1. From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives
MDPI Open Acess Journal
Fermentation
2023-10-23 | Journal article
DOI: https://doi.org/10.3390/fermentation9100922
part of ISSN: 2311-5637
PID of zenodo repository: https://zenodo.org/records/11082856
Contributors: Roshanak Agharafeie; Joao Rodrigues correria Ramos; Jorge M. Mendes; and Rui Oliveira*
Abstract: Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging compared to other industries. A promising approach is to combine deep neural networks (DNN) with prior knowledge in hybrid neural network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It reveals that HNNs have been applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs have been applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies have combined shallow feedforward neural networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, convolution neural networks (CNN), long short-term memory (LSTM) networks and physics-informed neural networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.
2. Life Cycle Assessment applied to polyhydroxyalkanoates production phases: A mini revie
Closed Access Conference Proceedings
IRIS | UNIBO | CRIS Current Research Information System
June 2023
30 anni di Life Cycle Assessment, sviluppi metodologici e applicativi
Page 287 – 295
Link: Life Cycle Assessment applied to polyhydroxyalkanoates production phases: a mini review (unibo.it)
PID of zenodo repository : 10.5281/zenodo.11105211
IRIS Alma Mater Studorium: https://cris.unibo.it/handle/11585/968302
Contributors: Martina Pelliconi; Serena Righi
Abstract: Polyhydroxyalkanoates (PHAs) are bio-based polyesters that are natural, renewables, biocompatible and biodegradable while having similar properties to commonly used plastic. However, their industrial production is still more expensive than the petroleum-based one. To make PHAs marketable on a large scale, it is therefore necessary to optimize every single phase of their production. This process must be accompanied by environmental assessments, to verify that PHAs are a greener alternative to conventional plastic. To do this, the best tool currently available is Life Cycle Assessment (LCA). The purpose of this study is to examine the state of the art regarding the application of the LCA methodology to all phases of PHAs production.