A Bounded Adaptive Random Local Mutation Algorithm for Sparse-Data Parameter Identification in Nonlinear Bioprocess Models

Sparse and unevenly informative observations can substantially limit parameter estimation in nonlinear bioprocess models. This proof-of-concept study investigates Bounded Adaptive Random Local Mutation (B-ARLM), a derivative-free algorithm for bounded parameter calibration, using a product-inhibited Andrews-type model of biological hydrogen production calibrated to six interval-average observations over 0–8 days. Andrews–P achieved the lowest in-sample calibration error among the three examined kinetic formulations, with a best-run NMSE of 1.29 × 10−3. Under a common budget of 2250 objective-function evaluations and 30 repeated runs, B-ARLM significantly outperformed the Fixed-EA ablation and the study-specific sCA-ES reference, while its terminal performance was not statistically distinguishable from canonical CMA-ES or jDE after Holm correction. Hyperparameter, broad-start, profile-NMSE, and observation-error sensitivity analyses were additionally performed. Broad-start optimization and two-dimensional profiling revealed widely separated near-optimal parameter vectors and pronounced parameter compensation, showing that accurate trajectory reconstruction does not imply unique kinetic-parameter recovery. The results support B-ARLM as a competitive bounded calibration strategy for the examined sparse-data problem while emphasizing the distinction between optimization performance, practical identifiability, and biological interpretation.