Selection Of Optimal Solution Research Articles

Comparative performance analysis of recuperative helium and supercritical CO₂ Brayton cycles for high-temperature energy systems

This study presents a comprehensive comparative analysis of recuperative Brayton cycles utilizing helium (He) and supercritical carbon dioxide (sCO2) as working fluids for high-temperature power generation, focusing on applications in next-generation concentrated solar power (CSP) systems. Thermodynamic cycle simulations were conducted across a temperature range of 1000 K–2000 K, exploring the effects of low-side pressure (1 bar–100 bar) and expansion ratio (1–5) on key performance indicators. These indicators included thermal efficiency, net specific work output, mass flow rate, volumetric flow rate, and system irreversibilities. Results demonstrate that helium-based Brayton cycles exhibit superior thermal efficiency at high temperatures, reaching up to 65 %, compared to sCO2 cycles, which achieve efficiencies up to 55 %. This advantage stems from helium's higher specific heat capacity and lower pressure drops within the cycle. Conversely, sCO2 cycles demonstrate higher net specific work output due to the fluid's higher density, leading to more compact turbomachinery. The study further investigates the impact of irreversibilities, including pressure losses, leakage losses, and heat transfer losses, on cycle performance. A detailed analysis of multi-stage Brayton cycles incorporating intercoolers and reheaters reveals the potential for further efficiency enhancements in both helium and sCO2 systems. This research provides valuable insights for the design and optimization of high-temperature power generation systems, particularly for next-generation CSP plants. The findings highlight the trade-offs between working fluid properties and cycle configurations, guiding the selection of optimal solutions based on specific application requirements and operating conditions.

Effects of steady flow and side hull layouts on motions and added resistance of a trimaran sailing in waves

As a high-performance ship, the trimaran holds significant potential in military and civilian fields. This study proposes a novel numerical method based on the Rankine panel method for predicting the time domain motion and wave loads of trimarans navigating in waves. This approach incorporates a more accurate steady flow model for trimarans and meticulously derives the boundary value problem for the unsteady disturbance potential, while retaining the steady terms related to steady flow throughout the process. This approach ensures a comprehensive consideration of the impacts of speed effect and ship wave. The method was innovatively validated using the trimaran hull form, and its accuracy and reliability were confirmed through comparisons with experimental data. Then, the influence of steady flow on time domain motions and added resistance of trimarans was thoroughly investigated. Comparative analysis of different steady flow models demonstrates that the steady flow model for trimaran proposed provides high precision and advantages in predicting the time domain motions of high-speed trimarans in waves. Furthermore, a systematic investigation into the effects of ship speed and side hull layouts on trimaran's motion and added resistance in waves has been conducted, offering crucial technical support for trimarans' seakeeping optimal solution selection.

Design and optimal scheduling of forecasting-based campus multi-energy complementary energy system

This study presents a complete campus multi-energy complementary energy system (MCES), including an accurate forecasting model, efficient MCES model, and effective multi-objective optimal scheduling strategy to better utilize renewable energy. A hybrid forecasting model, including multi-scale mathematical morphological decomposition, a bidirectional long short-term memory network, and subsequences to the original sequence (S2O) manner based on the rolling approach (RA), is utilized to forecast renewable energy variations. RA continuously updates input datasets to improve forecasting accuracy. Decomposition and forecasting modules are employed in an S2O manner to reduce the number of required modules and forecasting cost. The volatility of renewable energy is mitigated by supplementing energy sources with storage. During operation, the conversion times of different energies are reduced by reasonably planning the energy supply sequence based on different loads on the demand side, increasing the energy utilization rate. The proposed multi-objective optimal scheduling strategy includes a stacked multilevel-denoising autoencoder, non-dominated sorting genetic algorithm-II, and deep reinforcement learning (DRL) for surrogate-model building, Pareto frontier establishment, and optimal solution selection. This is the first study to use DRL to select the final optimal solution. A performance comparison confirms the proposed model effectively decreases costs and pollution while increasing thermal comfort.

The consistency discretization adjustment method of interval fuzzy preference relationship based on individual agreement and group consensus

Constrained by the limited knowledge and fuzzy cogitation, it is often difficult to guarantee the consistency of decision makers’ preference judgments. To effectively avoid the distortion of information about the original preferences, a discrete scale consistency adjustment model based on integer variables under the individual interval fuzzy preference relationship is established. The research shows that the proposed method can not only validly enhance the differentiation between alternatives, but also drop the occurrence of zero weights to a larger extent. Furthermore, aiming at the potential multiplicity of optimal adjustment strategies for interval fuzzy preference relations, an efficient algorithm for screening multiple optimal solutions under single-person decision making is proposed by setting multiple screening dimensions. The algorithm makes up for the deficiency of ignoring potential optimal solutions in traditional methods, and greatly raises the decision flexibility of the ranking optimization process. The validity and applicability of the algorithm are verified through filtering and analyzing multiple optimal solutions under different scenarios. Considering the differences in the decision making tendencies of exclusive and altruistic groups in the interval fuzzy environment, a discrete compatible adjustment model of group consensus under different decision making behaviors is established. Finally, the efficient screening system is matched for different groups, which provides a valid path for the optimal solution selection of group consensus.

Beyond surface: Unveiling ecological and economic ramifications of microplastic pollution in the oceans.

Every year, the global production of plastic waste reaches a staggering 400 million metric tons (Mt), precipitating adverse consequences for the environment, food safety, and biodiversity as it degrades into microplastics (MPs). The multifaceted nature of MP pollution, coupled with its intricate physiological impacts, underscores the pressing need for comprehensive policies and legislative frameworks. Such measures, alongside advancements in technology, hold promise in averting ecological catastrophe in the oceans. Mandated legislation represents a pivotal step towards restoring oceanic health and securing the well-being of the planet. This work offers an overview of the policy hurdles, legislative initiatives, and prospective strategies for addressing global pollution due to MP. Additionally, this work explores innovative approaches that yield fresh insights into combating plastic pollution across various sectors. Emphasizing the importance of a global plastics treaty, the article underscores its potential to galvanize collaborative efforts in mitigating MP pollution's deleterious effects on marine ecosystems. Successful implementation of such a treaty could revolutionize the plastics economy, steering it towards a circular, less polluting model operating within planetary boundaries. Failure to act decisively risks exacerbating the scourge of MP pollution and its attendant repercussions on both humanity and the environment. Central to this endeavor are the formulation, content, and execution of the treaty itself, which demand careful consideration. While recognizing that a global plastics treaty is not a panacea, it serves as a mechanism for enhancing plastics governance and elevating global ambitions towards achieving zero plastic pollution by 2040. Adopting a life cycle approach to plastic management allows for a nuanced understanding of possible trade-offs between environmental impact and economic growth, guiding the selection of optimal solutions with socio-economic implications in mind. By embracing a comprehensive strategy that integrates legislative measures and technological innovations, we can substantially reduce the influx of marine plastic litter at its sources, safeguarding the oceans for future generations.

Enhanced Model Predictive Control for Induction Motor Drives in Marine Electric Power Propulsion System

Marine electric propulsion is an important topic in the research of modern ships and underwater vehicles. The propulsion motor drives based on model predictive control (MPC) are becoming increasingly popular in marine propulsion systems as an emerging technology. However, the multi-objective optimization in conventional MPC requires cumbersome weighting factor tuning. The relatively large computational cost is also detrimental to the industrial application of MPC. Aiming at reducing the computational complexity of multi-objective optimization without weighting factors, this paper proposes an enhanced ranking-based MPC method for induction motor drives in marine electric power propulsion. The presented control set pre-optimization aims to reduce the computational complexity of enumeration and ranking. Based on the sign of torque prediction deviation, the proposed method avoids enumerating all fundamental voltage vectors. Consequently, the number of candidate elements in the initial control set are reduced to four without excessively excluding feasible solutions. By converting predicted numerical errors into ranking results, the proposed MPC seeks the optimal solution among the candidates through improved ranking evaluation. Considering the situation of simultaneous optimal ranking, the normalization error judgment is developed to further optimize the optimal solution selection process. The simulation and experimental results confirm that the proposed MPC is simple and effective. Without the involvement of tuning the weighting factors, the proposed method achieves good performance.

ALTERNATIF PENGENDALIAN LIMPASAN PERMUKAAN DI PERKOTAAN DENGAN MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS (AHP)

Control of surface runoff is a major challenge in sustainable urban stormwater management. High rainfall can cause various problems such as floods and landslides. Related to the problem of surface runoff includes a number of problems that require attention and good solutions to maintain environmental sustainability and prevent negative impacts from surface runoff. Surface runoff control activities generally tend to be social, technically costly, and relatively long. Collaboration with experts, government, and local stakeholders can also reinforce the concept of successful surface runoff control. The application of the AHP (Analytical Hierarchy Process) method is needed as a systematic approach to evaluate and select effective alternative surface runoff control systems, in this study four alternatives were taken based on three criteria. These alternatives involve solutions such as the application of biopores of infiltration wells, rainwater harvesting, retention ponds and green open spaces then these alternatives are evaluated based on relevant criteria, in this study the relevant criteria are taken namely technically, environmentally and cost. The results of this analysis using the AHP method provide relative priority values for each alternative, allowing for the selection of optimal solutions to surface runoff problems. Based on this assessment, it shows that alternative green open space is an optimal solution for surface runoff control systems in urban areas.

Extension of transmission performance evaluation for planar higher pair mechanisms

Comprehensive and accurate evaluation of performance is essential for profile synthesis and profile analysis of higher pair mechanisms. Aiming at the transmission performance of planar higher pair mechanisms, the evaluation indices and methods are proposed from different perspectives. On the one hand, the kinematic performance indices in lower pair mechanisms are extended to higher pair mechanisms, including the mechanical advantage and the generalized pressure angle. On the other hand, novel indices specific to higher pair mechanisms are developed, including the contact closure index and the singularity distance index. Considering the performance of individual elements and that of the overall joint, the transmission performance of higher pairs is subdivided into element-based performance and joint-based performance, and the evaluation indices are classified accordingly. A graphical mapping method based on element-based performance is further proposed for intuitive analysis and qualitative evaluation. Practical examples validate the effectiveness of the proposed indices and methods for evaluating the performance of higher pair mechanisms, as well as their potential to enable optimal profile solution selection and design in the profile synthesis process.

OPPORTUNITY OF USING A MIXED NEUTRAL TREATMENT SOLUTION IN THE DISTRIBUTION ELECTRICAL NETWORKS OF THE REPUBLIC OF MOLDOVA

The optimal solution selection for the treatment of the neutral point in distribution networks (6-35 kV) holds significant practical importance as it directly or indirectly affects the continuity and reliability of supplying electrical energy to consumers, the behavior of medium-voltage electrical networks under single-phase fault conditions, and its impact on the quality of the power distribution service (duration and frequency of power interruptions). It also affects resulting overvoltages, adopted technical solutions, level of electrical security assurance, investments, and so forth. Developing a mathematical model for various methods of treating the neutral point in distribution networks allows for the calculation and analysis of these operating conditions, identifying the optimal one, thus avoiding experimental attempts that are limited by the advanced wear of equipment within the electrical networks of the Republic of Moldova. Based on the developed mathematical model, comparative calculations are performed regarding the feasibility of implementing the combined neutral regime.

METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

Along with the growth of computing requirements, edge devices, such as satellites and unmanned aerial vehicles, are equipped with heterogeneous multiprocessor systems to cope with complicated missions, including attitude control, signal processing, and objective detection. Concerning the extremely limited power on these devices, reducing the energy and time overhead of task execution evolves into a crucial challenge. Therefore, a multiobjective energy-efficient task scheduling technique (METSM) is proposed. First, a mathematical model is established for the energy-efficient task scheduling problem on edge heterogeneous multiprocessor systems. In this model, both makespan and total energy consumption are the optimization objectives. The decision variables include the task execution sequence, processor assignment, and dynamic voltage and frequency scaling level for each processor. Second, a problem-specific algorithm, namely, iterated greedy-based multiobjective optimizer (IMO), is proposed. Specifically, destruction–reconstruction and local search are redesigned for optimizing task scheduling and resource allocation. Considering local optima avoidance, a probabilistic mutation operation is developed. In addition, multiobjective-oriented strategies of optimal solution selection and acceptance criteria are adopted to accelerate the convergence. Finally, our proposed IMO is compared with several of the latest algorithms through multiple performance metrics. The experimental results show that IMO can obtain optimal Pareto fronts among several multiobjective methods. Savings of approximately 10% and 12% in time and power consumption, respectively, can be achieved by IMO. Moreover, in comparison with classic list-based heuristics for solving test cases, while IMO maintains a similar makespan, energy is reduced by nearly 90%.

Incorporating users’ adaptive behaviors into multi-objective optimization of shading devices: A case study of an office room in Qingdao

Users' adaptive behaviors significantly affect the indoor environment and energy use. Neglecting them would introduce considerable uncertainty in the computer simulation results and lead to suboptimal or even invalid energy-efficiency solutions in optimization. To address this issue, we abstracted and simplified three user behavior patterns, including adjustment of operable shading devices, switching on/off artificial lighting, and opening windows for natural ventilation by a brief literature review. We then developed a new simulation approach that incorporates these behavior patterns and applied it to optimize the exterior fixed solar shade design of a south-facing office room in Qingdao as an example. The baseline model was established and validated in both daylight and energy simulations. The results demonstrate that the addition of user behavior models has a significant impact on both illuminance and load simulation results. The evaluation of each shading solution under the traditional approach and the newly-developed approach shows considerable differences, leading to the selection of different optimal solutions. Ultimately, the 0.6-meter-long type 8 (horizontal sunshade combined with vertical triangular panels on both sides) and 0.7-meter-long type 4 (double-sided vertical shading) were selected as the optimal solution using the traditional approach and new approach, respectively. This research offers valuable insights into the user behavior pattern and uses a shading optimization example to demonstrate the significant impact of incorporating adaptive behavior mechanisms on simulation and optimization results. The approach established in this paper has the potential to be extended to other solutions, being conducive to the study of refined building efficiency strategies for different conditions.

Kinetic resolution of thermal runaway for lithium-ion batteries: A Gaussian surrogate-assisted separate optimization approach

Accurate kinetic resolution of thermal runaway (TR) for lithium-ion batteries (LIBs) plays a central role in battery safety design and early warning. However, simultaneous extraction of the number and kinetic parameters of TR reactions from multi-peak overlapping differential scanning calorimetry (DSC) profiles is a complicated nonlinear programming optimization problem. A Gaussian surrogate-assisted separate optimization framework is proposed to address the kinetic resolution problem. First, an offline equivalent kinetic database, based on the geometric similarity of heat flow peaks and Gaussian functions, is designed to provide initial parameter estimations for multiple reactions. Second, an adaptive range division method, based on priori local extrema, is proposed to divide the DSC profile into separate ranges of fitting (ROF) to assist the key mechanism selection. Third, divisional kinetic resolution is performed for each ROF where kinetic models initialized by the above database are utilized to fit sub-range profiles and then potential solutions are searched based on a posteriori error-based criterion. Finally, Bayesian information criterion is utilized for the optimal solution selection among the combinations of potential solutions for multiple ROFs. The proposed framework is validated through experimental data from the DSC tests of types of LIBs. The results illustrate the applicability and efficiency of the proposed framework.

Emergency Material Scheduling Optimization Method Using Multi-Disaster Point Distribution Approach

The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies.

Evaluating near-optimal scenarios with EnergyPLAN to support policy makers

Energy system modelling may support policymakers in their energy planning efforts. Energy system modellers usually identify the optimal system configuration based on an economic objective function, or in multi-objective optimization, a combination of multiple objectives such as greenhouse gas emissions and total system cost. However, there could be political, socio-economic, or environmental reasons justifying a policymaker's selection of a solution that is slightly more costly or greenhouse gas polluting than the uniquely optimal solution. Solely focusing on the uniquely optimal solution disregards potentially diverse alternatives, which based on different evaluation metrics could even be preferable. In response to this challenge, the evaluation of near-optimal solutions is gaining attention in the energy system modelling field as an extension of traditional multi-objective optimization studies and as a way to bridge the gap between simulation and optimization approaches. In this study, we explore near-optimal solutions, outline the diversity of near-optimal solutions, and evaluate the relevance of these solutions in the context of energy planning. The proposed methodology is applied to the Italian case to determine its potential as a tool to support policymakers in evaluating energy system scenarios from a selection of optimal and near-optimal solutions.

Rating pressure factors affecting logistics systems during the pandemic and the ideal logistic decision selection under the Pythagorean fuzzy environment

PurposeDue to the current pandemic, the importance of logistics functions and decisions is well understood both at the level of companies and users. Logistics systems and related decisions are of vital importance in making supply chains effective, efficient and without disruption. Logistic pressure factors may emerge at different points along the logistics process, and given the role of logistics decisions as one of the important indicators of competitiveness, the determination of the logistics pressures that are likely to increase the costs of business, and their causative factors are a vital aspect of the logistics decision-making process. The study aims to provide assistance in the selection of the most ideal logistics decision by ranking the pressure factors affecting the logistics system, especially during the pandemic period for logistics enterprises operating in Ordu and Giresun provinces and which have a corporate identity.Design/methodology/approachIn this study, it is aimed to make the most ideal logistics decision selection by ranking the pressure factors affecting the logistics system, especially during the pandemic period for the logistics enterprises operating in Ordu and Giresun provinces and having a corporate identity. For that purpose interval-valued Pythagorean fuzzy (IVPF)–analytic hierarchy process (AHP) based combinative distance-based assessment (CODAS) methodology was used. Additionally sensitivity and comparison analysis were discussed.FindingsCompetitive pressure was found as the most important pressure factor affecting the logistics system during the pandemic period. Change in regulatory rules was the pressure factor found to have the least effect on the logistics system. Using the weights of logistics pressure factors, “Operational Decisions” was found to be the most ideal logistics decision selection.Research limitations/implicationsThe findings provide support for the evaluation of logistical pressures and decision options by presenting a decision model capable of processing ambiguous information. During a pandemic or similar period, the study assists decision makers in determining a new route. The findings will also call business managers' attention to logistical pressure factors and lead them toward more realistic and feasible practices in the logistics decision-making process.Originality/valueThis study provided an effective and applicable solution to a decision-making problem in the logistics sector including logistics pressure factors and the selection of logistics decisions. In this context, a methodology was presented that will allow businesses to self-evaluate their own logistics pressure factors and the selection of optimal solutions.

Evaluation of Elements for Selection of Optimal Solution for Facility Passive Protection from the Fire Spread over Façade

Evaluation of Elements for Selection of Optimal Solution for Facility Passive Protection from the Fire Spread over Façade

Topological Overview of Auxiliary Source Circuits for Grid-Tied Converters

This paper reviews different types of capacitors and auxiliary source circuit topologies and presents an introduction to control strategies used for circuit applications reducing DC-BUS capacitance. The paper argues in favor of replacing bulky electrolytic capacitors in capacitor-supported power electronic systems with auxiliary source circuits. DC-BUS capacitors are widely used in grid-tied power converters (rectifiers) and utilized for power balance, voltage ripple limitation, and short-term energy storage. The electrolyte capacitor is the Achilles heel of any rectifier and power converter due to its higher rate of failure than other circuitry components. Auxiliary source circuits are key components to qualitatively improve the reliability of the DC links, where they divert the instantaneous pulsating power into extra reliable storage components. Unlike previous work, this review serves to provide a clear picture of an auxiliary source circuit design, in favor of optimal solution selection according to the specific application. Therefore, energy storage components (capacitors), topologies, and control strategies of auxiliary source circuits are comprehensively reviewed in this paper. Additionally, detailed explanations, comparisons, and discussions of auxiliary source circuits are offered.

АНАЛИЗ И ОПТИМИЗАЦИЯ РАБОТЫ ИНФОРМАЦИОННЫХ СИСТЕМ С ПРИМЕНЕНИЕМ ВЕРОЯТНОСТНЫХ МЕТОДОВ

Probabilistic methods are one of the key tools for analyzing and optimizing the work of information systems in the modern world. They allow you to analyze and select optimal solutions for processing control algorithms and ensure an acceptable level of reliability of the system. When using probabilistic methods, it is important to take into account the influence of various factors on the results of the system. To do this, it is necessary to conduct a comprehensive analysis, which includes an assessment of the probability of malfunctions, an analysis of the consequences of these malfunctions, as well as an assessment of the probability of restoring the system after a malfunction. Comparison and selection of optimal solutions are also important steps when using probabilistic methods, this makes it possible to analyze various variants of control algorithms and choose the one that will provide the best results at the lowest cost.

Justification of the Marketing Strategy of the Business Plan of the Enterprise

The purpose of the article is to substantiate the parameters of the marketing strategy of business planning of enterprises.
 The study reveals the relevance of the problem of developing a business plan and in particular a marketing plan for an entrepreneurial project. The process of business planning at the enterprise is considered as a set of interrelated management processes in the form of: determining the definition of the structural unit of the enterprise, assessing the external environment, assessing the internal structure of the enterprise, developing alternative solu­tions, justification and selection of optimal solutions, implementation of optimal solutions and implementation of results.
 The practical significance of the research results is that the directions of strategic goals of the entrepreneurial project of creating a new strategic unit of the enterprise are generalized. The budget of expenses for advertising at realization of the enterprise project of creation of new division on retail trade of the enterprise is developed. The distribution of the budget of marketing costs in the implementation of the business project to create a new store of the enterprise. Forecasting of sales of products and gross income during the implementation (in the first year) of the entrepreneurial project of the enterprise was carried out.
 The scientific novelty of the research results is that the economic essence, main components and stages of development of the business plan as a document are determined; the role of business planning in the entrepreneurial activity of the enterprise is revealed; identified opportunities to use the practice of business planning of business processes at the enterprise and the development of its strategic units; the most important structural components of realization of the enterprise project of the enterprise are developed.

Research on product process design and optimisation model based on IoT intelligent computing

Abstract In this article, some complex parameters of the product and design processes, how to match and optimise the sub-parts of related industrial products and how to improve the quality of the corresponding products and the competitiveness of the product in the international market are discussed in this article. We also build an algorithm based on the particle swarm and XGBoost algorithms, combined with the intelligent computing of the Internet of Things (IoT). We transform some uncertain factors in the process of the industrial product design process through the fuzzy matrix, select the optimal design through the optimised intelligent computing of the IoT scheme and compare the influence of the scheme before and after optimisation on production efficiency. The results show that the method proposed in this article can reduce the time-consumption of optimal solution selection by 42.85%–52.94%. In addition, selecting the optimal solution for each field in a targeted manner can increase the overall production efficiency of the product by about 5%, reaching between 93.6% and 96.5%, which may save raw materials and create more economic value.