Two-phase flow modeling of solid dissolution in liquid for nutrient mixing improvement in algal raceway ponds

Achieving optimal nutrient concentrations is essential to increasing the biomass productivity of algal raceway ponds. Nutrient mixing or distribution in raceway ponds is significantly affected by hydrodynamic and geometric properties. The nutrient mixing in algal raceway ponds under the influence of hydrodynamic and geometric properties of ponds is yet to be explored. Such a study is required to ensure optimal nutrient concentrations in algal raceway ponds. A novel computational fluid dynamics (CFD) model based on the Euler–Euler numerical scheme was developed to investigate nutrient mixing in raceway ponds under the effects of hydrodynamic and geometric properties. Nutrient mixing was investigated by estimating the dissolution of nutrients in raceway pond water. Experimental and CFD results were compared and verified using solid–liquid mass transfer coefficient and nutrient concentrations. Solid–liquid mass transfer coefficient, solid holdup, and nutrient concentrations in algal pond were estimated with the effects of pond aspect ratios, water depths, paddle wheel speeds, and particle sizes of nutrients. From the results, it was found that the proposed CFD model effectively simulated nutrient mixing in raceway ponds. Nutrient mixing increased in narrow and shallow raceway ponds due to effective solid–liquid mass transfer. High paddle wheel speeds increased the dissolution rate of nutrients in raceway ponds

Photocatalytic production of bisabolene from green microalgae mutant: process analysis and kinetic modeling

Currently, algal fuel research has commenced to shift toward genetically engineered mutants able to express and excrete desired products directly into the culture. In this study, a mutant strain of Chlamydomonas reinhardtii, engineered for bisabolene (alternative biodiesel) excretion, was cultivated at different illumination and temperatures to investigate their effects on cell growth and bisabolene production. Moreover, a kinetic model was constructed to identify the desirable conditions for biofuel synthesis. Three original contributions were concluded. First, this work confirmed that bisabolene was partially synthesized independently of biomass growth, indicating its feasibility for continuous production. Second, it was found that while bisabolene synthesis was independent of light intensity, it was strongly affected by temperature, resulting in conflicting desirable conditions for cell growth and product synthesis. Finally, through model prediction, optimal operating conditions were identified for mutant growth and bisabolene synthesis. This study therefore paves the way toward chemostat production and process scale-up

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

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 modelling. 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 modelling 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 modelling and optimization

Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation

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

CFD and kinetic‐based modeling to optimize the sparger design of a large‐scale photobioreactor for scaling up of biofuel production

Microalgal biofuels have not yet achieved wide‐spread commercialization, partially as a result of the complexities involved with designing and scaling up of their biosystems. The sparger design of a pilot‐scale photobioreactor (120 L) was optimized to enable the scale‐up of biofuel production. An integrated model coupling computational fluid dynamics and microalgal biofuel synthesis kinetics was used to simulate the biomass growth and novel biofuel production (i.e., bisabolene) in the photobioreactor. Bisabolene production from Chlamydomonas reinhardtii mutant was used as an example to test the proposed model. To select the optimal sparger configuration, a rigorous procedure was followed by examining the effects of sparger design parameters (number and diameter of sparger holes and gas flow rates) on spatially averaged bubble volume fraction, light intensity, friction velocity, power input, biomass concentration, and bisabolene production. The optimized sparger design increases the final biomass concentration by 18%, thereby facilitating the scaling up of biofuel production

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

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

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

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