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

doi:10.1016/j.compchemeng.2026.109861

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

2026-08-23

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DOI：10.1016/j.compchemeng.2026.109861

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Abstract

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• Adjacent-batch differencing reduces repeated disturbance effects in batch data. • Continuous-time nominal models are identified without signal differentiation. • Laguerre-basis refinement reconstructs the residual in differenced output. • Combined differencing and basis use yields stable models in all four benchmarks. • Noise, mismatch, and a nonlinear case define the method’s operating scope.

Keywords:

Batch-process identification

Deterministic disturbance

Repetitive disturbance

Differenced data

Laguerre polynomials

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

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