Kinetic modeling and process analysis for Desmodesmus sp. lutein photo‐production

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L-tryptophan is an essential amino acid widely used in food and pharmaceutical industries. However, its production via Escherichia coli fermentation suffers severely from both low glucose conversion efficiency and acetic acid inhibition, and to date effective process control methods have rarely been explored to facilitate its industrial scale production. To resolve these challenges, in the current research an engineered strain of E. coli was used to overproduce L-tryptophan. To achieve this, a novel dynamic control strategy which incorporates an optimized anthranilic acid feeding into a dissolved oxygen-stat (DO-stat) glucose feeding framework was proposed for the first time. Three original contributions were observed. Firstly, compared to previous DO control methods, the current strategy was able to inhibit completely the production of acetic acid, and its glucose to L-tryptophan yield reached 0.211 g/g, 62.3% higher than the previously reported. Secondly, a rigorous kinetic model was constructed to simulate the underlying biochemical process and identify the effect of anthranilic acid on both glucose conversion and L-tryptophan synthesis. Finally, a thorough investigation was conducted to testify the capability of both the kinetic model and the novel control strategy for process scale-up. It was found that the model possesses great predictive power, and the presented strategy achieved the highest glucose to L-tryptophan yield (0.224 g/g) ever reported in large scale processes, which approaches the theoretical maximum yield of 0.227 g/g. This research, therefore, paves the way to significantly enhance the profitability of the investigated bioprocess.

Section: Methodsmentioning

confidence: 99%

Overproduction of L‐tryptophan via simultaneous feed of glucose and anthranilic acid from recombinant Escherichia coli W3110: Kinetic modeling and process scale‐up

Jing

Tang

Yao

et al. 2017

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“…The first simulation approach to explore the effects of light intensity and nitrate concentration on lutein synthesis and biomass growth is to construct a high‐fidelity physics‐based model using the available biological knowledge. In our recent work, we presented a rigorous kinetic model capable of effectively simulating and predicting biomass growth and lutein production under different operating conditions (del Rio‐Chanona et al, ). This model is presented in Equations )–(1f), and the parameter values and units are listed in Table .…”

Section: Methodsmentioning

confidence: 99%

“…As a result, the tubular PBR in this study has been approximated as a column with a square cross‐section, illuminated from two opposite sides of the reactor, allowing the application of the Trapezoidal rule. A more detailed explanation for model construction, simplification, and verification can be found in our previous work (del Rio‐Chanona et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have shown that lutein production is primarily affected by nitrate concentration and light intensity. However, the effects of these two factors on lutein synthesis and biomass growth are yet to be determined and inconsistent observations regarding lutein accumulation and cell growth have been presented in several publications when nitrate concentration and light intensity are fixed at different levels (del Rio‐Chanona et al, ). Therefore, it is worthy using simulation techniques to further investigate this system.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of physics‐based and data‐driven modelling techniques for dynamic optimisation of fed‐batch bioprocesses

Rio‐Chanona

Ahmed

Wagner

et al. 2019

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The development of digital bioprocessing technologies is critical to operate modern industrial bioprocesses. This study conducted the first investigation on the efficiency of using physics‐based and data‐driven models for the dynamic optimisation of long‐term bioprocess. More specifically, this study exploits a predictive kinetic model and a cutting‐edge data‐driven model to compute open‐loop optimisation strategies for the production of microalgal lutein during a fed‐batch operation. Light intensity and nitrate inflow rate are used as control variables given their key impact on biomass growth and lutein synthesis. By employing different optimisation algorithms, several optimal control sequences were computed. Due to the distinct model construction principles and sophisticated process mechanisms, the physics‐based and the data‐driven models yielded contradictory optimisation strategies. The experimental verification confirms that the data‐driven model predicted a closer result to the experiments than the physics‐based model. Both models succeeded in improving lutein intracellular content by over 40% compared to the highest previous record; however, the data‐driven model outperformed the kinetic model when optimising total lutein production and achieved an increase of 40–50%. This indicates the possible advantages of using data‐driven modelling for optimisation and prediction of complex dynamic bioprocesses, and its potential in industrial bio‐manufacturing systems.

“…As the temperature was fixed in this study,

μ_{d}

reduces to a constant. To model the effect of nitrate concentration on biomass growth, the Droop model was Equation , as it is predominantly applied under nutrient limiting conditions (Adesanya et al, ; Ahmed, Zhang, Lu, & Jing, ).

\frac{d X}{d t} = μ_{0} \cdot X - μ_{d} \cdot X

μ_{0} = μ_{m} (I) \cdot (1 - \frac{k_{q}}{q})

where

X

is biomass concentration (g L −1 ),

μ_{0}

is specific growth rate (h −1 ),

μ_{d}

is specific decay rate (h −1 ),

μ_{m} true(I true)

denotes the effect of light intensity (

I

) on biomass growth,

k_{q}

is minimum nitrogen quota (mg g −1 ), and

q

is nitrogen quota (mg g −1 ).…”

Section: Materials and Modeling Methodologymentioning

confidence: 99%

Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

Rio‐Chanona

Liu

Wagner

et al. 2017

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Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long-term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real-time optimization.