Model-based operational optimisation of a microbial bioprocess converting terephthalic acid to biomass

Beagan, Niall; O’Connor, Kevin E.; Val, Ioscani Jiménez del

doi:10.1016/j.bej.2020.107576

Cited by 9 publications

(4 citation statements)

References 41 publications

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“…The widespread use is due to their intrinsic properties, i.e., chemical inertness, mechanical, pressure resistance, durability, versatility, flexibility, and cheap production cost, along with the thermal stability from the additives and stabilisers used in the production phase [2]. Over the last 60 years, consumer plastic use has increased by approximately 20 times, where the annual production had dramatically increased from 15 million tonnes (Mt) in 1964, to 335 Mt in 2016, and is expected to reach 1124 Mt by the year 2050 [3,4]. The annual consumption of water bottles alone has reached 500 billion units globally [5].…”

Section: Introductionmentioning

confidence: 99%

Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring

et al. 2020

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Background: Recycling the ever-increasing plastic waste has become an urgent global concern. One of the most convenient methods for plastic recycling is pyrolysis, owing to its environmentally friendly nature and its intrinsic properties. Understanding the pyrolysis process and the degradation mechanism is crucial for scale-up and reactor design. Therefore, we studied kinetic modelling of the pyrolysis process for one of the most common plastics, polyethylene terephthalate (PET). The focus was to better understand and predict PET pyrolysis when transitioning to a low carbon economy and adhering to environmental and governmental legislation. This work aims at presenting for the first time, the kinetic triplet (activation energy, pre-exponential constant, and reaction rate) for PET pyrolysis using the differential iso-conversional method. This is coupled with the in-situ online tracking of the gaseous emissions using mass spectrometry. Results: The differential iso-conversional method showed activation energy (E a) values of 165-195 kJ mol −1 , R 2 = 0.99659. While the ASTM-E698 method showed 165.6 kJ mol −1 and integral methods such as Flynn-Wall and Ozawa (FWO) (166-180 kJ mol −1). The in-situ Mass Spectrometry results showed the gaseous pyrolysis emissions, which are C 1 hydrocarbons and H-O-C=O along with C 2 hydrocarbons, C 5-C 6 hydrocarbons, acetaldehyde, the fragment of O-CH=CH 2 , hydrogen, and water. Conclusions: From the obtained results herein, thermal predictions (isothermal, non-isothermal and step-based heating) were determined based on the kinetic parameters. They can be used at numerous scale with a high level of accuracy compared with the literature.

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Section: Introductionmentioning

confidence: 99%

Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring

et al. 2020

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“…On the other hand, unstructured kinetic models generally contain few parameters, which, from a computational perspective, is usually advantageous. At present, a significant portion of recent studies concern bioethanol production [ 38 , 39 , 40 ], biosurfactant production [ 41 , 42 ], bacterial and fungal biomass production [ 43 , 44 ], as well as microalgal growth [ 45 , 46 , 47 ].…”

Section: Methods and Applications Of Mechanistic Modelingmentioning

confidence: 99%

Optimization and Scale-Up of Fermentation Processes Driven by Models

Wang

Yang

et al. 2022

Bioengineering

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In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

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“…The scientific literature abounds with very recent references to modelled bioprocesses: the xanthan biosynthesis by Xanthomonas campestris ATCC 13951 using winery wastewater [1], the uricase production by Aspergillus welwitschiae strain 1-4 [2], the use of polyethylene terephthalate (PET) for the cultivation of Pseudomonas sp. GO16 [3], the extracellular protease production by a native Bacillus aryabhattai Ab15-ES [4] and many other examples could be found. Usually, however, the large number of variables involved in a process, and their mutual interactions, require careful analysis and specific optimization methods with a view to establishing their optimum values for the intended purpose.…”

Section: Introductionmentioning

confidence: 98%

Optimization of the Acetification Stage in the Production of Wine Vinegar by Use of Two Serial Bioreactors

et al. 2021

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In the scope of a broader study about wine acetification, previous works concluded that using a single bioreactor hindered simultaneously reaching high productivities with high substrate consumption and the use of two serially arranged bioreactors (TSAB) could achieve such goal. Then, the aim of this work is the optimization, using Karush–Kuhn–Tucker (KKT) conditions, of this TSAB using polynomial models previously obtained. The ranges for the operational variables leading to either maximum and minimum mean rate of acetification of 0.11 ≤ (rA)global ≤ 0.27 g acetic acid·(100 mL·h)−1 and acetic acid production of 14.7 ≤ Pm ≤ 36.6 g acetic acid·h−1 were identified; the results show that simultaneously maximizing (rA)global and Pm is not possible so, depending on the specific objective, different operational ranges must be used. Additionally, it is possible to reach a productivity close to the maximum one (34.6 ≤ Pm ≤ 35.5 g acetic acid·h−1) with an almost complete substrate use [0.2% ≤ Eu2 ≤ 1.5% (v/v)]. Finally, comparing the performance of the bioreactors operating in series and in parallel revealed that the former choice resulted in greater production.

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Model-based operational optimisation of a microbial bioprocess converting terephthalic acid to biomass

Cited by 9 publications

References 41 publications

Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring

Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring

Optimization and Scale-Up of Fermentation Processes Driven by Models

Optimization of the Acetification Stage in the Production of Wine Vinegar by Use of Two Serial Bioreactors

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