Dynamic Pareto Optimization of Consolidated Bioprocessing for Ethanol Titer, Productivity, Conversion, and Operating...

Consolidated bioprocessing (CBP), in which enzyme production, substrate hydrolysis, and fermentation occur in a single bioreactor, offers a promising pathway for lignocellulosic ethanol production. However, CBP operation involves competing objectives, including ethanol titer, volumetric productivity, substrate conversion, soluble sugar accumulation, batch duration, control effort, and the operating severity associated with temperature and pH profiles. This study introduces a feasibility-aware multi-objective dynamic optimization framework for identifying Pareto-optimal operating policies for batch CBP. A reduced-order mechanistic model is developed to represent biomass growth, enzyme activity, insoluble substrate hydrolysis, soluble sugar formation and consumption, ethanol production, and inhibition under time-varying temperature and pH conditions. The optimization simultaneously maximizes ethanol titer, productivity, and substrate conversion while minimizing sugar accumulation, operating severity, control movement, and batch time. In the main simulation run, 120,000 dynamic policies were retained for analysis, resulting in 5017 feasible policies and 328 feasible Pareto-optimal policies under a minimum conversion threshold of 0.42. Within the feasible Pareto archive, the highest ethanol titer reached 1.265gL-1, the highest productivity reached 0.017gL-1h-1, and the maximum conversion reached 0.440. Compared with the best criterion-specific static constant-operation baselines, the dynamic Pareto policies improved ethanol titer, productivity, and conversion by 10.6%, 8.3%, and 14.3%, respectively. A feasibility analysis showed that a conversion threshold of 0.42 was stringent but attainable, whereas thresholds of 0.44 and 0.55 were not attainable under the present model and operating bounds. Independent-seed repetitions confirmed a consistent high-performing region across stochastic searches. The resulting Pareto fronts and operating policy maps provide a model-based decision-support basis for selecting dynamic temperature and pH profiles for CBP operation. Because this study is in silico, future experimental validation is required before direct pilot- or industrial-scale application.