctuations of supply side are positive, the rate of change in critical fluctuation amplitudes on the supply side is 4.63 times than that of the demand side. This underscores that the power decline in the supply side has a more pronounced detrimental effect on the stability of RHPS than an increase in the demand side. The proposed strategy for optimizing RHPS offers valuable theoretical insights for the system design and retrofitting under uncertainty.

Enzymatic Kinetic Resolution of Racemic 1-(Isopropylamine)-3-phenoxy-2-propanol: A Building Block for β-Blockers.

This study aimed to optimize the kinetic resolution of building blocks for the synthesis of β-blockers using Candida rugosa lipases, which could be potentially used to synthesize enantiomerically pure β-blockers further. Reaction mixtures were incubated in a thermostated shaker. Qualitative and quantitative analyses of the reaction mixtures were performed using chiral stationary phases and the UPLC-IT-TOF system. Of the 24 catalytic systems prepared, a system containing lipase from Candida rugosa MY, [EMIM][BF4] and toluene as a two-phase reaction medium and isopropenyl acetate as an acetylating agent was optimal. This resulted in a product with high enantiomeric purity produced via biotransformation, whose enantioselectivity was E = 67.5. Using lipases from Candida rugosa enables the enantioselective biotransformation of the β-blockers building block. The biocatalyst used, the reaction environment, and the acetylating agent significantly influence the efficiency of performer kinetic resolutions. The studies made it possible to select an optimum system, a prerequisite for obtaining a product of high enantiomeric purity. As a result of the performed biotransformation, the (S)-enantiomer of the β-blocker derivative was obtained, which can be used to further synthesize enantiomerically pure β-blockers.

The impact of automobile warm-up noise characteristics on annoyance

In the automotive industry, the sound of a vehicle plays a crucial role in its perceived value, highlighting the importance of understanding the relationship between subjective auditory perceptions and objective physical measures. This study investigates the psychoacoustic properties of warm-up noise emitted by vehicles equipped with an accelerated warm-up system (AWS). Our main goal is to analyze the discomforting aspects of this noise and reveal that its cause is related to tonality. Through extensive auditory experiments, we aim to uncover the nuances of this relationship, finding that both tonality and roughness significantly contribute to annoyance perception, particularly at higher frequencies. This investigation provides crucial insights for acoustic optimization in automotive systems, focusing on improving the user experience and product quality during the AWS warm-up phase.

Application of hydrogen storage in polygeneration microgrids: Case study of wind microgrid in India

Renewable energy based stand-alone microgrids are highly promising to supply electricity to isolated communities and remote locations. However, both buffer and long-term energy storage are essential due to the highly intermittent nature of renewable sources. By employing hydrogen energy storage with fuel cell and electrolyser in these microgrids, multiple outputs such as electricity, heat, water, and fuel, can be achieved. Design, development and optimization of efficient energy storage system remain a key challenge for the stand-alone microgrid technology. One must ensure that the microgrid satisfies the required electric load but is not over-designed, which is achieved by limiting the two performance parameters i.e., unmet electrical load fraction and excess electricity fraction. The capacities of wind turbine, electrolyser and hydrogen storage are optimized corresponding to the limiting values of unmet electrical load (fUL) and excess electricity (fEX) below 10 %. For the location of Ladakh which has rich availability of wind resource, the optimum size of microgrid is observed to be smallest as WT-20 kW, El-20 kW, H2-15 kg with fUL and fEX as 6.4 % and 8.9 % respectively. The Levelised Cost of Energy (LCOE) is also minimum as 0.89 $/kWh for the location of Ladakh. The distribution of wind resource has significant impact on the size of hydrogen storage. Uniform wind energy with higher velocity round the year reduces the capacity of hydrogen energy storage. Kanyakumari has non-uniform wind resource across different months of the year corresponding to which the capacity of H2 storage is as high as 370 kg.

Optimization of energy system of natural gas hydrate offshore platform considering wind power uncertainty

Natural gas hydrate has potentially become a clean energy source in the future. Most natural gas hydrates are stored in the ocean, and the exploitation of deep-sea gas hydrates requires the construction of offshore platforms. Generally, the platform consumes a lot of energy due to its impact on the marine environment. Therefore, the adoption of a reasonable energy equipment operation scheme can effectively reduce the platform operation cost. Compared with the way of directly consuming natural gas to provide power to the platform, introducing wind energy may be cleaner and more economical. However, the uncertainty and volatility of wind power must be carefully considered when it is introduced. The paper establishes a mixed integer linear programming (MILP) model to solve this problem. A flexible distributed energy system framework for the natural gas hydrate mining platform is constructed to design and optimize the equipment scale and the number of wind turbines with the minimum total annual cost of the energy system as the objective function. A case study is conducted by an established model and the results show that access to wind energy can significantly reduce the cost of the energy system by 18.9 % and CO2 emissions by 46.9 %. Meanwhile, the uncertainty of electric energy demand is the most sensitive to the optimization results.

Optimization of a novel liquid carbon dioxide energy storage system by thermodynamic analysis and use of solar energy and liquefied natural gas

Liquid carbon dioxide energy storage with its advantages in terms of geographical constraints and economic performance has garnered significant attention. In this study, a novel liquid carbon dioxide storage system was proposed which utilizes the waste cold energy from LNG and achieves high liquefaction efficiency. By integrating solar energy, net output of the system is increased. The results show that the net output is sensitive to turbine 1 outlet pressure. Round-trip efficiency and energy storage density increases with the rise of the pump outlet pressure first and subsequently exhibits a gradual decline with an inflection point. With the increase in ambient temperature, the system efficiency shows a slight decrease, yet energy storage density shows a significant increase. At basic operating conditions, the system demonstrates energy storage efficiency of 66.64 %, round-trip efficiency of 74.33 %, and energy storage density of 23.51 kWh/m3. In addition, optimization was conducted. Single-objective optimization results show that round-trip efficiency, energy storage density and levelized cost of storage can reach 89.86 %, 25.37 kWh/m3 and 0.1873 $/kWh, respectively. Multi-objective optimization obtained compromise results of round-trip efficiency and levelized cost of storage of 85.33 % and 0.1947 $/kWh when integrated solar energy, showing a 33.77 % increase and a 13.20 % decrease compared to 63.79 % and 0.2243 $/kWh when not integrated solar energy.

Optimization of Enterprise Financial Risk Management and Crisis Early Warning System Supported by AI

The stability of the financial market plays a key role in the overall health of the economic system, and optimizing corporate financial risk management and crisis warning systems is of great significance. Traditional financial risk management methods often face challenges when processing large-scale, resulting in a decrease in the accuracy of risk assessment. This study combining gated recurrent units (GRU), temporal convolutional networks (TCN) and attention mechanisms. Leverage diverse data sets to improve the accuracy of financial risk assessments and the effectiveness of crisis warnings. Experimental results show that the algorithm outperforms the baseline model in terms of risk prediction accuracy, efficiency and stability. For example, on the LendingClub loan dataset, FLOPs are reduced by more than 46.8%, inference time is improved by more than 48.5%. This method provides new perspectives and solutions for optimizing corporate financial risk management and crisis warning systems. This will have a positive impact on the stable operation of enterprises and other aspects.

Time-delay feedback control on vertical vibration of maglev train under unsteady aerodynamic force

The stability and safety of maglev trains are significantly affected by unsteady aerodynamic forces during high-speed operation. In this paper, we employ a time-delay feedback control strategy to suppress the nonlinear vertical vibrations of maglev train under periodic unsteady aerodynamic force. Firstly, the amplitude-frequency response equation of the maglev vehicle is derived using the multiple scales method, and the stability of the steady-state solutions is determined. Then, the influences of velocity feedback gain coefficient and velocity time delay on the nonlinear vibrations of the maglev vehicle are investigated. The optimal value range of the time delay is identified. The results demonstrate that the time delay feedback control can effectively suppress the nonlinear vibration of maglev vehicles and enhance their operational stability under the premise of reasonable selection of time delay parameters. The theoretical research presented in this paper can serve as a theoretical reference for the design, improvement, and optimization of control systems for high-speed maglev trains.

Design optimization of phononic-fluidic resonator systems

Phononic-fluidic resonators utilize an acoustic fluid domain as a defect inside a solid metamaterial lattice, which can be used used to precisely measure acoustic properties of liquids. The design goal is to offer ideal boundary conditions for the acoustic resonator by matching defect resonance with phononic band gap. The design space of metamaterial or phononic crystal and fluidic resonator is nearly unlimited, especially when utilizing fully three-dimensional structures. In this work, we explore numerical shape and topology optimization to focus acoustic energy into the fluid domain and maximize the quality factor of the defect resonance. This requires the use of complex multi-frequency objective functions, while taking into account practical limits such as finite size and losses in the fluid and solid domains. Results highlight notable but limited improvements of quality factor with shape optimization starting from an initial design constrained by the maximum deformation possible. On the other hand, topology optimization results offer unique and unexpected ideas, and thus a deeper dive into the design space.

A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm

Building load prediction plays an important role in building energy savings and mechanical and electrical system optimization control. The dynamic energy consumption can be accurately calculated using the traditional physical energy simulation method that entails a complex setup and verification process due to the numerous input parameters required. It is also difficult to change the physical model once it has been determined. The data-mining method is fast in calculation and simple to use, but its prediction accuracy is limited by historical data quality. It is difficult to predict the load of new buildings without historical data. To solve these problems, this study proposes a hybrid building load prediction method for office buildings. The proposed method uses EnergyPlus to generate a building load database that includes than 25.14 million data cases, covering 35 types of building geometry and 2870 building examples. Based on the above database, the LightGBM algorithm was selected to extract feature variables that affect the load and build a load prediction model. The training results show that there are 24 key feature variables for office building load prediction. The hourly MAPE of the cooling load prediction model is 6.95 % and RMSE is 4.31 W/m2, and the hourly MAPE of the heating load prediction model is 7.09 % and RMSE is 11.64 W/m2 compared with EnergyPlus model. Two actual office buildings are selected as case studies to validate the model prediction accuracy. Results show that comparing predicted results with measured data, the hourly cooling load of the MAPE is 12.42 %. Coparing predicted results with actual heating load, daily MAPE is 7.97 %.

Energy system models should consider evolving charging profiles

Abstract Globally, sales of battery electric vehicles (BEVs) are surpassing records every year, and their growing charging needs will ultimately reshape power infrastructure planning practices. While studies have analyzed the impact of light-duty BEVs on the electricity sector, they have overlooked the prospective evolution in charging profiles. We developed a framework for analyzing passenger vehicle electrification futures accounting for the evolution in charging infrastructure, BEVs technical features, and socio-demographics. We soft-link a BEV charging profiles generator with an energy system optimization model to analyze a light-duty vehicle (LDV) electrification scenario in the U.S. from 2020-2050. Compared to static charging profiles, common in prior work, evolving profiles lead to substantial differences in projected power plant installed capacity (up to ~300 GW more solar PV) and activity (up to ~460 TWh more solar PV generation). Hence, future studies should consider not only different charging profiles (e.g., day, night, uncontrolled) but also how these evolve over time.

Experimental and numerical study on dynamic responses of a TLP-type modular floating structure system

This paper explores a proposed TLP-type modular floating structure system, incorporating both central TLP modules and peripheral floating artificial reef modules. The outermost floating artificial reef module can function as a floating breakwater, and also serving as a habitat for surrounding marine organisms. To assess the system's multi-body coupling dynamics under various wave conditions, both scale model tests (1:80) and time-domain numerical simulations were performed. The simulations accounted for hydrodynamic interactions among the bodies and the mechanical coupling effects. During the scale model test, a survival strategy has been proposed to prevent collision risks between reef modules and adjacent TLP modules, which involving a hinge connector with additional linear spring. A preliminary stiffness recommendation was suggested by a trade-off between the pitch of the reef module and the spring force. Numerical and experimental results have been widely compared and discussed, including the multi-body motion responses and the tension leg loads. A good agreement has been achieved, although responses are slightly overestimated by numerical simulation due to the neglect of the viscous damping effect. In addition, the responses of the proposed system, connected with and without the outermost floating artificial reef module, have been compared and analyzed. The results suggest that the connection of the reef module can significantly reduce the system's surge response and enhance its survivability under severe sea conditions. The validated numerical model can serve as a valuable reference for subsequent system optimization and further engineering applications.

Potential use of district heating networks and the prospects for the advancements within urban areas of Nottingham as a case study

Buildings in urban areas significantly contribute to global energy consumption, necessitating solutions to reduce energy demands and mitigate environmental impacts. This study investigates district heating networks in urban areas, using Nottingham as a case study. A literature review addresses key barriers to decarbonising building energy in the UK, focusing on: (1) Information, engagement, and behaviour, (2) Strategy, policy, and regulation, (3) Infrastructure, (4) Supply chain and skills, and (5) Distribution of costs and impacts. The study examines the network's location, current processes, energy performance, and potential future impacts. Heat mapping and energy flow assessments identify areas for energy savings, guiding system optimisation. The findings suggest that increasing the energy output from the heat station to the network can enhance performance. The analysis highlights the potential of a life cycle perspective for district heating networks, proposing a framework that considers all stages from material sourcing to system utilisation. This approach helps assess environmental impacts, including greenhouse gas emissions and resource depletion, identifying opportunities to improve resource efficiency and reduce environmental impacts. By evaluating current sustainability goals and regulatory compliance, the study provides insights into the environmental and social implications of the district heating network's operations, pinpointing areas for improvement. It emphasises the need for continuous optimisation and innovation throughout the network's life cycle. The potential of a full life cycle assessment (LCA) approach is highlighted to evaluate four main future advancements: (1) heating network with added energy storage, (2) advanced system operations through model predictive control (MPC) for demand prediction, (3) general network improvements, and (4) network expansion. Overall, this study aims to enhance the sustainability, efficiency, and resilience of district heating networks, contributing to the transition towards sustainable energy systems while ensuring environmental and economic viability over time.

Data-Driven Control Method Based on Koopman Operator for Suspension System of Maglev Train

The suspension system of the Electromagnetic Suspension (EMS) maglev train is crucial for ensuring safe operation. This article focuses on data-driven modeling and control optimization of the suspension system. By the Extended Dynamic Mode Decomposition (EDMD) method based on the Koopman theory, the state and input data of the suspension system are collected to construct a high-dimensional linearized model of the system without detailed parameters of the system, preserving the nonlinear characteristics. With the data-driven model, the LQR controller and Extended State Observer (ESO) are applied to optimize the suspension control. Compared with baseline feedback methods, the optimization control with data-driven modeling reduces the maximum system fluctuation by 75.0% in total. Furthermore, considering the high-speed operating environment and vertical dynamic response of the maglev train, a rolling-update modeling method is proposed to achieve online modeling optimization of the suspension system. The simulation results show that this method reduces the maximum fluctuation amplitude of the suspension system by 40.0% and the vibration acceleration of the vehicle body by 46.8%, achieving significant optimization of the suspension control.

RDBSB: a database for catalytic bioparts with experimental evidence.

Catalytic bioparts are fundamental to the design, construction and optimization of biological systems for specific metabolic pathways. However, the functional characterization information of these bioparts is frequently dispersed across multiple databases and literature sources, posing significant challenges to the effective design and optimization of specific chassis or cell factories. We developed the Registry and Database of Bioparts for Synthetic Biology (RDBSB), a comprehensive resource encompassing 83 193 curated catalytic bioparts with experimental evidences. RDBSB offers their detailed qualitative and quantitative catalytic information, including critical parameters such as activities, substrates, optimal pH and temperature, and chassis specificity. The platform features an interactive search engine, visualization tools and analysis utilities such as biopart finder, structure prediction and pathway design tools. Additionally, RDBSB promotes community engagement through a catalytic bioparts submission system to facilitate rapid data sharing and utilization. To date, RDBSB has supported the contribution of >1000 catalytic bioparts. We anticipate that the database will significantly enhance the resources available for pathway design in synthetic biology and serve essential tools for researchers. RDBSB is freely available at https://www.biosino.org/rdbsb/.

The Optimization of a Slow-Walking System in a Higher Education Campus—Taking the Huangjiahu Campus of Wuhan University of Science and Technology as an Example

The Optimization of a Slow-Walking System in a Higher Education Campus—Taking the Huangjiahu Campus of Wuhan University of Science and Technology as an Example

Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba

As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems.