EFFICIENT MODEL IDENTIFICATION THROUGH SENSOR LOCATION IN BATCH DISTILLATION

Prime areas in model based process control are process identification and optimization. This paper presents their linkage for a binary batch distillation pilot plant using minimum experimental data which is necessary in practice if the batch column is to be used as a multiproduct unit. Here, minimum experimental data from plant runs is used to determine the model parameters with minimum error. The main motivation behind this work is the efficient state and parameter estimation for which measurement locations play an important role. Hence optimum measurement locations are first determined and model identification is done based on these measurements.