Multi-state analysis functional models using Bayesian networks

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

Growth of Scenedesmus dimorphus in different algal media and pH profile due to secreted metabolites

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.Keywords: Scenedesmus dimorphus, bold basal medium, algal growthAfrican Journal of Biotechnology, Vol 13(16), 1714-172

Control and optimization of aromatic compounds in multivariable distillation column

Product separations in petroleum refineries depend significantly on distillation process, which is known to be challenging to be optimally managed, especially when multiple products with variety of purity requirements are involved due to nonlinear dynamics and high degree of process interactions. In this paper, control and optimization aspects of a multivariable distillation process are discussed. A mathematical model of the system is simulated in MATLAB programming environment, and analyses of process behavior and control performances are carried out. The controllers are tuned using conventional Ziegler-Nichols method and L-V control configuration was adopted. The results on disturbance rejection and set point tracking capabilities, in order to maintain the purity of benzene in the distillate above 98.5 % are discussed. Based on these insights, the optimum operating conditions were determined, which serves as a good starting point for further works in addressing variety of problems related to process operations