Batch-process identification under repetitive and deterministic disturbances using differenced batch data

초록

Reliable model identification for batch processes is difficult when the measured data contain both repetitive disturbances that recur at similar stages across batches and deterministic disturbances that vary from batch to batch. This study proposes a batch-process identification method that uses input–output data from the current batch and the immediately preceding batch. Differenced signals are formed to reduce the repeated effect of the repetitive disturbance on the output while preserving informative variation for nominal-model identification. A continuous-time nominal process model is then estimated from the differenced data by an integral-transform-based least-squares method. The residual component remaining in the differenced output is represented by Laguerre polynomials, and its coefficients and the model initial states are refined by the prediction error method. The proposed method is evaluated using four benchmark processes representing high-order, non-minimum-phase, and underdamped dynamics, supplemented by measurement-noise analyses and a nonlinear semi-batch case. Under the nominal benchmark configuration, the proposed method produced stable models in all four processes, whereas the previous method did so in only two cases. These results support the use of adjacent-batch differencing under the considered repetitive-disturbance conditions. © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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