Affiliations: Department of Chemical Engineering, National Institute of Technology, Calicut, Kerala, India
Correspondence:
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Corresponding author. Abdul Jaleel, Department of Chemical Engineering, National Institute of Technology, Calicut, Kerala, India. E:mail: [email protected].
Abstract: Nonlinear identification of a distillation column process is a challenging problem in the process industry. Controller performance of nonlinear and dynamic column can be viewed or analyzed using this type of identification. In this work, a novel identification method is proposed for distillation column simulated in realistic conditions using HYSYS using hybrid artificial bee colony (ABC) and artificial neural network (ANN). Since real distillation columns are dynamic in nature, this hybrid system is used as a nonlinear function in nonlinear autoregressive with exogenous input (NARX) structure. This hybrid NARX model is called NARX ANN ABC. In NARX ANN ABC, NARX ANN is trained using ABC algorithm. ABC training process benefit of training neural network without trapping at local optimal points. Reflux rate and reboiler temperature used as variable inputs while top and bottom compositions have been used as variable outputs. HYSYS software used for generating data. 1000 samples of data was collected from HYSYS. 800 samples of data was used for training, and remaining 200 samples of that was used for validation of the proposed model. The performance of proposed model has been compared with ANN, NARX ANN, and ANN ABC. The result showed NARX ANN ABC outperformed others.
