Evidence map›Paper›PMID 42351850›Full record

ArticleBioengineering (Basel, Switzerland)2026

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

Mark Korang Yeboah, Nana Yaw Asiedu, Ahmad Addo

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mark Korang YeboahChair of Dynamics and Control, University of Duisburg-Essen, Lotharstraße, 47057 Duisburg, Germany.
Nana Yaw AsieduFaculty of Mechanical and Chemical Engineering, Kwame Nkrumah University of Science and Technology, Kumasi 00233, Ghana.
Ahmad AddoFaculty of Mechanical and Chemical Engineering, Kwame Nkrumah University of Science and Technology, Kumasi 00233, Ghana.

Funding

German Academic Exchange Service 57693451
6 · The paper itself

Abstract

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.

Indexed as

bioprocess modelingconsolidated bioprocessingdynamic optimizationlignocellulosic ethanolmulti-objective optimizationoptimal controlpareto optimalitytemperature and pH policies

Identifiers

PMID42351850
PMCPMC13295681

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.