BACKGROUND: A continuous stirred tank bioreactor (CSTBR) represents an open dynamic system with high probability to show nonlinear behavior such as parametric sensitivity and multiplicity of steady state. A priori determination of this behavior is needed to decide on the strategy of reactor operation.

In this paper, the inhibitory effects of added hydrogen in reactor headspace on fermentative hydrogen production from acidogenesis of glucose by a bacterium, Clostridium acetobutylicum, was investigated experimentally in a batch reactor. It was observed that hydrogen itself became an acute inhibitor of hydrogen production if it accumulated excessively in the reactor headspace. A mathematical model to simulate and predict biological hydrogen production process was developed. The Monod model, which is a simple growth model, was modified to take inhibition kinetics on microbial growth into account. The modified model was then used to investigate the effect of hydrogen concentration on microbial growth and production rate of hydrogen. The inhibition was moderate as hydrogen concentration increased from 10% to 30% (v/v). However, a strong inhibition in microbial growth and hydrogen production rate was observed as the addition of H2 increased from 30% to 40% (v/v). Practically, an extended lag in microbial growth and considerably low hydrogen production rate were detected when 50% (v/v) of the reactor headspace was filled with hydrogen. The maximum specific growth rate (µmax), substrate saturation constant (ks), a critical hydrogen concentration at which microbial growth ceased (H2*) and degree of inhibition were found to be 0.976 h−1, 0.63 ± 0.01 gL, 24.74 mM, and 0.4786, respectively.

Microbial fuel cells (MFCs) are a kind of bioreactor for generating electricity, facilitated by exoelectrogens while treating wastewater. The present article focuses on the performance of an air cathode plexiglass MFC in terms of chemical oxygen demand (COD) removal efficiency and power output by performing two sets of experiments. The proton exchange membrane and electrode materials were Nafion 117 and carbon felts, whereas, for stable biofilm formation on the anode surface, a pure culture of Shewanella baltica 20 was used. Firstly, sterile Luria-Bertani (LB) media containing lactate, ranging from 20 to 100 mM, was continuously fed to an MFC, and a maximum power density of 55 mW/m2 was observed. Similarly, artificial wastewater with COD ranging from 3250 mg/L to 10272 mg/L was supplied to the MFC in the second set of experiments. In this case, the maximum power density and COD removal efficiency were 12 mW/m2 and 57%, respectively. In both cases, the hydraulic retention time (HRT) was 1.5 h. It was found that electricity generation depends on the characteristics of the wastewater. These initial findings confirm that the design aspects of an MFC, i.e., surface area to volume ratio, and external resistance with respect to the quality of influent need to be optimised to improve the MFC’s performance.

While designing the power requirements of a ship, the most important factor to be considered is the ship resistance, or the sea drag forces acting on the ship. It is important to have an estimate of the ship resistance while designing the propulsion system since the power required to overcome the sea drag forces contribute to 'losses' in the propulsion system. There are three main methods to calculate ship resistance: Statistical methods like the Holtrop-Mennen (HM) method, numerical analysis or CFD (Computational Fluid Dynamics) simulations, and model testing, i.e. scaled model tests in towing tanks. At the start of the design stage, when only basic ship parameters are available, only statistical models like the HM method can be used. Numerical analysis/ CFD simulations and model tests can be performed only when the complete 3D design of the ship is completed. The present paper aims at predicting the calm water ship resistance using CFD simulations, using the Flow-3D software package. A case study of a roll-on/roll-off passenger (RoPax) ferry was investigated. Ship resistance was calculated at various ship speeds. Since the mesh affects the results in any CFD simulation, multiple meshes were used to check the mesh sensitivity. The results from the simulations were compared with the estimate from the HM method. The results from simulations agreed well with the HM method for low ship speeds. The difference in the results was considerably high compared to the HM method for higher ship speeds. The capability of Flow-3D to perform ship resistance analysis was demonstrated.

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