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Ali Moridi - One of the best experts on this subject based on the ideXlab platform.
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Pressure Management Model for Urban Water Distribution Networks
Water Resources Management, 2010Co-Authors: Sara Nazif, Massoud Tabesh, Mohammad Karamouz, Ali MoridiAbstract:A technique for leakage reduction is Pressure Management, which considers the direct relationship between leakage and Pressure. To control the hydraulic Pressure in a water distribution system, water levels in the storage tanks should be maintained as much as the variations in the water demand allows. The problem is bounded by minimum and maximum allowable Pressure at the demand nodes. In this study, a Genetic Algorithm (GA) based optimization model is used to develop the optimal hourly water level variations in a storage tank in different seasons in order to minimize the leakage level. Resiliency and failure indices of the system have been considered as constraints in the optimization model to achieve the minimum required performance. In the proposed model, the results of a water distribution simulation model are used to train an Artificial Neural Network (ANN) model. Outputs of the ANN model as a hydraulic Pressure function is then linked to a GA based optimization model to simulate hydraulic Pressure and leakage at each node of the water distribution network based on the water level in the storage tank, water consumption and elevation of each node. The proposed model is applied for Pressure Management of a major Pressure zone with an integrated storage facility in the northwest part of Tehran Metropolitan area. The results show that network leakage can be reduced more than 30% during a year when tank water level is optimized by the proposed model.
Dongming Liu - One of the best experts on this subject based on the ideXlab platform.
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Distributed zone MPC of Pressure Management for water distribution network systems
IET Control Theory & Applications, 2019Co-Authors: Dongming Liu, Yi ZhengAbstract:In the Pressure Management of large scale water distribution network (WDN), a distributed zone model predictive control (MPC) is proposed to keep the terminal water head in thedesired Pressure range for satisfying the customer's demand, avoiding frequently operating of the actuator and reducing the correlation between subsystems. To ensure the existence of feasible solutions of constrained distributed zone MPC, a new reference trajectory of the tank level is introduced as an optimised variable. With the consideration of the desired Pressure range constraints on the new reference trajectory and some physical constraints on the corresponding physical variables, a distributed zone MPC is presented to minimise the weighted sum of the three terms in the proposed performance index. To achieve the convergence of distributed zone MPC optimisation problem, an augmented Lagrangian formulation is applied to the distributed coordinated strategy. The proposed distributed zone MPC is applied to the WDN in the Shinan district of Shanghai, and the effectiveness of the method is illustrated.
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Wiener model of Pressure Management for water distribution network
International Journal of Modelling Identification and Control, 2018Co-Authors: Dongming LiuAbstract:In this paper, the Pressure Management model of water distribution network (WDN) is established, which relies on the hydraulic law of the whole WDN, not the relationship between the Pressure and flow based on the leakage. In view of the specific WDN, the configuration is pumped into a closed system with Pressure control. And the Pressure Management model of WDN is divided into two modules by all the tanks which are extracted from the WDN. The two modules are linear dynamic module and nonlinear static module in series. A Wiener model is proposed to describe the nonlinear dynamic relationship of the Pressure Management model. Moreover, the theoretical data calculated by the Wiener model are compared with the real data generated by EPANET to show the effectiveness and high accuracy of our proposed modelling method.
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Predictive zone control of Pressure Management for water supply network systems
International Journal of Automation and Computing, 2016Co-Authors: Dongming LiuAbstract:In this paper we address the problem of Pressure Management in water supply system (WSS) network. The model-based predictive control (MPC) strategies have some important features to deal with WSS. By hydraulic analysis of WSS, the predictive model is derived from the dynamic model and static model of WSS. Through WSS, the consumers' demands are required to be met at all times according to some operational constraints that must be satisfied. The constraints of flow through actuators, the water level of reservoirs and the consumer areas' Pressure demand are determined by a specific system. In this work, we develop a constrained MPC controller that considers the zone control of the Pressure outputs and incorporates steady state economic targets in the control cost function. The designed Management strategies are applied to a case study and simulation results, covering different aspects, are provided. The output nodal Pressure can be controlled in the desired zone by optimal scheduling the actuators of the WSS. If the variation range of reservoir's water level is broader, the rate of flow through the actuators is gentle, and vice versa.
Aonghus Mcnabola - One of the best experts on this subject based on the ideXlab platform.
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Pressure Management and energy recovery in water distribution networks: Development of design and selection methodologies using three pump-as-turbine case studies
Renewable Energy, 2017Co-Authors: Tracey Lydon, Paul Coughlan, Aonghus McnabolaAbstract:Abstract Energy consumption in water distribution networks is a widely publicised problem. Similarly the control of leakage through Pressure Management in water networks has also received significant attention in the literature. This paper outlines progress on the development of micro-hydropower systems for energy recovery and Pressure Management in water distribution networks. Design and selection methodologies are outlined for pump-as-turbines (PAT) to recover energy and control Pressure at 3 case study Pressure reducing valves (PRV) in the water distribution network (WDN) of Dublin, Ireland. This investigation comprised the use of experimental characterisation of a laboratory scale prototype PAT, extrapolation of these results to larger scales using Suter and Affinity laws, and the assessment of their performance against real-world flow and Pressure data. An assessment of existing PAT selection methodologies was also conducted and compared against the experimental data. The results of this investigation highlight that up to 40% of the gross power potential of an existing PRV could be converted to electrical energy using a PAT while also controlling Pressure. Existing PAT selection methodologies did not concur well with the experimental results. The use of 2 PATs in parallel to increase the efficiency of the overall system achieved marginal improvements in performance.
Luciano Fasotti - One of the best experts on this subject based on the ideXlab platform.
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Training patients in Time Pressure Management, a cognitive strategy for mental slowness
Clinical rehabilitation, 2009Co-Authors: Ieke Winkens, Caroline M. Van Heugten, Derick T Wade, Luciano FasottiAbstract:Purpose: To provide clinical practitioners with a framework for teaching patients Time Pressure Management, a cognitive strategy that aims to reduce disabilities arising from mental slowness due to acquired brain injury. Time Pressure Management provides patients with compensatory strategies to deal with time Pressure in daily life. Application of the training in clinical practice is illustrated using two case examples from a randomized controlled trial on the effectiveness of Time Pressure Management for patients with stroke. Rationale: The Time Pressure Management approach is based on Michon's task analysis, describing levels of decision-making in complex cognitive tasks. Decisions with little or no time Pressure are not impaired by mental slowness. Therefore, patients should try to transfer actions from situations with high time Pressure to situations where the preserved decision levels with little or no time Pressure can work. Theory into practice: Several factors are required to teach patients to use Time Pressure Management. First, sufficient awareness is needed to recognize that there is a deficit and behavioural change is necessary. Sufficient awareness is also required to recognize and anticipate time Pressure situations and to realize that the strategy is helpful and might also be useful in new and more difficult circumstances. Second, adequate motivation is needed to learn the strategy. And finally, the training should be adjusted to the patient's individual learning abilities and cognitive skills.
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Efficacy of Time Pressure Management in Stroke Patients With Slowed Information Processing: A Randomized Controlled Trial
Archives of physical medicine and rehabilitation, 2009Co-Authors: Ieke Winkens, Caroline M. Van Heugten, Derick T Wade, Esther J. Habets, Luciano FasottiAbstract:Abstract Winkens I, Van Heugten CM, Wade DT, Habets EJ, Fasotti L. Efficacy of Time Pressure Management in stroke patients with slowed information processing: a randomized controlled trial. Objective To examine the effects of a Time Pressure Management (TPM) strategy taught to stroke patients with mental slowness, compared with the effects of care as usual. Design Randomized controlled trial with outcome assessments conducted at baseline, at the end of treatment (at 5–10wk), and at 3 months. Setting Eight Dutch rehabilitation centers. Participants Stroke patients (N=37; mean age ± SD, 51.5±9.7y) in rehabilitation programs who had a mean Barthel score ± SD at baseline of 19.6±1.1. Intervention Ten hours of treatment teaching patients a TPM strategy to compensate for mental slowness in real-life tasks. Main Outcome Measures Mental Slowness Observation Test and Mental Slowness Questionnaire. Results Patients were randomly assigned to the experimental treatment (n=20) and to care as usual (n=17). After 10 hours of treatment, both groups showed a significant decline in number of complaints on the Mental Slowness Questionnaire. This decline was still present at 3 months. At 3 months, the Mental Slowness Observation Test revealed significantly higher increases in speed of performance of the TPM group in comparison with the care-as-usual group ( t =–2.7, P =.01). Conclusions Although the TPM group and the care-as-usual group both showed fewer complaints after a 3-month follow-up period, only the TPM group showed improved speed of performance on everyday tasks. Use of TPM treatment therefore is recommended when treating stroke patients with mental slowness.
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time Pressure Management as a compensatory strategy training after closed head injury
Neuropsychological Rehabilitation, 2000Co-Authors: Luciano Fasotti, Feri Kovacs, P A T M Eling, Wiebo BrouwerAbstract:Following severe closed head injury, deficits in speed of information processing are common. As a result, many head-injured patients experience a feeling of “information overload” in daily tasks that once were relatively easy. Many remedial programmes have been designed that treat different aspects of attention (often including mental speed requirements) by repetitive exercises. In the present study, a different approach to slow information processing has been taken, namely Time Pressure Management (TPM). TPM consists of a set of alternative cognitive strategies that allow head-injured patients in real-life tasks to compensate for their mental slowness. In a randomised pre-training vs. post-training vs. follow-up group study, the effectiveness of TPM training was compared with concentration training in which verbal instruction was the key element. The results indicate that specific TPM strategies are learned by the experimental subjects but that both treatments improve task performance significantly for a...
Sara Nazif - One of the best experts on this subject based on the ideXlab platform.
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Pressure Management Model for Urban Water Distribution Networks
Water Resources Management, 2010Co-Authors: Sara Nazif, Massoud Tabesh, Mohammad Karamouz, Ali MoridiAbstract:A technique for leakage reduction is Pressure Management, which considers the direct relationship between leakage and Pressure. To control the hydraulic Pressure in a water distribution system, water levels in the storage tanks should be maintained as much as the variations in the water demand allows. The problem is bounded by minimum and maximum allowable Pressure at the demand nodes. In this study, a Genetic Algorithm (GA) based optimization model is used to develop the optimal hourly water level variations in a storage tank in different seasons in order to minimize the leakage level. Resiliency and failure indices of the system have been considered as constraints in the optimization model to achieve the minimum required performance. In the proposed model, the results of a water distribution simulation model are used to train an Artificial Neural Network (ANN) model. Outputs of the ANN model as a hydraulic Pressure function is then linked to a GA based optimization model to simulate hydraulic Pressure and leakage at each node of the water distribution network based on the water level in the storage tank, water consumption and elevation of each node. The proposed model is applied for Pressure Management of a major Pressure zone with an integrated storage facility in the northwest part of Tehran Metropolitan area. The results show that network leakage can be reduced more than 30% during a year when tank water level is optimized by the proposed model.