Utilising localised exhaust and air curtain to reduce airborne particle settlement on surgical patients: potential future application in operating rooms?

Journal of Thermal Analysis and Calorimetry, Год журнала: 2024, Номер 149(19), С. 11323 - 11336

Опубликована: Апрель 23, 2024

Язык: Английский

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Transforming pollution into solutions: A bibliometric analysis and sustainable strategies for reducing indoor microplastics while converting to value-added products

Environmental Research, Год журнала: 2024, Номер 252, С. 118928 - 118928

Опубликована: Апрель 16, 2024

Язык: Английский

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Prediction of Dust Emissions in Highway Subgrade-Filling Construction Based on Deep Neural Network

HighTech and Innovation Journal, Год журнала: 2024, Номер 5(2), С. 259 - 271

Опубликована: Июнь 1, 2024

Dust pollution can harm the urban environment and health of citizens. Each stage in highway construction generates unorganized dust emissions to varying degrees, which complicates their quantification. To precisely forecast during subgrades reduce associated risks, this study introduces a predictive model based on deep neural network (DNN) for subgrade-filling operations. concentration is treated as nonlinear multivariate problem, with indicators encompassing particulate matter 2.5 (PM2.5), 10 (PM10), ground surface temperature, wind speed, air pressure, relative humidity. Using DNN model, forecasts concentrations PM2.5 PM10 at sites. Based project Hebei Province, predicts dust-emission via field monitoring conducted using self-developed equipment. The model’s predictions exhibit small mean-absolute-percentage error root-mean-square compared actual values, accuracy significantly surpasses that conventional regression models. Accurate forecasting facilitate timely control sites, thus facilitating more environmentally friendly efficient construction. Doi: 10.28991/HIJ-2024-05-02-03 Full Text: PDF

Язык: Английский

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Revolutionizing indoor air quality monitoring through IoT innovations: a comprehensive systematic review and bibliometric analysis

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(32), С. 44463 - 44488

Опубликована: Июнь 28, 2024

Язык: Английский

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Load Forecasting and Operation Optimization of Ice-Storage Air Conditioners Based on Improved Deep-Belief Network

Processes, Год журнала: 2024, Номер 12(3), С. 523 - 523

Опубликована: Март 5, 2024

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role optimizing operations, significantly contributing to the equilibrium regional electricity supply and demand, mitigating power grid stress, curtailing energy consumption grids. Addressing issues minimal correlation between input output data suboptimal accuracy inherent traditional deep-belief neural-network models, this study introduces an enhanced combination model. This model is refined through advanced genetic algorithm conjunction with “Statistical Products Services Solution” version 25.0 software, aiming augment precision predictions. Initially, undergo processing via which facilitates exclusion samples exhibiting low coupling. Subsequently, improved implements adaptive adjustments surmount challenge random weight parameter initialization prevalent networks. Consequently, optimized model, predicated on algorithm, established subjected training. Ultimately, undergoes simulation validation across three critical dimensions: operational performance, evaluation indices, operating costs conditioners. results indicate that, compared existing methods for predicting cooling conditioning, proposed achieves 96.52%. It also shows average improvement 14.12% computational performance 14.32% reduction consumption. outcomes align actual cooling-load variation patterns. Furthermore, daily cost derived from predicted data, has error margin only 2.36%. contributes optimization operations.

Язык: Английский

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Forecasting the cost premium of certified green building in China: A cutting-edge methodology incorporating radial basis function neural network and various optimization algorithms

Energy and Buildings, Год журнала: 2024, Номер 317, С. 114385 - 114385

Опубликована: Июнь 5, 2024

Язык: Английский

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Computational Fluid Dynamics as a Digital Tool for Enhancing Safety Uptake in Advanced Manufacturing Environments Within a Safe-by-Design Strategy

Materials, Год журнала: 2025, Номер 18(2), С. 262 - 262

Опубликована: Янв. 9, 2025

In modern manufacturing environments, pollution management is critical as exposure to harmful substances can cause serious health issues. This study presents a two-stage computational fluid dynamic (CFD) model estimate the distribution of pollutants in indoor production spaces. first stage, Reynolds-averaged Navier-Stokes (RANS) method was used simulate airflow and temperature. second Lagrangian applied for particle tracing. The theoretical acrylonitrile butadiene styrene (ABS) filament 3D printing process evaluate factors affecting ultrafine particles (30 nm). Key parameters such ventilation system effects, presence cooling fans print bed, nozzle temperatures were considered. results show that highest flow velocities (1.97 × 10-6 m/s 3.38 m/s) occur near system's inlet outlet, accompanied by regions high turbulent kinetic energy (0.66 m2/s2). These conditions promote airflow, facilitating particulate removal reducing stagnant zones prone pollutant buildup. effect thermal sources investigated, showing limited contribution on removal. findings emphasize importance digital twins better worker safety air quality environments.

Язык: Английский

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A Brief Outline of Indoor Air Quality: Monitoring, Modeling, and Impacts

Journal of Environmental Engineering, Год журнала: 2025, Номер 151(5)

Опубликована: Фев. 27, 2025

Язык: Английский

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Mitigating Air Pollution Risks with Deep Learning: A Quantum-Optimized Approach for Nitrogen Dioxide Prediction in Los Angeles

Journal of Machine and Computing, Год журнала: 2025, Номер unknown, С. 709 - 719

Опубликована: Апрель 5, 2025

Air pollution causes about seven million pre mature deaths globally every year, making it a critical issue that requires urgent attention. The key to mitigating its devastating effects lies in understanding nature, identifying sources and trends, predicting its. Accurate Real-time air forecasting is challenging task due spatiotemporal dynamics, requiring sophisticated modeling approaches. In our study, employed the Sequential Array-based Convolutional LSTM (SACLSTM) framework, which captures spatial temporal correlations by integrating deep CNNs for analysis with models prediction. To further enhance model's accuracy, optimized SACLSTM parameters using Quantum-based Draft Mongoose Optimization Algorithm (QDMOA). Using ten days of nitrogen dioxide (NO₂) data from Los Angeles County, developed sequential encoder-decoder network capable levels into future. By reformatting satellite quality images 5D tensor, achieved precise predictions concentrations across various locations time periods Angeles. Our results are thoroughly documented metrics visualizations, clearly demonstrating factors behind improved accuracy. comparison highlights effectiveness approach providing reliable forecasts.

Язык: Английский

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Indoor CO2 capture driven energy load reduction and ventilation management for plus energy building applications

Energy, Год журнала: 2025, Номер unknown, С. 136259 - 136259

Опубликована: Апрель 1, 2025

Язык: Английский

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