Precise Performance Evaluation Research Articles

Evaluating the Performance of Taiwan Airport Renovation Projects: An Application of Multiple Attributes Intelligent Decision Analysis

Performance evaluation is a vital tool for measuring whether construction projects meet their established objectives, particularly in complex tasks. It helps identify key areas for improvement and enhances resource allocation efficiency. Through precise performance evaluation, managers can make optimal decisions regarding resource use, ultimately increasing project productivity and overall performance. The objective of this study is to measure the production efficiency of airport renovation projects in Taiwan using data envelopment analysis (DEA) and to apply artificial neural networks (ANN) for predicting project quality. DEA effectively handles scenarios with multiple inputs and outputs, providing relative efficiency comparisons among projects and quantifying the potential for improvement. ANN, on the other hand, can learn nonlinear patterns from the data, allowing for accurate predictions of project quality. As construction projects become more complex, ensuring efficient operation within limited resources becomes increasingly crucial. Traditional performance evaluation methods are inadequate for addressing scenarios involving multiple inputs and outputs; therefore, using DEA and ANN offers a more accurate framework for analysis and prediction. The results of this study indicate that, through the DEA model, five projects achieved an efficiency score of 1, while twelve inefficient projects need to reduce defect frequency by 54.76% and increase the progress and budget implementation efficiency by an average of 10.33% to improve performance. The ANN model achieved a classification accuracy of 94.1% and a mean squared error (MSE) of 0.11 in regression predictions. By adopting a data-driven approach, ANN facilitates intelligent decision making and forecasting throughout the construction process. This study provides construction managers with concrete guidelines for resource allocation and quality prediction, thus enhancing project management effectiveness.

Application of the non-linear three-component model for simulating accelerated creep behavior of polymer-alloy geocell sheets

The polymer-alloy geocell sheets (PAGS) represent a novel geocell material developed to replace conventional geocell materials. Accelerated creep testing, a convenient and precise performance evaluation method, presents a viable alternative to traditional creep testing for obtaining long-term creep strains. Nonetheless, there is a lack of prediction and in-depth exploration of accelerated creep testing. This paper aims to assess the efficacy of using the non-linear three-component (NLTC) model to simulate the accelerated creep behavior of PAGS. The predictive accuracy of the NLTC model has undergone evaluation through a comparison between stepped isothermal method (SIM) accelerated creep experimental tests and numerical simulations. Subsequently, the validated NLTC model was employed to simulate the time-temperature superposition method (TTSM), time-stress superposition method (TSSM), and stepped isostress method (SSM) accelerated creep tests, thereby verifying its effectiveness in predicting all accelerated creep tests. The results indicate that the NLTC model can effectively simulate creep deformation induced by temperature increases, particularly the temperatures below 41 °C. Although some errors are observed at elevated temperatures, it is within the acceptable range of 17.4%. Numerical simulation results of TTSM, TSSM, and SSM tests also suggest the model's proficiency in simulating the accelerated creep behavior by temperature and creep load increasing.

On the performance of OFDM-IM systems in the presence of CFO effects

This study presents a comprehensive analysis of the performance degradation effects of carrier frequency offset (CFO) on orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems operating over frequency-selective multipath fading channels. CFO is an impairing factor that degrades the signal-to-noise ratio (SNR) through signal attenuation and inter-carrier interference (ICI). We derive a closed-form expression to quantify the SNR degradation under CFO for OFDM-IM systems. Additionally, we formulate a very tight upper bound for the bit error rate (BER), accounting for index modulation errors, CFO distortion, and multipath fading.The presented analytical formulations capture the unique characteristics of OFDM-IM systems and facilitate precise performance evaluation. The findings yield valuable insights into mitigating CFO-induced BER degradation through appropriate system parameter selection and CFO compensation techniques. Moreover, this investigation makes significant contributions towards designing reliable OFDM-IM communication links resilient to the combined effects of index modulation, frequency offsets, and dispersive channel conditions.

Evaluating Rainwater Harvesting Potential of Concrete and Tile Roofings for Detached Houses in Aydın, Turkey

This study conducts a comprehensive analysis of rainwater harvesting (RWH) efficiency concerning distinct roofing materials, specifically comparing concrete and tile-covered roofs. RWH strategies were applied to two separate detached residences in Aydın, Turkey, representing varied roof scenarios. Tailored prototype RWH systems enabled precise performance evaluation. Hydrological data and municipal water usage statistics were analyzed to measure RWH effectiveness. Hydraulic principles governing system components are outlined. Results indicate the implemented RWH system significantly enhances water reuse, aligning with sustainable goals. Circular and cost considerations provide a holistic view. This study advances RWH understanding, highlighting material influence on system performance and contributing to water management progress.

Advancing Post‐Secondary Batteries under Lean Electrolyte Conditions through Interfacial Modification Strategies

AbstractIn response to the growing global demand for portable electronics and electric vehicles, there is an escalating interest in developing advanced battery technologies with superior energy density. Research efforts are focused on unveiling post‐lithium‐ion batteries (LIBs) that outperform the performance of current LIBs through the use of innovative active electrode materials. Yet, these technological advancements face significant hurdles, primarily due to intricate interfacial issues within battery components. In laboratory‐scale studies, these challenges often lead to the utilization of excess electrolytes, which complicates the precise evaluation of battery performance. This review emphasizes the significance of designing future batteries that operate effectively under lean electrolyte usage conditions. It discusses essential principles, obstacles, and diverse strategies for interfacial modification, including in situ growth, coating of supportive layers, and embedding of active substances in pre‐structured templates. Furthermore, it compiles and examines data on the lean electrolyte conditions achieved in various battery systems, contrasting their energy densities with those of commercially established batteries. Ultimately, the potential of future batteries to achieve or even exceed the energy densities of existing commercial batteries is assessed, thereby offering a strategic roadmap for the progression of next‐generation battery technologies.

Wind power anomaly data detection based on unsupervised methods

During the actual operation of the wind turbine, a large number of abnormal data will be generated due to environmental or human factors, which will have a great impact on the condition assessment and output prediction. In order to make wind energy a reliable source of energy, it is very important to establish an efficient and accurate wind power detection model. Therefore, it becomes essential to identify abnormal data for a more precise evaluation of wind turbine performance. Based on the data mining clustering technology K-means algorithm, this paper introduces an unsupervised abnormal wind power detection algorithm combining the variational autoencoders (VAE) model. The focus of this abnormal wind power detection method is primarily on assessing the reconstruction error, which, in turn, generates an abnormality score for wind power data. This score is then used to determine the presence of abnormal wind power data. Finally, the wind power data in 2021 is tested.

Traffic Performance of Successive Freeway Merge Segments

At freeway interchanges with high entering flows, implementing two successive merges can improve traffic flow and capacity by distributing the merging maneuvers. The German Guidelines for the Design of Motorways define different types of successive (“double”) merges. According to the quality of service assessment procedure for freeway merge segments given in the German Highway Capacity Manual, successive merge segments can be evaluated separately. However, depending on the distance between the merges, the capacity of the second (downstream) merge might be affected by the entering traffic at the first (upstream) merge, which influences the lane-flow distribution. For a more precise evaluation of traffic flow performance and traffic safety depending on the geometric design characteristics, vehicle interactions, lane changes, the resulting capacity of successive merge segments, accident rates, and crash types were analyzed in the study. The research was based on loop detector data as well as trajectory data obtained from video measurements, which provide an insight into the interactions between mainline and entering vehicles. For the analysis of traffic safety, crash data were evaluated over a period of 3 years. Furthermore, successive merge segments with different geometric and traffic characteristics were modeled with the microscopic traffic simulation tool BABSIM to analyze the influence on traffic flow and capacity. As a result, capacity models and recommendations for the geometric design of successive freeway merge segments are provided.

Providing a Comprehensive Framework for Human Resource Auditing in Line with Performance Evaluation

Objective: This study aimed to propose a comprehensive framework for human resource auditing to assess organizational performance. Methodology: This applied research employed a qualitative, exploratory approach. Data were collected through semi-structured interviews with experts and professionals in human resource management. The data were analyzed using qualitative content analysis, and the participants were selected through purposive sampling among senior managers and HR experts. Findings: The findings revealed the identification of three new levels for human resource auditing: the level of HR unit outcomes, the level of HR professionals and custodians, and the level of HR processes and activities. These levels were defined as subsets for operational auditing and contributed to a more precise evaluation of HR unit performance. Conclusion: The results demonstrate that human resource auditing can serve as a key tool in improving managerial processes and increasing organizational productivity. Additionally, the newly identified auditing levels in the proposed framework can help organizations evaluate HR unit performance more effectively and improve their management processes

Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.

High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medical supervision required for phenotyping algorithm (PA) estimation and evaluation. To address this challenge, numerous weakly-supervised learning methods have been proposed. However, there is a paucity of methods for reliably evaluating the predictive performance of PAs when a very small proportion of the data is labeled. To fill this gap, we introduce a semi-supervised approach (ssROC) for estimation of the receiver operating characteristic (ROC) parameters of PAs (eg, sensitivity, specificity). ssROC uses a small labeled dataset to nonparametrically impute missing labels. The imputations are then used for ROC parameter estimation to yield more precise estimates of PA performance relative to classical supervised ROC analysis (supROC) using only labeled data. We evaluated ssROC with synthetic, semi-synthetic, and EHR data from Mass General Brigham (MGB). ssROC produced ROC parameter estimates with minimal bias and significantly lower variance than supROC in the simulated and semi-synthetic data. For the 5 PAs from MGB, the estimates from ssROC are 30% to 60% less variable than supROC on average. ssROC enables precise evaluation of PA performance without demanding large volumes of labeled data. ssROC is also easily implementable in open-source R software. When used in conjunction with weakly-supervised PAs, ssROC facilitates the reliable and streamlined phenotyping necessary for EHR-based research.

Solar thermoelectric generator and thermoelectric cooler performance: analysis and comparison using a different shape geometry

Abstract Thermoelectric devices are one of the technologies used either to generate electricity by applying a temperature difference using thermal energy or as a heating/cooling system by applying an electrical voltage. The number of materials required to produce a product is an important factor in determining its price. Production costs associated with these materials, as well as their availability and quality, play a crucial role in price determination by manufacturers. In this context, a method that employs a uniform volume distribution was implemented. This approach enabled the analysis to focus on other variables, thereby promoting a more precise and relevant evaluation of overall performance. Based on the finite element method, this study investigated the influence of geometric shape, including Rect-leg, Y-leg, Pin-leg and X-leg designs, on the performance of solar thermoelectric generators and thermoelectric coolers. The study was conducted considering the same hot alumina junction surface that receives solar radiation; however, the effective surface, which corresponded to the heat flow area and had a similar area near the exposed surface, varied depending on the chosen leg geometry, thus impacting the heat flux due to the variation in thermal resistance. In the case of a solar thermoelectric generator, the Rect-leg model, having the same effective surface area, presented the lowest heat loss value resulting from convection and radiation in the heat spreader and the hot alumina plate. Under the same conditions, the Y-leg showed the highest value. The Rect-leg design generated, by using thermal and optical concentration, the highest output power of 0.028 and 0.054 W, and efficiency of 3.47% and 4.7%, respectively, whereas the Y-leg generated lower values of 0.006523 and 0.018744 W for power, and 2.83% and 2.71% for efficiency, respectively. In the case of the thermoelectric coolers, the Y-leg generated the highest temperature difference between the hot and cold sides of 67.28 K at an electric current value of 1.8 A, whereas the Rect-leg, Pin-leg and X-leg generated ~66.25, ~67.02 and ~67.19 K at 6.1, 2.7 and 2.6 A.

Critical considerations and effective assessment of extraction and recovery processes of RAP

With the increasing shortage of resources, the reuse of recycled asphalt pavements (RAP) in pavement engineering is considered as a sustainable technology. Challenges posed by common extraction and recovery methods may result in misjudgment of asphalt pavement performance. In this study, we investigate the optimization of extraction and recovery processes in recycled asphalt pavement (RAP) recycling, aiming to promote sustainable development within the pavement engineering sector. We prepared eleven asphalt samples to simulate common extraction and recovery scenarios, using virgin SBS-modified asphalt as a reference. Employing Fourier Transform Infrared (FTIR) analysis, thermogravimetric analysis (TGA), and Dynamic Shear Rheometer (DSR) testing, we assessed the samples' rheological and chemical properties. We pointed out three common but easily overlooked problems in the extraction and recovery process, namely residual mineral powder, residual trichloroethylene, and incomplete extraction. Residual mineral powder and trichloroethylene greatly influence extraction recovery accuracy; high-speed centrifugation effectively addresses trichloroethylene, but completely removing mineral powder remains challenging. Accurate evaluation of residual substances in recycled asphalt is achievable through FTIR, TGA, and rheological tests, providing valuable insights for material selection and processing. Additionally, it is crucial to fully recover the binder from RAP for precise performance evaluation, as the binder's interior exhibits lower aging levels compared to the surface. This aging heterogeneity should be considered when assessing RAP performance and developing effective rehabilitation strategies. Our findings hold significant implications for enhancing the efficiency and effectiveness of extraction and recovery processes in RAP recycling, ultimately contributing to sustainable development in pavement engineering.

A Comparison of the Checklist Scoring Systems, Global Rating Systems, and Borderline Regression Method for an Objective Structured Clinical Examination for a Small Cohort in a Saudi Medical School.

This study aims to compare the effectiveness of using the checklist and global rating scores to evaluate the clinical competency of medical students in Objective Structured Clinical Examinations (OSCEs). Additionally, the study assesses the appropriateness of using the borderline regression method to set standards for small-scale OSCE exams and determines if the estimated passing marks differ significantly from the university's prefixed passing score of 70%. The study also examines whether the university should utilize the borderline regression method to determine passing scores for each OSCE exam instead of a set passing score. The study analyzed medical students' grades in 11 OSCE exams in the 2022-2023 academic year at Alfaisal University, Riyadh, Saudi Arabia. Students received family medicine clerkship rotations, and after each rotation, they took an OSCE exam consisting of three stations that family medicine consultants graded. The exam included a checklist of 30 tasks and a five-level global rank scale. The study collected all the checklist marks and global rank grades and analyzed them using IBM® Statistical Package for Social Sciences (SPSS® Statistics) software. The statistical tests used were descriptive statistics, the T-test, chi-square tests, Fisher's exact test, and Pearson correlation. The study showed that students were more likely to pass when using the global rating system than the checklist scoring system. Additionally, students had a significantly lower passing rate when using the higher cut-off passing score estimated using the borderline regression method compared to the pre-set passing score of 70% established by the university (with a p-value of 0.00). Each scoring system has advantages and disadvantages, but they complement each other. Combining scoring systems can produce a more comprehensive and precise evaluation of a candidate's performance. The study also emphasizes the importance of carefully selecting and validating cut-off points in OSCE exams to ensure fairness and consistency in assessment.

Precise Ambiguity Performance Evaluation for Spaceborne SAR with Diverse Waveforms

The ambiguity suppression is a technical challenge for the present generation of spaceborne synthetic aperture radar (SAR) systems since this kind of suppression does not take the high spatial resolution and wide coverage into account simultaneously. The transmitting scheme based on the waveform diversity technique is a promising candidate for the conventional (one transmit, one receive channel) SAR systems and has been widely discussed, because it has almost no extra system costs and the ambiguity suppression performance is not closely related to pulse repetition frequency (PRF). However, the accurate method to evaluate the ratio of the intensities of the ambiguities to that of the signal is still a gap. To this end, starting from the precise signal model formulated in this paper, the ambiguity evaluation for spaceborne SAR with waveform diversity has been analyzed in detail. Particularly, the modified azimuth ambiguity-to-signal ratio (AASR) and range ambiguity-to-signal ratio (RASR) formulas are given for the single polarization SARs and quadrature-polarimetric (quad-pol) SARs, which contributes a lot, for the system designer, to precisely evaluating the ambiguity performance. Finally, detailed simulation experiments exploiting the system parameters of the LuTan (LT-1) system are carried out to corroborate the theoretical developments.

OEE approach applied to additive manufacturing systems in distributed manufacturing networks

Technological changes are transforming business models, such as distributed manufacturing networks, which have benefited from additive manufacturing (AM) to transform centralized into decentralized production. The main challenges of these networks are related to their global performance, which depends on several factors such as production responsiveness, machine efficiency, printing quality and the operation of equipment, which can suffer from unscheduled downtime. Decision support tools, such as optimization and simulation can contribute to an effective response to these challenges. To obtain a more precise performance evaluation this work proposes a methodology for Overall Equipment Effectiveness (OEE) as a decision-making approach in distributed manufacturing networks based on additive manufacturing, exploring technologies from industry 4.0 applied in the context of the production systems digitization in flexible markets. Using OEE to optimize the smart additive manufacturing units (SAMs) - a concept of an AM network - the demand of four cities in Brazil are distributed according to their performance in terms of lead-time, adjusted to the real expected performance, demonstrating that on average, a decreasing of 44% in OEE impacted in an increment of 75% in the total lead-time for parts delivering, changing the decision-making for order allocation in a distributed manufacturing network.

Provable Cloud Data Transfer with Efficient Integrity Auditing for Cloud Computing

Due to the promising market prospects, more and more enterprises invest cloud storage and offer on-demand data storage services, which are characterized by distinct quality; thus, users would dynamically change the cloud service providers and transfer their outsourced data. However, the original cloud server might not honestly transfer the outsourced data for saving overhead, and the outsourced data might be polluted during the transfer process. Therefore, how to achieve secure outsourced data transfer has become a primary concern of users. To solve this issue, we put forward a solution for the issue of provable cloud data transfer, which can also simultaneously realize efficient data integrity auditing. By taking the advantages of Merkle sum hash tree (MSHT), our presented scheme can satisfy verifiability without dependency on a third party auditor (TPA). Meanwhile, the formal security proof can demonstrate that our presented scheme meets all of the expected security requirements. Finally, we implement our presented scheme and offer the precise performance evaluation, which can intuitively show the high-efficiency and practicality of our presented scheme in the real-world applications.

Study on Influence Factors of Horizontal Ground Heat Exchanger Performance Considering Climatic Conditions and Groundwater Flow

Horizontal ground heat exchanger (HGHE) is a cost effective method to exploit geothermal energy due to its shallow installation depth, which meanwhile causes its performance sensitive to the climatic conditions and groundwater flow. However, effects of key factors on HGHE performance when considering climate and groundwater flow still remain unclear, which is vital for HGHE optimal design. To this end, based on a 3D numerical model considering atmosphere-HGHE-ground interaction for a HGHE, this paper comprehensively investigated effects of operation modes, heat storage, precipitation, air temperature, groundwater flow velocity, ground thermal properties, circulation flow rate and installation depth on HGHE performances. The influence degrees of these factors were compared through the gray correlation analysis. The results indicated that non-consideration of climatic conditions could underestimate HGHE outlet temperature in summer and winter. The intermittent operation mode showed much better performance than continuous one. Increasing installation depth and improving grout thermal conductivity were both beneficial for HGHE performance. The gray correlation analysis demonstrated that the most significant impact factors on HGHE performance were groundwater flow velocity > circulation flow rate > precipitation > air temperature. Consequently, it is necessary to consider groundwater flow and climatic conditions for the precise evaluation of HGHE performance.

A high-resolution daily experimental performance evaluation of a large-scale industrial vapor-compression refrigeration system based on real-time IoT data monitoring technology

This paper studies a novel comprehensive experimental dynamic performance analysis of a large-scale industrial vapor-compression refrigeration system as a part of a discrete cooling system with the aim of system daily parameter investigation. An innovative high-resolution real-time data monitoring is implemented on the system based on precise internet-of-things sensor technology. For precise evaluation of system performance, energy, exergy, and exergoeconomic aspects were considered to achieve daily results for future energy planning. The results demonstrates that high parameter variations occur during peak hours, while around 70% of both the total refrigeration cooling demand and the total power consumption was during the peak period. Moreover, the system's total exergy destruction rate raised by 88.4% at the peak hours, while the exergy efficiency attained between 27 and 35% throughout the day. In addition, the price of the evaporator outlet air, the desired product of the system, decreased from 96.9 $/GJ at midnight to 61.1 $/GJ at noon peak hours. In this period, the total cost rate for the system increased from 0.89 $/h to 1.10 $/h. The data obtained is not only beneficial regarding dynamic performance analysis of the refrigeration cycle but also can assist for executive purposes regarding energy management and further profit makings.

Kinetic gait analysis in healthy dogs and dogs with osteoarthritis: An evaluation of precision and overlap performance of a pressure-sensitive walkway and the use of symmetry indices.

In veterinary practice, a thorough gait examination is essential in the clinical workup of any orthopedic patient, including the large population of dogs with chronic pain as a result of osteoarthritis. The traditional visual gait examination is, however, a subjective discipline, and systems for kinetic gait analysis may potentially offer an objective alternative for gait assessment by the measurement of ground reaction forces. In order to avoid unnecessary testing of patients, a thorough, stepwise evaluation of the diagnostic performance of each system is recommended before clinical use for diagnostic purposes. The aim of the study was to evaluate the Tekscan pressure-sensitive walkway system by assessing precision (agreement between repetitive measurements in individual dogs) and overlap performance (the ability to distinguish dogs with lameness due to osteoarthritis from clinically healthy dogs). Direction of travel over the walkway was investigated as a possible bias. Symmetry indices are commonly used to assess lameness by comparing ground reaction forces across different combinations of limbs in each dog. However, SIs can be calculated in several different ways and specific recommendations for optimal use of individual indices are currently lacking. Therefore the present study also compared indices in order to recommend a specific index preferable for future studies of canine osteoarthritis. Forty-one clinically healthy dogs and 21 dogs with osteoarthritis were included in the study. High precision was demonstrated. The direction of travel over the walkway was excluded as a possible bias. A significant overlap was observed when comparing ground reaction forces measured in dogs with osteoarthritis compared to clinically healthy dogs. In some affected dogs, symmetry indices comparing contralateral limbs differed from clinically healthy dogs, but in general, the overlap performance was insufficient and, consequently, general use of this method for diagnostic purposes in dogs with osteoarthritis cannot be recommended.

Spatial-temporal constrained particle filter for cooperative target tracking

Localization is one of the most important topics of cyber physical system. In the last decades, much attention has been paid to the precise localization and performance evaluation in wireless sensor networks. Inertial-measurement-unit and Time-of-Arrival fusion is a state-of-the-art method to solve the accumulative error and drifting problem faced by sole inertial-measurement-unit positioning and navigation. Network cooperative technology could effectively suppress the accumulative error. This paper presents a spatial-temporal constrained particle filter algorithm for cooperative target tracking, so as to solve the problem of multi-target high-precision position tracking in complex and highly dynamic environments. Firstly, we propose an error-ellipse-resampling particle filter method. In the resampling stage of the particle filter, error ellipses with different confidence probabilities would be established with the use of the known estimated center and confidence scale, to achieve hierarchical resampling optimization based on the geometrical position of particles. As for cooperative tracking, an optimization method of state estimation based on spatial distance constraint is proposed, so that the Bayesian filter can benefit from spatial information and achieve cooperative tracking of spatial-temporal information fusion. Numerical experimental results show that the proposed error-ellipse-resampling particle filter could decrease the growth rate of cumulative errors and reach a positioning accuracy of 1.05 m, multi-target cooperative error-ellipse-resampling particle filter can effectively eliminate the cumulative error and achieve a positioning accuracy of 0.24 m.

A comprehensive analysis of a power-to-gas energy storage unit utilizing captured carbon dioxide as a raw material in a large-scale power plant

The global destructive effects of carbon dioxide emissions and the importance of energy storage for grid stability and peak shaving applications are in deep concern. In this regard, this paper investigates the recycling of carbon dioxide from gas-fired power plant emissions into synthetic natural gas based on a novel power-to-gas process with the aim of chemical energy storage and emission reduction. The hybrid power-to-gas process proposed in this paper integrates the power generated from the gas plant and renewable energies. To make a precise evaluation of system performance, the referenced system is analyzed from technical, economic, and environmental points of view. The process is optimized through the reaction operation conditions and the energy utilization of the process. The results demonstrate that the power-to-gas process achieves a reaction synthetic natural gas production yield of over 97% at the optimal operation conditions. Regarding energy and environmental outlook, the power plant provides 15.4% of the power-to-gas total energy consumption, with the remaining 84.6% supplied from renewable energy resources. From the synthetic natural gas produced, the power-to-gas process provides total annual energy storage of 2.9 × 107 GJ and recycles 1.6 Mton of carbon dioxide for the power plant flues, resulting in a reduction of the plant emissions by around 66%. According to the economic data, with the consideration of the selling price of oxygen, the cost of the synthetic natural gas produced results in 1738 $/ton. The obtained results indicate that the system designed can reduce production costs due to hybrid electricity and offers two unique attributes: energy storage capability and drastic reduction of carbon dioxide emissions.