Optimal System Research Articles

Profiling the bacterial microbiome diversity and assessing the potential to detect antimicrobial resistance bacteria in wastewater in Kimberley, South Africa.

Wastewater treatment plants (WWTPs) are hotspots for pathogens, and can facilitate horizontal gene transfer, potentially releasing harmful genetic material and antimicrobial resistance genes into the environment. Little information exists on the composition and behavior of microbes in WWTPs, especially in developing countries. This study used environmental DNA (eDNA) techniques to examine the microbiome load of wastewater from WWTPs. The DNA was isolated from wastewater samples collected from the treatment trains of three WWTPs in Kimberley, South Africa, and the microbial diversity and composition was compared through 16S rRNA gene sequencing. The microbes detected were of the Kingdom Bacteria, and of these, 48.27% were successfully identified to genus level. The majority of reads from the combined bacterial data fall within the class Gammaproteobacteria, which is known to adversely impact ecological and human health. Arcobacteraceae constituted 19% of the bacterial reads, which is expected as this family is widespread in aquatic environments. Interestingly, the most abundant bacterial group was Bacteroides, which contain a variety of antibiotic-resistant members. Overall, various antibiotic-resistant taxa were detected in the wastewater, indicating a concerning level of antibiotic resistance within the bacterial community. Therefore, eDNA analysis can be a valuable tool in monitoring and assessing the bacterial microbiome in wastewater, thus providing important information for the optimization and improvement of wastewater treatment systems and mitigate public health risks.

A Matrix-Based Approach to Unified Synthesis of Planar Four-Bar Mechanisms for Motion Generation With Position, Velocity, and Acceleration Constraints

Abstract This paper introduces a novel matrix-based approach for the simultaneous type and dimensional synthesis of planar four-bar linkage mechanisms, accommodating various practical constraints, including position, velocity, acceleration, and joint placements. Traditional design processes segregate type synthesis, the determination of joint and link configurations, from dimensional synthesis, which involves specifying link sizes and pivot locations. This segregation often leads to complexities in addressing the complete design challenge. The novel methodology proposed in this paper departs from the conventional sequential design approach by concurrently evaluating type and dimensional parameters using a data-driven matrix formulation. The crux of the paper’s methodology involves formulating a singular design equation through a transformation matrix, parameterized by the Cartesian parameters of the mechanism’s dyads. This formulation linearly expresses a broad range of constraints, facilitating the identification of viable solutions through singular value decomposition and null space analysis. This integrated approach not only simplifies the synthesis process but also provides direct insights into the mechanism’s parameters, encompassing both type and dimensions, thereby obviating the need for further interpretative steps common to the use of quaternions and kinematic mapping. In essence, the paper presents two main contributions: the development of a unified design equation capable of encompassing a wide array of constraints within the mechanism synthesis process, and the introduction of an algorithm that effectively identifies all potential planar four-bar linkage mechanisms by accurately satisfying up to five constraints. This approach promises to enhance the design and optimization of mechanical systems by offering a more holistic and efficient pathway to mechanism synthesis.

Intelligent Optimization and Impact Analysis of Energy Efficiency and Carbon Reduction in the High-Temperature Sintered Ore Production Process

The coordinated optimization of energy conservation, efficiency improvement, and pollution reduction in the sintering production process is vital for the efficient and sustainable development of the sintering department. However, previous studies have shown shortcomings in the multi-objective collaborative optimization of sintering systems and the quantification of pollutant impacts. To address these, this paper proposes a multi-objective optimization method integrated with the NSGA-III algorithm and establishes an integrated system optimization model for sintered ore production and high-temperature waste heat recovery. The results demonstrate significant improvements: energy utilization efficiency increased by 0.67%, energy consumption decreased by 17.3 MJ/t, production costs were reduced by 11.45 CNY/t, and the emissions of CO2, SO2, and NOx were reduced by 0.464 kg/t, 0.034 kg/t, and 0.008 kg/t, respectively. Additionally, the study identified optimal configuration parameters and analyzed the quantitative impact of several key factors on multiple indicators. The results also show that reducing the water content of the mixture, decreasing the middling coal content in the fuel, and increasing the thickness of the material layer are effective strategies to reduce energy consumption and pollutant emissions in the sintering process. Overall, implementing these optimizations can bring significant economic and environmental benefits to the steel industry.

Tax System Optimization: A Comparative Analysis Between Personal Income Tax and Corporate Income Tax in Indonesia

This study adopts a qualitative approach to analyze the comparison between Personal Income Tax (PPh) and Corporate Income Tax (PPh Corporate) in Indonesia. Through literature studies and observations, this study explores the tariff structure, tax relief, and tax compliance level of the two tax systems. The economic implications of the implementation of Income Tax and Corporate Income Tax on economic growth, investment, and income distribution were also discussed. The findings show that tax reform, as stated in the Law on Harmonization of Tax Regulations (UU HPP), has a significant impact in improving the effectiveness and fairness of the tax system. The results of this study provide in-depth insights into the importance of appropriate tax policies to support inclusive and sustainable economic growth in Indonesia.

Research on dynamic solution analysis and applications based on the Rydberg atom superheterodyne structure

This paper investigates the dynamic solution of the density matrix equation based on the Rydberg atom superheterodyne structure. Compared to the current analytical method relying on the steady-state solution, the dynamic solution is related to the Rabi frequency and the frequency of the signal to be measured. Therefore, it can comprehensively describe the instantaneous bandwidth and gain characteristics of the receiver and is in good agreement with experimental results. Additionally, we propose an atomic all-heterodyne receiver architecture that combines electric-field heterodyne and optical heterodyne techniques and demonstrates the reception and recovery of modulated signals under this architecture with linear frequency modulation (LFM) signals as an example. Our research offers interesting theoretical insights that can be applied to the performance analysis and system optimization of atomic receivers.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework

This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets.

R-DOCO: Resilient Distributed Online Convex Optimization Against Adversarial Attacks

This paper addresses the problem of distributed constrained optimization in a multi-agent system where some agents may deviate from the prescribed update rules due to failures or malicious adversarial attacks. The objective is to minimize the collective cost of the unattacked agents while respecting the constraint limitations. To tackle this, we propose a resilient distributed projected gradient descent algorithm for online optimization that achieves sublinear individual regret, defined as the difference between the online and offline solutions. Additionally, we extend the cost function from convex combinations to more general distributed optimization scenarios. The proposed algorithm demonstrates resilience under adversarial conditions, allowing it to handle an unknown number of adversarial nodes while maintaining performance. Compared to existing methods, this approach offers a robust solution to adversarial attacks in constrained distributed optimization problems.

Implications of accelerated and delayed climate action for Ireland’s energy transition under carbon budgets

Limiting global warming requires the effective implementation of energy mitigation measures by individual countries. However, the consequences of the timing of these efforts on the technical feasibility of adhering to cumulative carbon budgets—which determines future global warming—are underexplored. Moreover, existing national studies on carbon budgets either overlook integrated sectoral interactions, path dependencies, or comprehensive demand-side strategies. To address this, we analyse Ireland’s mitigation pathways under equal per-capita carbon budgets using an energy systems optimisation model. Our findings reveal that delayed mitigation brings forward the need for a net-zero target by five years, risks carbon lock-in and stranded assets, increase reliance on carbon dioxide removal technologies and leads to higher long-term mitigation costs. To keep the Paris Agreement targets, countries must set and meet accelerated mid-term mitigation goals and address energy demand.

Understanding prescribing errors for system optimisation: the technology-related error mechanism classification.

Technology-related prescribing errors curtail the positive impacts of computerised provider order entry (CPOE) on medication safety. Understanding how technology-related errors (TREs) occur can inform CPOE optimisation. Previously, we developed a classification of the underlying mechanisms of TREs using prescribing error data from two adult hospitals. Our objective was to update the classification using paediatric prescribing error data and to assess the reliability with which reviewers could independently apply the classification. Using data on 1696 prescribing errors identified by chart review in 2016 and 2017 at a tertiary paediatric hospital, we identified errors that were technology-related. These errors were investigated to classify their underlying mechanisms using our previously developed classification, and new categories were added based on the data. A two-step process was used to identify and classify TREs involving a review of the error in the CPOE and simulating the error in the CPOE testing environment. The technology-related error mechanism (TREM) classification comprises six mechanism categories, one contributing factor and 19 subcategories. The categories are as follows: (1) incorrect system configuration or system malfunction, (2) opening or using the wrong patient record, (3) selection errors, (4) construction errors, (5) editing errors, (6) errors that occur when using workflows that differ from a paper-based system (7) contributing factor: use of hybrid systems. TREs remain a critical issue for CPOE. The updated TREM classification provides a systematic means of assessing and monitoring TREs to inform and prioritise system improvements and has now been updated for the paediatric setting.

Performance evaluation and multi-objective optimization of hydrogen-based integrated energy systems driven by renewable energy sources

Performance evaluation and multi-objective optimization of hydrogen-based integrated energy systems driven by renewable energy sources

Proposal design and thermodynamic optimization of an afterburning-type isothermal compressed air energy storage system integrated with molten salt thermal storage

The isothermal compressed air energy storage is a potential technique for large-scale energy storage. In this study, the molten salt thermal storage is integrated with the afterburning-type isothermal compressed air energy storage system, which uses liquid piston compression technique, to enhance the thermal performances. Thermodynamic models of the hybrid energy storage system were developed, and the genetic algorithm was used to optimize multiple parameters to enhance the energy efficiency. Four operation modes, i.e., the high, medium-high, medium-low, and the low output power modes were proposed, to enhance the operational flexibility of the hybrid energy storage system. When the system operates in the operation mode of medium-low output power, a portion of afterburning heat is stored in the molten salt and used in the operation mode of low output power. Results show that the roundtrip efficiency of high, medium-high, medium-low, and the low output power modes are 63.57 %, 59.33 %, 57.13 %, and 83.28 %, respectively. Exergy analysis was carried out to quantify the irreversibilities of key components. In the three modes of operation with afterburning, the combustion chamber has the highest portion of exergy loss, and in the operation mode without afterburning, the exergy loss of heat exchangers is higher than that of expanders.

Two-parameter dynamics and multistability of a non-smooth railway wheelset system with dry friction damping.

A deep understanding of non-smooth dynamics of vehicle systems, particularly with dry friction damping offer valuable insights into the design and optimization of railway vehicle systems, ultimately enhancing the safety and reliability of railway operations. In this paper, the two-parameter dynamics of a non-smooth railway wheelset system incorporating dry friction damping are investigated. The effect of the crucial parameters on the complexity of the evolution process is comprehensively exposed by identifying different dynamic responses in the two-parameter plane. In addition, the multistability and the various routes transition to chaos for the system are also discussed. It is found that dry friction induces highly complex dynamics in the system, encompassing a range of behaviors such as periodic, quasi-periodic, and chaotic motions. These intricate dynamics are a direct result of the interplay between multiple parameters, such as speed and damping coefficients, which are critical in determining the system's stability and performance. The presence of multistability further complicates the system, resulting in unpredictable transitions between different motion states.

Evaluation of household electricity consumption in multi-apartment buildings for optimization of rooftop PV systems

Evaluation of household electricity consumption in multi-apartment buildings for optimization of rooftop PV systems

Energy Efficiency Optimization in IRS-Aided Multiuser MISO Communication Systems

Energy Efficiency Optimization in IRS-Aided Multiuser MISO Communication Systems

Capacity planning of storage batteries for remote island microgrids with physical energy storage with CO2 phase changes

Energy storage devices based on the CO2 thermal cycle (CTC) lose competitiveness in cold regions because of the decreased energy volume density and charge–discharge efficiency at low ambient temperatures. The CO2 hybrid thermal cycle (CHS) combines the CTC with the CO2 hydrate thermal cycle (CHT) to maintain a high energy volume density and charge–discharge efficiency regardless of the ambient temperature. The CHS-based energy storage system has been shown to have an energy volume density of 80–140 kWh/m3 and charge–discharge efficiency of 60 %–106 %. However, the economic feasibility of this system has not been considered. In this study, a numerical analysis was performed on the practical application and economic feasibility of CHS-based energy storage for the 100 % renewable energy microgrid of a pair of remote islands. In a case analysis of CHS installation in a microgrid for a remote island with 100 % renewable energy, the construction cost of CHS was 2/3 of that of pumped storage; the equipment cost reduction of CHS strongly depends on the type and amount of renewable energy to be interconnected, and therefore, the overall optimization of the power system is necessary.

AoI Optimization in Multi-Source Update Network Systems Under Stochastic Energy Harvesting Model

AoI Optimization in Multi-Source Update Network Systems Under Stochastic Energy Harvesting Model

Cathodic Supply Optimization of PEMFC System Under Variable Altitude

Cathodic Supply Optimization of PEMFC System Under Variable Altitude

Kinetics of continuously catalyzed peroxymonosulfate in a fixed-bed reactor

Most studies have been conducted in batch reactors. Therefore, understanding the catalytic reaction kinetics and mass transfer in continuous catalytic reactors is essential for the design of heterogeneous catalysts and optimisation of catalytic degradation processes in heterogeneous systems. In this research, granular catalysts (Co-600/AlSi-0.25/AP) is used to investigate the kinetic constants and mass transfer resistance for the continuous catalytic decomposition of peroxymonosulfate (PMS) and degradation of p-nitrophenol (PNP) in a fixed bed reactor. The flow rate is 5 mL/min and reaction temperature is 30 °C, intrinsic kinetic constants of PMS decomposition and PNP degradation are 48 and 195 kg−1·min−1·L, respectively, which are 20–30 times of the apparent kinetic constants of PMS decomposition and PNP degradation in fixed bed reactors, respectively. The decomposition rates of PMS and PNP are controlled by mass transfer, and the reaction takes place in the diffusion-controlled regime. The mass transfer kinetic model based on the plug-flow model shows a good agreement between the calculated data and the experimental data. Which indicate that PMS decomposition and pollutant degradation in a fixed-bed reactor can be predicted by mass transfer kinetic modelling, guide the design of catalysts and the optimisation of heterogeneous catalytic reaction system in a fixed-bed reactor.

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