Menu
Menu
Menu
Menu
Menu
Menu
Respira® is a control system based on Artificial Intelligence that enhance ventilation operation & maintenance in road and rail tunnels. Thanks to its predictive algorithms,respira® provides energy savings and better environmental conditions in normal operation, more safety in case of fire with a real data calibrated AI algorithms and fast-tracking failure detection in jet fans, axial fans, anemometers & environmental devices.
Respira® can be understood as an intelligent and automatic virtual operator of the existing ventilation control system.
Ventilation algorithms are commonly defined at the design stage and tested on commissioning days under specific weather conditions and with all newly installed equipment. In addition, these algorithms depend on the environmental devices installed (anemometers, opacimeters, NO or CO devices), so they are usually compromised by the malfunction of the devices, a very common problem in tunnel infrastructure.
Respira® uses historical data to learn but also continuously analyzes all available information (fan efficiency, weather and traffic conditions) and applies AI to correlate the data, continuously adapting its algorithm to the real reality of the infrastructure and alerting maintenance teams when a device deviates from its behavior pattern.
Control algorithms are mostly reactive, using only instantaneous values in their operation. Respira®, instead, uses the prediction of environmental conditions to anticipate possible stress situations of the installation in order to improve the performance of the system.
Respira® AI algorithm enables predictive maintenance with early fault detection. In addition, it allows the integration of maintenance alerts with Computer-Aided Maintenance Management (CMMS) systems.
Respira® uses real data to calibrate the reality of the installation and provides a layer of intelligence to the existing control system of the ventilation installation (SCADA or similar).
Normal operation. Respira® receives data from the SCADA and obtains data from weather agencies, traffic agencies, energy management systems and/or other sources of information related to the behavior of the facility; correlates all this information and applies AI to predict airspeed and pollutants within the tunnel under different traffic, weather and fan states. With these predictions, respira® automatically modifies the operation of the fans in order to achieve the required environmental conditions with the lowest energy expenditure.
Funcionamiento en caso de incendio. respira® predice la dinámica de fluidos dentro del túnel y combina esta predicción con un modelo físico que considera el efecto del fuego. En caso de incendio, conocida su posición, este modelo híbrido permite determinar la potencia del fuego a partir de los datos, alertar a los operadores si la ventilación del túnel no puede alcanzar la velocidad crítica y definir la mejor secuencia de funcionamiento de los ventiladores para garantizar la evacuación de los usuarios.
Respira® can be used with different kinds of ventilation such as longitudinal, transversal or semi-transversal and can be integrated in different ways depending on the existing SCADA and the established requirements. It can be deployed as a cloud system, Edge Computing or local servers and be integrated via API or web server, database servers or directly into the communication protocol layer (OPC, Modbus, Bacnet,…)
Efficient use of energy and improvement of environmental conditions inside the tunnel.
Greater safety in case of fire. It allows a continuous analysis of risks.
Improvement of the life cycle of the equipment. It allows a quick detection of failures of fans and devices, speeding up maintenance work.
Agnostic to the existing installation. It manages, autonomously and intelligently, any type of installation and /or any control system. It allows the integration of IoT equipment outside the installation if necessary.
Respira® is an intelligent control system that improves the functioning and maintenance of ventilation in railway and road networks by using Artificial Intelligence thanks to its predictive algorythms calibrated with real data. respira® provides energy savings and better environmental conditions in normal operation, more safety in case of fire with a real data calibrated AI algorithms and fast-tracking failure detection in jet fans, axial fans, anemometers & environmental devices.
Respira® can be understood as an intelligent and automatic virtual operator of the existing ventilation control system.
Ventilation algorithms are commonly defined at the design stage and tested on commissioning days under specific weather conditions and with all newly installed equipment. In addition, these algorithms depend on the environmental devices installed (anemometers, opacimeters, NO or CO devices), so they are usually compromised by the malfunction of those devices, a very common problem in tunnel infrastructure works.
Respira® uses historical data to learn but also continuously analyzes all available information (fan efficiency, weather and traffic conditions) and applies AI to correlate the data, continuously adapting its algorithm to the real reality of the infrastructure and alerting maintenance teams when a device deviates from its behavior pattern.
Control algorithms are mostly reactive, using only instantaneous values in their operation. Respira®, instead, uses the prediction of environmental conditions to anticipate possible stress situations of the installation in order to improve the performance of the system.
El algoritmo de IA de respira® AI algorithm enables predictive maintenance with early fault detection. In addition, it allows the integration of maintenance alerts with Computer-Aided Maintenance Management (CMMS) systems.
Respira® uses real data to calibrate the reality of the installation and provides a layer of intelligence to the existing control system of the ventilation installation (SCADA or similar).
Normal operation: Respira® receives data from the SCADA and obtains data from weather agencies, traffic agencies, energy management systems and/or other sources of information related to the behavior of the facility; correlates all this information and applies AI to predict airspeed and pollutants within the tunnel under different traffic, weather and fan states. With these predictions, respira® automatically modifies the operation of the fans in order to achieve the required environmental conditions with the lowest energy expenditure.
Operation in case of fire: Respira® predicts the fluid dynamics within the tunnel and combines this prediction with a physical model that considers the effect of fire. In case of fire, known its position, this hybrid model allows to determine the firepower from the data, alert the operators if the ventilation of the tunnel can not reach the critical speed and define the best sequence of operation of the fans to guarantee the evacuation of the users.
Efficient use of energy and improvement of environmental conditions inside the tunnel.
Greater safety in case of fire. It allows a continuous analysis of risks.
Improvement of the life cycle of the equipment. It allows a quick detection of failures of fans and devices, speeding up maintenance work.
Agnostic to the existing installation. It manages, autonomously and intelligently, any type of installation and /or any control system. It allows the integration of IoT equipment outside the installation if necessary.
Respira® is a control system based on Artificial Intelligence that enhance ventilation operation & maintenance in road and rail tunnels. Thanks to its predictive algorithms,respira® provides energy savings and better environmental conditions in normal operation, more safety in case of fire with a real data calibrated AI algorithms and fast-tracking failure detection in jet fans, axial fans, anemometers & environmental devices.
Respira® can be understood as an intelligent and automatic virtual operator of the existing ventilation control system.
Ventilation algorithms are commonly defined at the design stage and tested on commissioning days under specific weather conditions and with all newly installed equipment. In addition, these algorithms depend on the environmental devices installed (anemometers, opacimeters, NO or CO devices), so they are usually compromised by the malfunction of those devices, a very common problem in tunnel infrastructure works.
Respira® uses historical data to learn but also continuously analyzes all available information (fan efficiency, weather and traffic conditions) and applies AI to correlate the data, continuously adapting its algorithm to the real reality of the infrastructure and alerting maintenance teams when a device deviates from its behavior pattern.
Control algorithms are mostly reactive, using only instantaneous values in their operation. Respira®, instead, uses the prediction of environmental conditions to anticipate possible stress situations of the installation in order to improve the performance of the system.
El algoritmo de IA de respira® AI algorithm enables predictive maintenance with early fault detection. In addition, it allows the integration of maintenance alerts with Computer-Aided Maintenance Management (CMMS) systems.
Respira® uses real data to calibrate the reality of the installation and provides a layer of intelligence to the existing control system of the ventilation installation (SCADA or similar).
Normal operation: Respira® receives data from the SCADA and obtains data from weather agencies, traffic agencies, energy management systems and/or other sources of information related to the behavior of the facility; correlates all this information and applies AI to predict airspeed and pollutants within the tunnel under different traffic, weather and fan states. With these predictions, respira® automatically modifies the operation of the fans in order to achieve the required environmental conditions with the lowest energy expenditure.
Operation in case of fire: Respira® predicts the fluid dynamics within the tunnel and combines this prediction with a physical model that considers the effect of fire. In case of fire, known its position, this hybrid model allows to determine the firepower from the data, alert the operators if the ventilation of the tunnel can not reach the critical speed and define the best sequence of operation of the fans to guarantee the evacuation of the users.
Efficient use of energy and improvement of environmental conditions inside the tunnel.
Greater safety in case of fire. It allows a continuous analysis of risks.
Improvement of the life cycle of the equipment. It allows a quick detection of failures of fans and devices, speeding up maintenance work.
Agnostic to the existing installation. It manages, autonomously and intelligently, any type of installation and /or any control system. It allows the integration of IoT equipment outside the installation if necessary.