Photocatalytic Production of Bisabolene from Green Microalgae Mutant: Process Analysis and Kinetic Modeling

Harun, Irina; Rio‐Chanona, Ehecatl Antonio del; Wagner, Jonathan L.; Lauersen, Kyle J.; Zhang, Dongda; Hellgardt, Klaus

doi:10.1021/acs.iecr.8b02509

Cited by 14 publications

(20 citation statements)

References 41 publications

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“…To illustrate the above procedure and demonstrate the efficiency and accuracy of the modeling framework, a case study is provided with the aim to investigate simultaneously the optimal configurations of a 120 L flat‐plate PBR (i.e., number and diameter of holes on the sparger) and the optimal batch operating conditions (i.e., incident light intensity and gas recycling rate) for the production of bisabolene, a novel sustainable biofuel synthesized from green microalga Chlamydomonas reinhardtii …”

Section: Methodsmentioning

confidence: 99%

“…A kinetic model presented in Eq. 3 was developed in our recent work to simulate the effects of light intensity and cultivation temperature on algal biomass growth and bisabolene production under nutrient‐sufficient conditions . Equation represents biomass growth rate, and its specific growth rate ( μ ) is a function of local light intensity and temperature formulated as Eqs.…”

Section: Methodsmentioning

confidence: 99%

“…was constructed to simulate bisabolene production by modifying the Luedeking–Piret model . The detailed explanation of this model and its parameter estimation method can be found in our recent work

\frac{dX}{dt} = μ \cdot X - μ_{d} \cdot X^{2}

μ = μ_{m} \cdot k ((), I) \cdot k ((), T)

k ((), I) = \frac{I ((), z)}{I ((), z) + k_{s} + \frac{I {(z)}^{2}}{k_{i}}}

k ((), T) = \exp ([], - ((), \frac{E_{a}}{RT} - \frac{E_{a}}{R T_{a}})) - \exp ([], - ((), \frac{E_{b}}{RT} - \frac{E_{b}}{R T_{b}}))

I ((), z) = I_{0} \cdot \exp ([], - ((), τ \cdot X + K_{a}) \cdot z)

k ((), I) = \frac{1}{20} \cdot \sum_{n = 1}^{9} ((), \frac{I_{i = 0}}{I_{i = 0} + k_{s} + \frac{I_{i = 0}^{2}}{k_{i}}} + 2 \cdot \frac{I_{i = \frac{n \cdot L}{10}}}{I_{i = \frac{n \cdot L}{10}} + k_{s} + I_{i = n...}})

…”

Section: Methodsmentioning

confidence: 99%

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Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

et al. 2018

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Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae‐derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modeling. However, this approach presents computational intractability and numerical instabilities when simulating large‐scale systems, causing time‐intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data‐driven surrogate modeling framework, which considerably reduces computing time from months to days by exploiting state‐of‐the‐art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi‐objective optimization was incorporated to generate a Pareto‐frontier for decision‐making, advancing its applications in complex biosystems modeling and optimization. © 2018 American Institute of Chemical Engineers AIChE J, 65: 915–923, 2019

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

\frac{dX}{dt} = μ \cdot X - μ_{d} \cdot X^{2}

μ = μ_{m} \cdot k ((), I) \cdot k ((), T)

k ((), I) = \frac{I ((), z)}{I ((), z) + k_{s} + \frac{I {(z)}^{2}}{k_{i}}}

k ((), T) = \exp ([], - ((), \frac{E_{a}}{RT} - \frac{E_{a}}{R T_{a}})) - \exp ([], - ((), \frac{E_{b}}{RT} - \frac{E_{b}}{R T_{b}}))

I ((), z) = I_{0} \cdot \exp ([], - ((), τ \cdot X + K_{a}) \cdot z)

k ((), I) = \frac{1}{20} \cdot \sum_{n = 1}^{9} ((), \frac{I_{i = 0}}{I_{i = 0} + k_{s} + \frac{I_{i = 0}^{2}}{k_{i}}} + 2 \cdot \frac{I_{i = \frac{n \cdot L}{10}}}{I_{i = \frac{n \cdot L}{10}} + k_{s} + I_{i = n...}})

…”

Section: Methodsmentioning

confidence: 99%

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Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

et al. 2018

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“…With the rapid development of digital computing technologies, industrially focused mathematical modeling tools have been extensively applied to chemical engineering systems for process simulation, optimization, control, and design (Marchetti, François, Faulwasser, & Bonvin, ; Voll & Marquardt, ; Zhang, del Rio‐Chanona, & Shah, ). Strong global demand for innovative biotechnologies to sustainably produce energy, food, pharmaceuticals, and platform chemicals (Harun et al, ; Jeandet, Vasserot, Chastang, & Courot, ; Jing et al, ) has opened up great opportunities for computer‐aided technologies in various bio‐production industries, for example, fermentation and photo‐production (Jing et al, ; Wagner, Lee‐Lane, Monaghan, Sharifzadeh, & Hellgardt, ). As most bioprocesses rely on microorganisms to synthesize the desired products, the simulation of the complex microbial activities has become a critical task, requiring the use of state‐of‐the‐art process systems engineering tools for bioprocess optimization and scale‐up.…”

Section: Introductionmentioning

confidence: 99%

Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization

Zhang

Rio‐Chanona

Petsagkourakis

et al. 2019

Biotech & Bioengineering

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Model‐based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics‐based and data‐driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high‐quality data by correcting raw process measurements via a physics‐based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data‐driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re‐fitting the simple kinetic model (soft sensor) using the data‐driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed‐batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open‐loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.

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“…For instance, bacteria and yeasts have been utilised extensively to yield a large variety of commercial products ranging from fuels and platform chemicals to pharmaceuticals (Bankar, Dhumal, Bhotmange, Bhagwat, & Singhal, ; Jing et al, ). Recently, attention has increasingly shifted towards microalgae and cyanobacteria to produce renewable biofuels and sustainable high‐value products (Harun et al, ; Zhang & Vassiliadis, ). These approaches provide a competitive alternative to traditional synthetic routes and have considerable demand in the global market.…”

Section: Introductionmentioning

confidence: 99%

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

Rio‐Chanona

Ahmed

Wagner

et al. 2019

Biotech & Bioengineering

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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.

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Photocatalytic Production of Bisabolene from Green Microalgae Mutant: Process Analysis and Kinetic Modeling

Cited by 14 publications

References 41 publications

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization

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

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