Membrane fouling significantly hinders the widespread application of membrane technology. In the current study, a support vector machine (SVM) and artificial neural networks (ANN) modelling approach was adopted to optimize the membrane permeability in a novel membrane rotating biological contactor (MRBC). The MRBC utilizes the disk rotation mechanism to generate a shear rate at the membrane surface to scour off the foulants. The effect of operational parameters (disk rotational speed, hydraulic retention time (HRT), and sludge retention time (SRT)) was studied on the membrane permeability. ANN and SVM are machine learning algorithms that aim to predict the model based on the trained data sets. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. Feed-forward ANN with the back-propagation algorithm and SVN regression models for various kernel functions were trained to augment the membrane permeability. An overall comparison of predictive models for the test data sets reveals the model’s significance. ANN modelling with 13 hidden layers gives the highest R2 value of >0.99, and the SVM model with the Bayesian optimizer approach results in R2 values higher than 0.99. The MRBC is a promising substitute for traditional suspended growth processes, which aligns with the stipulations of ecological evolution and environmentally friendly treatment.
Fire-induced domino accidents are less probable but highly consequent. Although past studies conducted risk assessment of such events, classical models were used for impact estimation. Moreover the influence of varying weather on evolution of fire domino effect was not investigated, where past research necessitates the use of computational fluid dynamics to perform such analysis. This paper adopts a consolidated risk analysis approach to perform consequence modeling using fire dynamics simulator. Various atmospheric conditions were incorporated in the model. The outcomes were used in probabilistic analysis to estimate the escalation probabilities. Risk of domino events was presented as domino levels. It was found that 37% reduction in humidity ratio resulted in 10% decrease in tanks failure time. At 3 and 6 m/s winds, the failure time of tank in flame direction reduced by 56% and 80%, whereas the escalation probability increased by 3 and 4 orders respectively. The tank farm failed in 11.3 and 12.85 min at 3 and 6 m/s respectively, which is less than the suggested mitigation time.
Pool fires cause immense damage to fuel storage tank farms. Reduced fire escalation risk in tank farms improves fire safety. Computational fluid dynamics (CFD) has proven effective in assessing escalation of fire-related domino effects and is being utilized for pool fire consequences in tank farms. The past CFD-based analysis focused on primary fire effects on secondary targets. This study used fire dynamics simulator (FDS) to model complete evolution of the domino effect under different wind speeds and primary pool fire locations. Dynamic escalation probability (DEP) and fire spread probability of the tank farm were calculated. Offset tank failure increased by 3% and 31%, while inline tank failure dropped by 36% and 90%, at 2 and 8 m/s, respectively. An artificial neural network (ANN) incorporating the Levenberg–Marquardt algorithm is used to predict fire spread probability based on numerical data set. The use of ANNs for this purpose is one of the first attempts in this regard. ANNs can reliably predict dynamic fire spread probability and could be utilized to manage fire-induced domino effects. Moreover, dynamic fire spread probability in tank farms obtained from ANN modelling can be used for safety applications, such as updating mitigation time when fire spread probability is unacceptable for a specific wind speed.
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