Irrigated agricultural uses 72% of total water diversions in Australia and its potential to generate returns flows to river systems in terms of both quantity and quality is significant. Increased hydraulic loading under irrigation and changes in land use has lead to high water tables and land salinisation and sodification. Drainage schemes have been implemented to reduce the consequences of land salinisation. These drainage schemes contribute large amounts of salt, nutrients and sediments into natural water courses and have lead to a decline in water quality in rivers and reduced health in riverine ecosystems.The implications of management and interventions to drainage systems are complex and often have the potential to cause significant impacts on stakeholders in the system unless careful consideration is given to all aspects of the system. There is a need to clearly understand the tradeoffs between management options and interventions and impacts on drainage return flows, and to conduct water accounting (quantity and quality) at these scales.Hence tools or frameworks which allow all aspects of the drainage intervention to be considered and trade-offs between stakeholders investigated allow improved decision making to safeguard against solutions which only address the symptoms of a particular problem, leading to a further different set of problems, often transferring the problem downstream.This paper presents the conceptualisation and model development of an irrigation return flow model, called "Tiddalik", for the prediction of drainage return flow volumes and salt loads to streams and river systems. Conceptualisation of the major drivers of return flows are presented along with the interaction of land use management variables that determine generated return flow volumes and salt loads.The Tiddalik model can be used to look at a range of management and operational options for meeting license conditions that are applied to return flows from irrigation areas. These may include flow conditions and/or quality conditions such as salinity limits/EC credits. Various scenarios are presented from increasing irrigation efficiency to large scale land use changes (i.e. changed cropping systems, drainage implementation) for their effect on drainage volumes and salt loads.Core building blocks of the model which include evapotranspiration, soil water balance, upflux, watertable, subsurface drainage, irrigation system and on farm storage/recycling system modules and their limitations are described and discussed.The Tiddalik model seeks to provide a transparent framework whereby users have the ability to investigate management options and trade-offs for meeting environmental targets in relation to drainage return flow quantity and quality.
Surface irrigation currently accounts for 70-80% of irrigation water use in Australia and surface application is by far the dominant irrigation method applied throughout the world. However, water use efficiencies with surface irrigation methods tend to be low. In recent years a number of surface irrigation simulation models for assessing surface irrigation system performance have been developed. One of the most commonly used models SIRMOD, developed by Utah State University, has seen wide use and evaluation throughout the world particularly by researchers and has been shown to offer potential for increasing surface irrigation water use efficiencies.Considerable efforts are now being undertaken to move use of the model from the realm of a research domain to the farmer domain. Maximum benefit from the use of such models will only occur when farmers have the ability to use Decision Support Systems (DSS) such as SIRMOD in a near real time environment i.e. for individual irrigations.An extensive field investigation of the model was undertaken on a range of irrigation layouts and two different soil types commonly found in south eastern Australia. The regression analysis of measured infiltrated volumes showed a strong correlation with modeled results (Figure 1 r(2) = 0.9474).The SIRMOD simulation model was found to adequately predict furrow irrigation characteristics on irrigation layouts and soil conditions typically found in the Murrumbidgee Irrigation Area (MIA). Comparisons of infiltrated volumes predicted by SIRMOD with measured infiltrated volumes gave a strong relationship providing confidence that SIRMOD was able to adequately model furrow irrigation systems typically used in the MIA.[GRAPHICS]This paper presents the results from field testing and evaluation of the model directly with irrigation farmers and user experiences with using SIRMOD as a quasi real time decision support tool.Four areas have the potential to dictate the uptake of the SIRMOD model as DSS:1. Ease of gathering input data for the DSS2. Platform delivery of the DSS3. Relating model outputs to end user needs4. Level of irrigation water availability and the cost: benefit ratio of using the DSSThe use of the SIRMOD model as a management tool for improving irrigation efficiencies was found to be a valuable aid. Adoption of the model in use as a DSS has great potential. Future directions in research should focus on providing a streamlined solution which delivers an integrated system and addresses the four points outlined above. This will ensure irrigation farmers harness the benefit of the model.
Rivers and streams around the world are being affected by declining water quality. When designing remediation strategies, we must first understand the key factors affecting spatial and temporal variability in stream water quality. As such, the objective of this investigation was to investigate the relationships between in-stream constituent concentrations and streamflow and to understand how these relationships vary across space. We intend to use these findings to add a temporal component into existing statistical models of spatial variability in water quality. Monthly water quality data for total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjedahl nitrogen (TKN), nitrate-nitrite (NOx) and electrical conductivity (EC), in addition to streamflow collected between 1994 and 2014 from 107 water quality monitoring sites in Victoria were used for this study. Using these data, we characterized the interaction between constituent concentrations and streamflow in terms of (i) the ratio of the coefficient of variation (CV) of constituent concentrations to the CV of streamflow (CVC/CVQ) , and (ii) the slope of the linear regression between the log-transformed constituent concentrations and log-transformed streamflow (the C-Q slope). We then linked the spatial variations in CVC/CVQ and the C-Q slope to catchment characteristics (e.g., land use and climate). We found that the interaction between constituents and streamflow depends significantly on the reactivity of the constituent, and whether the constituent is in the dissolved or particulate state. TSS, TP, TKN, FRP and NOx demonstrated chemodynamic behavior, with the concentrations varying with streamflow (i.e., high CVC/CVQ and large absolute value in C-Q slope). On the other hand, EC demonstrated chemostatic behavior for the selected sites, with low CVC/CVQ values and C-Q slopes. The interaction between streamflow and constituents varied significantly across space. The variability in CVC/CVQ for TSS, nutrients and salts correlated positively with catchment characteristics such as mean catchment slope, average annual rainfall and woodland cover. This could be due to the weaker sources of TSS due to reduced erosion, nutrients due to zero or low application and salts due to high leaching in steeply sloping, vegetated and high rainfall catchments (as they tend to be less disturbed). Lower magnitude and less temporal consistency of constituent sources can lead to greater variability in constituent concentrations relative to streamflow. The spatial variability in C-Q slopes generally did not correlate strongly to catchment characteristics, likely due to the presence of major dams in approximately half of the water quality monitoring sites. However, once these sites were removed, we found that the TSS C-Q slope correlated strongly to average annual rainfall and the mean 7-day low flow. This suggests that there is a stronger positive linear relationship between TSS concentrations and streamflow in catchments with temporally consistent rainfall and streamflow. There were weak correlations between catchment characteristics and the C-Q slopes for nutrients regardless of the exclusion of the water quality monitoring sites with dams. This could be due to the reactive nature of these compounds, leading to less predictable interactions between streamflow and in-stream concentrations. The results of the analysis will be used to develop statistically-based predictive models of spatio-temporal variability in stream water quality.
We outline the challenges of situation awareness with early and accurate recognition of traffic maneuvers and how to assess them.This includes also an overview of the available data and derived situation features, handling of data uncertainties, modelling and the approach for maneuver recognition.An efficient and effective solution, meeting the automotive requirements, is successfully deployed and tested on a prototype car.Test driving results show that earlier recognition of intended maneuver is feasible on average 1 second (and up to 6.72 s) before the actual lane marking crossing.The even earlier maneuver recognition is dependent on the earlier recognition of surrounding vehicles.
In this paper, inspired by the herding behavior of rhinos, a new kind of swarm-based metaheuristic search method, namely Rhino Herd (RH), is proposed for solving global continuous optimization problems.In various studies of rhinos in nature, the synoptic model is used to describe rhino's space use and estimate its probability of occurrence within a given domain.The number of rhinos increases year by year, and this increment can be forecasted by several population size updating models.Synoptic model and a population size updating model are formalized and generalized to a general-purpose metaheuristic optimization algorithm.In RH, null model without introducing any influences is generated as the initial herding.This is followed by rhino modification via synoptic model.After that, the population size is updated by a certain population size updating model, and newly-generated rhinos are randomly initialized within the given conditions.RH is benchmarked by fifteen test problems in comparison with biogeography-based optimization (BBO) and stud genetic algorithm (SGA).The results clearly show the superiority of RH in searching for the better function values on most benchmark problems over BBO and SGA.
A mean value engine model of a two-stroke ma-rine diesel engine with EGR that is capable of simulatingduring low load operation is developed. In order to beable to perform low load simulations, a c ...
This paper presents a method for monitoring the sludge profiles of a secondary settler using a Gaussian Mixture Model (GMM). A GMM is a parametric probability density function represented as a weig ...
Global trends in higher education including e-learning, massive open online courses, and new teaching methods have positively affected control education.Control course content has evolved due to changes in industrial practices and the increasing availability of affordable computer hardware and software.Continuous developments in virtual remote and real laboratories have made hands-on tasks more accessible and affordable.In this article, we share our experiences of undergraduate and graduate control education at the University College of Southeast Norway (USN), and Oslo and Akershus University College of Applied Sciences (HiOA).First, we present an overview of the course content at our institutions, and then, we give examples of the development of real and virtual laboratories, online course materials, new learning platforms, and teaching methods.
Semantic gap, high retrieval efficiency, and speed are important factors for content-based image retrieval system (CBIR).Recent research towards semantic gap reduction to improve the retrieval accuracy of CBIR is shifting towards machine learning methods, relevance feedback, object ontology etc.In this research study, we have put forward the idea that semantic gap can be reduced to improve the performance accuracy of image retrieval through a two-step process.It should be initiated with the identification of the semantic category of the query image in the first step, followed by retrieving of similar images from the identified semantic category in the second step.We have later demonstrated this idea through constructing a global feature vector using wavelet decomposition of color and texture information of the query image and then used feature vector to identify its semantic category.We have trained a stacked classifier consisting of deep neural network and logistic regression as base classifiers for identifying the semantic category of input image.The image retrieval process in the identified semantic category was achieved through gabor filter of the texture information of query image.This proposed algorithm has shown better precision rate of image retrieval than that of other researchers work
The Saint-Venant equation is a mathematical model which could be used to study water flow in an open channel, river, etc.The Kurganov-Petrova (KP) method, which is a second-order scheme, is used to solve the Saint-Venant equations with good stability.The water flow of a river between two hydropower stations in Norway has been simulated in this study using MATLAB and OpenModelica.The KP scheme has been used to discretize the Saint-Venant equations in the spatial domain, yielding a collection of Ordinary Differential Equations (ODEs).These are then integrated with time using the variable steplength solvers in MATLAB: ode23t, ode23s, ode45, and fixed step-length solvers: The Euler method, the second and fourth order Runge Kutta method (RK2 and RK4).In OpenModelica built-in, variable step-length DASSL solver has been used.From the simulation, it was observed that all solvers produce more or less similar results.Volumetric flowrate calculation indicated numerical oscillation with variable step-length solvers in MATLAB.The results indicated that it is reasonable to match the order of space and time discretization.
Heat transfer and pressure loss characteristics of a fin and tube heat exchanger are numerically investigated based on parametric fin geometry. The cross-flow type heat exchanger with circular tubes and rectangular fin profile is selected as a reference design. The fin geometry is varied using a design aspect ratio as a variable parameter in a range of 0.1-1.0 to predict the impact on overall performance of the heat exchanger. In this paper, geometric profiles with a constant thickness of fin base are studied. Threedimensional, steady state CFD model is developed using commercially available Multiphysics software COMSOL v5.2. The numerical results are obtained for Reynolds number in a range from 5000 to 13000 and verified with the experimentally developed correlations. Dimensionless performance parameters such as Nusselt number, Euler number, efficiency index, and area-goodness factor are determined. The best performed geometric fin profile based on the higher heat transfer and lower pressure loss is predicted. The study provides insights into the impact of fin geometry on the heat transfer performance which help escalate the understanding of heat exchanger designing and manufacturing at a minimum cost.
Under conditions of water scarcity, energy saving in operation of water pumping plants and the minimisation of water deficit for users and activities are frequently contrasting requirements, which should be considered when optimising large-scale multi-reservoirs and multi-users water supply systems. Undoubtedly, a high uncertainty level in predicted water resources due to hydrologic input variability and water demand behaviour characterizes this problem. The aim of this paper is to provide an efficient decision support system considering emergency water pumping plants activation schedules. The obtained results should allow the water system’s authority to adopt a robust decision policy, minimising the risk of harmful future decisions concerning the water resource management. The model has been here developed to manage this problem, in order to reduce the damages due to shortage of water and the energy-cost requirements of pumping plants. Particularly, in optimisation, we look for optimal rules considering both historical and generated synthetic scenarios of hydrologic inputs to reservoirs. Hence, using synthetic series, we can analyse climate change impacts and optimise the activation rules considering future hydrologic occurrences. A simulation model has been coupled with an optimization module using the stochastic gradient method to get robust pumping activation thresholds. This method allows to solve complex problems, solving efficiently large size real cases due to high number of data and variables. Thresholds values are identified in terms of critical storage levels in supply-reservoirs. Application of the modelling approach has been developed on a real case study in a water-shortage prone area in south-Sardinia (Italy), characterized by Mediterranean climate and high annual variability in hydrological input to reservoirs. By applying the combined simulation procedure, a robust decision strategy in pumping activation was obtained. Developing the stochastic gradient model, a main programming supports has been built by MATLAB efficiently interfaced with CPLEX for optimisation and Excel for inputs and results representation.
Predicting druggability and prioritising certain disease modifying targets is critical in drug discovery.Expanding the spectrum of disease-relevant targets to pharmacological manipulation is vital to reducing morbidity and mortality.We test a druggability rule, based on 10 molecular parameters (scores counting violations, denoted by score10), which uses cutpoints for each molecular parameter based on mixture clustering discriminant analysis (MC/DA) (Hudson et al., 2014).A total of 1279 small molecules from the DrugBank chem-informatics database (Knox et al., 2011), combining detailed drug (i.e.chemical, pharmacological and pharmaceutical) data with drug disease target information, were analysed and these were shown to be aligned with 173 targets.The score10 function comprised 4 traditional parameters of the rule of five (Ro5) (Lipinski, 2016), plus 5 extra parameters (polar surface area PSA, number of rotatable bonds, rings and halogens, N and O atoms) with an extra candidate of lipophicility, log D (the distribution coefficient) recently suggested by Bhal et al., 2007 as a possible preferable predictor for permeation (Zafar, Hudson et al., 2016, 2013;) to Lipinski's traditional partition coefficient, Log P, a predictor for permeation.Multivariate skew normal (SN) (Lee and Mc Lachlan 2013) and Gaussian (MN) mixture clustering identified 5 molecule groups based on the 10 predictors, or 9 predictors when the number of halogen atoms was omitted.MN clusters were highly differentiable with 3 of the 5 clusters classified as poor druggable candidates, similarly the SN clusters.Logistic regression was used to determine the best cutpoint, C, for the total number of violations, score10 (< C versus greater or equal to C, for C= 3, 4 or 5) using predictor models containing the molecule's Ro5 status (if Ro5 compliant the molecule is druggable by Lipinski's rule), oral status, and poor vs good druggability grouping based on the clustering.We studied the performance of a support vector machine (SVM) and Recursive partitioning (RP) based on the 10 molecular descriptors, to classify compounds with high or low violator scores (defined by our optimal cutpoint, C).RP was applied to find simple hierarchical rules to classify the high score violators from the low (< C).PRoC analyses (Robin et al., 2011) and logit analyses showed that a cutpoint of 5 is best in partitioning chemo-space.For either partition of the score10 function, logistic models with the MN10 cluster predictor were superior to that of the (SN10).The best model was obtained for a cutpoint of 5 (AIC = 1403.79)and established that molecules with 5 or more violations tended to be non-oral candidates (p <0.00001), MN10 poor (p <0.00001) and be Ro5 violators (p <0.00001), with a significant oral by cluster interaction (P< 0.03) found.The SVM classifier of the score10 partition (C=5) gave a Matthews coefficient C= 0.887.PROC analyses gave high values for the area under the curve (AUC) of 98.7%, with 95% CI (98.2%-99.3%),sensitivity (r) and specificity (s), 0.961 and 0.924, respectively for the training set.For the validation set SVM gave an AUC of 98.1%, 95% CI (97%-99.2%),r=0.927, s=0.983 and likewise a high C=0.818.The RP classification gave similar but slightly lower AUC and C values as the SVM.Specifically, the RP classifier for the score10 partition yielded an AUC of 95.1% with 95%CI (93.8%-96.4%),sensitivity of 0.918, specificity 0.936, and C= 0.845 for the training set; for the validation set an AUC of 95.3% with 95% CI (93.1%-97.5%),with r=0.924, s=0.886 and C=0.809.The RP rules to classify the high score violators from the low (< 5) confirmed the value of log D's inclusion in the scoring function and supported the original MC/DA cutpoints established for each molecular descriptor (Hudson et al., 2014).Our work illustrated that SVM used in combination with simple molecular descriptors can provide a reliable assessment of our simple scoring function of counts of violations partition.Moreover, molecules with score10 representing 5 or more violations were shown to be associated with specific disease targets, namely, Anti-Bacterial, Antineoplastic, Antihypertensive and Anti-allergic, within which most of the drugs have a non-oral delivery mode.Target drugs with a median score10 < 5 were Adrenergic, Dietary, Analgesics, Anti-infective, Anesthetics, Adjuvants, Anti-convulsants, Antimetabolites and Antidepressants, all of which, except Dietary and Anesthetics, were non-oral.
With climate change, it is expected that frequency and intensity of extreme weather events will increase. An example of extreme weather event is heat waves, which are defined as three or more consecutive days of unusually high temperatures. These long periods of hot weather have serious effects on health and have caused many mortalities in the past. To better cope with such weather conditions, many people have installed air-conditioners. However, when many such cooling appliances are all switched on at the same time and industry is operating, the electricity demand peaks, often leading to great stress on the network. Understanding the impact of heatwaves on the health of the electricity network is therefore important, especially as these types of events are expected to increase. We have analysed the impact of heatwave on electricity consumption in Queensland, and investigated rooftop solar panels as an option to reduce peak demand during such weather events. Two analyses were undertaken for the February 2017 heatwave for two levels of details: a global analysis at the state level and a detailed analysis on a low voltage network. The first study compares the electricity demand for the whole state of New South Wales during the heatwave event and quantifies the contribution of solar panels output in reducing electricity demand. It showed that the peak consumption was reduced by about 4% (where PV generated 597 MW over half an hour) thanks to the rooftop solar panels contribution. The second study focusses on the consumption of nine individual premises located in a same street in Townsville. The contribution of the solar panels in reducing the households’ demand from the grid varied quite significantly amongst the different users, from 0.6% to 25%, with a median of 3.5%. While the first study gives an estimate of how much solar panels can help reduce the demand on the state overall, the second one which is location specific takes into account the network constraints in terms of consumption and production of electricity. This location-specific study highlights the importance of understanding where, when and how electricity is consumed and produced. It provides a better estimate when considering solar panels to alleviate the impact of extreme weather events which can be used when planning the network.
This paper presents the simplified mechanistic model of a Multiple Hearth Furnace (MHF), developed for process control implementation.The detailed mechanistic model of the MHF and its solving procedure are introduced.Based on the detailed model, the simplified model is developed in the nonlinear Hammerstein-Wiener form, which defines a specific type of nonlinear state space models suitable for example for Model Predictive Control (MPC) implementation.The simplified model aims to preserve the key physicalchemical phenomena taking place in the furnace and to reproduce the nonlinear dependencies between the input and output variables.Finally, the paper presents the simulation results to compare the mechanistic and the simplified models.The comparison confirms that the dynamics of the simplified model accurately follows the mechanistic model outputs.
