Forecasting sustainable water production in convex tubular solar stills using gradient boosting analysis

Desalination and Water Treatment, Год журнала: 2024, Номер 318, С. 100344 - 100344

Опубликована: Апрель 1, 2024

Water scarcity is an important global issue that necessitates the development of sufficient and sustainable desalination technologies. This study forecasts productivity two solar distillation technologies, namely conventional tubular still (TSS) convex (CTSS). The research objectives included assessing distillate yield both stills investigating application advanced gradient boosting machine learning (ML) technique for forecasting production. Compared to TSS, CTSS demonstrated a calculated increase in which indicates its potential as effective water technology. correlation analysis revealed TSS exhibited 10 significant correlations while 4 correlations. model exceptional predictive precision stills. R-squared (R2) was 0.86, Root Mean Squared Error (RMSE) 58.2%, Coefficient Variation (CVRMSE) 29.3%. In contrast, displayed impressive performance metrics, including R2 value 0.99, RMSE 1.2%, CVRMSE 4%. Valuable insights were provided enhancement stills, addition highlighting ML techniques accurately predicting productivity.

Язык: Английский

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Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features

Heliyon, Год журнала: 2024, Номер 10(13), С. e34202 - e34202

Опубликована: Июль 1, 2024

Predictive maintenance to avoid fatigue and failure enhances the reliability of mechanics, herewith, this paper explores vibrational time-domain data in advancing fault diagnosis predictive maintenance. This study leveraged a belt-drive system with properties: operating rotational speeds 500–2000 RPM, belt pretensions at 70 150 N, three operational cases healthy, faulty unbalanced, which leads 12 studied cases. In analysis, two one-axis piezoelectric accelerometers were utilized capture vibration signals near driver pulley. Five advanced statistics calculated during signal processing, namely Variance, Mean Absolute Deviation (MAD), Zero Crossing Rate (ZCR), Autocorrelation Coefficient, signal's Energy. The Taguchi method was used test five selected features on basis Signal-to-Noise (S/N) ratio. For classifications, an expert based artificial intelligence where Random Forest (RF) model trained untraditional parameters for optimizing accuracy. resulted 0.990 0.999, accuracy AUC, demonstrate RF model's high dependability. Evidently, methodology highlights potential when progressed into systems, advances strategies systems.

Язык: Английский

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Statistical analysis and open innovation in economic growth of scottish business SMEs for sustainable development

Journal of Open Innovation Technology Market and Complexity, Год журнала: 2024, Номер 10(2), С. 100275 - 100275

Опубликована: Апрель 12, 2024

The propulsion of economic growth is a multifaceted construct, influenced by technology, capital, and resource management, with Small Medium-sized Enterprises (SMEs) being significant contributors to innovation employment. This study examines the Scottish SMEs across four sectors over 14-year period (2008-2021) employing novel statistical approach understand their development trajectories within framework open innovation. paper engaged in longitudinal analysis assess trends inform policy for sustainable utilization open-access data. Theoretical metrics such as Average Growth Rate, Compound Rate (CAGR), Year-Over-Year (YoY) were applied evaluate sectoral performance. findings revealed varied patterns; information technology sector exhibited robust increase, while telecommunications showed percentage growth. Conversely, Information Communication Technologies (ICT) experienced decline that suggests potential market gap. An affirmative trajectory CAGR was observed most except ICT, which corroborates shift technological evolution needs. illuminates dynamic landscape SMEs, telecoms emerging prosperous terms rates, thus indicates shifting focus. study's limitations are briefly discussed, addition need updated databases further research predictive modeling machine learning enhance forecasting capabilities foster development.

Язык: Английский

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Enhanced Fault Detection of Wind Turbine Using eXtreme Gradient Boosting Technique Based on Nonstationary Vibration Analysis

Journal of Failure Analysis and Prevention, Год журнала: 2024, Номер 24(2), С. 877 - 895

Опубликована: Фев. 27, 2024

Язык: Английский

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Emerging Technologies in Water Desalination: A Review and Future Outlook

Energy Nexus, Год журнала: 2025, Номер unknown, С. 100373 - 100373

Опубликована: Фев. 1, 2025

Язык: Английский

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Enhancing building sustainability through aerodynamic shading devices: an integrated design methodology using finite element analysis and optimized neural networks

Asian Journal of Civil Engineering, Год журнала: 2024, Номер 25(5), С. 4281 - 4294

Опубликована: Апрель 20, 2024

Язык: Английский

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Comparative Analysis of Design Parameters Impacting the Performance of Pyramidal and Spherical Solar Stills: A Review

Desalination and Water Treatment, Год журнала: 2024, Номер 319, С. 100545 - 100545

Опубликована: Июнь 19, 2024

Population growth, urbanization, and the effects of climate change all exacerbate problem global water scarcity, which poses a serious obstacle to sustainable development. Solar distillation emerges as critical solution, converting brackish or saline into potable using alternative energy. Despite wealth information on solar still adaptations, identifying most efficient design for residential industrial settings remains challenging. Hence, comparative analysis various designs is essential, considering practical financial aspects. This study aims showcase work researchers who are trying make systems more productive by looking at new techniques used in spherical pyramidal stills. The goal this research identify variables that influence efficiency, enabling achievement desired results with ease. Researchers have investigated interventions, such integrating moving parts other components, modifying basin's shape size, incorporating filaments wick, reducing surface tension through use floats balls, magnetic fields, improving electric field. According research, field (220 mT) above below basin increases molecular motion evaporation, resulting 41 % efficiency gain. Spherical stills don't require tracking because their uniform exposure radiation makes them than pyramid Reviews find adding rotating ball phase-changing materials significantly enhances stills, making best design. Mirrors reflect sunlight; an overview related literature shows it leads additional production. Future will focus comprehending annual production rates associated costs, aiming enhance our application strategies technology.

Язык: Английский

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Evaluating electrical power yield of photovoltaic solar cells with k-Nearest neighbors: A machine learning statistical analysis approach

e-Prime - Advances in Electrical Engineering Electronics and Energy, Год журнала: 2024, Номер 9, С. 100674 - 100674

Опубликована: Июль 4, 2024

The increasing demand for sustainable and renewable energy solutions reflects the critical importance of advancing photovoltaic (PV) technology its operational efficiency. In response, this study introduces a novel application k-Nearest Neighbor (k-NN) algorithm to assess reliability applicability solar panel simulation data which aimed classify current states partial shading, open, short circuit conditions, alongside regression-based analysis predicting specific operating parameters. research, published dataset that involved various PV module configurations under different environmental conditions was tested evaluated. k-NN technique applied both status predict performance metrics modules. diagnosis model demonstrated an accuracy 99.2 % F1 score %, indicating high degree in identifying Concurrently, regression exhibited Root Mean Square Error (RMSE) 0.036 R2 value unity showcased effectiveness parameters based on data. concluded results are further enriching simulation-based generation be endorsed implemented before jumping into real experimental applications, addition highlighting potential machine learning cells productivity statistical analysis.

Язык: Английский

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Automated wind turbines gearbox condition monitoring: A comparative study of machine learning techniques based on vibration analysis

FME Transaction, Год журнала: 2024, Номер 52(3), С. 471 - 485

Опубликована: Янв. 1, 2024

Wind turbines play a role in the adoption of renewable energy production, but they are susceptible to shutdowns that require thorough monitoring. Gearbox failures an issue leading maintenance and operational downtime. This study investigates application machine learning methods enhance diagnosis gearbox problems using vibration analysis. Through fault scenarios impact bearings gears, researchers successfully extracted time domain features from data 750 kW turbine testbed order detect indications damage. Support Vector Machine (SVM), Naive Bayes, K Nearest Neighbour (KNN) models were used classify faults. Among these models, Bayes achieved accuracy rate 95.7%, which exceeded established benchmarks. The probabilistic approach was able associate symptom characteristics with patterns. Intelligent monitoring systems could improve efficiency. data-driven highlights potential supporting wind power development by eliminating inefficiencies improving reliability, further research is being conducted ensure this works concert diversity real world. shows how contributing advances helping analyze predictive prevent costly failures.

Язык: Английский

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UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel–Ziv complexity and Teager–Kaiser energy features

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 10, 2024

Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel-Ziv Complexity (LZC), Teager-Kaiser Energy Operator (TKEO), PADRE dataset, which encapsulates various rotor configurations. The extracted features were subjected to a Chi-Square (χ

Язык: Английский

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