Stream Flow Rate Research Articles

Systematic Approach for Synthesizing Carbon–Hydrogen–Oxygen Networks Involving Detailed Process Simulations

Carbon–hydrogen–oxygen symbiotic networks (CHOSYNs) provide a systematic multiscale framework for integrating multiple hydrocarbon processing plants. Earlier approaches have focused on high-level performance benchmarking using atomic and stoichiometric targeting techniques, which are subsequently detailed to identify the implementation strategies for allocation and processing. These sequential approaches are very powerful for the cases involving benchmarks that are independent of the system details (e.g., minimum usage of raw materials, minimum discharge of wastes, maximum revenue). For objectives involving detailed process information (e.g., capital cost minimization), there is a need to include the system details early enough in optimization. This paper proposes a systematic approach for incorporating detailed process simulations throughout the various stages of synthesizing a CHOSYN. The proposed approach involves the use of computer-aided process simulators and simultaneous optimization for synthesizing a CHOSYN. Critical data are generated and used at each design stage. For existing facilities, targeting uses the simulated data on available resources and sinks (e.g., flow rate, composition, pressure, and temperature). Process synthesis is used to generate a set of candidate new units and plants to be added, while detailed simulation is used to size these plants and identify the specific needs for flows and compositions. The proposed approach is implemented for a case study that involves five existing plants and a set of seven candidate plants that can induce symbiosis. The results show final configurations with the selection of new plants, the reduction of raw material, the allocation of internal and external sources, and the details of process information, capital and operating costs, and flow rate, pressure, and temperature of the exchangeable streams across the network.

Random forest and extreme gradient boosting algorithms for streamflow modeling using vessel features and tree-rings

Monitoring temporal variation of streamflow is necessary for many water resources management plans, yet, such practices are constrained by the absence or paucity of data in many rivers around the world. Using a permanent river in the north of Iran as a test site, a machine learning framework was proposed to model the streamflow data in the three periods of growing seasons based on tree-rings and vessel features of the Zelkova carpinifolia species. First, full-disc samples were taken from 30 trees near the river, and the samples went through preprocessing, cross-dating, standardization, and time series analysis. Two machine learning algorithms, namely random forest (RF) and extreme gradient boosting (XGB), were used to model the relationships between dendrochronology variables (tree-rings and vessel features in the three periods of growing seasons) and the corresponding streamflow rates. The performance of each model was evaluated using statistical coefficients [coefficient of determination (R-squared), Nash–Sutcliffe efficiency (NSE), and root-mean-square error (NRMSE)]. Findings demonstrate that consideration should be given to the XGB model in streamflow modeling given its apparent enhanced performance (R-squared: 0.87; NSE: 0.81; and NRMSE: 0.43) over the RF model (R-squared: 0.82; NSE: 0.71; and NRMSE: 0.52). Furthermore, the results showed that the models perform better in modeling the normal and low flows compared to extremely high flows. Finally, the tested models were used to reconstruct the temporal streamflow during the past decades (1970–1981).

Microfluidic synthesis of magnetic nanoparticles in droplet-based microreactors

Microfluidic devices have been widely used to achieve highly controlled synthesis of various nanomaterials including inorganic materials, polymers and composite materials via continuous microreactors. Here, droplet-based continuous microreactors generated in a microfluidic device are used to synthesize magnetic nanoparticles (Fe3O4) via simple coprecipitation method. The chip is composed of multifunctional units, including T-junction for droplet generation, Y-junction and S-channels for droplet fusion and rapid mixing as well as the observation of the formation process of nanoparticles. The size of water-in-oil droplets was inspected by tuning the flow rate of aqueous and oil phase. By changing the temperature and flow rate of aqueous and oil streams, we investigated the variation of the morphology, size, saturation magnetization and coercivity of nanoparticles. This work presents that chemical reaction can be initiated by fusing two aqueous droplets due to the hydrodynamic coupling, which is superiorly efficient compared to the conventional coprecipitation method for synthesizing Fe3O4 nanoparticles of high saturation magnetization and low coercivity.

Estimation of the error in calculating the maximum flow of small rivers

The share of the studied small watercourses on the territory of the Russian Federation is 0.01% of their total number. In this regard, when conducting hydrological calculations for the vast majority of projected structures, especially linear ones, the surveyor deals with unexplored rivers. To design and ensure safety in the operation of various water management and hydraulic structures on water bodies, it is recommended to use the Set of Rules SP 33-101-2003 and methodological recommendations for determining the calculated hydrological characteristics in the absence of hydrometric observations. However, until now, a full-fledged analysis of the error in calculating the values of the maximum runoff of a given repeatability according to the methods proposed in the joint venture, in comparison with the observed ones, has not been carried out. The proposed work fills this gap for streams (catchment areas <1 km2) and small rivers (up to 2,000 km2) in the forest zone of the European part of the Russian Federation. Before calculations, the necessary preliminary analysis of the initial observation data on the analogous rivers was carried out in order to assess the quality of the initial hydrometeorological information. The calculation of the maximum water flow rates of streams and small rivers of spring flood is considered using the reduction formula. The maximum urgent water flow rates of rain floods of a given probability were calculated using: the type I reduction formula in the presence of one or more analogous rivers; the type II calculation formula in the absence of analogous rivers; and the type III limit intensity formula. One of the calculation option is considered according to the type I formula when using various analogues that meet the requirements of the joint venture for such. As a result of calculations, it is concluded that for streams and rivers with a catchment area of up to 100 km2, the discrepancy in the values of the measured and calculated 1% of the spring flood flow rate can reach 300%, and rain floods – 2,000%. Such an error in any case can not ensure the safety of the operation of the designed structures

The Seasonal Water Balance of Western-Juniper-Dominated and Big-Sagebrush-Dominated Watersheds

The combined impacts of woody plant encroachment and climate variability have the potential to alter the water balance in many sagebrush steppe ecosystems in the Western USA, leading to reduced water availability in these already water-scarce regions. This study compared the water-balance characteristics of two adjacent semiarid watersheds in central Oregon, USA: one dominated by big sagebrush and one dominated by western juniper. Precipitation, springflow, streamflow, shallow groundwater levels, and soil moisture were measured. The potential evapotranspiration was calculated using the Hargreaves–Samani method. Potential evapotranspiration and a water-balance approach were used to calculate seasonal actual evapotranspiration. The shallow aquifer recharge was calculated using the Water-Table-Fluctuation-Method. Evapotranspiration, followed by deep percolation, accounted for the largest portion (83% to 86% of annual precipitation) of water output for both watersheds. Springflow and streamflow rates were generally greater at the sagebrush-dominated watershed. Snow-dominated years showed greater amounts of groundwater recharge and deep percolation than years where a larger portion of precipitation fell as rain, even when total annual precipitation amounts were similar. This study’s results highlight the role of vegetation dynamics, such as juniper encroachment, and seasonal precipitation characteristics, on water availability in semiarid rangeland ecosystems.

Independent or complementary power system configuration: A decision making approach for sustainable electrification of an urban environment in Sierra Leone

Providing quality and reliable power to grid-isolated areas has long been a difficulty in Africa, particularly in Sierra Leone, where government regulations are either weak or non-existent to assist the developing endeavour. Hybrid off-grid systems have grown in popularity as a way to offer electricity to people who live remote from power grids. This strategy, on the other hand, is capital demanding and prone to interruptions. This paper considers a decision-making process based on the Government of Sierra Leone's initiative to undertake a project to provide sustainable electricity to the people of Bo and Kenema in Sierra Leone's South-eastern region, with the assistance of the Africa Development Bank (ADB) and the Department for International Development (DFID). Two operating scenarios were modeled, and a comparative study was performed to determine which of the two should be considered. The results showed that the complementary scenario's proposed optimized hybrid configuration performed best in terms of technical, economic, and environmental features. Technically, it produced an excess energy of 2.29 %, compared to the combined total of the independent scenarios with 49.4 % (15.3 % excess energy from Bo and 34.1 % from Kenema). In terms of cost, it has a net present cost (NPC) of $45.6 million (M), compared to a combined NPC of $52 M ($37.8 M for Bo and $14.2 M for Kenema) for the independent networks. In terms of the environment, it generated 2,402,380 kg/yr of CO2 which is 42 % less than the combined total produced by Bo and Kenema (3,408,018 kg/yr). Sensitivity analysis revealed that the overall system cost is heavily reliant on the diesel fuel price and average annual stream flow rate, as well as the hydro turbine's deplorable efficiency status, with the intention of project leaders' ignoring the impact of variations in the PV derating factor.

Separation of minor cannabinoids from hemp extract with trapping multiple dual mode liquid-liquid chromatography

Aside from Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), other less common cannabinoids have recently gained an increasing popularity, mostly due to their promising biological potential. However, time-saving and cost-effective methods for their preparative purification are missing. In this study, trapping multiple dual mode (MDM), a flow-reversal liquid-liquid chromatography (LLC) operating mode, was used for the separation of different minor cannabinoids from a hemp extract. Separation task specific biphasic solvent systems were selected for the purification of the target constituents, as follows: n-hexane/methanol/water 10/6.5/3.5 for cannabielsoin (CBE); n-hexane/methanol/water 10/7/3 for cannabidivarin (CBDV) and cannabigerol (CBG); n-hexane/methanol/water 10/8/2 for cannabinol (CBN) and n-hexane/methanol/water 10/9/1 for cannabichromene (CBC) and cannabicylol (CBL). For each separation task, the concentration of the hemp extract in the feed stream and mobile phase flow rate were selected by shake-flask and stationary phase retention experiments, respectively. For the determination of the trapping MDM operating parameters, the short-cut method was implemented and followed by equilibrium-cell model-based simulations. The trapping MDM allowed the separation of the targeted cannabinoids with purities of 93-99%, yields of 73-95%, solvent consumption 2-4-fold lower and productivities almost double than those obtained using batch separation.

Sources of stream base flow in blanket peat covered catchments

A two year hydrological characterisation programme of relatively intact blanket peat-covered catchments, underlain by geochemically distinct substrates, investigated the influence of sub-peat materials on the runoff regimes of the first order and second order streams draining them. Semi-continuous monitoring of flow and water quality revealed a strong inverse relationship between stream flow rates and specific electrical conductance (SEC), with runoff chemistries during storm flow proving comparable in all three catchments. By contrast, differences in water quality between catchments became more pronounced at lower flow, where increased ion concentrations in stream water reflected reactions with peat substrate materials. Water table monitoring in peat at all three sites revealed groundwater levels to vary by less than 0.6 m over the investigation period. Findings suggest runoff consists of near constant inputs of higher specific electrical conductance peat substrate discharge, mixing with variable proportions of less mineralised and more compositionally variable bog water. Although making up less than 5% to total runoff, the contribution of more mineralised waters plays an important role in maintaining stream flow, particularly during prolonged dry periods, and may help explain the aquatic biodiversity encountered in blanket peat covered areas.

Control and Preparation of Quaternized Chitosan and Carboxymethyl Chitosan Nanoscale Polyelectrolyte Complexes Based on Reactive Flash Nanoprecipitation.

Nanoscale polyelectrolyte complex materials have been extensively investigated for their promising application in protocell, drug carriers, imaging, and catalysis. However, the conventional preparation approach involving positive and negative polyelectrolytes leads to large size, wide size distribution, instability, and aggregation due to the nonhomogeneous mixing process. Herein, we employ reactive flash nanoprecipitation (RFNP) to control the mixing and preparation of the nanoscale polyelectrolyte complex. With RFNP, homogeneous mixing complexation between oppositely charged chitosan derivatives could be achieved, resulting in stable nanoscale complexes (NCs) with controllable size and narrow size distribution. The smallest size of NCs is found at specific pH due to the maximum attraction of positive and negative molecules of chitosan. The size can be modulated by altering the volumetric flow rates of inlet streams, concentration, and charge molar ratio of two oppositely charged chitosan derivatives. The charge molar ratio is also tuned to create NCs with positive and negative shells. There is no significant variation in the size of NCs produced at different intervals of time. This method allows continuous and tunable NC production and could have the potential for fast, practical translation.

Laboratory Flume and Numerical Modeling Experiments Show Log Jams and Branching Channels Increase Hyporheic Exchange

AbstractLog jams alter gradients in hydraulic head, increase the area available for hyporheic exchange by creating backwater areas, and lead to the formation of multiple channel branches and bars that drive additional exchange. Here, we numerically simulated stream‐groundwater interactions for two constructed flume systems—one without jams and one with a series of three jams—to quantify the effects of interacting jam structures and channel branches on hyporheic exchange at three stream flow rates. In simulations without jams, average hyporheic exchange rates ranged from 2.1 × 10−4 to 2.9 × 10−4 m/s for various stream discharge scenarios, but with jams, exchange rates increased to a range of 1.3 × 10−3–3.5 × 10−3 m/s. Largely due to these increased hyporheic exchange rates, jams increased stream‐groundwater connectivity or decreased the turnover length that stream water travels before interacting with the hyporheic zone, by an order of magnitude, and drove long flow paths that connected multiple jams and channel threads. Decreased turnover lengths corresponded with greater reaction significance per km, a measure of the potential for the hyporheic zone to influence stream water chemistry. For low‐flow conditions, log jams increased reaction significance per km five‐fold, from 0.07 to 0.35. Jams with larger volumes led to longer hyporheic residence times and path lengths that exhibited multiple scales of exchange. Additionally, the longest flow paths connecting multiple jams occurred in the reach with multiple channel branches. These findings suggest that large gains in hydrologic connectivity can be achieved by promoting in‐stream wood accumulation and the natural formation of both jams and branching channels.

Mechanisms of passing through short exits for the elderly and young adults

The enhancement of the passage efficiency and the safety of pedestrians in public places have being paid increasing attentions. The increase of the elderly in crowds may impact the level of service of transportation systems due to the lower physical abilities. In this work, we focus on the movement characteristics through short bottlenecks for both the elderly and young adults experimentally. It is found that the elderly adopts the distance-oriented strategy when moving forward to an exit, while the young adults adopt the time-oriented strategy. These strategies lead to a 26% decline in the space utilization for the elderly and more waiting phase for the young adults. Besides, ellipse and teardrop-shape spatial distribution of the high-density regions are observed for the elderly and the young adults respectively. The time headway for two consecutive elders through the exit is 0.20 ± 0.06 s longer than that of the young adults. For a certain exit, the flow rate of the elderly stream is lower than that of the young stream. The capacity for the former is 0.51 p/(m s) lower than that for the latter. Consequently, the exit width for the elderly is suggested to be widened to 1.25b − 0.05 m from the width b for young adults. The results presented in this study can be used in management strategies of pedestrian flow and design of traffic facilities for the old people.

A Comparison of Multiple Imputation Methods for Recovering Missing Data in Hydrological Studies

Missing data is a common problem in hydrological studies; therefore, data reconstruction is critical, especially when it is crucial to employ all available resources, even incomplete records. Furthermore, missing data could have an impact on statistical analysis results, and the amount of variability in the data would not be fittingly anticipated. As a result, this study compared the performance of three imputation methods in predicting recurrence in streamflow datasets: robust random regression imputation (RRRI), k-nearest neighbours (k-NN), and classification and regression tree (CART). Furthermore, entire historical daily streamflow data from 2012 to 2014 (as training dataset) were utilised to assess and validate the effectiveness of the imputation methods in addressing missing streamflow data. Following that, all three methods coupled with multiple linear regression (MLR), were used to restore streamflow rates in Malaysia's Langat River Basin from 1978 to 2016. The estimation techniques effectiveness was evaluated using metrics inclusive of the Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). The results confirmed that RRRI coupled with MLR (RRRI-MLR) had the lowest RMSE and MAPE values, outperforming all other techniques tested for filling missing data in daily streamflow datasets. This indicates that the RRRI-MLR is the best method for dealing with missing data in streamflow datasets. Doi: 10.28991/cej-2021-03091747 Full Text: PDF

Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation

This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge.

Solar desalination unit coupled with a novel humidifier

The solar desalination system is experimentally investigated according to humidification dehumidification methodology (HDH) with hot air stream flow and heat pump as a condensation unit. The effects of high frequency ultrasound atomizer number, water height and hot air stream flow rate on distillate yield are studied. The results illustrated that increasing of atomizer number and decreasing water height increases the daily distillate production. The maximum daily freshwater productivity occurred at six atomizer number with an increase of 38.6% and 115% compared with atomizer number four and two, respectively. Furthermore, water height at 1 cm is the most efficient with an increment of 16% and 28.6% compared with 2 cm and 3 cm, respectively. The optimum hot air stream flow rate is 0.011 kg/s with an increment of 36.88%, 31.07%, 6.48%, 11.72%, 23.52% and 38.60% compared with 0.009, 0.010, 0.013, 0.014, 0.016, and 0.017 kg/s, flow rates, respectively. The air mass flow rate has a significant impact on system performance. The maximum gain output ratio (GOR) of the system is about 1.54. The daily production reaches 7.72 kg∖day, the system energy efficiency is 33.84% and 1.43% exergy efficiency the estimated cost is 0.0112 US$/L.

The Local Distribution of Temperatures and Entropy Generation Rate in an Ideal Counterflow Heat Exchanger

The process of heat exchange between two fluids of different temperatures and separated by a solid wall occurs in many engineering applications. Log mean temperature difference and effectiveness-NTU methods are widely used to assist in the design of heat exchangers. However, the two methods focus on overall analysis and cannot show the local temperature distributions. This paper obtains the mathematical solutions to the temperature profiles in an ideal counterflow heat exchanger. The aim of this research is to explain the phenomenon called the “entropy generation paradox”, which indicates a discrepancy between effectiveness and optimal entropy generation. The theoretical analysis reveals that the temperature curves are exponential functions when the heat capacity rates of the two streams are different; otherwise, the curves are linear functions. A heat exchanger is demonstrated to draw the temperature profiles under different working conditions. Local entropy generation rates are determined by the ratio of local stream temperatures in the form of a hook function. To realize a certain heat duty, there are many stream flow rate couples, and each couple results in a different entropy generation profile and obtains a corresponding total entropy generation. The helical steam generator of a high-temperature gas-cooled reactor is analyzed in this article and the principle of equipartition of entropy generation is confirmed. This principle indicates that, among the many working conditions to achieve a certain heat duty, a heat exchanger characterized by a nearly constant entropy production gives the best second law efficiency possible in order to achieve the best energy conversion.

Evaluating landscape influences on hydrologic behavior with a fully-integrated groundwater – surface water model

Optimizing hydrologic resiliency in agricultural landscapes is critical for the sustainability of food production and ecosystem services, and optimization strategies must include a holistic understanding of the hydrologic cycle, including groundwater-surface water interactions. Here, a large-scale (2056 km2), high resolution (50–150 m), structurally complex, fully-integrated groundwater-surface water model was constructed for an agriculturally-dominated watershed in the Northern Great Plains. The sensitivity of surface water flow and groundwater conditions were examined in the context of wetland/depression water storage capacity, soil hydraulic conductivity (Ks), field surface roughness (Manning’s n), as well as variability in precipitation and potential evapotranspiration (PET). The model evaluation interval extended from 2010 to 2015, which included both flood and drought conditions. Results showed that stream flow rates in the watershed exhibit a larger response to 10 % changes in either precipitation or PET relative to the response associated with a 50 % change in depression storage, a 4x change in Ks, or a 50 % change in n, thus highlighting the region’s susceptibility to weather variability. Of the landscape parameters examined, increasing depression storage provided a greater reduction in peak flood flows than an increase in Ks or n; however, increases in all three landscape parameters contributed to flood peak mitigation as well as groundwater recharge. When model configuration approximated predevelopment (primarily native prairie) landscape conditions, simulated flood peaks were reduced by 22 to 25 %. This study presents a fully-integrated groundwater-surface water modeling framework to determine the hydrologic influences and risk mitigation value of landscape management practices.

Comparison of the performance of packed column and jet electro-scrubbers for the removal of toluene

Toluene removal by electro-scrubbing was carried out in facilities that operate in continuous mode, comparing the performance of packed column and jet electro-scrubber systems, both in terms of retention in the absorbent and electrochemical degradation. It was found that about half of the toluene carried by the air flow is retained only by absorption. The packed column electro-scrubber showed better performance in electrochemical degradation, while the jet mixing device showed the highest concentrations of toluene retained in the electrolyte/absorbent, reaching levels of supersaturation. The flow rate of the gas stream has a great influence on the efficiency of the electro-scrubbing system: the column appears to be more efficient at lower flow rates while the jet scrubber works better at higher flow rates. Entrained species of the industrial solvent were detected (2-methylphenol and 4-methylphenol, principally) that were efficiently degraded. Benzaldehyde and benzoic acids were detected as intermediates.

Separation of multicomponent aromatic/aliphatic mixtures by simulated moving bed adsorption: Modeling and experiments

AbstractSimulated moving bed (SMB) adsorption has potential for efficient separation of many valuable chemical mixtures, but considerably less attention has been devoted to multicomponent feeds relative to binary mixtures. We take a rigorous experimental and modeling approach to study multicomponent separation of aromatics and aliphatics with a mesoporous silica adsorbent, which is relevant in many petrochemical applications such as separation of reformate and distillate streams. Our approach involves refining multicomponent adsorption, mass transfer, and SMB process parameters based upon detailed experimental inputs, with progressive addition of components. We develop a robust model that quantitatively predicts the influence of key operating parameters such as stream flow rates, desorbent/feed ratio, and switch time on the separation results and concentration profiles. The model is validated as a function of feed complexity by SMB experiments and column concentration profile measurements in a 16‐column mini‐plant. Furthermore, conditions for clear separation of each mixture are developed.

A hybrid deep learning algorithm and its application to streamflow prediction

Process-based streamflow prediction is subjected to large uncertainties in model parameters and parameterizations related to the complex processes involved in streamflow generation. The data-driven models offer efficient alternatives without considering the physical processes, but their applications are limited by non-stationarity existing in observations. In this study, we propose a hybrid model, namely the DIFF-FFNN-LSTM model, to predict hourly streamflow. The model comprises three components, namely the first-order difference (DIFF), feedforward neural network (FFNN), and long short-term memory network (LSTM). When applied to the Andun basin of China, the proposed DIFF-FFNN-LSTM model performs very well in hourly streamflow prediction, with a RMSE of 9.31 m3/s with average streamflow rate of 54 m3/s and a MAE of 3.63 m3/s for all the flood events in the testing period. The comparison with five other machine learning models (of similar complexity or model structure) and four statistical models show superiority of our proposed DIFF-FFNN-LSTM model. The Shapley Additive exPlanations was used to quantify the contribution of each model input to the prediction skill. Streamflow at the previous hour was identified as the most important input, and streamflow generally contribute more than precipitation. Inputs closer to the prediction time do not necessarily have a greater impact on the model prediction. The study highlights the power of the combining of different data-driven methods and the promising prospect of our hybrid model in hydrological predictions.

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

AbstractA neuro‐based computing technique is used for simulation of olefin plants at industrial scale. Artificial neural networks are applied to estimate the flow rate of the main products of the olefin unit from available information in terms of flow rate of feed streams and operating condition of furnaces. The structure of the smart model is determined through a trial‐and‐error procedure taking the real plant information over four successive years. The proposed paradigm estimates the tonnage of the product streams by an absolute average relative deviation in the range of 0.9 % for methane to 3.14 % for propylene. Results confirmed that this smart simulation not only presents accurate predictions, but is easy to use, straightforward, and can be simply employed for optimization and control of the unit.