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The quest to reduce transport resistance in separations using nanomaterials has led to considerable interest in nanoscale adsorbents and ultrathin membranes. It is now established that interfacial resistance limits the performance of such nanosized materials; however, the origin of this resistance is uncertain. While it is associated with surface pore blockages and distortions in some materials, its existence even in ideal materials is largely putative. Here, we report equilibrium molecular dynamics (EMD) simulations with ideal zeolite-based nanosheets, indicating the transport resistance to be entirely distributed within the solid, without contribution from an interfacial effect. We demonstrate the presence of an internal entry region over which fluid decorrelation occurs, and in which the local transport coefficient inside the crystal is nonuniform and position-dependent, increasing to the uniform value in the bulk material at larger distances. Our EMD-based diffusivity profiles within the nanomaterial enable us to unequivocally determine the entry length, and reveal an internal excess resistance, frequently assumed to be an interfacial resistance, due to significant reduction of the internal transport coefficient in the entrance and exit regions. A decrease in the entry length with loading in PON zeolite nanosheets is seen. We demonstrate a reduction in external resistance in the external bulk chambers used in simulations, triggered by the interplay of incomplete decorrelation in the nanosheet and periodic boundary conditions imposed on the system comprising the nanosheet and surrounding bulk reservoirs when the nanosheet thickness is less than the entry length. Our analysis of the transport dynamics within the nanosheet demonstrates that, at least for ideal systems, decomposition of the inhomogeneous diffusivity-based internal resistance into an interfacial and a uniform transport coefficient-based internal contribution is not appropriate for finite-sized systems. Our results will enable the improved design of nanoscale membranes and materials for applications in separation and other processes.

Fluid transport in finite-sized nanoporous materials is critically affected by apparent interfacial barriers, which severely restrict efficiency improvement on reduction in system size; however, th...

Diffused aeration is a sensitive process for wastewater treatment. Because of the nonlinearity and complexity of aerator dynamics due to microorganism metabolism and oxygen transfer, reliable mathematical models are needed to perform control-oriented tasks. To this end, in this study we develop a phenomenological based semi-physical model (PBSM) to predict and describe the dynamic behavior of the oxygen transfer in a diffused aeration process by means of a formal modeling methodology. This model will then be validated by using data from an aeration pilot plant. In this paper, we also show a lack of agreement in the literature in terms of the different available ways to represent the volumetric oxygen transfer coefficient kLa. Reasonable agreement between the developed model and plant data is found by considering a phenomenological approach of the kLa instead of considering many of the available empirical correlations in the literature.

Objective: Our aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D). Methods: Structural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion. Results: A reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours. Conclusion: This study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.

Process modeling is a fundamental tool for control and estimation design. Pressure control is a basic process used for academic and even research purposes. This article addresses a modeling methodology to achieve a phenomenological based semi-physical model describing the pressure dynamic behavior in the buffer tank of a laboratory-scale plant. The used constitutive equations are based on critical flow conditions and from the representation of the inherent flow characteristics in valves. Finally, the model was validated using experimental data, showing that the model behavior is highly dependent to the pressure input.

Batch processes are inherently irreversible. In fact, design parameters and initial states can affect irreversibility and controllability of a batch process. Current design methodologies do not include controllability as a criterion for process specification. In this work, the case of Bacillus thuringiensis process is studied in batch operation, and its operational conditions are defined according to a phenomenological-based model. Regarding process design, a novel methodology is presented using set-theoretic methods and a controllability index to find the best design parameter and initial state values. Using this methodology the controllability index is doubled improving the dynamic behavior of the process.

Modeling of processes is becoming a powerful tool for understanding their dynamic behavior. In coffee drying process, the current models are based on empiric approximations and fluid dynamic studies. These last models are very computationally demanding for control and dynamic analysis purposes. The proposed model is an approximation that permits prediction, optimization and control systems design without losing rigorousness. Model is based on conservative principles and causal equations. Model validation is qualitative and this model can be used for analysis and design.