The Architecture, Engineering, Construction, and Facility Management (AEC‐FM) industry is increasingly affected by digital technologies that monitor sensor network data and control automation systems. Advances in digital technologies like Digital Twin offer a high‐level representation of buildings and their assets by integrating the physical and digital world. This paper examines patterns, gaps, and trends in the AEC‐FM sector and contributes to digitalization and automation solutions for building management. This work covers a broad range of research topics, from intelligent information management of complex models to building information management and the interaction of building systems, where researchers are increasingly interested in using the Digital Twin to manage their information and in developing new research lines focused on data interchange and the interoperability of building information modeling (BIM) and facility management (FM). After a complete bibliometric search of several databases and following selection criteria, 77 academic publications about the Digital Twin application in the AEC‐FM industry were labeled and clustered accordingly. This study analyzed in detail the concept of key technologies, including “Digital Twin in Facility Lifecycle Management,” “Digital Twin‐Information Integration Standards,” “Digital Twin‐Based Occupants Centric Building Design,” “Digital Twin‐Based Predictive Maintenance,” “Semantic Digital Twin for Facility Maintenance,” and “Digital Twin‐Based Human Knowledge.” The findings show that information standardization is the first major hurdle that must be overcome before the actual use of Digital Twin can be realized in the AEC‐FM industry. Based on that, this paper provides a conceptual framework of Digital Twin for building management as a starting point for future research.

The efficiency of a fluidized bed reactor depends on the bed fluid dynamic behavior, which is significantly influenced by the bubble properties. This work investigates the bubble properties of a bubbling fluidized bed reactor using computational particle fluid dynamic (CPFD) simulations and electrical capacitance tomography (ECT) measurements. The two-dimensional images (along the reactor horizontal and vertical planes) of the fluidized bed are obtained from the CPFD simulations at different operating conditions. The CPFD model was developed in a commercial CPFD software Barracuda Virtual Reactor 20.0.1. The bubble behavior and bed fluidization behavior are characterized form the bubble properties: average bubble diameter, bubble rise velocity, and bubble frequency. The bubble properties were determined by processing the extracted images with script developed in MATLAB. The CPFD simulation results are compared with experimental data (obtained from the ECT sensors) and correlations in the literature. The results from the CPFD model and experimental measurement depicted that the average bubble diameter increased with an increase in superficial gas velocities up to $4.2 U_{mf}$ and decreased with a further increase in gas velocities due to the onset of large bubbles (potential slugging regime). The bubble rise velocity increased as it moved from the lower region to the bed surface. The Fourier transform of the transient solid volume fraction illustrated that multiple bubbles pass the plane with varying amplitude and frequency in the range of 1–6 Hz. Further, the bubble frequency increased with an increase in superficial gas velocity up to $2.5U_{mf}$ and decreased with a further increase in gas velocity. The CPFD model and method employed in this work can be useful for studying the influence of bubble properties on conversion efficiency of a gasification reactor operating at high temperatures.

The combination of local renewable energy production, dynamic loads, and multiple energy storage systems with different dynamics requires sophisticated control systems to maximize the energy cost efficiency of the combined energy system. Battery and thermal energy storage systems can be combined to increase the local use of on-site renewable energy, reduce peak power demand, and exploit time-of-use energy pricing. In this paper, we focus on how the augmented random search algorithm and artificial neural networks can be used together to solve an energy cost optimization problem involving the control of a battery energy storage system and a thermal energy storage system at the same time in a smart warehouse. As part of this work, a simulated training environment made using the data from the smart warehouse’s operations. In addition to the energy storage systems, the warehouse energy system has integrated a large roof mounted photovoltaic power plant and an industrial-scale cooling system. The developed solution is able to minimize the energy costs by modulating both energy systems, depending on the situation. Additionally, when it is tested against the state-of-the-art solutions, our developed solution at worst matches performance when the alternative algorithm is allowed to increase training time by a factor of nearly three. On average, our presented solution doubles the performance of the benchmark algorithm with much less computational resource expenditure.

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