No abstract

In this study investigation was made to evaluate the effects of different algal media components to get optimized cell count of Scenedesmus dimorphus. Five different fresh water algal media such as Bold's Basal Medium (BBM), M4N medium, BG-11 medium, N-8 medium and M-8 medium were used for culturing S. dimorphus in flask culture. A set of environmental factors including light, temperature, air flow rate and nutritional components was standardized to obtain the highest productivity of 0.1406 g/L with specific growth rate of 0.10483/day. This study designates the bold basal medium as advantageous one for S. dimorphus and also reveals that production of metabolites by the same algal strain depends mostly on the nature of constituents of media and might have different influence on the pH.

Torrefaction of pelletised oil palm empty fruit bunches (OPEFBs) is a promising pretreatment technique for improving its solid biofuel properties and energy recovery potential. Therefore, this paper investigates the torrefaction of OPEFB pellets to examine the effects of temperature and purge gas flow rate on mass yield (MY), energy yield (EY), and mass loss (ML). The results revealed that MY and EY decreased due to significant ML during torrefaction. Furthermore, significant improvements in the higher heating value (HHV) and energy density (DE) were observed. The torrefaction temperature increased liquid (tar) and gas yields mainly above 300 °C at the expense of solid products. However, the effect of purge gas flow rate on the torrefaction products was found to be negligible. Consequently, the torrefaction of OPEFB pellets were limited to 250-300 °C, 30 min, and nitrogen (N2) gas flow rate of 200 ml min-1.

Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method.

No abstract