System Performance Indicators Research Articles

Finite MX/M/C Queue with State Dependent Service, Two-Class Arrivals and Impatient Customers

Objectives: 1. To compute the transition probabilities for the queueing system to be in different states. 2. To calculate the average system length and mean waiting time under the specified parameters. 3. To investigate and test the impact of system characteristics on the anticipated system length as well as mean waiting times. Method: MATLAB software is used for the Runge-Kutta method to calculate probabilities and system constants. Findings: An increase in Type-I arrival rate λ1 and Type-II arrival rate λ2 will cause both mean waiting times and queue length to increase, and they are reduced with an increase in service rates µ1 and µ2 of Type-I and Type-II classes. A rise in parameters such as special service rate µ3, probability of balking and reneging will result in a reduction in both mean waiting times and queue length. Novelty: This article covers a batch arrival finite Markovian queueing system with multi-server support and consumer impatience. In real time, all arrivals need not enter the queue at the same arrival rates, as it is based on the needs of the customer. In view of this, we have assumed two types of customers along with two different rates of arrival and state-dependent service. We did our work in transient mode, as time can influence the waiting lines. Also determined the system's performance indices and demonstrated the impact of the input parameters on the system's constants. Keywords: Batch Arrival, Customer Impatience, Two Class Customers, State Dependent Service, Multi Server Facility

Energy, exergy, economic, and environmental analyses and optimization of a multigeneration system considering high-temperature island environment

Renewable energy based multigeneration systems help satisfy the diverse demand for marine resources exploration in off-grid island area, where the storage of fluctuating renewable energy for continuous power supply becomes necessary. Therefore, this paper proposes a solar-driven multigeneration system for island environment integrated with energy storage and production of electricity, cooling, heating, and freshwater. The system comprises an ejector power subsystem, a multiple-effect distillation subsystem, and a CO2 binary mixture-based compressed gas energy storage subsystem. The system performance from the perspectives of energy, exergy, economics, and environment, as well as the impact on performance of the working fluid and several key system parameters, are investigated in this paper. According to the results, an increase in the mass fraction of organic in the CO2 mixture leads to an improvement in system energy efficiency and exergy density at the cost of lower system power efficiency. Increasing the high-pressure tank pressure enhances system performance except for system power efficiency. Increased low-pressure tank pressure leads to improved system energy efficiency and exergy density, accompanied by lower system power efficiency and exergy efficiency. When the ambient temperature increases from 298 K to 303 K, the system energy efficiency noticeably decreases, whereas an increase in direct normal irradiance improves various system performance indicators. Among several mixtures, CO2/R32 mixture was chosen for further analysis and optimization for its wider operating range with R32 mass fraction and better system performance. The optimal operating point had system energy efficiency, exergy density, and modified levelized cost of energy of 1.281, 22.216 kWh/m3, and 0.131 $/kWh, respectively. The proposed system in this research is proven to be a novel technical solution to meet the diverse energy demands on unique island environments.

Key performance indicators of marketing mix effectiveness in the pharmaceutical business

The article is devoted to the study of determining key performance indicators of the marketing mix effectiveness in the pharmaceutical business. The problems and contradictions in ensuring the effectiveness of marketing in pharmaceutical companies are emphasized. Theoretical and applied foundations for implementing the marketing mix in the pharmaceutical business are highlighted. The expediency of following the «5Ps» and «6Ps» marketing mix is justified due to their correspondence to the specific features of marketing in the pharmaceutical business. Based on the research, a system of key performance indicators of the marketing mix effectiveness in the pharmaceutical business is identified.

Commercialization Efforts of Capacitive Deionization Technology in Water Treatment Processes

AbstractAlthough capacitive deionization (CDI) technology has been studied intensively for more than 20 years, its commercialization remains in the initial stage, which is partly caused by the insufficient knowledge exchange between academia and industry. This concept reviews multiple scaling‐up efforts in the CDI technology for treating real water streams, following a case‐by‐case fashion. While the cell architecture in pilot scales is limited to the membrane CDI, highlighting the necessary role of ion‐exchange components during scaling‐ups, different ways of electrode stacking, i. e., monopolar and bipolar, are available. The performance indicators of these CDI systems when treating real water streams are summarized to gain insights into industrial practices. Importantly, key discrepancies in cell components and performances between pilot‐scale and lab‐scale studies are emphasized. The main challenges in large‐scale CDI systems for industrial purposes are discussed, providing hints for a better integration of research and applications.

Stochastic modeling and optimization of discrete-time cold standby repairable systems with unreliable repair facility and retrial mechanism

This study develops novel reliability models for discrete-time cold standby repairable systems with unreliable repair facility and retrial mechanism. In view of the situation that the state of some engineering systems cannot be continuously monitored, discrete distribution is used to describe the distribution of random variables involved in this paper. For the case that multiple events can occur simultaneously in the discrete-time system model, the priority order of multiple events occurring simultaneously is defined. Two different models are proposed based on two types of priority rules. The retrial mechanism of the components is considered in the case that the failure information of the system components cannot be successfully transmitted to the repair facility. The preventive maintenance (PM) and repair after the breakdown of the repair facility are considered. The key reliability indices are derived by using the iterative algorithm of the difference equation and the generative function method. The algorithms for calculating system state probability, key reliability indices and cost–benefit ratio (CBR) are designed. To demonstrate the impact of each parameter on the system reliability indices, numerical results are provided. The optimal repair rate configuration scheme is chosen using the Particle Swarm Optimization (PSO) algorithm to achieve the lowest possible CBR. The impact of the system model with or without PM on stationary availability and CBR is shown. In addition, the impact of the number of system components on a series of system performance indices is analyzed, and the optimal number of components of the system corresponding to the minimum value of CBR is obtained.

Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models

The rapid growth of the automotive industry has exacerbated the conflict between the complex traffic environment, increasing communication demands, and limited resources. Given the imperative to mitigate traffic and network congestion, analyzing the performance of Internet of Vehicles (IoV) mesh networks is of great practical significance. Most studies focus solely on individual performance metrics and influencing factors, and the adopted simulation tools, such as OPNET, cannot achieve the dynamic link generation of IoV mesh networks. To address these problems, a network performance analysis model based on actual switches is proposed. First, a typical IoV mesh network architecture is constructed and abstracted into a mathematical model that describes how the link and topology changes over time. Then, the task generation model and the task forwarding model based on actual switches are proposed to obtain the real traffic distribution of the network. Finally, a scientific network performance indicator system is constructed. Simulation results demonstrate that, with rising task traffic and decreasing node caching capacity, the packet loss rate increases, and the task arrival rate decreases in the network. The proposed model can effectively evaluate the network performance across various traffic states and provide valuable insights for network construction and enhancement.

Innovative and efficient integrations of desalination plants coupled absorption, adsorption, and humidification-dehumidification desalination units employing external heat recovery techniques

Thermal desalination technologies have been developed recently to solve some pressing problems, such as high energy expenditure, increasing fuel costs, and environmental problems related to conventional plants. However, these technologies still need to be enhanced in terms of water production and gain output ratio (GOR). This paper presents innovative and efficient combined thermal desalination plants consisting of absorption (ABDS), adsorption (ADDS), and humidification-dehumidification (HDH) desalination units to enhance energy utilization, water production, and overall system performance. Four innovative combined desalination plants employing different external heat recovery scenarios are presented and compared. The result from each scenario is evaluated and compared with those of the standalone ABDS and with other studies found in the literature at the same heat source temperatures. The heat source is mainly utilized to drive the ABDS plant. The ADDS and HDH are driven by the heat rejection from the ABDS components with different external heat recovery scenarios. Results illustrated that the maximum freshwater production generated from the proposed scenarios ranges between 1.57 and 2.94 m3.day−1 with a GOR of between 1.38 and 1.75 at 120 °C. Raising heat source temperatures from 55 to 120 °C decreased the improvement in the water productivity from 618.5 to 224 % and decreased the enhancement in the GOR from 222.8 to 99 %. In scenarios where the ADDS and HDH are driven by the heat rejection from the absorption and condensation process of the ABDS, the maximum freshwater production results from the ABDS unit. On the other hand, in scenarios where the ADDS and HDH are driven by the outlet hot water from the ABDS-generator, the maximum freshwater production is obtained from the HDH unit. The finding provided that the proposed innovative scenarios have an effective improvement in overall system performance indicators.

A Novel Techno-Economical Control of UPFC against Cyber-Physical Attacks Considering Power System Interarea Oscillations

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system’s security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system’s resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks.

Optimization of the Load Command for a Coal-Fired Power Unit via Particle Swarm Optimization–Long Short-Term Memory Model

This study addresses the challenges faced by coal-fired power plants in adapting to energy fluctuations following the integration of renewable energy sources into the power grid. The flexible operation of thermal power plants has become a focal point in academic research. A numerical model of a coal-fired power plant was developed in this study using the Long Short-Term Memory (LSTM) algorithm and the Particle Swarm Optimization (PSO) algorithm based on actual operation data analysis. The combined PSO-LSTM approach improved the accuracy of the model by optimizing parameters. Validation of the model was performed using a Dymola physical simulation model, demonstrating that the PSO-LSTM coupled numerical model accurately simulates coal-fired power plant operations with a goodness of fit reaching 0.998. Overall system performance for comprehensively evaluating the rate and accuracy of unit operation is proposed. Furthermore, the model’s capability to simulate the load variation process of automatic generation control (AGC) under different load command groups was assessed, aiding in optimizing the best load command group. Optimization experiments show that the performance index of output power is optimal within the experimental range when the set load starts and stops are the same and the power of load command γ = 1.8. Specifically, the 50–75% Turbine Heat Acceptance (THA) load rise process enhanced the overall system performance index by 55.1%, while the 75–50% THA load fall process improved the overall system performance index by 54.2%. These findings highlight the effectiveness of the PSO-LSTM approach in optimizing thermal power plant operations and enhancing system performance under varying load conditions.

Performance evaluation and degradation analysis of 20 MW photovoltaic power plant located in the southwestern highlands of Algeria

This article evaluates the performance of a 20 MW grid-connected photovoltaic system installed in the Naama region, southwestern highlands of Algeria. The production database used in this work was recorded between January 1, 2017, and December 2022, from the plant’s SCADA system. During the first 5 years of production, the final yield varied from 3.88 in winter 2020 to 6.30 h/day in summer 2017, while the reference yield varied from 4.45 to 7.05 h/day, the annual average performance ratio is equal to 83.35%, and the annual average capacity factor was found to be 20.05% for the monitored period. These data are then compared with the predicted outcomes by a detailed difference analysis using three widely used PV simulation tools: PVsyst, SAM, and PVSol. Results indicate that the actual data strongly coincide with those predicted by the PVsyst simulation, as confirmed by statistical indices. However, the degradation of the PV plants over the monitored period is quantified using a linear model and a CSD model and is estimated at −1.43 and −1.55 %/year, respectively. In summary, the photovoltaic system performance indices obtained are satisfactory compared to large-scale photovoltaic systems reported in other studies. Practical applications This study evaluates the performance and degradation of a 20 MWp photovoltaic system located in the highlands of Algeria. The results of this article will serve as a baseline for the future construction of PV plants to raise awareness among decision-makers about the beginnings of the deterioration of installations to allocate a budget for the maintenance and operation of these installations. The findings of this study have the potential to contribute to the wider adoption of Photovoltaic systems as a viable alternative to traditional energy in Algeria and especially in the Highlands.

Distributed l2−l∞ filtering over wireless sensors based on adaptive event triggering

This paper investigates the design problem of a set of distributed filters. These adaptive event-triggered (AET) filters have the [Formula: see text] property and are suitable for sensor networks characterized by random measurement data loss and dynamic topology switching. In a distributed filtering network, each local filter estimates the system’s state not only relying on the node’s individual information but also using information from neighboring nodes in the network topology. The adaptive event triggering condition is determined by the combination of the latest release data of the filter itself, the estimated value of the current moment, and the latest release data of the neighboring nodes. The event triggering mechanism adopts a threshold adaptive adjustment scheme, which dynamically changes the threshold parameter according to the filtering error under the premise of guaranteeing the filter performance, thus maximally saving the network communication resources. Initially, we used a binary sequence to describe the random loss of sensor measurements, and a Markov chain is used to describe the switching of the filtering network topology. Secondly, a Lyapunov function is created to examine the [Formula: see text] performance index of the filtering error system and investigate its mean square exponential stability. Subsequently, the distributed filter is formulated, and its parameters are determined using the linear matrix inequality approach. Ultimately, the validity of the proposed method is substantiated through numerical illustrations.

УДОСКОНАЛЕННЯ КОНЦЕПТУАЛЬНИХ ЗАСАД ЄВРОІНТЕГРАЦІЇ ТА ФІНАНСОВО-ПРАВОВІ МЕХАНІЗМИ ПРИСКОРЕННЯ ВСТУПУ УКРАЇНИ В ЄС

Introduction. Since 2022, Ukraine has been following the path of “classical accession” to the EU, which involves the compliance with the Copenhagen criteria and adoption of the common EU legislation (acquis) in 33 policy areas/ chapters. But advancement withing this scenario is a long and resource-intensive process. Problem Statement. The urgent task is to find an optimal model of joining the EU with coherence to a reasonable time frame of this course, achieving high results of European integration and identification of the sources of funding for carrying out reforms and overcoming the consequences of the war after reducing the potential of the Ukraine Facility. Purpose - identification of the current financial and legal problems of the European integration process for Ukraine, generalization of the methodological pillars of the concept of staged accession to the EU as a more progressive version over the classic accession model, design of the policy proposals for updating monitoring tools and financial incentives for progressing across the subsequent stages towards the EU membership. Methods. General scientific and special methods are applied: reviews of theoretical sources, advisory and research papers, analysis of statistical data and legal documents, historical method, logical method, methods of comparison and synthesis. Results. The article summarizes the methodological pillars of the concept of staged accession to the EU, systematizes the legal mechanisms of negotiations and obtaining EU membership, shows the financial aspects of the European integration process, including access to the EU cohesion funds, and elaborates practically important proposals for the activity of the EU institutions and national bodies as to the operationalization of the model of staged accession. Conclusions. The staged accession model is the most holistic and logically structured concept that can evolve a predictable progress towards the EU membership. The model should be based on the European Commission's quantitative assessments of countries' preparedness for the membership within the framework of negotiating clusters and chapters. A system of performance indicators is also required that will record progress of the candidate countries across stages and will set the ground for disbursements from the EU funds in line with specified ratios. Since 2028, the new EU medium-term fiscal framework should provide for the formation of new funds to finance the recovery of Ukraine's economy and the application of the EU's cohesion policy towards Ukraine.

Performance analysis of different control models for smart demand–supply energy management system

Several features of innovative grid technologies can be deployed to improve the overall performance of the power system environment. This can be seen from the generation to the consumption of energy. The two-way communication of smart metering introduces the novel functionalities of the energy management system. This paper presents a practical implementation of using the intelligent metering system. It consists of implementing a nanogrid that optimally coordinates the energy from the solar panel, battery storage and utility grid to supply the end user. The developed model is validated with an optimal value of the state of charge of the distributed energy storage to maximise energy from the solar panel and battery storage while minimising the power received from the utility grid. A demand response scheme is employed to formulate the performance index of the energy management system using three optimal control models: adaptive open-loop control, adaptive closed-loop control and model predictive control schemes. The formulation of the performance index of each approach is a function of the energy flow from different resources depending on the power consumption. The three models have given different insights into the performance of the smart nanogrid, which may be used to the advantage of the grid owner or end user. Through the performance of the optimal strategies, it can be observed that energy management is ensured, and real-time monitoring of the entire system is guaranteed. The performance models facilitate the minimisation of the power from the utility, resulting in savings between 23.7% and 39.240% of the total energy demand from the end user. Besides, the system design is validated by an electrical system to form a real-world innovative nanogrid application in residential environments.

Heat flow topology-driven thermo-mass decoupling strategy: Cross-scale regularization modeling and optimization analysis

The liquid desiccant dehumidification (LDD) is both eco-friendly and highly effective in humidity control, widely investigated across diverse angles. This study expands the application of the newly devised heat current method to examine the liquid desiccant dehumidification process. It introduces a new regularized temperature, which includes using a heat flow topology driven thermo-mass decoupling strategy, and constructing the heat current model by defining both the thermal resistance and moisture resistance. It significantly extends the application of the heat current method, clarifying the thermo-mass coupling energy transfer mechanism. The findings indicate that key factors influencing system performance indicators include total thermal conductivity (αA), solution mass flow rate (ms), air-solution mass flow rate ratio (RA/S,D), and heat source temperature (tH). Furthermore, under the influence of distinct thermal conductivities, the optimal output values for performance indicators, including moisture removal rate, exergy efficiency and entransy efficiency, are determined as 8.4 g/s, 71.2%, and 12.3%, respectively. The results of synergistic influence analysis reveal that, for the performance indicator entransy efficiency, msand RA/S,D display a significant degree of synergistic influence, resulting in a strong positive effect and benefiting 68.53% of the total area. Multi-objective optimization indicates a trade-off relationship among the system performance evaluation indicators moisture removal rate, exergy efficiency and entransy efficiency, with optimal solution values of 3.25 g/s, 65.92%, and 12.01%, respectively.

A decision-making approach on control techniques for an inverted pendulum based on, neuro-fuzzy, indirect adaptive and PID controllers

The operation of an inverted pendulum and its respective type of control are affected by the change of the values of its internal parameters. Changes with high uncertainty result in responses with undesirable outputs. In this work, a comparison is presented for the control of an inverted pendulum to determine the operation and characteristics of three types of control systems: Neuro-Fuzzy Control (NFC), Indirect Adaptive Control (IAC) and a Proportional Integral Derivative control (PID). The study considers several indices such as stabilization time, rise time, mean square error, overshoots, convergence, computational load, error, mathematical requirements, and performance indices for control systems. To demonstrate its operation, the controls are implemented in hardware, one for the NFC and another for the IAC under an Arduino UNO platform. The results indicate that the NFC and IAC controls do not generate a transient or impulse response, only a small delay and the rise and stabilization time are minimal. While PID presents a transient response and overshoot, as well as a stabilization time to reach the steady state response.

Impact of early death recording on international comparison of acute myocardial infarction mortality – administrative hospital data study using the example of Germany and the United States

BackgroundIn-hospital mortality from acute myocardial infarction (AMI) is widely used in international comparisons as an indicator of health system performance. Because of the high risk of early death after AMI, international comparisons may be biased by differences in the recording of early death cases in hospital inpatient data. This study examined whether differences in the recording of early deaths affect international comparisons of AMI in-hospital mortality by using the example of Germany and the United States, and explored approaches to address this issue.MethodsThe German Diagnosis-Related Groups Statistics (DRG Statistics), the U.S. National Inpatient Sample (NIS) and the U.S. Nationwide Emergency Department Sample (NEDS) were analysed from 2014 to 2019. Cases with treatment for AMI were identified in German and U.S. inpatient data. AMI deaths occurring in the emergency department (ED) without inpatient admission were extracted from NEDS data. 30-day in-hospital mortality figures were calculated according to the OECD indicator definition (unlinked data) and modified by including ED deaths, or excluding all same-day cases.ResultsGerman age-and-sex standardized 30-day in-hospital mortality was substantially higher compared to the U.S. (in 2019, 7.3% vs. 4.6%). The ratio of German vs. U.S. mortality was 1.6. After inclusion of ED deaths in U.S. data this ratio declined to 1.4. Exclusion of same-day cases in German and U.S. data led to a similar ratio.ConclusionsWhile short-duration treatments due to early death are generally recorded in German inpatient data, in U.S. inpatient data those cases are partially missing. Excluding cases with short-duration treatment from the calculation of mortality indicators could be a feasible approach to account for differences in the recording of early deaths, that might be existent in other countries as well.

Adaptive reinforcement learning-based control using proximal policy optimization and slime mould algorithm with experimental tower crane system validation

This paper presents a novel optimal reference tracking control approach resulted from the combination of a popular policy gradient Reinforcement Learning (RL) algorithm, namely Proximal Policy Optimization (PPO), and a metaheuristic Slime Mould Algorithm (SMA). One of the most important parameters in the PPO-based RL process is the learning rate, which has a big impact on how the parameters of the actor neural network (NN) are iteratively updated. In every episode of the RL process, the weights and the biases of the actor NN are multiplied with the learning rate, determining how much the learning agent will step into a certain direction computed based on previous experiences. The classical PPO algorithm usually relies on fixed values for the learning rates which rarely change, or not at all, during the learning process. However, its main drawback is that the learning agent cannot take advantage of positive momentum in the learning process by accelerating towards good learning experiences or slow down and quickly change the direction in the case of consecutive negative learning experiences. The main objective of the combination proposed in this paper is to create an adaptive SMA-based PPO approach applied to control systems, which instead of using fixed learning rate values, it uses the SMA to compute optimal values of the learning rates in each time step of the learning process based on the progress of the learning agent. This paper investigates if the adaptive SMA-based PPO control approach can be considered as an alternative to the classical PPO version, which employs fixed values of the learning rate. A comparison is carried out using control system performance indices gathered while performing an optimal reference tracking control task on tower crane system laboratory equipment.

Proposal of Performance Indicators for Situational Awareness Systems from Viewpoint of STCW Convention

Proposal of Performance Indicators for Situational Awareness Systems from Viewpoint of STCW Convention

A Novel Designation of Solid Oxide Fuel Cell-Integrated System Using LPG as Fuel for Marine Vessels

A Novel Designation of Solid Oxide Fuel Cell-Integrated System Using LPG as Fuel for Marine Vessels

A novel designation of LNG solid oxide fuel cells combined system for marine application

This paper presents a pioneering multigeneration system for marine vessel applications, involving the integration of solid oxide fuel cells (SOFCs) fueled by liquefied natural gas (LNG), coupled with LNG cold energy utilization cycles and exhaust gas heat recovery systems. The system comprises a tandem arrangement of organic Rankine cycle (ORC) and trans-critical carbon dioxide cycle (TCO2), specifically engineered to harness the cold energy released during LNG regasification process. The elevated temperature exhaust gases emanating from the SOFC are efficiently recuperated through a series of components including a gas turbine (GT), regenerative, steam Rankine cycle (SRC), and waste heat boiler (WHB). The proton exchange membrane fuel cells (PEMFC) is designed to enhance the overall operational flexibility and responsiveness of the multigeneration system. The Aspen Hysys V12.1 is used to simulate the propose system and numerical method is employed to parameter optimize of system. The first and second laws of thermodynamics are applied to establish the thermodynamics equations and calculated the system performances. The described system is estimated to obtain energy efficiency of 69.32 % and exergy efficiency of 33.85 %. The high-temperature exhaust gas harvesting integrated systems are sufficiently generated 2091.24 kW which accounted 35.49 % of entire power generate. Furthermore, the parametric researches are implemented to examine the influence of current density, β values to the performance indicators of systems. The WHB is designed to generate 1081 kg/h of superheated vapor at 170 °C and 405 kPa for various purposes of seafarers and equipment's onboard ship. The economic feasibility is performed to view the possibility of investment, maintenance cost and payback period for the described system.