Performance Prediction Analysis Research Articles

Performance prediction and regression analysis of scroll expander based on response surface methodology

The Organic Rankine Cycle (ORC) plays a pivotal role in the domain of renewable energy, adeptly harnessing thermal energy from sources such as solar and geothermal to generate electrical power. Central to this system is the scroll expander, whose operational efficacy directly influences the ORC's overall efficiency. This study initiates with a detailed numerical analysis of the impact of various operational parameters on both the steady and transient output performance of the scroll expander. Following this, a sophisticated performance prediction model is developed employing the response surface methodology. The model boasts high coefficients of determination, R2,values of 0.9967 and 0.9916, respectively, underscoring its superior predictive accuracy. Further, this model delves into the complex interactions among operational parameters and their consequent effects on the expander's performance. Validation of this predictive model is achieved through rigorous testing on an experimental platform designed for the ORC low-temperature waste heat power generation system. The discrepancies between predicted and actual performance measurements fall within a commendable margin of 15 %, affirming the model's robustness in forecasting scroll expander performance. The culmination of this research furnishes a novel and efficacious optimization strategy, establishing a robust theoretical foundation and offering fresh insights for the enhancement and prediction of scroll expander performance. This not only underscores the innovation of the approach but also its practical applicability in advancing the efficiency of renewable energy systems.

High-Temperature Characteristics of Polyphosphoric Acid-Modified Asphalt and High-Temperature Performance Prediction Analysis of Its Mixtures

To promote the application of economical and sustainable polyphosphoric acid (PPA)-modified asphalt in road engineering, styrene-butadiene block copolymer (SBS), styrene-butadiene rubber (SBR), and PPA were used to prepare PPA/SBS and PPA/SBR composite-modified asphalts, which were tested and the data analyzed. Fourier transform infrared spectroscopy (FTIR) tests and thermogravimetric analysis (TG) tests were carried out to study the modification mechanisms of the composite-modified asphalts, and the high-temperature performance of the PPA-modified asphalt and asphalt mixtures was analyzed by dynamic shear rheology (DSR) tests and wheel tracking tests. A gray correlation analysis and a back-propagation (BP) neural network were utilized to construct a prediction model of the high-temperature performance of the asphalt and asphalt mixtures. The test results indicate that PPA chemically interacts with the base asphalt and physically integrates with SBS and SBR. The PPA-modified asphalt has a higher decomposition temperature than the base asphalt, indicating superior thermal stability. As the PPA dosage increases, the G*/sinδ value of the PPA-modified asphalt also increases. In particular, when 0.6% PPA is combined with 2% SBS/SBR, it surpasses the high-temperature performance achieved with 4% SBS/SBR, suggesting that PPA may be a good alternative for polymer modifiers. In addition, the creep recovery of PPA-modified asphalt is influenced by the stress level, and as the stress increases, the R-value decreases, resulting in reduced elastic deformation. Furthermore, the BP neural network model achieved a fit of 0.991 in predicting dynamic stability, with a mean percentage of relative error (MAPE) of 6.15% between measured and predicted values. This underscores the feasibility of using BP neural networks in predictive dynamic stability models.

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS.

A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry

Assessing the retail industry's efficiency is pivotal for economic growth and corporate productivity. This study employs a novel approach, utilizing a regression-based Stochastic Data Envelopment Analysis (SDEA) model, Balanced Scorecard (BSC), and Decision Tree. The integration of these methods is a pioneering effort in the retail sector. This is a data-driven decision-making framework, aiding managers in predicting efficient and inefficient Decision-Making Units (DMUs). Results from a case study in 44 retail store chains in Iran indicate that the accuracy of the SDEA model is 99%. The Decision Tree highlights low branch efficiency due to a low customer count, a unique finding in comparison to prior studies.

Performance evaluation of conductive materials in conductive mortar based on machine learning

Conductive mortar auxiliary anode (CMAA) is one of the main materials to realize stable operation of cathodic protection system. However, the conductive material in CMAA is complex and diverse, and its basic physical properties have a complex functional relation with the type and content of conductive material. At present, there is no uniform evaluation criterion, which can realize the evaluation of the action properties of different conductive substances. To address this issue, machine learning was proposed to establish predictive models between input and output factors. The SHAP-value was used to analyze the model's global and local features, and the partial dependency of prediction results. The results show that: on the basis of 241*3 groups of databases, the predictive performance analysis of RFR, GB, XGBoost and LGBM models shows that LGBM is the optimal model, and the determination coefficients of its training set and test set are 0.98 and 0.97, respectively. The LGBM model, combined with SHAP-Value analysis and PDP analysis, reveals the importance ranking of the input features that affect the compressive strength, and can also provide a quantitative evaluation of the effect of the input factors against the compressive strength, providing a new analysis method for material design and performance optimization.

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression.

In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.

Leverage Learning Behaviour Data for Students' Learning Performance Prediction and Influence Factor Analysis

Leverage Learning Behaviour Data for Students' Learning Performance Prediction and Influence Factor Analysis

Dynamic performance prediction and experimental analysis of wet clutch actuator considering thermal flow characteristics

The hydraulic actuator system (HAS) for wet clutches in heavy-duty vehicle transmissions is required to have a fast response and high precision in the pressure regulation procedure. Conventional pressure control valves for HAS are almost slide spool structures, which perform a large flow force at a high flow rate. An open-center spool structure is designed and developed in this paper, which is a combination of a traditional slide spool and a valve sleeve. It has an orifice-groove combination throttling orifice, which can take into account the large through-flow capacity and small flow force. Firstly, to deeply understand the mechanism of pressure variation in the clutch chamber, a high-fidelity dynamic characteristic mathematical model of HAS considering the oil temperature is proposed. Secondly, the functional relationship between the oil temperature on the medium physical property parameters and the orifice thermal flow parameters in the model is deduced in detail. An experimental bench to measure the pressure valve dynamic performance and actuator pressure regulation characteristics in a wide temperature domain is then designed and implemented to validate the accuracy of the simulation model. Finally, the mechanisms of oil temperature and system pressure on pressure impact and pressure response are quantitatively analyzed in association with a supervised learning algorithm. The feasible domains of the two adjustable variables are also explored. This study provides a new perspective on dynamic control and energy saving of HAS pressure valves and a new method for the prediction of HAS performance under complicated operating conditions.

Quantitative analysis of energy loss and performance prediction of regenerative compressor

The intricate internal flow structure within the regenerative compressor leads to substantial energy loss, presenting a long-standing challenge in accurately predicting its performance under various operating conditions. This paper establishes a performance prediction model that considers the impact of blade side exit angle and generalizes energy loss coefficient to accurately predict the performance of regenerative compressor under different operating conditions. Through numerical simulation analysis, the internal flow structure of the regenerative compressor is visualized, and the distribution and variation patterns of internal energy loss are studied. The magnitudes of loss rates for various components are quantitatively analyzed, revealing the underlying energy loss mechanism. A link between loss coefficients and the operating condition parameters of the regenerative compressor is established. Finally, the prediction model is applied to various similar compressor models for performance prediction, and compared with numerical simulation results. The results indicate that the prediction model can effectively predict the performance of various regenerative compressor models under different operating conditions, with maximum errors in total pressure and efficiency of 3.8% and 3.4%, and at rated conditions of 1.13% and 1.78%, respectively.

Predictive thermal performance analysis of T-wall based adsorption thermal battery for solar building heating

Moisture-based adsorption thermal battery (ATB) holds great potential for addressing energy storage and utilization challenges. In this work, a proof-of-concept solar harvesting building envelope using the Trombe-wall (T-wall) based ATB design is proposed and investigated, featuring a developed composite sorbent as the porous wall for effective heat storage and utilization. To demonstrate the feasibility of employing the ATB-based building envelope for day-and-night space heating, a 3-dimensional simulation model of the ATB wall is meticulously designed and comprehensively studied. Parametric analyses of various working conditions, including examining the effects of solar radiation intensity, air temperature, air humidity, and airflow velocity on the heat charging and discharging performances of the ATB wall, are conducted using the numerical model. Simulation results indicate that, under a solar irradiance level of 700 W m−2 during the daytime, an average output air temperature of 42.4 °C and an average heating power density of 2.5 kW m−3 are achieved. Extending the heat charging time to 8 h and 12 h significantly improves the desorption efficiency, which is able to reach 47.2% and 75.0%, respectively. In terms of heat discharging performances, various working conditions investigated in the ATB wall model will lead to different thermal output performances.

BIM-driven energy simulation and optimization for net-zero tall buildings: sustainable construction management

The growing demand for sustainable and energy-efficient buildings, particularly in the context of tall structures, has prompted increased attention to innovative solutions. Despite advancements in Building Information Modelling (BIM) technology, there exists a critical gap in understanding its comprehensive application for achieving net-zero energy consumption in tall buildings, particularly in the Malaysian construction industry. This research addresses this gap by presenting a novel strategy that integrates BIM technology with energy analysis tools for net-zero tall buildings in Malaysia. The aim of the study is to contribute valuable insights to the construction industry, policymakers, and researchers by conducting empirical research, utilizing case studies, validating the proposed framework, advancing sustainable design practices, and supporting the transition towards net-zero energy tall buildings in Malaysia. The methodology involves a three-phase approach, including qualitative analysis, a pilot survey, and a main questionnaire. Exploratory factor analysis (EFA) validates the categorization derived from qualitative interviews, while Partial Least Squares Structural Equation Modelling (PLS-SEM) assesses the convergent and discriminant validity of the measurement model. Hypotheses testing using bootstrapping establishes the significance of correlations between BIM deployment and key factors such as early design integration, enhanced energy efficiency, optimized system integration, predictive performance analysis, and validation of sustainable design. The research findings support the positive associations between BIM deployment and the mentioned factors, providing statistical significance through T-statistics and p-values. The implications of this research extend beyond the Malaysian context, offering valuable insights for architects, engineers, and stakeholders involved in designing and managing sustainable tall buildings. By addressing the identified gaps and leveraging BIM technology effectively, stakeholders can contribute to the construction of net-zero energy structures, aligning with global efforts towards sustainable and energy-efficient building practices.

Performance prediction, optimization and economic analysis of a combined PEMFC-ORC system

Performance prediction, optimization and economic analysis of a combined PEMFC-ORC system

Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process.

A climate-spatial matrix growth model for major tree species in Lesser Khingan Mountains and responses of forest dynamics change to different representative concentration path scenarios

IntroductionClimate change affects forest distribution, species composition, structure, and yield due to the sensitivity of forests to temperature, precipitation, and CO2. Therefore, for forest management decisions regarding climate change, it is crucial to explore the response of forest growth, mortality, and recruitment to future climate. We aimed to establish tree species’ responses by introducing variables such as climate, stand spatial structure parameters, and diversity indices.MethodsWe produced fixed parameter transition matrix model (FM), climate-sensitive matrix growth model (CM) and climate-spatial matrix growth model (SCM) using data from 786 plots collected during the 7th (2010), and 8th (2015), Chinese National Forest Inventories in Heilongjiang Province, and long-term predictive performance of CM, SCM, and FM were compared using same data. The models were compared using tenfold cross-validation and long-term predictive performance analysis. To predict the response of major tree species in the Lesser Khingan Mountains to three future climate change scenarios (RCP2.6, RCP4.5, RCP8.5).Results and discussionThe cross-validation results show small significant differences among the three models for short-term prediction (5 years), with the FM performing slightly better than the CM and the SCM. In contrast, for long-term projections (85 years), SCM outperformed FM and CM under three different RCPs, and SCM and CM under three representative concentration paths (RCPs), i.e., RCP2.6, RCP4.5, and RCP8.5, suggesting that rather different dynamics are more reliable, since climatic factors are taken into account which may significantly affect forest dynamics, while changes in stand spatial structure also affect the sensitivity of trees to climate, especially in long-term prediction interval, the results of this paper may provide a theoretical basis for optimizing forest management strategies under climate change.

HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins.

Current predictors of DNA-binding residues (DBRs) from protein sequences belong to two distinct groups, those trained on binding annotations extracted from structured protein-DNA complexes (structure-trained) vs. intrinsically disordered proteins (disorder-trained). We complete the first empirical analysis of predictive performance across the structure- and disorder-annotated proteins for a representative collection of ten predictors. Majority of the structure-trained tools perform well on the structure-annotated proteins while doing relatively poorly on the disorder-annotated proteins, and vice versa. Several methods make accurate predictions for the structure-annotated proteins or the disorder-annotated proteins, but none performs highly accurately for both annotation types. Moreover, most predictors make excessive cross-predictions for the disorder-annotated proteins, where residues that interact with non-DNA ligand types are predicted as DBRs. Motivated by these results, we design, validate and deploy an innovative meta-model, hybridDBRpred, that uses deep transformer network to combine predictions generated by three best current predictors. HybridDBRpred provides accurate predictions and low levels of cross-predictions across the two annotation types, and is statistically more accurate than each of the ten tools and baseline meta-predictors that rely on averaging and logistic regression. We deploy hybridDBRpred as a convenient web server at http://biomine.cs.vcu.edu/servers/hybridDBRpred/ and provide the corresponding source code at https://github.com/jianzhang-xynu/hybridDBRpred.

Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures.

Exploration of Enterprise Financial Management Innovation Based on Accounting Informatization under the Background of Big Data

Abstract This paper first demonstrates an enterprise-intelligent financial management platform built on big data technology. Secondly, KPCA is used to preprocess financial data to improve the accuracy of financial data warnings and combined with the XGBoost algorithm to construct a financial warning model to analyze the enterprise’s economic crisis. Finally, the enterprise’s intelligent financial management platform underwent performance testing and prediction analysis. The results show that when the number of concurrent users is 100, the average response time of the platform is 1.94s, and the error rate of the system is about 0.5%. The platform can also predict and analyze financial data of the enterprise, which can help the enterprise better understand its financial situation and formulate a development plan. In summary, accounting automation can promote the development of enterprise financial management innovation. Intelligent financial management platforms can further realize enterprise accounting informatization to help enterprises achieve good economic development.

Dimensionality Reduction using Feature Selection Techniques on EDM for Student Academic Performance Prediction

The aim of this study is to perform dimensionality reduction on student datasets using different feature selection strategies in order to better forecast students' academic progress and minimize high-dimensionality concerns. The effectiveness of models built utilizing classification algorithms and pertinent features chosen using different feature selection strategies has been experimentally evaluated in this study. Nine feature selection methods, including Chi-Square, Pearson Correlation, Variance Threshold, Feature Importance, Recursive Feature Elimination (RFE), Lasso, Ridge, Random Forest, and XGBoost, as well as one classification method, logistic regression, were applied to the student dataset to determine the results of the analysis. In this experiment, dimensionality reduction techniques were used to reduce the number of features from 70 to the optimal 21 feature subsets for analysis of student academic performance prediction. Five measures were used to assess the effectiveness of the techniques: r2, precision, recall, f-score, and accuracy. With the logistic regression classifier, the feature subset chosen by the chi-square had the highest accuracy of 72.4%, precision of 71.4%, recall of 72.4%, f-score of 70%, and r-square of -0.221%.

Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar

The inspection and monitoring of structural conditions are crucial for the maintenance of semi-rigid base pavement. To achieve the inverse calculation of material parameters and obtain the mechanical response of asphalt pavement, a method of modulus correction by reducing the error between tested and simulated strains was first developed. The relationship between the temperature at various depths within the pavement structure and atmospheric temperature was effectively demonstrated using a dual sinusoidal regression model. Subsequently, pavement monitoring data illustrated that as loading weight and temperature increased and loading speed decreased, the three-way strain of the asphalt layer increased. Thus, the relationship model between loading conditions and three-way strain was established with a good fitting degree (R2 > 0.95). The corrected modulus was obtained by approximating the error between simulated and measured strains. Then, the finite element analysis was performed to calculate key mechanical index values under various working conditions and predict the fatigue life of asphalt and base layers. Finally, ground-penetrating radar (GPR) detection was performed, and the internal pavement condition index was defined for quantitative assessment of structure conditions. The results show that there is a good correlation between the internal pavement condition index (IPCI) and remaining life of pavement structure. Therefore, our works solve the problems of the parameter reliability of pavement structures and quantitative assessment for structural conditions, which could support the performance prediction and maintenance analysis on asphalt pavement with a semi-rigid base.

A Climate-Sensitive Transition Matrix Growth Model for Masson Pine (Pinus massoniana Lamb.) Natural Forests in Hunan Province, South-Central China

Masson pine natural forests are ecologically and economically valuable forest ecosystems extensively distributed across China. However, they have been subject to deforestation due to human disturbance. Moreover, climate change affects the growth, mortality, and recruitment of forests, yet available forest growth models do not effectively analyze the impacts of climate. A climate-sensitive transition matrix model (CM) was developed using data from 330 sample plots collected during the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventories in Hunan Province. To assess model robustness, two additional models were created using the same data: a non-climate-sensitive transition matrix model (NCM) and a fixed probability transition matrix model (FM). The models were compared using tenfold cross-validation and long-term predictive performance analysis. The cross-validation results did not show any significant differences among the three models, with the FM performing slightly better than the NCM. However, the application of the CM for long-term prediction (over a span of 100 years) under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) revealed distinct dynamics that demonstrated enhanced reliability. This is attributed to the consideration of climate variables that impact forest dynamics during long-term prediction periods. The CM model offers valuable guidance for the management of Masson pine natural forests within the context of changing climatic conditions.