Faisal Khan

TL;DR: In this article, a nonlinear Gaussian belief network (NLGBN) based fault diagnosis technique is proposed for industrial processes, where the nonlinear relationships between the process variables and the latent variables are modelled by a set of sigmoidal functions.

TL;DR: A fugacity approach is adopted to model the fate and transport of released oil, taking into account the uncertainty of input variables, which assists in predicting the 95th percentile Predicted Exposure concentration of pollutants in different media.

TL;DR: The innovation of the proposed CBN model is in integrating the advantage of copula functions in modelling complex dependence structures with the cause-effect relationship reasoning of process variables using BNs, offering a great flexibility in probabilistic analysis of individual risk factors while considering their uncertainty and stochastic dependence.

TL;DR: In this article, a methodology for process design that considers the assessment and minimisation of the environmental impacts of the complete process system (including upstream processes) is proposed. But, it is only applied to the early design and decision-making stages and does not consider environmental soundness as one of the important parameters.

TL;DR: A fresh look at definitions of resilience and its measurement is taken and a new set of metrics based on the concept of reliability and maintainability combined with the system modeling approach is offered.