In industrial scenarios,nanoparticles are incidentally generated in high concentrations during diverse material transformation processes,presenting potential health hazards for exposed workers.Consequently,as an indoo...In industrial scenarios,nanoparticles are incidentally generated in high concentrations during diverse material transformation processes,presenting potential health hazards for exposed workers.Consequently,as an indoor air quality management measure,their concentration is commonly reduced through localized forced ventilation.However,the control of these systems usually relies on traditional rule-based algorithms,which cannot deploy efficient control strategies such as model predictive control.To solve this issue,we propose a novel grey-box reduced order model method,never used before for industrial indoor nanoparticles.This approach can be deployed in model predictive control algorithms in buildings and does not present the data-reliance and transferability issues of black-box modeling.To test this model,a data collection campaign was conducted under real-world operating conditions in an industrial-scale thermal spraying booth,aiming to test the method’s viability for model calibration and validation of indoor total nanoparticle concentration through the maximum likelihood method,statistical validation tests,and physical viability assessment.Results for three different lumped sum models illustrate the effectiveness of grey-box modeling in industrial scenarios with confined processes and forced ventilation systems,handling observations’noise and background concentration fluctuations,and allowing a performance comparison between models.Further research could be conducted to study the viability of indoor total nanoparticle concentration reduced order models with higher spatial resolution,non-confined sources,and natural airflows.展开更多
基金LIFE-funded project(LIFE20 ENV/ES/000187)It was also supported by the Spanish Ministry of Science and Innovation(Project CEX2018000794-S)and by AGAUR(project 2021 SGR 00341).
文摘In industrial scenarios,nanoparticles are incidentally generated in high concentrations during diverse material transformation processes,presenting potential health hazards for exposed workers.Consequently,as an indoor air quality management measure,their concentration is commonly reduced through localized forced ventilation.However,the control of these systems usually relies on traditional rule-based algorithms,which cannot deploy efficient control strategies such as model predictive control.To solve this issue,we propose a novel grey-box reduced order model method,never used before for industrial indoor nanoparticles.This approach can be deployed in model predictive control algorithms in buildings and does not present the data-reliance and transferability issues of black-box modeling.To test this model,a data collection campaign was conducted under real-world operating conditions in an industrial-scale thermal spraying booth,aiming to test the method’s viability for model calibration and validation of indoor total nanoparticle concentration through the maximum likelihood method,statistical validation tests,and physical viability assessment.Results for three different lumped sum models illustrate the effectiveness of grey-box modeling in industrial scenarios with confined processes and forced ventilation systems,handling observations’noise and background concentration fluctuations,and allowing a performance comparison between models.Further research could be conducted to study the viability of indoor total nanoparticle concentration reduced order models with higher spatial resolution,non-confined sources,and natural airflows.
