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A gestão de edifícios de saúde apresenta desafios crescentes de complexidade operacional,
eficiência energética e sustentabilidade, sendo as infraestruturas hospitalares responsáveis
por cerca de 4,4% das emissões globais de gases com efeito de estufa. O conceito de Digital
Twin (DT) — representação virtual dinâmica de um sistema físico — surge como uma
ferramenta promissora, ao permitir a contextualização espacial de dados operacionais num
modelo tridimensional do edifício.
A presente dissertação tem como objetivo desenvolver e avaliar um modelo de Digital Twin
orientado para a gestão de espaços numa farmácia, através de: (i) modelação de uma
unidade funcional em ambiente BIM (Revit) e integração na plataforma Autodesk Tandem;
(ii) integração de dados representativos das condições operacionais; e (iii) avaliação dos
benefícios do DT nas dimensões de eficiência operacional, manutenção, sustentabilidade e
apoio à decisão.
A metodologia seguiu uma abordagem de caso de estudo aplicada a uma farmácia de
pequena dimensão, suportada por uma arquitetura modular com três componentes: o
modelo BIM em Revit, a plataforma Tandem como camada de integração e visualização, e
um gerador de dados sintéticos em Python que simula o comportamento de sensores IoT.
Foram definidos e implementados três cenários de demonstração: (1) impacto térmico de
um evento físico (abertura de porta), (2) deteção de ineficiência energética (HVAC em
funcionamento desnecessário) e (3) degradação progressiva de um equipamento de
refrigeração. Para cada cenário, estabeleceram-se thresholds e uma classificação de
estados (normal, alerta, crítico), permitindo a identificação automática de anomalias e a
sua visualização espacial.
Os três cenários foram reproduzidos com sucesso na plataforma Tandem, com transições
de estado claramente visíveis e contextualizadas espacialmente. A principal vantagem
identificada reside na capacidade do DT de associar cada indicador ao compartimento, piso
ou equipamento correspondente, superando as limitações dos dashboards convencionais. As principais limitações incluem o uso de dados sintéticos e a ausência de capacidade nativa
de simulação ou predição no Tandem; ainda assim, a arquitetura modular permite
substituir o gerador por fontes reais (sensores IoT, BMS, CMMS) sem alterações estruturais.
Em suma, demonstrou-se a viabilidade técnica e o potencial operacional da integração
BIM–Digital Twin para a gestão inteligente de espaços numa farmácia. Como trabalho
futuro, propõe-se a integração de sensores IoT reais, o acoplamento com motores de
simulação energética (e.g., EnergyPlus), a aplicação de machine learning para manutenção
preditiva e a extensão a outras áreas funcionais e sistemas de gestão hospitalar.
The management of healthcare buildings presents growing challenges in operational complexity, energy efficiency, and sustainability, with hospital infrastructures accounting for approximately 4.4% of global greenhouse gas emissions. The Digital Twin (DT) concept — a dynamic virtual representation of a physical system — emerges as a promising tool, enabling the spatial contextualization of operational data within a three-dimensional building model. This dissertation aims to develop and evaluate a Digital Twin model for pharmacy space management through: (i) modeling a functional pharmacy in a BIM environment (Revit) and integrating it into the Autodesk Tandem platform; (ii) integrating data representative of operational conditions; and (iii) evaluating the benefits of the DT in terms of operational efficiency, maintenance, sustainability, and decision support. The methodology followed a case study approach applied to a small pharmacy, supported by a modular architecture with three components: the BIM model developed in Revit, the Tandem platform as the integration and visualization layer, and a synthetic data generator developed in Python simulating IoT sensor behavior. Three demonstration scenarios were defined and implemented: (1) the thermal impact of a physical event (door opening), (2) the detection of energy inefficiency (HVAC running unnecessarily), and (3) the progressive degradation of refrigeration equipment. For each scenario, thresholds and a state classification (normal, alert, critical) were established, enabling automatic anomaly identification and spatial visualization. All three scenarios were successfully reproduced on the Tandem platform, with state transitions clearly visible and spatially contextualized. The primary advantage identified lies in the DT's ability to associate each indicator with the corresponding room, floor, or equipment, surpassing the limitations of conventional dashboards. The main limitations include the use of synthetic data and the absence of native simulation or prediction capabilities in Tandem; nevertheless, the modular architecture allows the generator to be replaced by real sources (IoT sensors, BMS, CMMS) without structural changes. In summary, this work demonstrated the technical feasibility and operational potential of BIM–Digital Twin integration for the intelligent management of pharmacy spaces. Future work includes the integration of real IoT sensors, coupling with energy simulation engines (e.g., EnergyPlus), the application of machine learning for predictive maintenance, and extension to other functional areas and hospital management systems.
The management of healthcare buildings presents growing challenges in operational complexity, energy efficiency, and sustainability, with hospital infrastructures accounting for approximately 4.4% of global greenhouse gas emissions. The Digital Twin (DT) concept — a dynamic virtual representation of a physical system — emerges as a promising tool, enabling the spatial contextualization of operational data within a three-dimensional building model. This dissertation aims to develop and evaluate a Digital Twin model for pharmacy space management through: (i) modeling a functional pharmacy in a BIM environment (Revit) and integrating it into the Autodesk Tandem platform; (ii) integrating data representative of operational conditions; and (iii) evaluating the benefits of the DT in terms of operational efficiency, maintenance, sustainability, and decision support. The methodology followed a case study approach applied to a small pharmacy, supported by a modular architecture with three components: the BIM model developed in Revit, the Tandem platform as the integration and visualization layer, and a synthetic data generator developed in Python simulating IoT sensor behavior. Three demonstration scenarios were defined and implemented: (1) the thermal impact of a physical event (door opening), (2) the detection of energy inefficiency (HVAC running unnecessarily), and (3) the progressive degradation of refrigeration equipment. For each scenario, thresholds and a state classification (normal, alert, critical) were established, enabling automatic anomaly identification and spatial visualization. All three scenarios were successfully reproduced on the Tandem platform, with state transitions clearly visible and spatially contextualized. The primary advantage identified lies in the DT's ability to associate each indicator with the corresponding room, floor, or equipment, surpassing the limitations of conventional dashboards. The main limitations include the use of synthetic data and the absence of native simulation or prediction capabilities in Tandem; nevertheless, the modular architecture allows the generator to be replaced by real sources (IoT sensors, BMS, CMMS) without structural changes. In summary, this work demonstrated the technical feasibility and operational potential of BIM–Digital Twin integration for the intelligent management of pharmacy spaces. Future work includes the integration of real IoT sensors, coupling with energy simulation engines (e.g., EnergyPlus), the application of machine learning for predictive maintenance, and extension to other functional areas and hospital management systems.
Descrição
Palavras-chave
Digital Twin BIM Space Management Pharmacy Autodesk Tandem Revit IoT Real-Time Monitoring Energy Efficiency Sustainability Predictive Maintenance Farmácia Gestão de espaços Monitorização em tempo real Eficiência energética Sustentabilidade Manutenção preditiva