Multi-objective optimization for the biotechnological conversion of lingocellulosic biomass to value-added products

Castillo-Romo, Arturo Álvarez del; Morales-Rodríguez, Ricardo; Román-Martínez, Alicia

doi:10.1016/b978-0-444-63428-3.50257-5

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…These models are mathematical expressions of a process that are not time-dependent and hence carry no accumulation term. In general, these types of process models are employed in the first pass optimisation and calculation procedures at an initial design stage and for uncertainty analyses [69][70][71]. In areas such as monoclonal antibodies production, a lack of kinetic information might make steady-state models the only possibility [72].…”

Section: Step 1: Steady-state Modelmentioning

confidence: 99%

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Udugama

López

Gargalo

et al. 2021

Syst Microbiol and Biomanuf

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The domain of industrial biomanufacturing is enthusiastically embracing the concept of Digital Twin, owing to its promises of increased process efficiency and resource utilisation. However, Digital Twin in biomanufacturing is not yet clearly defined and this sector of the industry is falling behind the others in terms of its implementation. On the other hand, some of the benefits of Digital Twin seem to overlap with the more established practices of process control and optimization, and the term is vaguely used in different scenarios. In an attempt to clarify this issue, we investigate this overlap for the specific case of fermentation operation, a central step in many biomanufacturing processes. Based on this investigation, a framework built upon a five-step pathway starting from a basic steady-state process model is proposed to develop a fully-fledged Digital Twin. For demonstration purposes, the framework is applied to a bench-scale second-generation ethanol fermentation process as a case study. It is proposed that the success or failure of a fully-fledged Digital Twin implementation is determined by key factors that comprise the role of modelling, human operator actions, and other propositions of economic value.

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Section: Step 1: Steady-state Modelmentioning

confidence: 99%

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Udugama

López

Gargalo

et al. 2021

Syst Microbiol and Biomanuf

View full text Add to dashboard Cite

show abstract

“…The first step consists of a steady-state model that describes the process with the help of timeindependent mass and energy balances. This is used in the first design phase of the process for initial optimisation and calculation procedures [36][37][38][39]. If the steady-state model is extended by accumulation terms and the system dynamics, the second stage of a digital twin is reached.…”

Section: Qbd-based Process Developmentmentioning

confidence: 99%

Digital Twins for Continuous mRNA Production

et al. 2021

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The global coronavirus pandemic continues to restrict public life worldwide. An effective means of limiting the pandemic is vaccination. Messenger ribonucleic acid (mRNA) vaccines currently available on the market have proven to be a well-tolerated and effective class of vaccine against coronavirus type 2 (CoV2). Accordingly, demand is presently outstripping mRNA vaccine production. One way to increase productivity is to switch from the currently performed batch to continuous in vitro transcription, which has proven to be a crucial material-consuming step. In this article, a physico-chemical model of in vitro mRNA transcription in a tubular reactor is presented and compared to classical batch and continuous in vitro transcription in a stirred tank. The three models are validated based on a distinct and quantitative validation workflow. Statistically significant parameters are identified as part of the parameter determination concept. Monte Carlo simulations showed that the model is precise, with a deviation of less than 1%. The advantages of continuous production are pointed out compared to batchwise in vitro transcription by optimization of the space–time yield. Improvements of a factor of 56 (0.011 µM/min) in the case of the continuously stirred tank reactor (CSTR) and 68 (0.013 µM/min) in the case of the plug flow reactor (PFR) were found.

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“…The first stage is a steady-state model that describes the process by means of time-independent mass and energy balances. The steady-state model is used in the first design phase of the process for the initial optimization and calculation procedures [32][33][34][35]. The second stage of a digital twin is achieved by extending the steady-state model with accumulation terms and the system dynamics.…”

Section: Introductionmentioning

confidence: 99%