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At least 181 records · Page 10

Prevention and management of silica scaling in membrane distillation using pH adjustment

Membrane scaling by silica is a major challenge in desalination, particularly for inland desalination of brackish groundwater or geothermal resources, which often contain high concentrations of silica and dissolved solids. Adjustment of feed pH may reduce silica scaling risk, which is important for inland facilities that operate at high water recoveries to reduce brine disposal costs. However, water recovery of reverse osmosis is also limited due to increased osmotic pressure with feed water concentration. Membrane distillation (MD) is a thermally driven membrane desalination technique that is not limited by increased osmotic pressure of the feed. In this investigation, pH adjustment was tested as a strategy to reduce silica scaling risk in the MD process. With feed water pH less than 5 or higher than 10, scaling impacts were negligible at silica concentrations up to 600 mg/L. Scaling rates were highest at neutral pH between 6 and 8. Cleaning strategies were also explored to remove silica scale from membranes. Cleaning using NaOH solutions at pH higher than 11 to induce dissolution of silica scale was effective at temporarily restoring performance; however, some silica remained on membrane surfaces and scaling upon re-exposure to supersaturated silica concentrations occurred faster than with new membranes.

15 GEOTHERMAL ENERGY↗

Cost optimization of high recovery single stage gap membrane distillation

Estimating the cost of membrane distillation (MD) processes at scale is confounded by the vast array of possible process configurations, module configurations, and operating conditions and the extreme sensitivity of MD costs to these parameters. This work develops a comprehensive MD cost optimization framework for minimizing the levelized cost of water (LCOW) as a function of feed salinity and temperature. Our single stage gap MD model fully captures solution properties, heat transfer, and mass transfer relationships in the modules to determine the cost optimal design as a function of multiple decision variables, including process configuration (feed location and connectivity between the hot and cold-side), the gap type (air, permeate, conductive), equipment sizes (membrane, heater, chiller, and heat exchanger units), and operating conditions (flow rates, hot and cold side temperatures, and heating and cooling duties). We estimate that the minimum LCOW of conductive gap MD ranges between $\$$10 and $\$$16 per m 3 for feed salinities of 25 g/L to 200 g/L and water recoveries of 30%–75%. Further, we update several heuristic assumptions for cost optimal system operation, including: increased thermal conductivity of the gap decreases system costs; cost optimal MD operates at the highest allowable temperature; and the LCOW is a strong function of brine salinity. Finally, we find that improving membrane properties and lowering heating and cooling costs will provide only modest reductions in costs (<25%).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Life-cycle energy use and greenhouse gas emissions of palm fatty acid distillate derived renewable diesel

This study aims to quantify life-cycle fossil energy use and greenhouse gas (GHG) emissions for palm fatty acid distillate (PFAD) derived renewable diesel (RD) taking into consideration different feedstock classifications that are applicable to PFAD (residue, byproduct, or coproduct), and incorporating updated data for key processes. Under the three classifications, the PFAD to RD pathway was modeled using the Greenhouse gases, Regulated Emissions, and Energy Use in Technologies (GREET®) model. PFAD-derived RD could reduce fossil energy consumption by 77%–88%, relative to petroleum diesel. GHG emissions are very sensitive to PFAD classification and coproduct handling methods. Considering the production of palm oil and PFAD and economic value, we maintain that PFAD should be treated as a byproduct in palm oil refineries. With this treatment, PFAD-derived RD could achieve 84% GHG emissions reductions, compared to the emissions of petroleum diesel. We also employed a substitution method to address the substitution of PFAD by other materials in the marketplace. Compared to coproduct allocation results, we found substituting PFAD by tallow, soy oil, barley, and canola oil results in lower GHG emissions. Due to high induced land-use change emissions associated with palm farming, if PFAD is treated as a coproduct with refined palm oil, PFAD-derived RD may not deliver GHG reductions. A sensitivity analysis identified key parameters such as palm fruit yield, oil extraction efficiency in oil mills, and energy use intensity for RD production affects LCA results significantly; future efforts to improve these parameters could result in further GHG reductions.

09 BIOMASS FUELS↗

Molecular thermodynamics for scaling prediction: Case of membrane distillation

Membrane distillation (MD) for water treatment is significantly impaired by the scaling of dissolved minerals. The type and content of minerals generally measured as total dissolved solids (TDS) in hypersaline brines not only reduce the MD flux but also control the scaling behavior on the membrane surface. The scaling-induced pore blockage further reduces water flux and eventually leads to membrane wetting. The scaling problem is even more pronounced in the treatment of produced water (PW) as it contains 3–7 times higher TDS concentrations, compared to seawater. Theoretically, the necessary conditions for a salt to precipitate can be traced from its solubility product constant and activity of the constituents within the solution. Therefore, a comprehensive thermodynamic model is necessary to represent the electrolyte behavior and to predict the precipitation of different salts in a complex solution like PW. We pursued electrolyte Nonrandom Two Liquid Theory (eNRTL), a state-of-the-art electrolyte model, to address the PW fluid phase equilibria. With a fully parameterized eNRTL model, we predicted salt precipitations in two different PW samples and compared the results against the experimental findings. Furthermore, here we forecast the precipitation of salts in response to the change in PW concentration and temperature in the MD operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Energy and exergy analysis of multi-stage vacuum membrane distillation integrated with mechanical vapor compression

Membrane distillation (MD) is a promising candidate for desalinating hypersaline brine, but its poor energy efficiency has remained a major barrier for widespread application. One possible solution to this issue is to recover the latent heat in the process. In this work, a multi-stage vacuum MD (MSVMD) was integrated with a mechanical vapor compressor (MVC) to enhance the latent heat recovery, and the energetic and exergetic performance of this integrated process was examined. A comprehensive energy and exergy analysis is provided to compare MSVMD and MSVMD-MVC processes for desalination of hypersaline brine. This analysis was conducted by examining the effect of the compression ratio on the energetic and exergetic performance, and the findings are reported in terms of specific thermal energy consumption (STEC), specific electricity consumption (SEC), and exergetic efficiency. The energy analysis shows that thermal energy consumption can be reduced as the compression ratio increases, due to the enhancement of latent heat recovery. The MSVMD-MVC process can be operated in a steady-state condition, without the need for thermal heat input; with STEC and SEC of 0 and 49 kWh/m 3 at the feed temperature of 50 °C and MVC compression ratio of 2.14. Moreover, exergy analysis demonstrates the efficacy of the eNRTL model in exergy calculation. Exergy destruction can be greatly reduced by increasing the compression ratio to an optimal value. For high salinity brine (124 g/L), MSVMD-MVC achieved a higher exergetic efficiency of 6.85%, compared to 2.42% in MSVMD. Furthermore, the result suggests that the application of MVC can intensify the energy efficiency and exergetic efficiency of the MSVMD system, although this process cannot outperform the current desalination technologies from the standard primary energy point of view.

42 ENGINEERING↗

Understanding flow dynamics in membrane distillation: Effects of reactor design on polarization

Optimization and design of full-scale membrane distillation (MD) systems usually require Sherwood and Nusselt correlations that are developed from lab-scale systems. However, entrance effects in lab-scale systems can significantly impact heat, mass and momentum transfer in the reactor, therefore affect the accuracy of the developed experimental Sherwood and Nusselt correlations. Here, Computational Fluid Dynamics (CFD) simulations using OpenFOAM are performed to understand the effects of right-angled bends and inlet design on flow dynamics, temperature and concentration polarization in MD systems. Simulation results show that the presence of right-angled bends and inlets with sudden expansions lead to the formation of Dean vortices. Dean vortices enhance perpendicular mixing in MD systems and reduce both temperature and concentration polarization. Temperature and concentration polarization coefficients in MD systems with right-angled bends and inlets with sudden expansions vary significantly for the same volumetric flow rate. Our studies show that lab-scale systems with the same volumetric flow rate but different designs lead to significantly different Nusselt and Sherwood correlations. Here, this study demonstrates the importance of CFD-informed design of lab-scale systems to minimize entrance effects and suppress Dean vortices for consistent model development and calibration across multiple scales.

42 ENGINEERING↗

Dynamic Process Intensification via Data-Driven Dynamic Optimization: Concept and Application to Ternary Distillation

Process intensification is a design philosophy aimed at making chemical processes safer and more efficient. Its implementation often results in significant modifications to the design and structure of the process, with several conventional unit operations occurring in the same physical device. Traditionally, process intensification has focused on steady-state operation. In our previous works, we introduced dynamic process intensification (DPI) as a new intensification paradigm based on operational changes for conventional or intensified units. DPI is predicated on switching operation between two auxiliary steady states selected via a steady-state optimization calculation that ensures that the system generates, on average and over time, the same products as in nominal steady-state operation, but with favorable economics. This paper extends the DPI concept and introduces a novel dynamic optimization-based DPI strategy (Do-DPI) that involves imposing a true cyclic operation rather than switching between two discrete states. We discuss its implementation using surrogate dynamic models learned via system identification. Here, an extensive case study concerning a ternary distillation column separating a canonical hydrocarbon mixture shows that Do-DPI can reduce energy use by more than 4% relative to steady-state operation, with no significant deviations in product quality and production rate.

42 ENGINEERING↗

Short-cut Methods versus Rigorous Methods for Performance-evaluation of Distillation Configurations

Here, this study demonstrates the efficacy of a short-cut method such as the Global Minimization Algorithm (GMA), that uses assumptions of ideal mixtures, constant molar overflow (CMO) and pinched columns, in pruning the search-space of distillation column configurations for zeotropic multicomponent separation, to provide a small subset of attractive configurations with low minimum heat duties. The short-cut method, due to its simplifying assumptions, is computationally efficient, yet reliable in identifying the small subset of useful configurations for further detailed process evaluation. This two-tier approach allows expedient search of the configuration space containing hundreds to thousands of candidate configurations for a given application.

42 ENGINEERING↗

Enhanced Flux and Electrochemical Cleaning of Silicate Scaling on Carbon Nanotube-Coated Membrane Distillation Membranes Treating Geothermal Brines

The desalination of inland brackish groundwater offers the opportunity to provide potable drinking water to residents and industrial cooling water to industries located in arid regions. Geothermal brines are used to generate electricity, but often contain high concentrations of dissolved salt. Here in this paper, we demonstrate how the residual heat left in spent geothermal brines can be used to drive a membrane distillation (MD) process and recover desalinated water. Porous polypropylene membranes were coated with a carbon nanotube (CNT)/poly(vinyl alcohol) layer, resulting in composite membranes having a binary structure that combines the hydrophobic properties critical for MD with the hydrophilic and conductive properties of the CNTs. We demonstrate that the addition of the CNT layer increases membrane flux due to enhanced heat transport from the bulk feed to the membrane surface, a result of CNT's high thermal transport properties. Furthermore, we show how hydroxide ion generation, driven by water electrolysis on the electrically conducting membrane surface, can be used to efficiently dissolve silicate scaling that developed during the process of desalinating the geothermal brine, negating the need for chemical cleaning.

15 GEOTHERMAL ENERGY↗

Impact of Organic and Volatile Compounds in Produced Water from Unconventional Reservoirs on Direct Contact Membrane Distillation Permeate Quality

The expansion of oil and gas extraction from unconventional reservoirs has led to an increase in the amount of produced water that has to be managed by this industry. Direct contact membrane distillation (DCMD) is a promising technology for treatment of produced water to enable water recovery and reduce the environmental footprint of this industry. The feasibility of DCMD for the treatment of highly saline produced water from the Permian Basin in TX with commercially available polyethylene and polytetrafluoroethylene membranes was evaluated in this study. An increase in water recovery by a DCMD system operated in the batch (concentrating) mode led to an increase in permeate conductivity. Partial removal of organic compounds from the produced water by biodegradation, chemical oxidation, and/or activated carbon adsorption could not resolve deterioration in permeate quality, and none of the organics observed in the permeate contributed to its conductivity. The observed increase in permeate conductivity was attributed to the passage of ammonia vapor from the feed side followed by protonation on the permeate side. Furthermore, this study revealed that permeate conductivity may not always be a reliable indicator of membrane wetting and underscores the importance of understanding the interactions between specific solutes and membrane materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports

One of the goals of the Surveillance, Epidemiology, and End Results (SEER) program is to estimate incidence, prevalence, and mortality of all cancers. To that end, cancer registries across the country maintain a massive database of cancer pathology reports which contain rich information to understand cancer trends. However, these reports are stored in the form of unstructured text, and human annotators are required to read and extract relevant information. In this article, we show that existing deep learning models for automating information extraction from cancer pathology reports can be significantly improved by using ensemble model distillation. We found that by training multiple predictive models and transferring their knowledge to a single, low-resource model, we can reduce the number of highly confident wrong predictions. Our results show that our implemented methods could save 1000s of manual annotation hours.

60 APPLIED LIFE SCIENCES↗

Robustness of topological persistence in knowledge distillation for wearable sensor data

Topological data analysis (TDA) has shown great success in various applications involving wearable sensor data. However, there are difficulties in leveraging topological features in machine learning and wearable sensors because of the large time consumption and computational resources required to extract the features. To address this problem, knowledge distillation (KD) is utilized to generate a small model and accommodate topological features with persistence image (PI) representations from the raw time series data. Deploying topological knowledge in KD enables the student to achieve better performance compared to the one trained solely on raw time series data. However, it is not yet known if there are coherent characteristics for topological features in PI, which can aid in improving the performance during KD. In this paper, we investigate the suitability and challenges of utilizing topological features in KD for wearable sensor data, thereby contributing to the advancement of the field. Our study explores the impact of transferred topological features by comparing the Teacher-to-Student framework with Multiple Teachers-to-Student where teachers utilize both time series data and persistence images obtained by TDA as inputs. Additionally, we conduct a rigorous examination of topological knowledge effects by testing under various corruptions, knowledge types, and learning strategies in the context of human activity recognition tasks. Our analysis of topological features in KD presents the optimal strategy for incorporating these features. This study includes datasets of varying scales, window lengths, and activity classes, providing a comprehensive evaluation. Our results demonstrate that leveraging topological features in KD to enhance performance across databases.

97 MATHEMATICS AND COMPUTING↗

Bioconversion of distillers’ grains hydrolysates to advanced biofuels by an Escherichia coli co-culture

Here, the first generation bioethanol production utilizes the starch fraction of maize, which accounts for approximately 60% of the ash-free dry weight of the grain. Scale-up of this technology for fuels applications has resulted in a massive supply of distillers’ grains with solubles (DGS) coproduct, which is rich in cellulosic polysaccharides and protein. It was surmised that DGS would be rapidly adopted for animal feed applications, however, this has not been observed based on inconsistency of the product stream and other logistics-related risks, especially toxigenic contaminants. Therefore, efficient valorization of DGS for production of petroleum displacing products will significantly improve the techno-economic feasibility and net energy return of the established starch bioethanol process. In this study, we demonstrate ‘one-pot’ bioconversion of the protein and carbohydrate fractions of a DGS hydrolysate into C4 and C5 fusel alcohols through development of a microbial consortium incorporating two engineered Escherichia coli biocatalyst strains.

09 BIOMASS FUELS↗

Membrane Specifications for Multi-Configuration Membrane Distillation Model

The data includes the membrane properties and specifications used for multi-configuration membrane distillation desalination. In this study, membranes from CLARCOR, 3M, and Aquastill are tested in counter-current, co-current and air-gap configurations at Colorado School of Mines (CSM), Advanced Water technology Center ( Aqwatech) laboratories. In the data sheets: The "theoretical" worksheet, contains steady-state values of the experimental runs and also provides several calculated values. The "Specifications" worksheet contains the inputs to the experiment. The "Data" spreadsheet contains the entire set of data and the rest of the sheets "20-40", "20-45", ...etc., contain individual portions of the data with variation of feed temperatures.

15 GEOTHERMAL ENERGY↗

Geothermal Thermoelectric Generation (G-TEG) with Integrated Temperature Driven Membrane Distillation and Novel Manganese Oxide for Lithium Extraction

Southern Research Institute (Southern) teamed with partners Novus Energy Technologies (Novus), Carus Corporation (Carus), and Applied Membrane Technology, Inc. (AMT) to develop an innovative Geothermal ThermoElectric Generation (G-TEG) system specially designed to both generate electricity and extract high-value lithium (Li) from low-temperature geothermal brines. The process combined five modular technologies including – silica removal, nanofiltration (NF), membrane distillation (MD), Mn-oxide sorbent for Li recovery, and TEG. This project provides a proof of concept for each of these technologies. The first step in the process is silica precipitation through metal addition and pH adjustment to prevent downstream scaling in membrane processes. Next, the geothermal brine is concentrated with the first of a two stage MD system. The first stage MD system is made of a high-temperature material to withstand geothermal brine temperatures up to 150C.° The first stage MD is integrated with a G-TEG module for simultaneous energy generation. The release of energy from the MD permeate drives heat transfer across the TE module, producing electricity. The first stage MD concentrate is then treated utilizing an NF system to remove Ca 2+ and Mg 2+ . The NF concentrate will be disposed in the well by reinjection. The NF permeate undergoes concentration in a second stage of MD (polymeric material) to further concentrate Li in the NF permeate and enhance the efficiency of the downstream Li recovery process utilizing a Mn-oxide sorbent. Permeate from both the stages of the MD can be beneficially utilized as the permeates will contain less contaminants than the feed water. The concentrated geothermal brines are then contacted with the Mn-oxide sorbent. After Li from the geothermal brine is adsorbed on the sorbent, HCl is then utilized to regenerate the sorbent and recover the Li. The research and development project showed that the Si removal goal (>80%) could be achieved by increasing the pH of the brine and adding Fe 3+ under several scenarios. The NF was also successful in achieving significant Ca 2+ and Mg 2+ removal (~80%) while retaining most Li in the permeate for high strength brines. MD experiments showed that geothermal brines could be significantly concentrated with little fouling due to pre-treatment.

15 GEOTHERMAL ENERGY↗

Isotope Enrichment Using Microchannel Distillation Technology (Final Report)

The Pacific Northwest National Laboratory has successfully demonstrated isotopic enrichment with microchannel distillation (MCD) technology, a capability for process intensification of chemical boiling point separations (CBPS). Sixty separation stages were achieved in a single device, a new record for process intensification technologies. The minimum length of a separation stage was 0.41 cm, a factor of 5 smaller than the best commercial advanced structured packing. The reduced separation stage length dramatically shortens the column length, which is particularly important for isotopic enrichment that typically requires thousands of stages. A key advantage of MCD technology is scalability, which was demonstrated by increasing the length of the test device by a factor of 2.5 and doubling the number of channels while improving performance. Results were obtained for two separations, propane from propylene and isotopic enrichment of methane, which along with previous results with other separations, demonstrates the versatility and utility of MCD for difficult separations. By employing capillary forces, MCD devices can operated in a horizontal configuration that facilitates a lower equipment profile and compact systems. Compact systems are easier and cheaper to insulate and operate at cryogenic temperatures that are typical for isotopic enrichment. MCD devices have been operated as low as the normal boiling point of methane, -162°C, and as high as the boiling point of diesel fuel, ~300°C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

New Design Methods and Algorithms for Energy Efficient Multicomponent Distillation Column Trains (Final Report)

The major objectives of this project are to; first develop new methods and tools, and then incorporate them in a user-friendly software that will readily generate low-energy solutions for a large class of multicomponent distillation applications found in typical chemical and petrochemical plants. Having such methods and corresponding software at the disposal of the process engineers will allow chemical manufacturers of all sizes to reduce energy consumption of their grassroots as well as existing separation plants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Maximizing energy savings attainable by dynamic intensification of binary distillation

Dynamic intensification of distillation columns has shown significant promise in achieving energy savings with minimal investment in new equipment. Conceptually, it entails making a desired product as a blend of two auxiliary products (one with higher purity, the other with lower purity, but both having lower energy consumption). Practically, dynamic intensification means periodically switching between two operating states corresponding to the aforementioned products. Past work has relied on ad-hoc choices of auxiliary products. In this paper, we introduce a new optimization framework for selecting auxiliary products for dynamic intensification. An extensive case study concerning the separation of a methanol/propanol mixture is then presented. We show that optimizing the choice of auxiliary products can lead to significant energy savings (more than 3.6% compared to a column operated at steady state) derived from dynamic intensification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗