•We propose a model for direct contact membrane distillation that accounts for fouling.•We design an adaptive observer for descriptor systems.•We use the adaptive observer for the estimation of the ...thermal resistance of the fouling.•Simulations are presented to show the effectiveness of the approach.
This paper develops a lumped parameter model-based method for membrane fouling detection in Direct Contact Membrane Distillation (DCMD) process. First, a previously published mathematical model of DCMD is extended to account for the thermal resistance of the fouling layer. Then, an adaptive nonlinear descriptor observer is developed. The adaptive observer estimates the thermal resistance of the fouling layer in addition to the temporal and spatial temperature distribution of the bulk feed and permeate solutions and at membrane interface layers. Simulation results are presented to illustrate the performance of the proposed method.
Membrane distillation (MD) is an emerging technology that has a great potential for sustainable water desalination. In order to pave the way for successful commercialization of MD-based water ...desalination techniques, adequate and accurate dynamical models of the process are essential. This paper presents the predictive capabilities of a lumped-parameter dynamic model for direct contact membrane distillation (DCMD) and discusses the results under wide range of steady-state and dynamic conditions. Unlike previous studies, the proposed model captures the time response of the spacial temperature distribution along the flow direction. It also directly solves for the local temperatures at the membrane interfaces, which allows to accurately model and calculate local flux values along with other intrinsic variables of great influence on the process, like the temperature polarization coefficient (TPC). The proposed model is based on energy and mass conservation principles and analogy between thermal and electrical systems. Experimental data was collected to validated the steady-state and dynamic responses of the model. The obtained results shows great agreement with the experimental data. The paper discusses the results of several simulations under various conditions to optimize the DCMD process efficiency and analyze its response. This demonstrates some potential applications of the proposed model to carry out scale up and design studies.
•We present a reduced order dynamic predictive model for direct contact membrane distillation process.•Both time varying and steady-state responses are well captured and have been validated with experimental data.•Analysis shows the response of several intrinsic variables under various conditions.
•We propose a novel dynamic model for direct contact membrane distillation process.•The model is a reduced version of a distributed system.•It is based on the analogy between electrical and thermal ...systems.•The model has been validated with experimental data.•The model is written in the form of differential algebraic system.
Membrane distillation (MD) is an emerging water desalination technology that offers several advantages compared to conventional desalination methods. Although progress has been made to model the physics of the process, there are two common limitations of existing models. Firstly, many of the models are based on the steady-state analysis of the process and secondly, some of the models are based on partial differential equations, which when discretized introduce many states which are not accessible in practice. This paper presents the derivation of a novel dynamic model, based on the analogy between electrical and thermal systems, for direct contact membrane distillation (DCMD). An analogous electrical thermal network is constructed and its elements are parameterized such that the response of the network models the DCMD process. The proposed model captures the spatial and temporal responses of the temperature distribution along the flow direction and is able to accurately predict the distilled water flux output. To demonstrate the adequacy of the proposed model, validation with time varying and steady-state experimental data is presented.
The present work investigates the potential role of metformin nanoparticles (MTF-NPs) as a radio-protector against cardiac fibrosis and inflammation induced by gamma radiation via CXCL1/TGF-β ...pathway. Lethal dose fifty of nano-metformin was determined in mice, then 21 rats (male albino) were equally divided into three groups: normal control (G1), irradiated control (G2), and MTF-NPs + IRR (G3). The possible protective effect of MTF-NPs is illustrated via decreasing cardiac contents of troponin, C-X-C motif Ligand 1 (CXCL1), tumor growth factor β (TGF-β), protein kinase B (AKT), and nuclear factor-κB (NF-κB). Also, the positive effect of MTF-NPs on insulin-like growth factor (IGF) and platelet-derived growth factor (PDGF) in heart tissues using immunohistochemical technique is illustrated in the present study. Histopathological examination emphasizes the biochemical findings. The current investigation suggests that MTF-NPs might be considered as a potent novel treatment for the management of cardiac fibrosis and inflammation in patients who receive radiotherapy or workers who may be exposed to gamma radiation.
Magnetic resonance imaging (MRI) has been considered for the quantification of iron overload in the liver. Iron overload was found to correlate with T2* measurement using T2* weighted images. In this ...work, we address the problem of iron overload estimation in the liver using MRI. We propose a general framework for all liver models proposed in the literature. The iron overload estimation task is then formulated as a minimization problem, and suitable regularization functions are assigned to the unknown model parameters. Subsequently, an alternating direction method of multipliers (ADMM) is used to estimate these unknown parameters. Three different models are derived, tested and compared; namely the single exponential (SEXP), the bi-exponential (BiEXP), and the exponential plus constant (CEXP). Simulations conducted using synthetic datasets indicate good correlation between estimated and ground truth T2* for all models. Moreover, the algorithms are evaluated using MRI scans of nine patients of different iron concentrations, using a 3-Tesla MRI scanner. The estimated T2* values of the proposed approaches are found to correlate with those obtained by the MRI scanner console. Moreover, the proposed approaches outperform several existing methods in the literature for iron overload estimation.
This paper presents a real time optimization scheme for a solar powered direct contact membrane distillation (DCMD) water desalination system. The sun and weather conditions vary and are inconsistent ...throughout the day. Therefore, the solar powered DCMD feed inlet temperature is never constant, which influences the distilled water flux. The problem of DCMD process optimization has not been studied enough. In this work, the response of the process under various feed inlet temperatures is investigated, which demonstrates the need for an optimal controller. To address this issue, we propose a multivariable Newton-based extremum seeking controller which optimizes the inlet feed and permeate mass flow rates as the feed inlet temperature varies. Results are presented and discussed for a realistic temperature profile.
•We implemented NARX networks to estimate the hemodynamic states.•The method has been used to estimate the neural activity.•We optimized the structure of the NARX networks.•Blocked and event-related ...BOLD real data were used.•The method is accurate and robust even in the presence of signal noise.
Originally inspired by biological neural networks, artificial neural networks (ANNs) are powerful mathematical tools that can solve complex nonlinear problems such as filtering, classification, prediction and more. This paper demonstrates the first successful implementation of ANN, specifically nonlinear autoregressive with exogenous input (NARX) networks, to estimate the hemodynamic states and neural activity from simulated and measured real blood oxygenation level dependent (BOLD) signals. Blocked and event-related BOLD data are used to test the algorithm on real experiments. The proposed method is accurate and robust even in the presence of signal noise and it does not depend on sampling interval. Moreover, the structure of the NARX networks is optimized to yield the best estimate with minimal network architecture. The results of the estimated neural activity are also discussed in terms of their potential use.
Diaphragmatic hernias are well-known sequelae of abdominal and chest wall trauma. However, they may go undiagnosed in the acute setting but present later due to gastrointestinal or respiratory ...complications. A distinctive presentation of a diaphragmatic hernia 15 years after a traumatic insult is herein described. Management strategies are also discussed.
Membrane distillation (MD) is a thermally driven process where only water vapor is passed through a hydrophobic membrane. Several models have been proposed to study this process, yet most of them ...assume steady-state conditions. This work presents a new approach to model Direct Contact Membrane Distillation (DCMD) dynamics based on the analogy between electrical and thermal systems. A lumped-capacitance dynamical model accounting for mass, energy, and momentum balance was derived and simulated in Simulink. First, the model was validated by considering three measures: the temperature distribution along the flow direction, the effect of feed linear velocity on the output temperatures, and total mass flux. The simulation results were compared with experimental data reported in the literature and showed very close match to the experimental measurements.
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