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Fire outbreak contributes greatly to loss of lives, properties and valuables. In order to avert such losses, reliable fire detection, notification, and prevention systems are crucial. Heat, the common indicator for fire development, requires to be monitored and controlled in a flammable environment. This paper has therefore developed a distributed approach to the detection and prevention of fire development by monitoring the inducing heat, smoke and early fire. The study proposes a model for the heat sensing and also for the control function of the system. It designs and implements the four sensing nodes, one base node, and the control and alarm system with resourceful hardware devices. The heat and smoke status of the environment is relayed for prompt action through a local user window on the administrator's personal computer (PC) and mobile phone. The local user interfaces and the internet cloud are designed for data relay and recording. The system is tested and evaluated for real-time deployment and operations. The resulting data are analyzed and reported.

One major feature of a granary is the uneven distribution of temperature and airflow. Due to the large variability in the parameters to be considered in characterizing the feature, a pilot test serves as the better way to performing the experiment, which subsequently affects the airflow velocity distribution, and is very difficult to determine by natural experiment. This paper develops a model for uneven airflow and temperature distribution through the layers of stored grains, relative to the indicated parameters. The study aims at predicting the various thermo-physical properties of maize grains using the developed model with the incorporated several expressions obtained, and compare with the measured values through the deployed pilot mini silo. To validate the model, the bin was aerated with forced air at constant humidity and temperature. A mini cylindrical silo was also developed and deployed with bulk grains for a pilot test. The predicted results were compared with the measured values of the temperatures obtained in the various locations of the pilot silo. The two results were closely related, thereby establishing the validity of our model. The model provides information on the direction of flow and velocity in each location within the stored volume of grains, and data for grain cooling, airing and drying in the bin. The developed model is useful for predicting the temperature distribution, airflow and the cooling time for bulk grains under varying aeration conditions, and suitable for optimizing the design and operation of granary systems.

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