• Energy Research

Abstract In the tubular reactor models the mass and heat source terms due to chemical reactions in the species mass balances and temperature equation are conventionally determined from kinetic rate expressions. In many cases a kinetic rate model is not available but the chemical equilibrium conversion can be determined from reaction equilibrium calculations minimizing the Gibbs or Helmholtz free energies. Although a process is believed to behave physically like a tubular reactor, previous feasibility and design studies have typically disregarded fluid flow and mass- and heat transfer limitations and performed a classical chemical equilibrium calculation. For non-adiabatic cases, valuable information on the heat transfer flux limitations of the chemical process at the wall (often provided by a specified axial heating/cooling media temperature profile) is lost when simplifying a model representing the physical tubular reactor process behavior by considering a classical thermodynamic system having uniform state properties. For this reason, in the present study, a new type of tubular reactor model, which we name the differential Gibbs (or Helmholtz) reactor model, is presented to improve on the conventional feasibility and design model. In the differential Gibbs (or Helmholtz) reactor model, the chemical conversion and the reaction heat are determined assuming chemical reaction equilibrium conditions along the axial flow direction by minimizing the Gibbs (or Helmholtz) free energy. The new model is verified through comparison with the conventional differential tubular model using the fast reaction kinetics of the steam–methane reforming process and neglected mass diffusion limitation of the catalyst.

AbstractComputational demanding two‐ and three‐dimensional two‐fluid models are frequently adopted simulating gas–solid flows in fluidised beds. Reduced computational cost is favourable for efficient numerical studies of technologies such as the novel chemical looping combustion (CLC), chemical looping reforming (CLR), and sorption‐enhanced steam methane reforming (SE‐SMR) processes. In this study, we elucidate the potential of a one‐dimensional two‐fluid model to describe gas–solid cold‐flows in fluidised beds. The validity of the numerical simulation results of the bubbling beds and risers have been compared to experimental data in the literature. Moreover, sensitivity analyses on drag closure laws and operation condition have been performed and a number of model solution techniques and algorithms are studied. In addition, simulation results of the one‐dimensional model are compared to results of a two‐dimensional model.For particular sets of operating conditions and flow characteristics, the one‐dimensional model compares fairly well to the simulation results of the two‐dimensional model and to experimental data. Under other operating conditions, large quantitative deviations can be observed. However, the one‐dimensional model is assumed to be sufficently accurate for particular reactor process optimisation and design evaluations.

AbstractMicroalgal 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.