In order to determine the water vapor adsorption performance of a rotary desiccant-based air conditioning system, the behavior of water adsorption on cylindrical pores of different sizes was studied by using classical density functional theory (CDFT) based on perturbated chain statistical associating fluid theory (PC-SAFT). Firstly, the structural parameters of the desiccant material were characterized by scanning electron microscopy (SEM), X-ray Energy Dispersive Spectrum (EDS), and N2 adsorption/desorption isotherms, as well as adsorption equilibrium measurements of water vapor at temperature range 293–308 K. Secondly, the potential energy equation of water molecules in cylindrical pores was determined, and contribution of various terms of PC-SAFT for simulating fluid in cylindrical pores were established. Finally, the pore size distribution (PSD) of the desiccant materials is determined by the PC-SAFT kernel. Moreover, water vapor condensation was investigated with the PC-SAFT model in micropores. The results indicate that the rotary desiccant materials have a large number of micropores with a volume of 0.3669 cm3/g and the amount of water adsorption is about 0.285 g/g. The condensation pressure and the pore width corresponding to the saturated pressure P0 grow with an increase in the temperature, signifying that adjusting the PSD of the material has a significant effect on improving the dehumidification performance. The research concludes that the PSD range of the oxide cylindrical pore between 1.09 and 1.53 nm is particularly beneficial for dehumidification. This study provides valuable theoretical guidance for optimizing dehumidification materials.

Water Vapor Adsorption on Desiccant Materials for Rotary Desiccant Air Conditioning Systems

The European Union is actively promoting the digitalization of energy systems as a mean of achieving urban decarbonization. In addition, the future expansion of distributed energy networks, prosumers (for both heating and electricity) and electric vehicles, will create the need for a smart real-time and predictive management. In this framework, Digital Twins will play a key role offering management tools and planning strategies in urban energy systems. Given the recent emergence of this technology, this work aims to elucidate the definition of Digital Twin by conducting a comprehensive review of various practical applications and methodologies outlined in the current literature. This analysis will examine different applications in the context of buildings, district heating networks, and micro-grids, exploring different scopes, such as fault detection, energy saving and support for grid operators. Additionally, several methodologies for developing Digital Twins will be highlighted including Deep Learning and physical-based algorithms. Finally, the benefits of smart networks and buildings using Digital Twins in different pilots across Europe will be demonstrated through future activities of the DigiBUILD project. Within this context the project aims to develop various innovative applications, such as Electric Vehicles smart charging to increase renewable energy consumption, optimization of heat generation in 4th generation District Heating, and pro-active management and fault detection in buildings with a focus on enhancing occupant comfort. 

Towards Digital Twins of buildings and smart energy networks: Current and future trends

Monitoring individual exposure to indoor air pollutants is crucial for human health and well-being. Due to the high spatiotemporal variations of indoor air pollutants, ubiquitous sensing is essential. However, the cost and maintenance associated with physical sensors make this currently infeasible. Consequently, this study investigates the feasibility of virtually sensing indoor air pollutants, such as particulate matter, volatile organic compounds (VOCs), and CO2, using a long short-term memory (LSTM) deep learning model. Several years of accumulated measurement data were employed to train the model, which predicts indoor air pollutant concentrations based on Building Management System (BMS) data (e.g., temperature, humidity, illumination, noise, motion, and window state) as well as meteorological and outdoor pollution data. A cross-validation scheme and hyperparameter optimization were utilized to determine the best model parameters and evaluate its performance using common evaluation metrics (R2, mean absolute error (MAE), root mean square error (RMSE)). The results demonstrate that the LSTM model can effectively replace physical indoor air pollutant sensors in the examined room, with evaluation metrics indicating a strong correlation in the testing set (MAE; CO2: 15.4 ppm, PM2.5: 0.3 μg/m3, VOC: 20.1 IAQI; R2; CO2: 0.47, PM2.5: 0.88, VOC:0.87). Additionally, the transferability of the model to other rooms was tested, with good results for CO2 and mixed results for VOC and particulate matter (MAE; CO2: 21.9 ppm, PM2.5: 0.3 μg/m3, VOC: 52.7 IAQI; R2; CO2: 0.45, PM2.5: 0.09, VOC:0.13). Despite these mixed results, they hint at the potential for a more broadly applicable approach to virtual sensing of indoor air pollutants, given the incorporation of more diverse datasets, thereby offering the potential for real-time occupant exposure monitoring and enhanced building operations. 

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LSTM Deep Learning Models for Virtual Sensing of Indoor Air Pollutants: A Feasible Alternative to Physical Sensors

The European Union is actively promoting the digitalization of energy systems as a mean of achieving urban decarbonization. In addition, the future expansion of distributed energy networks, prosumers (for both heating and electricity) and electric vehicles, will create the need for a smart real-time and predictive management. In this framework, Digital Twins will play a key role offering management tools and planning strategies in urban energy systems. Given the recent emergence of this technology, this work aims to elucidate the definition of Digital Twin by conducting a comprehensive review of various practical applications and methodologies outlined in the current literature. This analysis will examine different applications in the context of buildings, district heating networks, and micro-grids, exploring different scopes, such as fault detection, energy saving and support for grid operators. Additionally, several methodologies for developing Digital Twins will be highlighted including Deep Learning and physical-based algorithms. Finally, the benefits of smart networks and buildings using Digital Twins in different pilots across Europe will be demonstrated through future activities of the DigiBUILD project. Within this context the project aims to develop various innovative applications, such as Electric Vehicles smart charging to increase renewable energy consumption, optimization of heat generation in 4th generation District Heating, and pro-active management and fault detection in buildings with a focus on enhancing occupant comfort.

This paper presents a comprehensive understanding of current digital twin (DT) implementations in the construction industry, along with providing an overview of technologies enabling the operation of DTs in the industry. To this end, 145 publications were identified using a systematic literature review. The results revealed eight key areas of DT implementation including (i) virtual design, (ii) project planning and management, (iii) asset management and maintenance, (iv) safety management, (v) energy efficiency and sustainability, (vi) quality control and management, (vii) supply chain management and logistics, and (viii) structural health monitoring. The findings demonstrate that DT technology has the capacity to revolutionise the construction industry across these areas, enabling optimised designs, improved collaboration, real-time monitoring, predictive maintenance, enhanced safety practices, energy performance optimisation, quality inspections, efficient supply chain management, and proactive maintenance. This study also identified several challenges that hinder the widespread implementation of DT in construction, including (i) data integration and interoperability, (ii) data accuracy and completeness, (iii) scalability and complexity, (iv) privacy and security, and (v) standards and governance. To address these challenges, this paper recommends prioritising standardised data formats, protocols, and APIs for seamless collaboration, exploring semantic data modelling and ontologies for data integration, implementing validation processes and robust data governance for accuracy and completeness, harnessing high-performance computing and advanced modelling techniques for scalability and complexity, establishing comprehensive data protection and access controls for privacy and security, and developing widely accepted standards and governance frameworks with industry-wide collaboration. By addressing these challenges, the construction industry can unlock the full potential of DT technology, thus enhancing safety, reliability, and efficiency in construction projects.

Mohammed Zoher

Papers

Digital twin with Machine learning for predictive monitoring of CO2 equivalent from existing buildings