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\begin{document}

\title{Trend of the Application of Hybrid Artificial Neural Network Models in
Bioprocesses}



\author[1]{Roshanak Agharafeie}%
\author[2]{Rui Oliveira}%
\author[1]{Jorge M. Mendes}%
\affil[1]{Universidade Nova de Lisboa}%
\affil[2]{Universidade NOVA de Lisboa Faculdade de Ciencias e Tecnologia}%


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  \date{May 28, 2024}


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\begin{abstract}
Due to the complexity of biological transformations, developing
model-based strategies to optimize and control bioprocesses is
nontrivial. Hybrid models combining a mechanistic description of known
influential factors with machine learning to infer the missing
influential factors from data have been reported as powerful tools for
bioprocesses applications. The artificial neural network is one of the
most popular machine learning methods in this case. This paper presents
a systematic literature review by computerized search across two
databases: Scopus and Web of Science, and backward citation. The PRISMA
method was applied to selecting the publications and 159 research
articles were categorized as hybrid model applications to bioprocesses
problems. It was found that hybrid models were mainly applied in
upstream operation steps with a predominance of bioreaction steps. In
downstream processing, chromatography appeared as a more recent research
topic, with a relatively small number of publications. Furthermore,
holistic hybrid modeling applications that integrate data and knowledge
from several bioprocess steps will likely emerge in the future, enabling
better optimization and control of the bioprocess's platform. The
combination of other machine learning methods with the hybrid neural
network model is another opportunity that could improve the output of
the model.%
\end{abstract}%



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