Design of an intelligent fire protection system

Fire protection systems utilize detectors that process signals from fire sensors using threshold-based methods and gen-erate a fire signal based on a logical function. Artificial neural networks can enhance these detectors by processing in-formation from a network of sensors after being trained. To train these neural networks, extensive data sets are neces-sary, which can be obtained through fire simulations on a supercomputer. Field tests are costly, subject to random fac-tors, limited to one or two rooms, and do not provide a comprehensive picture of fire development. Thus, designing intelligent fire systems falls under model-based design. Through modeling, large data sets were generated for training fire system algorithms, expanding the range of tasks they can address. A group of neural networks is proposed for op-timizing the placement of multi-parameter sensors, identifying the type of burning material, detecting fires at early stages, and localizing the fire zone to select appropriate extinguishing agents. Artificial neural networks enable the pre-diction of fire development, mapping hazardous factors' distribution to find optimal evacuation routes. An example of model-based design for a ship fire protection system is provided.