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multiple linear regression to predict the brightness of waste fibres mixtures before bleaching

CHEMICAL PAPERS 2022年第7期76卷 4351-4365页

作者： Tofani, Giorgio Cornet, Iris Tavernier, Serge Univ Antwerp Fac Appl Engn iPRACS Intelligence Proc Adv Catalysts & Solvents Groenenborgerlaan 171 B-2020 Antwerp Belgium Univ Antwerp Fac Appl Engn BioWAVE Biochem Wastewater Valorisat & Engn Groenenborgerlaan 171 B-2020 Antwerp Belgium Univ Pisa Dept Phys Largo Bruno Pontecorvo 3 I-56127 Pisa Italy

Waste paper is recovered and bleached to produce recycled newsprints and magazines. It is composed of a fibre mixture from different wood pulping processes. Each type of fibre shows a different reactivity towards bleaching. Consequently, if the composition of waste paper changes over time, the actual industrial bleaching process may no longer be suitable to achieve the intended brightness. This study aims to develop a multiple linear regression that correlates brightness and fibre composition to determine in advance whether a waste paper stream can achieve the intended brightness. Several samples of four of the most representative fibre types were bleached under specific laboratory conditions, and the resulting brightness was used to develop the regression. The resulting model is valid and consistent when the amount of bleached fibre chemically pulped type in the mixed fibre stream does not exceed 80%. Waste samples with a known fibre composition were then bleached to verify the model. The measured brightness followed the same trend predicted by the regression but was lower at a constant value. The use of a correction factor allowed for a good fit. The cause of this discrepancy could be the differences between the reference fibre mixtures and waste paper pulp not included in the model (e.g. contaminants or collapsed fibres). This work is a first step to develop a simple statistical tool to estimate the brightness of waste paper pulp, despite some limitations. [GRAPHICS] .

关键词： Waste paper Bleaching multiple linear regression Pulping Brightness Wood fibres

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Water quality evaluation based on water quality index and multiple linear regression: A research on Hanyuan Lake in southern Sichuan Province, China

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WATER ENVIRONMENT RESEARCH 2024年第6期96卷 e11055-e11055页

作者： Su, Kai Peng, Zhongshan Zhu, Dan Liu, Ruiqian Wang, Qin Cao, Rong He, Jun Southwest Jiaotong Univ Sch Environm Sci & Engn Chengdu 611756 Peoples R China Hanyuan Ecol Environm Monitoring Stn Yaan Yaan Peoples R China

This study aims to understand the changes in the water quality of Hanyuan Lake and to show these changes over time. In this study, monthly sampling was conducted at three sampling sites in Hanyuan Lake, and water samples were measured for water quality indicators in the laboratory according to the methods specified in the Environmental Quality Standards for Surface Water (GB3838-2002). Based on the monitoring data from January to December 2019, the WQI comprehensive evaluation method was used to conduct multiple linear stepwise regression analysis, extract key indicators, and establish the WQI(min) model. The results show that according to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, and the grade is excellent. The overall water quality of Hanyuan Lake is excellent, and most of the water quality indexes reach the Class I standard in the Environmental Quality Standards for Surface Water (GB3838-2002). WQI(min1) (R-2 = 0.86, p < 0.001, PE = 4.28) as the best WQI(min) model. In this study, a model with fewer parameters was established by multiple linear regression method, which is conducive to better monitoring of water quality at monitoring stations while saving costs. Practitioner Points According to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, the rating is excellent. From January 2019 to September 2020, the monthly change trend of each section is roughly the same, showing a trend of first decreasing, then rising, then decreasing, and finally rising and flattening. The WQI(min) model was developed to completely describe the change in the water body.

关键词： Hanyuan Lake multiple linear regression water quality evaluation water quality index (WQI)

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Prediction Models with multiple linear regression for Improving Acoustic Performance of Textile Industry Plants

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ARCHIVES OF ACOUSTICS 2025年第1期50卷 3-16页

作者： Yaman, Muammer Kurtay, Cuneyt Harputlugil, Gulsu U. L. U. K. A. V. A. K. Ondokuz Mayas Univ Fac Architecture Dept Architecture Samsun Turkiye Baskent Univ Fac Fine Arts Design & Architecture Dept Architecture Ankara Turkiye Cankaya Univ Fac Architecture Dept Architecture Ankara Turkiye

In industrial plants noise is a major threat to the mental and physical health of employees. The risk increases more due to the presence of high noise sources and the presence of too many employees in textile industry plants. This paper aims to predict the consequences of variables that may arise in the plants for acoustic improvement in textile industry plants. For this purpose, scenario plants have been created according to architectural properties and source-transmission path-receiver characteristics. The acoustic analyses of the scenario plants were performed in the ODEON Auditorium, and A-weighted sound pressure level (LA), noise reduction (NR), and reverberation time (RT) were determined. From the data, prediction equations were created with a multiple linear regression (MLR) model. To test the prediction equations, acoustic measurements were made, and acoustics improvements were carried out at a textile industry plant located in Turkiye. When the obtained results, the success, validity, and reliability of the prediction method are provided. In conclusion, the effect of architectural properties and the surface absorption on acoustic improvements in the textile industry was revealed. It was emphasized that prediction methods can be used to determine the effectiveness of interventions that can be applied in different facilities and can be improved in future studies.

关键词： industrial noise control acoustics simulation multiple linear regression prediction methods textile industry ODEON Auditorium noise reduction reverberation time

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Photovoltaic parameter extraction through an adaptive differential evolution algorithm with multiple linear regression

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APPLIED SOFT COMPUTING 2025年 176卷

作者： Chen, Bozhen Ouyang, Haibin Li, Steven Gao, Liqun Ding, Weiping Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China RMIT Univ Grad Sch Business & Law Melbourne 3000 Australia Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China

Solar cells play a crucial role in generating clean, renewable energy. Accurate modeling of photovoltaic (PV) systems is essential for their development, and simulating their behaviors requires precise estimation of their parameters. However, many optimization methods exhibit high or unstable root mean square error (RMSE) due to local optima entrapment and parameter interdependence. To address these challenges, we propose MLR-DE, a novel hybrid approach that integrates adaptive differential evolution (DE) with multiple linear regression (MLR). The main innovation is to decompose the PV model into linear coefficients and non-linear functions, the latter being iteratively estimated using DE. By treating nonlinear function outputs as independent variables and known measured currents as dependent variables, linear coefficients are analytically solved through MLR. Additionally, we introduce a data-fusion-based parameter generation scheme to improve DE's reliability by integrating historical crossover rates with estimated crossover rates. We validate MLR-DE through experiments across 11 PV configurations: 3 standard diode models and 8 environmental variants. The results demonstrate MLR-DE's superiority in all tests. It achieves the lowest average RMSE compared to other algorithms, with standard deviations at or below 2e-16. In the Friedman test, MLR-DE ranked first with a score of 1.94, outperforming the second-place (3.72) and last-place (7.58) competitors. The convergence curve shows that MLR-DE achieves convergence in less than 3,000 function evaluations over standard models, with an average convergence time of less than 0.6 s.

关键词： Adaptive differential evolution Single-objective optimization Photovoltaic model multiple linear regression

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multiple linear regression to Analyze the Effect of Emergency Diagnostic Procedures on the Hospitalization 2nd

Multiple Linear Regression to Analyze the Effect of Emergenc...

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2nd International Symposium on Biomedical and Computational Biology, BECB 2022

作者： Valente, Antonio Saverio Marino, Marta Rosaria Majolo, Massimo Longo, Giuseppe Balato, Giovanni Festa, Enrico Borrelli, Anna Triassi, Maria Amato, Francesco Romano, Maria Department of Electrical Engineering and Information Technology University of Study of Naples "Federico II" Naples Italy Department of Public Health University of Naples "Federico II" Naples Italy A.O.R.N. "A. Cardarelli" Naples Italy "San Giovanni di Dio e Ruggi d’Aragona" University Hospital Salerno Italy University of Naples "Federico II" Naples Italy

ISBN: (纸本)9783031251900

Emergency medicine is a discipline that today is increasingly the focus of attention. In the emergency department, to avoid overcrowding, it is important to assess the Length of the Stay (LOS). The length of stay (LOS) is a useful tool to monitor patients and for evaluating the efficiency and quality of the services offered. This study was conducted with the aim of providing LOS for all patients of Emergency Medicine unit of the University Hospital "San Giovanni di Dio e Ruggi d’Aragona" in Salerno (Italy) and the A.O.R.N. "Antonio Cardarelli" in Naples (Italy). Our aim is to evaluate the procedures about LOS in two different hospitals, also comparing the obtained results The analysis was conducted with multiple linear regression. In particular for the second an R2 equal to 0.880 was obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： multiple linear regression

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Comparing multiple linear regression and neural network models for predicting heating energy consumption in school buildings in the Federation of Bosnia and Herzegovina

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JOURNAL OF BUILDING ENGINEERING 2024年 97卷

作者： Katic, Dragan Krstic, Hrvoje Otkovic, Irena Istoka Juricic, Hana Begic Univ Mostar Fac Civil Engn Architecture & Geodesy Mostar Bosnia & Herceg Josip Juraj Strossmayer Univ Osijek Fac Civil Engn & Architecture Osijek Osijek Croatia

During the design of buildings in the preliminary stages of preparing project documentation and for the purpose of analysing the overall life cycle costs of buildings, particularly in the evaluation of alternative design solutions, there is often need for a rapid and straightforward assessment of thermal energy consumption expenditures. To fulfil this requirement, it is essential to develop new and to continuously refine existing mathematical estimation models. These models facilitate swift and uncomplicated assessments based on a reduced set of parameters or building characteristics that are readily determinable and available in the design phase. The paper compares the accuracy of multiple linear regression models and neural network models in predicting energy consumption for heating school buildings in the Federation of Bosnia and Herzegovina. The models were trained on a database, and their prediction ability was assessed through generalization and correlation analysis. The paper underscores neural networks' advantages, including their capacity for automatic network construction and management of nonlinear relationships. It specifically cites the cascade-correlation algorithm as a rapid learning technique for constructing deep neural networks. In essence, this paper offers discernment into the precision and efficacy of both multiple linear regression models and neural network models in predicting heating energy consumption of school buildings in Federation of Bosnia and Herzegovina. We evaluate the developed models for predicting delivered thermal energy consumption, and our discussion furnishes detailed results of an accuracy comparison among these models.

关键词： multiple linear regression Neural network models Energy consumption for space heating Evaluation of prediction model accuracy

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multiple linear regression Based Analysis of Weather Data: Assumptions and Limitations 2nd

Multiple Linear Regression Based Analysis of Weather Data: ...

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2nd International Conference on Advanced Communication and Intelligent Systems, ICACIS 2023

作者： Bansal, Savita Singh, Gurwinder Department of AIT-CSE Chandigarh University Punjab Mohali India

ISBN: (纸本)9783031451201

multiple linear regression is a statistical technique that is widely used in many fields, including weather forecasting. The primary aim of this chapter is to investigate the assumptions and limitations of multiple linear regression in analyzing weather data. Therefore, the main assumptions of multiple linear regression, including linearity, normality, independence, and homoscedasticity have been examined for weather data analysis. Also the limitations of multiple linear regression are explored in weather data analysis, such as multicollinearity, outliers, and model selection. The mean square error (MSE) and its rooted version (RMSE), and the mean absolute error (MAE), and the R-squared are employed to investigate the data (10 years with 4018 samples) for evaluating regression analysis. Finally, some recommendations and best practices to overcome these limitations and improve the accuracy of weather data analysis using multiple linear regression have been suggested to facilitate different stakeholders. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： multiple linear regression

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Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

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TEXTILE RESEARCH JOURNAL 2024年第11-12期94卷 1279-1296页

作者： Akter, Mahmuda Khalil, Elias Uddin, Md. Haris Chowdhury, Md. Kamrul Hassan Hasan, Shah Md. Maruf Bangladesh Univ Text Dept Apparel Engn Dhaka Bangladesh Bangabandhu Text Engn Coll Dept Text Kalihati Bangladesh Univ Dhaka Dept Stat Dhaka Bangladesh Bangladesh Univ Text Dept Apparel Engn Dhaka 1208 Bangladesh

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software's training function trainlm is used to modify its weight and basic values based on Levenberg-Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

关键词： Ends per inch (EPI) picks per inch (PPI) fabric thickness thermal transmittance artificial neural network multiple linear regression

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multiple linear regression Based Analysis of Weather Data for Precipitation and Visibility Prediction 1

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Proceedings of the 7th International Conference on Advances in Computing and Data Sciences, ICACDS 2023

作者： Singh, Gurwinder Harun Department of AIT-CSE Chandigarh University Punjab Chandigarh India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

When considering the operational rendition and safety of a road, factors such as traffic condition, vehicle characteristics, and driver behaviour are just as important as the weather condition. In particular, the visibility and, by extension, the frequency with which accidents occur on a certain route or the probability of getting involved in accidents are affected by weather conditions like fog and rain. In this paper, a multiple linear regression based analysis of weather data is performed for predicting the precipitation and visibility so that different stockholder can be facilitated. This analysis method looks at the relationship between several independent variables (such as temperature, humidity, cloud-cover) and a dependent variable (precipitation or visibility). To evaluate the accuracy of the regression models, several evaluation metrics, including the mean square error (MSE), root mean square error (RMSE), and the mean absolute error (MAE), as well as the R-squared value have been employed on a large dataset containing 10 years of weather data, with 4018 samples. The obtained results proves that the multiple linear regression models can provide more accurate predictions of future weather conditions, benefiting a wide range of industries and individuals who depend on it for their operations and decision making. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： multiple linear regression

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A multiple linear regression model for predicting characteristic frequencies in biological tissues

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AIP ADVANCES 2024年第11期14卷

作者： Apon, Imtiaz Ahamed Hasan, Md. Ratul Zafur, Abu Wahid, Md Ferdoush Haque, Mohammad Salman Bangladesh Army Univ Sci & Technol BAUST Dept Elect & Elect Engn Saidpur 5311 Bangladesh Khulna Univ Engn & Technol KUET Dept Mat Sci & Engn Khulna 9203 Bangladesh Bangladesh Univ Engn & Technol BUET Dept Mat & Met Engn Dhaka 1000 Bangladesh

This research introduces a novel mathematical methodology for identifying the distinctive frequency of human tissue. The model has been formulated using bioelectrical impedance analysis. The developed model can be utilized to detect a range of ailments, including those associated with the cardiovascular system, cancer, and dengue fever. A total of 3813 data points, including both males and females, were utilized. Data from a sample of both male and female individuals, including their age, height, bioelectrical impedance at frequencies ranging from 5 kHz to 1 MHz (for the Fc model), body mass index, and an impedance index of 2000, were utilized to create mathematical models. To validate the suggested models, data from a total of 1813 individuals (both male and female) were utilized. The statistical analysis of the proposed model (Fc) reveals a significant correlation (Pearson coefficient = 0.997, p < 0.001) between both male and female subjects, with a positive covariance. The model's 95% limits of agreement, ranging from -1.28 to 1.98 L for both males and females, are sufficiently minimal. All errors fall within this limit. In addition, the suggested model has undergone validation in terms of various types of error analysis, such as bias and root mean square (RMSE). The bias and RMSE values, which are indicators of error, reach a maximum of 0.32 and 0.38 L (for both male and female), respectively. These values are within the predicted range and can be considered minimal.

关键词： multiple linear regression

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