Abstract
Supply of dry air is of important use in industries because the products manufactured need to be moisture-free. The dry air can be generated by heating the air using a heater element through which the power needs to be supplied as input. This drying process needs to be controlled in order to obtain a desired output temperature. Dynamic modeling of the dryer is of great importance, because by controlling input power, the output temperature of dry air is varied so that it is used for drying of sugarcane at sugar factory. The mathematical model of the sugar processing plant fitted to its corresponding data is capable to tracking variations in the controller output and also can tackle the external disturbances. Simulation studies are carried out to compare the drying process output with conventional PI feedback control and the novel model-based predictive control (MPC). The proposed control algorithm is of advanced type and is used to control the temperature of drying process by varying the input power. The drying process has a ramp-shaped output which denotes that the MPC approach is beneficial as compared with the conventional methods.
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Sujatha, K. et al. (2022). Predictive Model-based Analytics to Control Drying Process in a Sugar Industry. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-4016-2_39
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DOI: https://doi.org/10.1007/978-981-16-4016-2_39
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