Real-time Performance Monitoring Research Articles

Real-time monitoring of sports performance based on ensemble learning algorithm and neural network

Real-time monitoring of sports performance based on ensemble learning algorithm and neural network

Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mode, the model predicts outlet temperatures with Mean Absolute Percentage Error (MAPE) between 2 and 5% and energy production and consumption with MAPE below 10%. Summarizing the five-minute interval predictions, the model predicts the hourly energy production and consumption with MAPE at 3% or less. Model B predicts energy consumption and coefficient of performance (COP) from measured inlet and outlet conditions with MAPE below 5%. The model may serve as a tool to develop system-specific compressor maps for part-load conditions and for real-time performance monitoring.

Impact of Industry (4.O) in Automobile Industry

The emergence of Industry 4.0, characterized by the fusion of cutting-edge technology and digitization, has significantly altered the automotive industry. Automation, data interchange, and digital technology are combined in Industry 4.0 to produce a more productive and adaptable production environment. This technological advancement is not without its difficulties, such as a call for improvement on cybersecurity to prevent online attacks, up skilling and reskilling of industry employees to ensure high level of competencies, to mention a few. The auto industry is moving toward a new paradigm of connected vehicles and intelligent mobility options outside of the production environment thanks to Industry 4.0. With the integration of (IoT) the Internet of Things, cars have become sophisticated devices that can gather, process, and communicate enormous amounts of data. Sensors placed throughout the vehicle provide real-time monitoring of performance, external conditions, and driver behaviour. This increases safety protections, maximizes fuel efficiency, and paves the road for preventative maintenance. This study aims at developing a workforce framework that will help increase organizational effectiveness within the industry 4.0 paradigm. It also provides recent views on the development of the automobile industry due to Industry 4.0 incorporation, the perceived and current difficulties and possible solutions to identified challenges.

The Design, Development and Assessment of a Novel De-centralised IoT-Based Remote Monitoring of a Small-Scale Anaerobic Digester Network

The implementation of community-level de-centralised anaerobic digestion (AD) systems offers a sustainable solution for organic waste management and energy provision, but there is currently a need for low-cost methods of system control and management at this scale. The problem becomes accentuated with increasing deployment, as greater complexities are created by the need to both monitor and control a wider network of smaller, community-scale plants. This paper describes research to design, deploy and test such a system by creating a network of two independent biogas generation AD reactor sites situated in the United Kingdom and Thailand. Internet of things (IoT) aspects such as inexpensive and widely available open-source control electronics was used in combination with small commercial gas analysers to log, transfer and share data with a central station, achieving real-time monitoring of performance for both networked digesters. The design proved beneficial for collaboration and knowledge exchange purposes along with providing off-site observations on reactor status. This information was used for early intervention to maintain biogas yields and enable the dynamic assessment of process economics. The concept showed potential for upscaling to larger multi-site networks of AD reactors that could maintain process optimisation without the need for skilled staff on site.

Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization

In the current era of e-mobility and for the planning of sustainable grid infrastructures, developing new efficient tools for real-time grid performance monitoring is essential. Thus, this paper presents the prediction of the voltage stability margin (VSM) of power systems by the critical boundary index (CBI) approach using the machine learning technique. Prediction models are based on an adaptive neuro-fuzzy inference system (ANFIS) and its enhanced model with particle swarm optimization (PSO). Standalone ANFIS and PSO-ANFIS models are implemented using the fuzzy ‘c-means’ clustering method (FCM) to predict the expected values of CBI as a veritable tool for measuring the VSM of power systems under different loading conditions. Six vital power system parameters, including the transmission line and bus parameters, the power injection, and the system voltage derived from load flow analysis, are used as the ANFIS model implementation input. The performances of the two ANFIS models on the standard IEEE 30-bus and the Nigerian 28-bus systems are evaluated using error and regression analysis metrics. The performance metrics are the root mean square error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (R) analyses. For the IEEE 30-bus system, RMSE is estimated to be 0.5833 for standalone ANFIS and 0.1795 for PSO-ANFIS; MAPE is estimated to be 13.6002% for ANFIS and 5.5876% for PSO-ANFIS; and R is estimated to be 0.9518 and 0.9829 for ANFIS and PSO-ANFIS, respectively. For the NIGERIAN 28-bus system, the RMSE values for ANFIS and PSO-ANFIS are 5.5024 and 2.3247, respectively; MAPE is 19.9504% and 8.1705% for both ANFIS and PSO-ANFIS variants, respectively, and the R is estimated to be 0.9277 for ANFIS and 0.9519 for ANFIS-PSO, respectively. Thus, the PSO-ANFIS model shows a superior performance for both test cases, as indicated by the percentage reduction in prediction error, although at the cost of a higher simulation time.

Evaluation of cognitive performance in high temperature with heart rate: A pilot study

Real-time monitoring and evaluation of cognitive performance based on physiological indicators is a potentially new measure to ensure high air temperature safety. Heart rate might be an appropriate physiological indicator, yet it still needs several direct shreds of evidence. This study analyzed the relation between heart rate and cognitive performance at high air temperatures, based on the experimental data obtained from three phases of climate-controlled experiments. The climate-controlled experiments include five temperature levels of 26, 30, 33, 37, and 39 °C, and two relative humidity levels of 50% and 70%. During the experiments, eight cognitive tests were conducted to assess the basial cognitive performance, heart rate was continuously monitored, and the ratings of alertness and thermal sensation, comfort, and acceptability were documented. A total of 104 healthy subjects took part in the three phases of experiments, and 1478 valid samples were obtained. By analyzing the experimental data comprehensively, the results show that when the heart rate went up to 90bpm, the speed and accuracy of Stroop and d2 test would decline significantly(p < 0.05). The accuracy of Visual learning, Addition, Multiplication, and Typing only dropped significantly when the heart rate reach a level of 100bpm(p < 0.05). There was an inverted U-shaped relation between the relative accuracy of cognitive tests and heart rate. Based on the quadratic regression equation with a downward opening, the Heart Rate-Cognitive performance (HR-CP) model was built up. The R2 value of all regression equations is close to or greater than 0.8. These results suggest that heart rate might be a potential physiological indicator to evaluate the effects of high air temperature on cognitive performance.

An industrial scale solution to achieving light-induced degradation (LID) free silicon solar systems: &gt;5% performance gain at system level with advanced hydrogenation technology

Light-induced degradation (LID) and light- and elevated-temperature induced degradation (LeTID) effects in p-type monocrystalline silicon (Si) passivated emitter and rear contact (PERC) cell structure have been extensively studied in recent years. Various advanced hydrogenation techniques were previously reported, and aimed to eliminate or at least minimize the effect of LID/LeTID on solar cell performance stability. However, there are limited reports on the long-term performance of solar modules which comprise of these treated solar cells and deployed in typical outdoor conditions. This work aims to provide quantified outdoor performance data for comparing solar modules that are comprised of either hydrogenated or non-hydrogenated reference p-PERC silicon solar cells. A total of 1150 solar modules (>82,000 p-PERC silicon solar cells) were deployed on a commercial building rooftop with identical placement and real-time performance monitoring over an extended period of 12 months under standard outdoor conditions. The results showed that effective advanced hydrogenation is achievable with process time as short as 5 s, although a longer process time of 10–15 s delivered optimal results. The advanced hydrogenation technology in this work can effectively address the LID and LeTID issues on the cell, module, and system levels, with >5% higher power generation capability on hydrogenated solar arrays as compared to the non-hydrogenated reference. Solar cell and module manufacturers strive to produce products with long-term stability, and the successful demonstration of the advanced hydrogenation technology in this work could serve this demand. A future perspective for advanced hydrogenation technologies is also presented.

Examining the Evolution of Management Accounting: A Qualitative Review

This qualitative review investigates the evolution of management accounting, focusing on its historical development, theoretical frameworks, and contemporary practices. The research aims to provide insights into the adaptive nature of management accounting in response to changing organizational needs, technological advancements, and societal trends. The methodology involves a comprehensive literature review encompassing historical analysis, theoretical perspectives, and empirical studies in the field of management accounting. Key findings reveal the historical roots of management accounting tracing back to the Industrial Revolution, with significant milestones including the emergence of cost accounting techniques, budgeting systems, and advanced methodologies like activity-based costing and strategic management accounting. The review also identifies theoretical frameworks such as agency theory, contingency theory, and institutional theory, offering diverse perspectives on the role and functioning of management accounting within organizations. Contemporary practices in management accounting emphasize strategic analysis, risk management, performance measurement, and the integration of financial and non-financial metrics. Technological advancements, particularly in information technology, have revolutionized decision support capabilities, enabling real-time monitoring and analysis of organizational performance. The synthesis of literature underscores the dynamic and multifaceted nature of management accounting, highlighting its pivotal role in facilitating strategic decision-making, enhancing organizational performance, and driving sustainable growth. The findings contribute to a deeper understanding of management accounting evolution and offer implications for practice and future research.

Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors

The immediate response to the state disturbances of anaerobic digestion is essential to prevent anaerobic digestion failure. However, frequent monitoring of the state and performance of anaerobic digestion is challenging. Thus, deep learning models were investigated to predict the state and performance variables from online sensor data. The online sensor data, including pH, electric conductivity, and oxidation–reduction potential, were used as the input features to build deep learning models. The state and performance data measured offline were used as the labels. The model performance was compared for several deep learning models of convolutional neural network (CNN), long short-term memory (LSTM), dense layer, and their combinations. The combined model of CNN and bidirectional LSTM was robust and well-generalized in predicting the state and performance variables (R2 = 0.978, root mean square error = 0.031). The combined model is an excellent soft sensor for monitoring the state and performance of anaerobic digestion from electrochemical sensors.

Stable image representation based stability performance monitoring and recovery for feedback control systems

This paper proposes a scheme to achieve real-time stability performance monitoring (SPM) and designs performance recovery controllers (PRCs) for feedback control systems with multiplicative faults. To be specific, a stability performance indicator is presented with the aid of stable image representation (SIR) for multiplicative faults through coprime factorization techniques. On this basis, a systematically hierarchical SPM and PRC scheme is proposed with three thresholds derived to evaluate the performance degradation degree. Subsequently, an integrated model-based and data-driven SIR-based PRC is presented to recover system stability performance. The embedded PRC parameters are adaptive to system variations by means of identifying the SIR of the faulty plant through a dual parity vector. Besides, some QR-decomposition based data-driven techniques are provided for the implementation of PRC to improve computation efficiency. Finally, the effectiveness of the proposed approach is demonstrated on a boost circuit model.

Identifying Antibiotic Use Targets for the Management of Antibiotic Resistance Using an Extended-Spectrum β-Lactamase-Producing Escherichia coli Case: A Threshold Logistic Modeling Approach.

The aim of this study was to develop a logistic modeling concept to improve understanding of the relationship between antibiotic use thresholds and the incidence of resistant pathogens. A combined approach of nonlinear modeling and logistic regression, named threshold logistic, was used to identify thresholds and risk scores in hospital-level antibiotic use associated with hospital-level incidence rates of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (E. coli). Threshold logistic models identified thresholds for fluoroquinolones (61.1 DDD/1000 occupied bed days (OBD)) and third-generation cephalosporins (9.2 DDD/1000 OBD) to control hospital ESBL-producing E. coli incidence. The 60th percentile of ESBL-producing E. coli was determined as the cutoff for defining high incidence rates. Threshold logistic analysis showed that for every one-unit increase in fluoroquinolones and third-generation cephalosporins above 61.1 and 9.2 DDD/1000 OBD levels, the average odds of the ESBL-producing E. coli incidence rate being ≥60th percentile of historical levels increased by 4.5% and 12%, respectively. Threshold logistic models estimated the risk scores of exceeding the 60th percentile of a historical ESBL-producing E. coli incidence rate. Threshold logistic models can help hospitals in defining critical levels of antibiotic use and resistant pathogen incidence and provide targets for antibiotic consumption and a near real-time performance monitoring feedback system.

ESG Poster ESG11: Lighting the way

Poster ESG11 Historically, Chevron Australia Pty Ltd have utilised Mobile Diesel Lighting Towers (MDLTs), with ‘turtle friendly’ luminaires at the Gorgon natural gas project (located on Barrow Island, Western Australia), operated by Chevron Australia on behalf of a joint venture of the Australian subsidiaries of Chevron (47.3%), ExxonMobil (25%), Shell (25%), Osaka Gas (1.25%), Tokyo Gas (1%) and JERA (0.417%). MDLTs are a known, relatively safe and reliable light source that can be utilised in most locations, though they emit noise, odour, vibrations, waste hydrocarbons and CO2 as well as requiring regular refuelling and general engine maintenance. With recent and significant improvements in solar energy harvest, and battery storage efficiencies, Mobile Solar Lighting Towers (MSLTs) have technical and performance characteristics that are comparable, and in many instances superior, to MDLTs. In addition to these characteristics, Chevron Australia has successfully worked with its Australian supplier, conducting trials on site to introduce a range of additional design and performance improvements. These improvements include simple Human–Machine Interface (HMI) touch screen interface allowing remote automation through WiFi or 4G, real-time battery and performance monitoring, CCTV, geofencing and GPS tracking. The benefits of replacing just 25 owned MDLTs with solar includes the abatement of over 600 tonnes of CO2 and direct operational cost savings of almost A$50 000 per year (not including indirect cost savings or avoided carbon emissions costs). Further, net present value calculations demonstrate a financial benefit exceeding A$1 000 000 over 5 years when the solar towers are leased compared with maintaining existing or purchasing new diesel units. These MSLT units are delivering environmental benefits, direct and indirect financial savings, and reduced health, safety and environment risk, which have been welcomed by the on-site workforce. To access the poster click the link on the right. To read the full paper click here

Lighting the way

Historically, Chevron Australia Pty Ltd have utilised Mobile Diesel Lighting Towers (MDLTs), with ‘turtle friendly’ luminaires at the Gorgon natural gas project (located on Barrow Island, Western Australia), operated by Chevron Australia on behalf of a joint venture of the Australian subsidiaries of Chevron (47.3%), ExxonMobil (25%), Shell (25%), Osaka Gas (1.25%), Tokyo Gas (1%) and JERA (0.417%). MDLTs are a known, relatively safe and reliable light source that can be utilised in most locations, though they emit noise, odour, vibrations, waste hydrocarbons and CO2 as well as requiring regular refuelling and general engine maintenance. With recent and significant improvements in solar energy harvest, and battery storage efficiencies, Mobile Solar Lighting Towers (MSLTs) have technical and performance characteristics that are comparable, and in many instances superior, to MDLTs. In addition to these characteristics, Chevron Australia has successfully worked with its Australian supplier, conducting trials on site to introduce a range of additional design and performance improvements. These improvements include simple Human–Machine Interface (HMI) touch screen interface allowing remote automation through WiFi or 4G, real-time battery and performance monitoring, CCTV, geofencing and GPS tracking. The benefits of replacing just 25 owned MDLTs with solar includes the abatement of over 600 tonnes of CO2 and direct operational cost savings of almost A$50 000 per year (not including indirect cost savings or avoided carbon emissions costs). Further, net present value calculations demonstrate a financial benefit exceeding A$1 000 000 over 5 years when the solar towers are leased compared with maintaining existing or purchasing new diesel units. These MSLT units are delivering environmental benefits, direct and indirect financial savings, and reduced health, safety and environment risk, which have been welcomed by the on-site workforce.

Evaluation of using building information modelling in green building in Saudi Arabia construction contracts

Building Information Modelling (BIM) enables, faster development, real-time monitoring of project performance, clash detection, knowledge management, more effective and efficient operation and maintenance, and provide more reliable and timely exchange of information to support the decision-making process. Despite the worldwide adoption of BIM, its implementation is the Kingdom of Saudi Arabia is not mature, especially when it comes to green buildings. The goal of this research is to investigate the use of BIM in green buildings in KSA and provide insights on the factors that need to be considered for optimal attainment of BIM outcomes. First, BIM implementation is analyzed in international contracts such as FIDIC and NEC4. Second, the local implementation of BIM in KSA is investigated through analysing the public works contract and the contract of case study of a mega project. Finally, a survey was conducted to identify and rate the (1) obstacles, and (2) benefits of BIM implementation in green building in KSA. Among the significant results was the fact that the strongest barrier to BIM implementation is the lack of support from the management to accept changing the current practices, and the weakest barrier is actually the costs of BIM implementation. The findings shed the light on significant aspects (such as increasing awareness about BIM and fostering flexibility in managers) that will enable successful implementation of BIM in green buildings in KSA; which in turn will result in cost and schedule savings.

Single-Ended Quadrature Coupler-Based VSWR Resilient Joint mm-Wave True Power Detector and Impedance Sensor

Intelligent reconfigurable RF/millimeter-wave (mm-Wave) front ends primarily rely on built-in-self-test (BiST) circuits for real-time performance monitoring, particularly in phased arrays, where antenna elements&#x2019; driving impedance varies substantially during beam steering due to the undesired element couplings. This severely degrades the power amplifier&#x2019;s (PA) performance. To address this problem, we propose a quadrature coupler-based voltage standing wave ratio (VSWR)-resilient mm-Wave joint impedance/true power detector integrated with a PA using the GlobalFoundries 45-nm CMOS silicon on insulator (SOI) process. A comprehensive theoretical comparison between the traditional and proposed coupler sensors is presented, including implementation details and design tradeoffs. Design guidelines for both the proposed coupler sensor and the integrated PA are presented to maximize sensing accuracy. The detailed data processing techniques are explained to accurately extract the sensor outputs and highlight hardware limitations. At 38 GHz, the proposed sensor measures the antenna impedance for VSWR <inline-formula> <tex-math notation="LaTeX">$=3$ </tex-math></inline-formula>:1 with a maximum <inline-formula> <tex-math notation="LaTeX">$\vert \Gamma \vert $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\angle \Gamma $ </tex-math></inline-formula> error of 0.238 and 28.9&#x00B0; respectively. The 90&#x00B0; coupler network demonstrates a 16-dB dynamic range with an error within &#x00B1;0.5 dB for 50-<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> power sensing, as well as sensing the real power delivered to a complex antenna load for VSWR <inline-formula> <tex-math notation="LaTeX">$=3$ </tex-math></inline-formula>:1 with less than &#x00B1;3.35-dB power sensing error. The chip die occupies an area of <inline-formula> <tex-math notation="LaTeX">$1.31\times1.36$ </tex-math></inline-formula> mm², while the sensor alone occupies an area of <inline-formula> <tex-math notation="LaTeX">$0.47\times0.971$ </tex-math></inline-formula> mm².

Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding

Global and local relational reasoning enable scene understanding models to perform human-like scene analysis and understanding. Scene understanding enables better semantic segmentation and object-to-object interaction detection. In the medical domain, a robust surgical scene understanding model allows the automation of surgical skill evaluation, real-time monitoring of surgeon's performance and post-surgical analysis. This paper introduces a globally-reasoned multi-task surgical scene understanding model capable of performing instrument segmentation and tool-tissue interaction detection. Here, we incorporate global relational reasoning in the latent interaction space and introduce multi-scale local (neighborhood) reasoning in the coordinate space to improve segmentation. Utilizing the multi-task model setup, the performance of the visual-semantic graph attention network in interaction detection is further enhanced through global reasoning. The global interaction space features from the segmentation module are introduced into the graph network, allowing it to detect interactions based on both node-to-node and global interaction reasoning. Our model reduces the computation cost compared to running two independent single-task models by sharing common modules, which is indispensable for practical applications. Using a sequential optimization technique, the proposed multi-task model outperforms other state-of-the-art single-task models on the MICCAI endoscopic vision challenge 2018 dataset. Additionally, we also observe the performance of the multi-task model when trained using the knowledge distillation technique. The official code implementation is made available in GitHub.

Proposed Design and Assessment Methodology of a Wearable Device for Prevention and Performance Evaluation of Athletes

The paper presents the design approach of a low power stretchable wearable epidermal electronic device as part of an advanced athlete performance management framework to perform multi-parametric, multi-modal measurements of vital physiological parameters through the skin and via sweat analysis. It can be used to seamlessly monitor athletes, to prevent serious health complications and injuries during training, sudden death and complications related to overtraining. The proposed framework allows real-time physiological monitoring, tracking, prediction and quantification of athletic performance and estimation of recovery time after an injury, an illness or the overtraining syndrome. It is based on low-cost and clean room-based fabrication techniques and interconnects and readout electronics based on commercially available off-the-shelf components and application-specific integrated circuits. A data analytics platform incorporates real-time monitoring to identify the susceptibility to sudden death, health complications or injury, for the athlete, executing a personalized prediction model

Faster Multidimensional Data Queries on Infrastructure Monitoring Systems

The analytics in online performance monitoring systems have often been limited due to the query performance of large scale multidimensional data. In this paper, we introduce a faster query approach using the bit-sliced index (BSI). Our study covers multidimensional grouping and preference top-k queries with the BSI, algorithms design, time complexity evaluation, and the query time comparison on a real-time production performance monitoring system. Our research work extended the BSI algorithms to cover attributes filtering and multidimensional grouping. We evaluated the query time with the single attribute, multiple attributes, feature filtering, and multidimensional grouping. To compare with the existing prior arts, we made a benchmarking comparison with the bitmap indexing, sequential scan, and collection streaming grouping. In the result of our experiments with large scale production data, the proposed BSI approach outperforms the existing prior arts: 3 times faster than the bitmap indexing approach on single attribute top-k queries, 10 times faster than the collection stream approach on the multidimensional grouping. While comparing with the baseline sequential scan approach, our proposed algorithm BSI approach outperforms the sequential scan approach with a factor of 10 on multiple attributes queries and a factor of 100 on single attribute queries. In the previous research, we had evaluated the BSI time complexity and space complexity on simulation data with various distributions, this research work further studied, evaluated, and concluded the BSI approach query performance with real production data.

Machine Learning Based Low-Cost Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature.

Exploring a Variant of PTC 4-2013 for Real-Time Performance Monitoring of Fossil Fuel Power Plants

Abstract This paper describes a real-time performance-monitoring method based on PTC 4-2013 that was developed for determining and reporting the annual heat rate for fossil fuel power plants. Unlike for the PTC 4 test, the coal composition is typically not known in real-time, so the procedure uses a modified output-loss approach applied to a control volume that closely conforms to the boiler. A calibration approach utilizes an ultimate analysis to describe the coal being burned during the calibration, while holding the plant load and other factors steady. This permits the calculation of correction factors used during real-time performance monitoring. Based on several assumptions that are justified within, a real-time estimate of coal composition is obtained. The losses are calculated in a similar manner to PTC 4-2013. However, the losses are expressed on a per-pound of as-fired coal basis, as opposed to a percentage of higher heating value of the coal, which is not known in real-time.