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This letter presents a state estimation algorithm for the legged robot by defining the problem as a Maximum A Posteriori (MAP) estimation problem and solving the problem with the Gauss-Newton algorithm. Moreover, marginalization by the Schur Complement method is adopted to make a fixed size problem. Each component of the cost function and its Jacobian are derived utilizing the SO(3) manifold structure, while we reparameterize the state with nominal state and variation to make linear algebra and vector calculus applied properly. Furthermore, a slip rejection method is proposed to reduce the erroneous effect of fault modeling of kinematics models. The proposed algorithm is verified by comparison with the Invariant Extended Kalman Filter (IEKF) in real robot experiments on various environments.

The numerical study was conducted to compare process performance depending on the pump type and process configuration. The daily monitoring data of seawater temperature and salinity offshore from Daesan, Republic of Korea was used to reflect the site-specific seawater conditions. An algorithm for reverse osmosis in constant permeate mode was developed to simulate the process in time-variant conditions. Two types of pumps with different maximum leachable efficiencies were employed to organize pump-train configuration: separated feed lines and common pressure center design. The results showed pump type and design configuration did not have a significant effect on process performance. The annual means of specific energy consumption (SEC) for every design configuration were under 2 kWh/m3, except for a worst-case. The worst-case was decided when the pump was operated out of the best operation range. The two operation strategies were evaluated to determine the optimal configuration. The permeate flow rate was reduced to 80% of the designed permeate flow rate with two approaches: feed flow rate reduction in every train and pump shutdown in a specific train. The operation mode with feed flow rate reduction was more efficient than the other. The operating pressure reduction led to a decrease in SEC.

The reduction of energy consumption in the reverse osmosis (RO) process is critical. The forward osmosis (FO)-RO hybrid process can be suggested to reduce RO energy consumption. In this study, a numerical FO-RO process model was developed to analyze the FO-RO hybrid process. The performance of the FO-RO hybrid process was compared with the stand-alone RO process. In addition, the impacts of the control parameters for operation, and the intrinsic membrane parameters, were analyzed using sensitivity analysis. In conclusion, the FO-RO hybrid process involves less RO energy consumption than the stand-alone RO process. The FO draw flow velocity and the RO applied pressure were derived as the major factors for controlling the FO-RO process. In addition, the control parameters for operation were found to be more important than the intrinsic membrane parameters in the minimization of RO energy consumption. Subsequently, the FO elements installed in front of the RO process should be configured with a parallel connection, in order to minimize RO energy consumption. The results in this study could be used to develop guidelines for the optimal design of the FO-RO hybrid process.

Osmotic and hydraulic pressures are both indispensable for operating membrane-based desalting processes, such as forward osmosis (FO), pressure-retarded osmosis (PRO), and reverse osmosis (RO). However, a clear relation between these driving pressures has not thus far been identified, hence, the effect of change in driving pressures on systems has not yet been sufficiently analyzed. In this context, this study formulates an actual mathematical relation between the driving pressures of membrane-based desalting processes by taking into consideration the presence of energy loss in each driving pressure. To do so, this study defines the pseudo-driving pressures representing the water transport direction of a system and the similarity coefficients that quantify the energy conservation rule. Consequently, this study finds three other theoretical constraints that are required to operate membrane-based desalting processes. Furthermore, along with the features of the similarity coefficients, this study diagnoses the commercial advantage of RO over FO/PRO and suggests desirable optimization sequences applicable to each process. Since this study provides researchers with guidelines regarding optimization sequences between membrane parameters and operational parameters for membrane-based desalting processes, it is expected that detailed optimization strategies for the processes could be established.

Background and aims The nonalcoholic fatty liver disease fibrosis score (NFS) is comprised of metabolic risk indicators that may accurately predict residual cardiovascular risk in patients with CAD. Method This is a nested case control study of the post PCI registry from single tertiary hospital. Among 5589 patients who underwent PCI from 2010 through 2014, previously diagnosed 1510 NAFLD patients and 296 consecutive NAFLD patients who had undergone both PCI and ultrasonography of abdomen within 1 year between the tests were enrolled. (median age, 64y (IQR 55–73)). We applied the NFS to the patients at baseline, using validated NFS cut-offs and 201 patients with higher NFS, defined as NFS >0.67 at PCI, were 1:1 matched with controls, based on propensity scores for higher NFS >0.67. Results Higher NFS (NFS >0.67) was more prevalent in patients with left ventricle (LV) dysfuction (LVEF at PCI Conclusion With a longitudinal assessment of the association between NAFLD status by NFS and the risk of CV death, we found post-PCI patients with persistent higher NFS were at higher risk of CV death compared with those without. KM rates of CV death according to NFS Funding Acknowledgement Type of funding source: None

Background Electrocardiographs (ECG) are obtained by a digital signal. However, they are still printed out of paper to read by physicians. Digitizing the analog ECG from the paper to digital signal make us much easier to access of bid data pool form the daily clinical practice and previous resources. Objective The goal of this study is to digitize paper ECG to detect premature ventricular contraction (PVC). Methods This system consists of 2 steps; digitization and PVC detection. Results First, for digitization, ECG are filtered by the specific cut-off value of red, green and blue, then the filtered ECG image is changed into gray scale. In order to extract ECG signal, the algorithm fine the only one of the biggest white body throughout the X-axis. The X and Y axis is matched with distance and amplitude, depending on dots per inch (DPI). Second, to detect PVC, ECG signal is filtered to eliminate baseline wandering. The characteristics of PVC is higher amplitude and longer duration than normal sinus rhythm, we set two criteria to detect the PVC: 1.5 times the duration, 1.2 points out of the amplitude. For the synchronization of timing, lead II rhythm strip was used by PVC detection and then the rest of 12-lead ECG is matched based on lead II synchronization (Figure 1). We applied this algorithm to the 300 real patient's ECG. 290 of 300 (96.7%) ECG are successfully digitized signal and PVC detection. Conclusion We successfully developed the algorithm analog ECG signal into digital signal to detect PVC. In the future, this method helps to gather big data from ECG papers to develop a new algorithm to localization of PVC. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Korean Heart Rhythm Society 2019 Research Fund

This paper presents a constrained nonlinear model predictive control (NMPC) framework for legged locomotion. The framework assumes a legged robot as a floating base single rigid body with contact forces being applied to the body as external forces. With consideration of orientation dynamics evolving on the rotation manifold SO(3), analytic Jacobians which are necessary for constructing the gradient and the Gauss-Newton Hessian approximation of the objective function are derived. This procedure also includes the reparameterization of the robot orientation on SO(3) to orientation error in the tangent space of that manifold. Obtained gradient and Gauss-Newton Hessian approximation are utilized to solve nonlinear least squares problems formulated from NMPC in a computationally efficient manner. The proposed algorithm is verified on various types of legged robots and gaits in a simulation environment.

Despite many kinds of research on pressure-retarded osmosis (PRO), feasibility studies for PRO are still insufficient. To provide a better understanding of PRO-hybridized processes, an energy efficiency analysis should reflect the hybridization features of three processes, including PRO, seawater reverse osmosis (SWRO), and membrane distillation (MD). To this end, a new dimensionless (i.e., unitless or scale-free) performance index is proposed to facilitate a fair comparison of energy efficiency between SWRO-PRO and SWRO-MD-PRO. The new performance index physically implies a ratio of the total energy recovered by PRO to the total energy consumed by SWRO and MD. The performances of hybridized processes were estimated with the new index and compared to each other after running several simulations. The simulation results showed that SWRO-MD-PRO generally has a higher energy efficiency than SWRO-PRO if an inexpensive heat source, such as waste heat, is used. However, the energy efficiencies of both PRO-hybridized processes were different according to the simulation conditions. Therefore, it can be concluded that energy optimization for PRO-hybridized processes should be conducted differently than for SWRO. The results from this study are expected to play a key role in providing a new roadmap for PRO-hybridized process research.

Heavy metals are naturally occurring trace elements present in the environment, including soil, water, and air. However, high heavy metal concentration contamination from waste is a serious environmental problem brought on by industrial activities. The research in this study evaluated different biomineralized CaCO3 forms from individual bacteria species in heavy-metal-contaminated soils and mine tailings. Scanning electron microscope (SEM) images of the crystals were used to characterize the precipitated CaCO3. Generally, urea-hydrolysis bacteria form minerals via a microbiologically induced calcite precipitation (MICP) process. These bacteria produce the urease enzyme, which leads to urea-hydrolysis. These bacteria were isolated from heavy-metal-contaminated soils and characterized for their potential utilization in the S/S process. Optimum conditions for indigenous bacterial growth were 30 °C and a pH range of 7–8; and growth patterns were further affected by the growth medium salinity. SEM and X-ray diffraction (XRD) analyses demonstrated that bioaccumulated heavy metal ions were deposited around the cell envelope as rhombohedral and sphere shaped crystalline carbonate minerals in optimum conditions. In this study, the authors hypothesize that the indigenous bacteria can effectively precipitate heavy metals in soil and mine tailing with the urea-hydrolysis enzyme, and play an important role in heavy metal stabilization.

Climate change is a primary driver that alters water pollution hotspots in watersheds, which complicates the use of Best management practices (BMPs) for diffuse pollution mitigation strategies. The objective of this study is to evaluate future changes in BMPs on total phosphorus (TP) loads to the river system, depending on climate change. Current weather data were collected from 2000 to 2010 and future weather data from 2040 to 2050 were obtained from regional climate model simulations based on Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 scenarios. This study describes various BMP implementation plans at affordable cost that are adapted to different weather conditions, current (for 2000–2010) and future climate scenarios (for 2040–2050). The Soil and Water Assessment Tool (SWAT), i.e., one of the deterministic models for watershed management, was used to estimate total phosphorus (TP) removal efficiency of various types of BMP at two crop fields (rice paddy and soybean fields) in the Yeongsan River (YR) watershed of Korea. The Non-dominated Sorting Genetic Algorithm II (NSGA-II), i.e., one of the artificial intelligence (AI) models for decision support framework, was applied to obtain allow for trade-off analysis between two conflict objectives, the maximum TP load reduction against implementation costs. The SWAT simulation results showed that the model performance based on the Nash-Sutcliffe efficiency and percent bias was acceptable for simulating three parameters; daily flow discharge, monthly sediment loads, and TP loads in the river. As a result, monthly sediment loads and TP loads increase remarkably under all future climate change scenarios except for July, when a comparable amount of precipitation is recorded during the present and future conditions. New implementation of BMPs, therefore, will be required for future climate change scenarios to achieve 50% TP load reduction in the given watershed condition. Interestingly, parallel terraces which showed good efficiency in reducing TP loads at both fields under the current weather condition cannot be effective any more under the worst-case weather scenario in the future climate. In such a case, detention ponds can be proposed as BMP alternative to parallel terraces. Overall, this study not only demonstrates that watershed management plans using BMPs should be adjusted according to climate change scenarios, but also that the hybrid use of SWAT model and AI can help in refining existing and future BMPs at affordable cost.

This paper introduces an obstacle-avoiding algorithm for bipedal robots, especially in push recovery situations. Typically, There are many algorithms that plan footstep to avoid obstacles based on vision recognition data. However, if the robot is pushed, the planned footprint will change, and thus, there is no guarantee that it will avoid obstacles. Although modified stepping positions can be limited, the robot’s stability is not assured. Our proposed algorithm focuses on avoiding obstacles through vision recognition in push recovery situations and generating compensation actions for instability by restricting modified footsteps. We fuse vision feedback with our previous push recovery algorithm, which optimizes the ankle, hip, and stepping strategies. We build simple grid data using vision recognition and apply it to the inequality constraint of the stepping position. We validate the effectiveness of our algorithm using the bipedal platform GAZELLE with the Kinect V2 RGBD sensor.

Land use and cover is one of the important factors that influence water flow within a watershed. Alteration of land use through time affects hydrological processes as well as the water budget in the watershed. In analyzing these processes that occur within basins and watershed, Soil and Water Assess...

Despite a growing awareness of the problems associated with cyanobacterial blooms in rivers, and particularly in regulated rivers, the drivers of bloom formation and abundance in rivers are not well understood. We developed a Bayesian hierarchical model to assess the relative importance of predictors of summer cyanobacteria abundance, and to test whether the relative importance of each predictor varies by site, using monitoring data from 16 sites in the four major rivers of South Korea. The results suggested that temperature and residence time, but not nutrient levels, are important predictors of summer cyanobacteria abundance in rivers. Although the two predictors were of similar significance across the sites, the residence time was marginally better in accounting for the variation in cyanobacteria abundance. The model with spatial hierarchy demonstrated that temperature played a consistently significant role at all sites, and showed no effect from site-specific factors. In contrast, the importance of residence time varied significantly from site to site. This variation was shown to depend on the trophic state, indicated by the chlorophyll-a and total phosphorus levels. Our results also suggested that the magnitude of weir inflow is a key factor determining the cyanobacteria abundance under baseline conditions.

In this paper, we present new distributed coordination for a set-point regulation of asymmetric compartmental systems under periodically time-varying external inputs. The feature of the proposed coordination is to adjust system parameters, which is directly related to interflow rate among compartments and outflow rate to environment. In contrast to symmetric compartmental systems, there is a difficulty in analysis resulted from a property of compartmental matrix in the asymmetric cases; however, we solve the problem by designing an appropriate coordination range of the system parameters. In order to compensate the time-varying effect in the inputs, we assume that the structure of the inputs is a finite Fourier series, and show that the coordination of the system parameters should have the same structure with the same frequencies. The remainder of the coordination is to determine coefficients of the Fourier series of the system parameters. We develop a distributed update law for obtaining the coefficients. We apply the distributed coordination to water irrigation systems and water distribution systems, and show the effectiveness of the proposed distributed coordination schemes through numerical simulations.

In this study, Internally Staged Design (ISD) was numerically optimized in order to demonstrate the long-term performance of full-scale seawater reverse osmosis (SWRO) membrane systems in the presence of colloidal foulants. To this end, a numerical model based on a finite difference method was developed and optimized with various feed water qualities, such as fouling potential and total dissolved solids (TDS). As a result, the optimized ISD exhibited greater water flux and higher energy efficiency in long-term operation (90 days) compared to conventional designs, where single-type membrane elements were employed over a pressure vessel while achieving potable TDS concentration in permeate (

The presence of flow control devices and/or obstacles in the fluid flow such as baffles and spacers complicates the solid-fluid mixing pattern, which remains difficult to describe by classical analytical solutions. In this study, the removal of fine particles in a tertiary sedimentation tank mounting an adjustable baffle was investigated using the computational fluid dynamics code-COMSOL. The solid-fluid motion was solved by consecutively applying the equations of the continuity and momentum using the finite element method. The experiment was conducted by the sedimentation tank with the adjustable baffle inclined at 30° in a pilot scale plant. It's used as the reference data set for numerical simulations that were run on a 2-dimensional domain by modifying the configuration settings of angles for an adjustable baffle (i.e., 30°, 45°, and 60°) and without one. Results showed that the simulation results matched well with the experimental data for an adjustable baffle at 30° (NSE=0.97). The sedimentation tank with the adjustable baffle at different angles had a lower overflow rate (in the area of flow rebound) and mixing intensity (in the area of flow curve) than without one, eventually leading to enhanced particle removal efficiency. This tendency became more pronounced as the particle motion stabilized over time. The sedimentation tank mounting the adjustable baffle at 30° provided the best settling efficiency among the four different flow patterns. However, the conventional index that represents the mixing properties did not correctly address their relative efficiency for fine particle removal. Therefore, a numerical simulation tailored to a given geometry should be conducted to fully elucidate the fluid dynamics in the sedimentation tank with complex devices or obstacles.

Background and purpose Based on the data of patients who measured office BP (OBP) in controlled condition, home BP (HBP), and 24-hour ambulatory BP (24hABP) for diagnosis of hypertension, we developed and validated a diagnostic algorithm for hypertension. Methods Patients who have high BP (≥140/90 mmHg) at the outpatient clinic were referred to measure OBP in controlled condition, HBP and 24hABP. The OBP was measured three times at each visit for 3 days in controlled condition by using a validated oscillometric device. HBP was measured for 7 days in triplicates every morning and evening. The 24hABP was measured on the 8th day. Same study protocol was used for development and validation population. Results In the development of algorithm, 319 patients were recruited and data of 256 patients (51.8±9.7 years, 119 men) with valid HBP and 24hABP measurements were analyzed. In the validation of algorithm, 300 patients were recruited and data of 257 patients (52.4±9.8 years, 126 men) with valid HBP and 24hABP measurements were analyzed. In the development population, the prevalence of masked hypertension was 19.5% (n=50) and most of them (n=47, 94%) was in the range of 130–139/80–89 mmHg. The BP in the range of 130–144/80–94 mmHg was defined as the grey zone of OBP, because prevalence of white-coat hypertension was lowered to 1.6% from 4.3% with threshold of 145/95 mmHg. The diagnostic sensitivity, specificity, positive and negative predictive value of OBP was improved to 97.4%, 76.5, 96.5% and 81.3% after excluding the grey zone of OBP (n=125). The diagnostic agreement between 24hABP and HBP in the grey zone of OBP was 64%. The diagnostic sensitivity, specificity, positive and negative predictive value of HBP were 88.8%, 74.7, 93.5% and 61.5% after excluding the grey zone of HBP (130–134/80–84 mmHg, n=28). Based upon these results, we developed a diagnostic algorithm for hypertension by using on out-of-office BP measurements to improved diagnostic accuracy of hypertension (Figure). In the validation population, the developed algorithm showed similar diagnostic accuracy. Diagnostic algorithm for hypertension Conclusion For accurate diagnosis of hypertension, OBP measurement according to guidelines and 24hABP measurement to grey zone of OBP is preferred. To improve diagnostic accuracy of HBP measurement, 24hABP measurement to grey zone of HBP is required. Acknowledgement/Funding Part of this study was supported by a grand from Dong-A ST Co. Ltd., Seoul, Korea.

Bacteria are a primary contaminant in natural surface water. The instream concentration of fecal coliform, a potential indicator of pathogens, is influenced by meteorological conditions and land-use characteristics. However, the relationships between these conditions and fecal coliforms are not fully understood. Furthermore, the sources of large variability in fecal coliform counts, e.g., temporal or spatial sources, remain unexplained, especially at large scales. This study proposes the use of Bayesian overdispersed Poisson models, whereby the combined effects of temperature, rainfall, and land-use characteristics on fecal coliform concentration are quantified with predictive uncertainty, and the sources of variability in fecal coliform concentration are assessed. The models were developed using 8-year weekly observations of fecal coliforms obtained from the Wachusett Reservoir watershed in Massachusetts, USA. The results highlight the importance of interactions among meteorological and land-use characteristics in controlling the instream fecal coliform concentration; the increase in fecal coliform concentration with temperature increase was more drastic when rainfall occurred. Also, the responses of fecal coliforms to temperature increases were more pronounced in forest-dominated than in urban-dominated areas. In contrast, the fecal coliform concentration increased more rapidly with rainfall increases in urban-dominated than in forest-dominated areas. The models also demonstrate that among the sources of variability, the monthly component made the most significant contribution to the variability in fecal coliform concentrations. Our results suggest that seasonally dependent processes, including surface runoff, are critical factors that regulate fecal coliform concentration in streams.

© 2016 Elsevier B.V. In this study, colloidal fouling behavior in pressure retarded osmosis (PRO) was systematically investigated in terms of the effects of draw solution concentration, applied hydraulic pressure at the draw side, feed solution pH, and particle size. Commercially-available cellulose triacetate (CTA) membranes were fouled with feed solution containing silica colloidal particles. Two different silica particles with mean diameter of 27 and 152 nm were used as model foulants. Our findings demonstrated that the colloidal fouling in PRO was dominantly affected by the cake layer buildup at the membrane surface. Fouling was further exacerbated by diffused salts from the draw side because retained salts within the cake layer elevated the salt concentration on the membrane surface, and consequently reduced the driving force of PRO. Substantial flux decline with the smaller particles was attributed to the high cake layer resistance due to the formation of the void-less cake layer. In addition, our approaches to mitigate the colloidal fouling revealed that the hydraulic cleaning by increasing the cross-flow rates was not effective to eliminate the compact cake layer. However, adjusting the feed solution pH showed the high potential to relieve the colloidal fouling resulting from the more stabilization of particles at low solution pH.

Providing high-resolution monitoring data is essential in promoting decision-making activities for surface water quality protection such as scientific modeling and data analysis. In this study, statistical models using multiple linear regression (MLR) were developed to estimate Chlorophyll-a (Chl-a) and total suspended solid (TSS) concentrations in a mesotrophic reservoir, the Yeongam Reservoir in Korea, from satellite observations. Two types of satellite data that covered different spectral regions, for wavelengths in 412–865 nm (for the Geostationary Ocean Color Imager) and those in 405–14,385 nm (for the Moderate Resolution Imaging Spectroradiometer), were used as inputs for statistical models, after bias correction. The MLR models for Chl-a and TSS were initially constructed and evaluated with 39 image data-sets obtained during 2011–2014. Subsequently, they compared with their corresponding algorithms that were developed under different environmental settings as well as the CE-QUAL-W2 model, a...

Fecal coliforms are indicators of pathogens and thereby, understanding of their fate and transport in surface waters is important to protect drinking water sources and public health. We compiled fecal coliform observations from four different sites in the USA and Korea and found a seasonal variability with a significant connection to temperature levels. In all observations, fecal coliform concentrations were relatively higher in summer and lower during the winter season. This could be explained by the seasonal dominance of growth or die-off of bacteria in soil and in-stream. Existing hydrologic models, however, have limitations in simulating the seasonal variability of fecal coliform. Soil and in-stream bacterial modules of the Soil and Water Assessment Tool (SWAT) model are oversimplified in that they exclude simulations of alternating bacterial growth. This study develops a new bacteria subroutine for the SWAT in an attempt to improve its prediction accuracy. We introduced critical temperatures as a parameter to simulate the onset of bacterial growth/die-off and to reproduce the seasonal variability of bacteria. The module developed in this study will improve modeling for environmental management schemes.

A performance analysis of a tri-combined process that consists of reverse osmosis (RO), membrane distillation (MD), and pressure-retarded osmosis (PRO) was conducted by using numerical approaches in order to evaluate its feasibility. In the hybrid process, the RO brine is partially used as the MD feed solution, and the concentrated MD brine is then mixed with the rest of the RO brine to be considered as the PRO draw solution. Here, the brine division ratio, incoming flow rate of RO, dimensions of the MD and PRO processes, and the supply cost of the MD heat source were considered as influential parameters. Previously validated process models were employed and the specific energy consumption (SEC) was calculated to examine the performance of the RO–MD–PRO hybrid process. The simulation results confirmed that the RO–MD–PRO hybrid process could outperform stand-alone RO in terms of reducing the SEC and the environmental footprint by dilution of the RO brine in locations where free or low-cost thermal energy can be exploited. Despite the need for further investigations and pilot-tests to determine its commercial practicability, this study provides insights into future directions for water and energy nexus processes for energy efficient desalination.

Reverse osmosis (RO) desalination of 35,000 ppm seawater consisting of sodium and chlorine ions with water was simulated using molecular dynamics. In this study, RO simulations were performed at different pressures using carbon nanotube (CNT) membranes having varying pore diameters (8–14 A). Results showed that a CNT membrane allowed high water fluxes because of the presence of a low energy barrier for water–carbon interactions, with continuous and ordered water chains observed inside the CNTs. The investigations into the ion rejection capability of CNT membranes revealed that (6, 6) CNTs could provide high rejection rates (>95%). The rejection of ions was mainly due to the presence of a high energy barrier caused by removing the hydrating water molecules necessary for ions to pass through the narrow CNT pore. The desalination performance of (6, 6) CNT membranes and conventional polymeric membranes was then compared. It was found that CNT membranes have a higher normalized flux and comparable ion ...

The pressure retarded osmosis (PRO) process has been considered as an alternative and renewable technology to generate electricity from mixing two solutions of different salinities. However, improving the osmotic performance of semi-permeable membrane is still a major challenge in the PRO system. Therefore, thin-film nanocomposite (TFN) membrane was synthesized by using carbon nanotubes (CNT)-embedded-polyethersulfone (PES) supporting layer and polyamide active layer in this study. The prepared membranes were further employed in the PRO process to harvest energy from saline water. The water flux increase of the TFN membrane was promoted by CNT-induced porosity and the hydrophilicity of the support layer as well as by the chemical etching of the active layer. The water flux and maximum power density of the developed TFN membrane was found to be 87% (averaged from 2 bar to 10 bar) and 110% greater than for bare thin-film composite (TFC) membranes, respectively. Furthermore, the TFN membrane preparation could easily be scaled up using conventional fabrication methods with less than 2% additional material cost. Therefore, this finding could contribute to the commercialization of sustainable energy generation by utilizing the tremendous potential of fresh- and salt-water mixing.

Statistical models play an important role in elucidating the dynamic behaviors of surface water quality, given limited data on a large scale. In this study, we examine alternative approaches to develop regression models that predict fecal coliform (FC) concentrations in a river using different methods for selecting important variables provided by a self-organizing map (SOM). The raw data used as input to the SOM included 11 water quality, 6 meteorological, and 7 land use parameters that were monitored along the Yeongsan River in Korea on various time scales (from daily to half a decade) during 1996–2008. In both test and validation data sets, (multiple) regressions using backward elimination were compared against regression models via forced entry, which included a set of ranked variables simultaneously based on four indices in the SOM (i.e. structuring index, relative importance, cluster description, and Spearman’s rank correlation). Results showed that the SOM effectively illustrated the complex...

A transient model of seawater reverse osmosis (SWRO) system enables systematic assessment of membrane performance in response to changes in time-series parameters, operating conditions, or ancillary equipment. In this study, we describe the effects of energy recovery device (ERD) and feed pressure control on the SWRO plant in terms of energy consumption (reduced) and water quantity (increased) using a numerical model at pilot scale. In the simulation, two types of isobaric ERD, i.e. pressure exchanger (PX) and dual work exchanger energy recovery (DWEER), were used to quantify changes of the mass flow rates of inflow and outflow in the system. Also, temporal variation in the raw feedwater quality was addressed in the model with adaptive feed pressure control to maintain the amount of produced water under fouled membrane conditions. Results showed that the observed recovery and rejection rates in the pilot-scale plant had an excellent agreement with their predicted values under different seawater fe...

Since the early 2000s, pressure retarded osmosis (PRO) has been continuously investigated, based on its potential to harvest energy from a salinity gradient. However, recent negative reports on its fundamental feasibility and the closure of the largest industrial implementation have slowed its momentum, with regard to its commercialization. In this respect, this review provides insights into the current status of PRO technologies in membrane fabrication, module design, process optimization, and industrial movements for the commercialization of PRO. Notably, despite dramatic advancements in lab-scale PRO membrane performance, recent modeling studies have revealed advanced membrane properties have only a minor impact on PRO performance. In contrast, developing PRO module designs are deemed to have paramount importance, due to the drastically low module performance compared to lab-scale membrane performance, and high potential of design modification to resolve inefficient PRO module structures. Various simulation methods for optimizing PRO processes using the classic mass transfer model, thermodynamics, and machine learning optimizations are comparatively analyzed. Integrations of seawater reverse osmosis and PRO and other hybrid processes with PRO are discussed in terms of recent pilot-scale implementations and modeling results. Finally, small and large industrial movements and projects for PRO commercialization are analyzed in detail, based on their quantitative outcomes.