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

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

SPM performance parameters.

<p>SPM performance parameters.</p

SPM PCB layout.

<p>SPM PCB layout.</p

Voltage versus current for different configurations of the Air-Coils.

<p>Voltage versus current for different configurations of the Air-Coils.</p

SPM thermal model.

<p>SPM thermal model.</p

Switches combination for specific configuration of coils.

<p>Switches combination for specific configuration of coils.</p

Design & thermal modeling of solar panel module with embedded reconfigurable Air-Coil for micro-satellites - Fig 9

<p>(a). Current versus generated torque. (b). Voltage versus generated torque in presence of 0.5-Gauss Earth magnetic field.</p

Time vs. temperature rise of the embedded Air-Coil.

<p>Time vs. temperature rise of the embedded Air-Coil.</p

Air-Coil design.

<p>Air-Coil design.</p