Neural and Hybrid Optimizations of the Fed-Batch Synthesis of Poly-β-Hydroxybutyrate by Ralstonia eutropha in a Nonideal Bioreactor

Abstract

ABSTRACT Poly-β-hydroxybutyrate (PHB) is synthesized by some microorganisms under stressful conditions. Despite its properties being comparable to those of synthetic polymers, and its biocompatibility and biodegradability, low productivities have dampened commercial interest in microbial PHB production. To increase production efficiency, a fed-batch fermentation with Ralstonia eutropha was optimized recently through a neural-cum-dispersion model (D-model) incorporating incomplete dispersion and noise in the feed streams. The approach described in the work has been improved in two ways: first by a model comprising neural networks only (N-model) and then by a hybrid neural model (H-model) with a mathematical component. At optimum dispersion, PHB production through the N-model optimization was 35% more than by the D-model, and this was enhanced by a further 58% using hybrid optimization. Recognizing that the D-model itself more than doubled the PHB production compared to a noise-free fully dispersed bioreactor, the present results establish hybrid neural optimization as a viable method for PHB production improvement under realistic conditions.