319 research outputs found
Entropy Generation Minimization for Energy-Efficient Desalination
Desalination systems can be conceptualized as power cycles, in which the useful work output is the work of separation of fresh water from saline water. In this framing, thermodynamic analysis provides powerful tools for raising energy efficiency. This paper discusses the use of entropy generation minimization for a spectrum of desalination systems, including those based on reverse osmosis, humidification-dehumidification, membrane distillation, electrodialysis, and forward osmosis. The energy efficiency of desalination is shown to be maximized when entropy generation is minimized. Equipartition of entropy generation is considered and applied to these systems. The mechanisms of entropy generation in these systems are characterized, including the identification of major causes of irreversibility. Methods to limit discarded exergy are also identified. Prospects and technology development needs for further improvement are mentioned briefly
Energy efficiency, primary energy, and apples vs. oranges
Energy is a major cost when desalinating seawater, and plant designers strive to reduce that cost. When water and power are coproduced, the energy cost is the additional fuel (primary energy) added to the power plant to drive the desalination plant. But comparing the energy for coproduction to the energy for a stand-alone plant brings complications
Design of Plate-Fin Tube Dehumidifiers for Humidification-Dehumidification Desalination Systems
A two-dimensional numerical model of a plate-fin tube heat exchanger for use as a dehumidifier in a humidification-dehumidification (HDH) desalination systems is developed, because typical heating, ventilating, and air conditioning (HVAC) dehumidifier models and plate-fin tube dehumidifier geometries are not intended for the considerably higher temperature and humidity ratio differences which drive heat and mass transfer in HDH desalination applications. The experimentally validated model is used to investigate the influence of various heat exchanger design parameters. Potential improvements on common plate-fin tube dehumidifier designs are identified by examining various methods of optimizing tube diameter, and longitudinal and transverse tube spacing to achieve maximum heat flow for a given quantity of fin material at a typical HDH operating point. Thicker fins are recommended than for HVAC geometries, as the thermal conductive resistance of HVAC fins restricts dehumidifier performance under HDH operating conditions.Elisabeth Meurer FoundationKarl H. Ditze FoundationGerman Academic Exchange ServiceCenter for Clean Water and Clean Energy at MIT and KFUP
Design of Flat-Plate Dehumidifiers for Humidification–Dehumidification Desalination Systems
Flat-plate heat exchangers are examined for use as dehumidifiers in humidification–dehumidification (HDH) desalination systems. The temperature and humidity ratio differences that drive mass transfer are considerably higher than in air-conditioning systems, making current air-conditioning dehumidifier designs and design software ill-suited to HDH desalination applications. In this work a numerical dehumidifier model is developed and validated against experimental data. The model uses a logarithmic mass transfer driving force and an accurate Lewis number. The heat exchanger is subdivided into many cells for high accuracy. The Ackermann correction takes into account the effect of noncondensable gases on heat transfer during condensation. The influence of various heat exchanger design parameters is thoroughly investigated and suitable geometries are identified. Among others, the relationship between heat flow, pressure drop, and heat transfer area is shown. The thermal resistance of the condensate layer is negligible for the investigated geometries and operating point. A particle-embedded polymer as a flat-plate heat exchanger material for seawater operation substantially improves the heat flux relative to pure polymers and approaches the performance of titanium alloys. Thus, the use of particle-embedded polymers is recommended. The dehumidifier model can be applied in design and optimization of HDH desalination systems.Center for Clean Water and Clean Energy at MIT and KFUP
Physics-constrained neural differential equations for learning multi-ionic transport
Continuum models for ion transport through polyamide nanopores require
solving partial differential equations (PDEs) through complex pore geometries.
Resolving spatiotemporal features at this length and time-scale can make
solving these equations computationally intractable. In addition, mechanistic
models frequently require functional relationships between ion interaction
parameters under nano-confinement, which are often too challenging to measure
experimentally or know a priori. In this work, we develop the first
physics-informed deep learning model to learn ion transport behaviour across
polyamide nanopores. The proposed architecture leverages neural differential
equations in conjunction with classical closure models as inductive biases
directly encoded into the neural framework. The neural differential equations
are pre-trained on simulated data from continuum models and fine-tuned on
independent experimental data to learn ion rejection behaviour. Gaussian noise
augmentations from experimental uncertainty estimates are also introduced into
the measured data to improve model generalization. Our approach is compared to
other physics-informed deep learning models and shows strong agreement with
experimental measurements across all studied datasets.Comment: 11 page
Attention-enhanced neural differential equations for physics-informed deep learning of ion transport
Species transport models typically combine partial differential equations
(PDEs) with relations from hindered transport theory to quantify
electromigrative, convective, and diffusive transport through complex
nanoporous systems; however, these formulations are frequently substantial
simplifications of the governing dynamics, leading to the poor generalization
performance of PDE-based models. Given the growing interest in deep learning
methods for the physical sciences, we develop a machine learning-based approach
to characterize ion transport across nanoporous membranes. Our proposed
framework centers around attention-enhanced neural differential equations that
incorporate electroneutrality-based inductive biases to improve generalization
performance relative to conventional PDE-based methods. In addition, we study
the role of the attention mechanism in illuminating physically-meaningful
ion-pairing relationships across diverse mixture compositions. Further, we
investigate the importance of pre-training on simulated data from PDE-based
models, as well as the performance benefits from hard vs. soft inductive
biases. Our results indicate that physics-informed deep learning solutions can
outperform their classical PDE-based counterparts and provide promising avenues
for modelling complex transport phenomena across diverse applications.Comment: 8 pages, 2 figures. Accepted in the NeurIPS Machine Learning and the
Physical Sciences Worksho
Entropy generation in condensation in the presence of high concentrations of noncondensable gases
The physical mechanisms of entropy generation in a condenser with high fractions of noncondensable gases are examined using scaling and boundary layer techniques, with the aim of defining a criterion for minimum entropy generation rate that is useful in engineering analyses. This process is particularly relevant in humidification-dehumidification desalination systems, where minimizing entropy generation per unit water produced is critical to maximizing system performance. The process is modeled by a consideration of the vapor/gas boundary layer alone, as it is the dominant thermal resistance and, consequently, the largest source of entropy production in many practical condensers with high fractions of noncondensable gases. Most previous studies of condensation have been restricted to a constant wall temperature, but it is shown here that for high concentrations of noncondensable gases, a varying wall temperature greatly reduces total entropy generation rate. Further, it is found that the diffusion of the condensing vapor through the vapor/noncondensable mixture boundary layer is the larger and often dominant mechanism of entropy production in such a condenser. As a result, when seeking to design a unit of desired heat transfer and condensation rates for minimum entropy generation, minimizing the variance in the driving force associated with diffusion yields a closer approximation to the minimum overall entropy generation rate than does equipartition of temperature difference.Center for Clean Water and Clean Energy at MIT and KFUPM (Project R4-CW-08)Eni S.p.A. (Firm) (Eni-MIT Energy Fellowship
Treating produced water from hydraulic fracturing: Composition effects on scale formation and desalination system selection
Produced water from unconventional gas and oil extraction may be hypersaline with uncommon combinations of dissolved ions. The aim of this analysis is to aid in the selection of produced water treatment technology by identifying the temperature, pH, and recovery ratio under which mineral solid formation from these produced waters is likely to occur. Eight samples of produced water from the Permian Basin and the Marcellus shale are discussed, with an average TDS of about 177 g/L but significant variability. Crystallization potential is quantified by the saturation index, and activity coefficients are calculated using the Pitzer model. The method is applied to estimate solid formation in the treatment of two design case samples: a 183 g/L sample representing the Permian Basin water and a 145 g/L sample representing the Marcellus. Without pretreatment, the most likely solids to form, defined by highest saturation index, are: CaCO[subscript 3], FeCO[subscript 3], MgCO[subscript 3], MnCO[subscript 3], SrCO[subscript 3], BaSO[subscript 4], CaSO[subscript 4], MgSO[subscript 4] and SrSO[subscript 4]. Some options for mitigating the formation of these scales are discussed. With appropriate pretreatment, it is estimated that recovery ratios of as high as 40–50% are achievable before NaCl, a major constituent, is likely to limit further concentration without significant crystallization.Center for Clean Water and Clean Energy at MIT and KFUPM (Project R4-CW-08)MIT Energy InitiativeMIT Martin Family Society of Fellows for Sustainabilit
Thermodynamic Analysis of a Reverse Osmosis Desalination System Using Forward Osmosis for Energy Recovery
Thermodynamic analysis is applied to assess the energy efficiency of hybrid desalination cycles that are driven by simultaneous mixed inputs, including heat, electrical work, and chemical energy. A seawater desalination cycle using work and a chemical input stream is analyzed using seawater properties. Two system models, a reversible separator and an irreversible component based model, are developed to find the least work required to operate the system with and without osmotic recovery. The component based model represents a proposed desalination system which uses a reverse osmosis membrane for solute separation, a pressure exchanger for recovering a fraction of the flow work associated with the pressurized discharge brine, and a forward osmosis (FO) module for recovering some of the chemical energy contained within the concentrated discharge brine. The energy attained by the addition of the chemical input stream serves to lower the amount of electrical work required for operation. For this analysis, a wastewater stream of varying solute concentration, ranging from feed to brackish water salinity, is considered as the chemical stream. Unlike other models available in the literature, the FO exchanger is numerically simulated as a mass exchanger of given size which accounts for changing stream concentration, and consequently, stream-wise variations of osmotic pressure throughout the length of the unit. A parametric study is performed on the models by varying input conditions. For the reversible case it is found that significant work reductions can be made through the use of an energy recovery device when the inlet wastewater salinity used is less than the feed salinity of 35 g/kg. For the irreversible case with a typical recovery ratio and feed salinity, significant work reductions were only noted for a wastewater inlet of less than half of the feed salinity due to pump work losses. In the irreversible case, the use of a numerical model to simulate the FO exchanger resulted in a maximum work reduction when the pressure difference between streams was around one half of the osmotic pressure difference as opposed to the precise value of one half found in zero-dimensional exchanger models.Center for Clean Water and Clean Energy at MIT and KFUPM (Project R4-CW-08
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