Molecular dynamics simulations are used to study the occupancy and flow of water through nanotubes comprised of hydrophobic and hydrophilic atoms, which are arranged on a honeycomb lattice to mimic functionalized carbon nanotubes (CNTs). We consider single-file motion of TIP3P water through narrow channels of (6,6) CNTs with varying fractions (f) of hydrophilic atoms. Various arrangements of hydrophilic atoms are used to create heterogeneous nanotubes with separate hydrophobic/hydrophilic domains along the tube as well as random mixtures of the two types of atoms. The water occupancy inside the nanotube channel is found to vary nonlinearly as a function of f, and a small fraction of hydrophilic atoms (f ≈ 0.4) are sufficient to induce spontaneous and continuous filling of the nanotube. Interestingly, the average number of water molecules inside the channel and water flux through the nanotube are less sensitive to the specific arrangement of hydrophilic atoms than to the fraction, f. Two different regimes are observed for the water flux dependence on f - an approximately linear increase in flux as a function of f for f < 0.4, and almost no change in flux for higher f values, similar to the change in water occupancy. We are able to define an effective interaction strength between nanotube atoms and water's oxygen, based on a linear combination of interaction strengths between hydrophobic and hydrophilic nanotube atoms and water, that can quantitatively capture the observed behavior.

This paper describes how dynamic simulations of a manufacturing process can be used to construct informed prior distributions for the failure probabilities of alarm and safety interlock systems. Bayesian analysis is used starting with prior distributions and enhancing them with likelihood distributions, constructed from real-time alarm data, to form posterior distributions, which are used to estimate failure probabilities. The use of alarm data to build likelihood distributions has previously been investigated [2,11] . Rareevent historical data are typically sparse and have high-variance likelihood distributions.When high-variance likelihood distributions are combined with typical high-variance prior distributions, the resulting posterior distributions naturally have high variances preventing reliable failure predictions. In contrast with prior distributions obtained by maximizing entropy [19] and those that are based on expert knowledge [2] , this paper introduces a new repeated-simulation method to construct informed prior distributions having smaller variances, which in turn leads to posterior distributions with lower variances and a more reliable prediction of the failure probabilities of alarm and safety interlock systems. The application of the proposed method is demonstrated for offline dynamic risk analysis of a steam-methane reformer (SMR) process.

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Dynamic risk analysis (DRA) has been used widely to analyze the performance of alarm and safety interlock systems of manufacturing processes. Because the most critical alarm and safety interlock systems are rarely activated, little or no data from these systems are often available to apply purely‐statistical DRA methods. Moskowitz et al. (2015)1 introduced a repeated‐simulation, process‐model‐based technique for constructing informed prior distributions, generating low‐variance posterior distributions for Bayesian analysis,1 and making alarm‐performance predictions. This article presents a method of quantifying process model quality, which impacts prior and posterior distributions used in Bayesian Analysis. The method uses higher‐frequency alarm and process data to select the most relevant constitutive equations and assumptions. New data‐based probabilistic models that describe important special‐cause event occurrences and operators’ response‐times are proposed and validated with industrial plant data. These models can be used to improve estimates of failure probabilities for alarm and safety interlock systems. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3461–3472, 2016