Optimal Network Configuration Research Articles

Multi-period water network management for industrial parks considering predictable variations

Designing processes in industrial parks by breaking plant boundaries will produce more resource and economic benefits than just focusing on every single plant. This article addresses the water management problem in industrial parks with considering two major points: (1) the water network synthesis problem towards park scenario in each time period, (2) the multi-period problem resulted from predictable development and adjustment variations of park. To achieve the optimal network configurations and water management plans for the operational scenarios in the future, an optimization mathematical model is formulated based on the proposed network superstructure including indirect water reuse and the planning strategies for multi-period concern. At last, three design cases are illustrated to demonstrate the application of the methodology.

A Highly Stretchable and Fatigue‐Free Transparent Electrode Based on an In‐Plane Buckled Au Nanotrough Network

Flexible transparent electrodes are an indispensable component of next‐generation soft optoelectronics such as wearable electronics and electronic artificial skins (E‐skins). Among the existing candidate materials, metal nanotrough networks exhibit optimal overall optoelectronic performance with impressive bendability. However, their further practical applications are hindered by their limited mechanical stretchability, which is highly desired in biointegrated systems. Here it is demonstrated that superior mechanical stretchability with tensile strains up to 300% can be achieved in Au nanotrough networks by introducing an in‐plane sinusoidal wavy structure. For the first time it is shown that with a precisely tuned nanotrough geometry along with an optimized network configuration, the buckled Au nanotrough network can be repeatedly stretched to strains up to 120% for 100 000 cycles, exhibiting excellent fatigue performance. Such highly stretchable and fatigue‐free (at a high tensile strain up to 100%) Au nanotrough networks present a competent sheet resistance (Rsh ≈ 10 Ω sq−1) with high optical transparency (T = 91%), which are demonstrated to be highly compatible with human skins as conformal flexible transparent electrodes.

Comparison between system design optimization strategies for more electric aircraft network

The aircraft electric network is a complex system, consisting of many different elements integrated to form a unique entity, designed to perform a well-defined mission. In the current state, the network conceptual design is based on standards defined by the aircraft manufacturer. As a consequence, electric subsystem suppliers are doing local optimizations to fulfill these standards in a separated way through a ``mechanistic approach''. This results in a set of optimized subsystems which is not necessarily ``optimal'' with respect to the network level. To overcome this problem, we present a design approach called EPFM (Extended Pareto Front Method) based on separated subsystem optimizations which aims at finding an optimal configuration of the electrical network at the system level. The EPFM is discussed with regard to the computational cost and the collaboration requirements in the aeronautical industrial context and compared with the classical mechanistic approach.

Distributed Optimization Framework for Energy Management of Multiple Smart Homes With Distributed Energy Resources

This paper proposes a distributed optimization algorithm for scheduling the energy consumption of multiple smart homes with distributed energy resources. In the proposed approach, the centralized optimization problem for home energy management is decomposed into a two-level optimization problem, corresponding to the local home energy management system (LHEMS) at the first level and the global home energy management system (GHEMS) at the second level. The controllable household appliances (e.g., air conditioner and washing machine) are scheduled in the LHEMS within the consumer’s preferred appliance scheduling and comfort level, while the energy storage system and power trading between households are scheduled in the GHEMS. In the simulation study, the proposed distributed algorithm shows almost equivalent performance to the centralized algorithm in terms of the electricity cost and the consumer’s comfort level. The impact of different network topologies on the proposed algorithm is also analyzed, and the result provides insight into the selection of the optimal network configuration in view of the consumer’s electricity cost saving.

Reliability and cost optimisation of complex electric power networks using ant colony algorithm

The article presents a new approach towards reducing an overall cost of electric power network with maintaining its reliability. Goals are achieved by implementing an ant colony algorithm with a cut-set method as a method for reliability evaluation. The algorithm solves the problem of multi-objective optimisation, where both the network cost and network reliability index, known as unavailability, should be minimalised. The network cost is considered as a linear function of overall length of network’s connections. For reliability evaluation in the cut-set method, real empiric data of hazard rate for overhead power lines are used. Parallel-series network structure, equivalent by means of reliability to analysed network, is generated through the cut-set method to compute unavailability of trial solutions. Sections of the structure are generated on the basis of minimum cut set, found by the algorithm for finding one- and two- minimum cuts. As used algorithm for finding minimum cuts has linear complexity, the evaluation of trial solutions is computationally effective. An example, presented in this article, provides figure of optimal network configurations found by the algorithm.

A Study of Energy Optimisation of Urban Water Distribution Systems Using Potential Elements

Energy use in water supply systems represents a significant portion of the global energy consumption. The electricity consumption due to the water pumping represents the highest proportion of the energy costs in these systems. This paper presents several comparative studies of energy efficiency in water distribution systems considering distinct configurations of the networks and also considers implementation of the variable-speed pumps. The main objective of this study is the energy optimisation of urban systems using optimal network configurations that reduce energy consumption and improve energy efficiency. The paper describes in detail four strategies for improving energy efficiency of water pumping: control systems to vary pump speed drive according to water demand, pumped storage tanks, intermediary pumping stations integrated in the network, and elevated storage tanks floating on the system. The improving energy efficiency of water pumping is briefly reviewed providing a representative real case study. In addition, a different approach for the hydraulic analysis of the networks and the determination of the optimal location of a pumped storage tank is provided. Finally, this study compares the results of the application of four water supply strategies to a real case in Romania. The results indicate high potential operating costs savings.

Reliability Surrogate Measures for Water Distribution System Design: Comparative Analysis

AbstractBecause of the large computational burden associated with the direct assessment of reliability, the indirect indices of reliability have recently received more attention in the framework of water distribution system design. Two new energy-related indices for reliability evaluation (i.e., available power index and pipe hydraulic resilience index) are developed in this paper. The performance of these new indices is evaluated and compared with that of four existing indices (three other energy-related indices—i.e., resilience index, network resilience index, and modified resilience index—and the entropy-based method, i.e., diameter-sensitive flow entropy) according to the following two-step methodology. In the first step, the application of the multiobjective optimization makes it possible to determine optimal network configurations that trade-off the installation cost (to be minimized) against the generic indirect reliability index (to be maximized). In the second step, the performance of the optimal...

Mixed-signal VLSI neural network based on Continuous Valued Number System

Mixed-signal neural networks have higher energy efficiency and lower area consumption when compared with their equivalent digital implementations. However, the signal processing precision in mixed-signal implementations is limited. A mixed-signal arithmetic method called Continuous Valued Number Systems (CVNS) is employed for development and implementation of different functions required in a neuronal network. Analog digits of this number system enable the higher accuracy of analog operations, and can efficiently relate with digital and binary values. In this paper, design and implementation of a 2-2-1 mixed-signal CVNS network structure is presented, to confirm the developed arithmetic method. In the proposed structure, weights are stored in the digital registers while the arithmetic is based on the CVNS. The CVNS features have been exploited to address the limited signal processing precision issue, which requires resolutions higher than 12-bit for online training of the network. As a result, the proposed structure satisfies this requirement of neural networks, while yielding an optimized network configuration. The proposed network is designed, fabricated, and tested in 0.18μm technology.

Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments

Self-organizing networks (SONs) are expected to minimize operational and capital expenditure of the operators while improving the end users’ quality of experience. To achieve these goals, the SON solutions are expected to learn from the environment and to be able to dynamically adapt to it. In this work, we propose a learning-based approach for self-optimization in SON deployments. In the proposed approach, the learning capability has the central role to perform the estimation of key performance indicators (KPIs) which are then exploited for the selection of the optimal network configuration. We apply this approach to the use case of dynamic frequency and bandwidth assignments (DFBA) in long-term evolution (LTE) residential small cell network deployments. For the implementation of the learning capability and the estimation of KPIs, we select and investigate various machine learning and statistical regression techniques. We provide a comprehensive analysis and comparison of these techniques evaluating the different factors that can influence the accuracy of the KPI predictions and consequently the performance of the network. Finally, we evaluate the performance of learning-based DFBA solution and compare it with the legacy approach and against an optimal exhaustive search for best configuration. The results show that the learning-based DFBA achieves on average a performance improvement of 33 % over approaches that are based on analytical models, reaching 95 % of the optimal network performance while leveraging just a small number of network measurements.

An optimisation-based framework for the conceptual design of reaction-separation processes

Prior to detailed process design, it is vital to first generate a good flowsheet that meets particular objective. This is particularly the case in bio-based materials and products, where, given a range of chemistries, the synthesis problem is not about the best way to make a particular product but rather the best way to convert a specific feedstock. In order to do so, an optimisation-based framework, which can be used to identify the optimal configuration of a process network that consists of both reactions and separation systems to achieve maximum economic potential, is presented in this paper. A process superstructure, which includes the concept of master reaction stages and subsidiary separation stages, is introduced to facilitate the theory. The problem is formulated as a generalised mixed integer linear programming (MILP) model which accounts for the simultaneous selection of products and identification of the process configuration. The solution of the optimisation problem includes the best possible economic performance, identification of active reactions, reaction ordering and separation sequences along with the corresponding flowsheet of the optimal process. The economic criterion takes account of raw materials costs, product values and separation related costs. Two bio-based chemical case studies are presented to illustrate the applicability of the proposed methodology.

A Genetic Based Algorithm for Intentional Islanding of Distribution Network to Maximize Social Welfare

An electricity distribution system is a vital part of a total electric power supply system, since it is a link between a bulk transmission system and customers. It has been estimated that 80% of supply interruptions faced by customers are due to failures that occur in the distribution network. To secure more costumers with increasing competition, the energy supply companies are now increasingly under pressure to maintain a high degree of uninterrupted power supply quality and reliability. Therefore a practice of disconnecting all distributed generation units once a fault occurs is not a practical solution in a deregulated electricity market environment since it relies on power outage. Operation of safe intentional islanding would be viable solution. The aim of this paper is to present a genetic-based coordinated methodology for islanding operation and management of active and reactive powers in a distribution network after occurrence of a fault with regard to cost related to active and reactive powers generation of distributed generations, price of energy purchasing from a retail market, and loads interruption cost. Since optimal operation and islands configuration of a distribution network is a complicated and discrete problem, the genetic algorithm has been used for implementation and solving of the proposed algorithm. Simulations have been applied on 33-bus IEEE test system in three scenarios according to faulty branches and DGs penetration rate.

Configuration Analysis of Two-Dimensional Resection Networks

AbstractThe optimal configuration of a resection network plays an important role in the precision analysis of free stations. The two conditions of the optimal configuration for distance-only resection networks were derived. Based on the two conditions, the regular sector configuration was proposed, and the character of the optimal configuration was determined. Furthermore, the geometric dilution of precision (GDOP) for a generic direction resection network with n known points was deduced. The conclusion was reached that A-D-E-optimality cannot be simultaneously achieved in the direction resection network. The pursuit of only the lowest GDOP is impractical in engineering, whereas the E-optimality criterion is suitable in such a situation, as shown by cases with three known points. There are typically two E-optimal configurations for a three-known-point direction resection network, and the regular pattern was explored. Finally, the configuration of a heterogeneous resection network was analyzed. An in-depth...

Flood Forecasting via Time Lag Forward Network; Kelantan, Malaysia

Forecasting water level is one of the critical issues in Malaysia for Kelantan region. Based on the flood events in 2014, this study investigates the hourly-forecasting of water level in one station namely Kg Jenob in Kelantan. For this issue, Time Lag Forward Network (TLFN) is evaluated for forecasting the water level as dynamic model. Heuristic method in stepwise forward methodology is performed. Rainfall and water level are the input and output of the modelling respectively. For selected flood period 15/12/2014 to 30/12/2014, 8 scenarios are developed to obtain a minimum error in water level forecasting. By monitoring the error, it will show that the optimum configuration of network has 2 processors in hidden layer and 7 lags have enough contribution on the result of hourly forecasting. Transfer functions in hidden and output layers are is Tangent hyperbolic and bias. Observed and simulated data are compared with usual error criteria called Mean Square Error (MSE) and Root Mean Square Error (RMSE) which obtained 0.005 and 0.07 respectively. In conclusion, this study will be as a baseline for Kelantan to show that TLFN has promising result to forecast the flood events.

A Planning Approach for the Network Configuration of AC-DC Hybrid Distribution Systems

This paper proposes a novel stochastic planning model for AC-DC hybrid distribution systems (DSs). Taking into account the possibility of each line/bus being AC or DC, the model finds the optimal AC-DC hybrid configuration of buses and lines in the DS. It incorporates consideration of the stochastic behavior of load demands and renewable-based distributed generators (DGs). The stochastic variations are addressed using a Monte-Carlo simulation technique. The objective of the planning model is the minimization of DS installation and operation costs. The optimal planning solution is obtained by dividing the hybrid planning problem into two nested optimization problems: 1) the main problem is formulated using a genetic algorithm (GA) to search for the optimal AC-DC configuration and 2) the subproblem is used for determining the optimal power flow solution for each configuration generated by the GA. The proposed model has been employed for finding the optimal configuration for a suggested case study that included photovoltaic panels, wind-based DG, and electric vehicle charging stations. The same case study was also solved using a traditional AC planning technique in order to evaluate the effectiveness of the proposed model and the associated cost-savings. The results demonstrate the advantages offered by the proposed model. The proposed framework represents an effective technique that can be used by DS operators to identify the optimal AC-DC network configuration of future DSs.

On Network Topology Reconfiguration for Remote State Estimation

In this paper, we investigate network topology reconfiguration in wireless sensor networks for remote state estimation, where sensor observations are transmitted, possibly via intermediate sensors, to a central gateway/estimator. The time-varying wireless network environment is modelled by the notion of a network state process, which is a randomly time-varying semi- Markov chain and determines the packet reception probabilities of links at different times. For each network state, different network configurations can be used, which govern the network topology and routing of packets. The problem addressed is to determine the optimal network configuration to use in each network state, in order to minimize an expected error covariance measure. Computation of the expected error covariance cost function has a complexity of O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MΔmax</sup> ), where M is the number of sensors and Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> is the maximum time between transitions of the semi- Markov chain. A sub-optimal method which minimizes the upper bound of the expected error covariance, that can be computed with a reduced complexity of O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> ), is proposed, which in many cases gives identical results to the optimal method. Conditions for estimator stability under both the optimal and suboptimal reconfiguration methods are derived using stochastic Lyapunov functions. Numerical results and comparisons with other low complexity approaches demonstrate the performance benefits of our approach.

Design of a sensitive air quality monitoring network using an integrated optimization approach

A new method is developed to design a multi-objective and multi-pollutant sensitive air quality monitoring network (AQMN) for an industrial district. A dispersion model is employed to estimate the ground level concentration of the air pollutants emitted from different emission sources. The primary objective of AQMN is providing the maximum information about the pollutant with respect to (1) maximum coverage area, (2) maximum detection of violations over ambient air standards and (3) sensitivity of monitoring stations to emission sources. Ant Colony Optimization algorithm (ACO) and Genetic Algorithm (GA) are adopted as the optimization tools to identify the optimal configuration of the monitoring network. The comparison between the results of ACO and GA shows that the performance of both algorithms is acceptable in finding the optimal configuration of AQMN. The application of the method to a network of existing refinery stacks indicates that three stations are suitable to cover the study area. The sensitivity of the three optimal station locations to emission sources is investigated and a database including the sensitivity of stations to each source is created.

1-Hub, 2-hub or fully connected network? A theoretical analysis of the optimality of airline network structure

Multi-hub network structures are an increasingly important phenomenon in today’s airline networks. With current theoretical arguments for the single-hub solution leaving little room for the kind of multi-hub networks that exist nowadays, this paper sets up a formal model to explore the optimality of multi-hub networks, with or without competition. It is shown that a single-hub or a fully connected network may not be the optimal network configuration, while a 2-hub configuration may be favored under certain circumstances. In addition, the paper shows that competition can also affect an airline’s optimal choice of network: a 2-hub network can be preferable if a competitor enters the market.

Optimal design of adhesive composition in footwear industry based on creep rate minimization

On the footwear industry, the composition of adhesives has a high contribution for the product quality. This paper aims to develop a model capable to predict and optimize the creep rate using the composition of the adhesive joints. The proposed mixed numerical and experimental approach is based on following stages: the planned experimental measurements, the learning procedure aiming to obtain the optimal artificial neural network (ANN) configuration, and the optimal design procedure for adhesive joint composition. The design variables are the weight percentages of the solid raw material constituents of the adhesive, such as polyurethanes (PUs), resins, and additives. Considering the experimental results obtained for Taguchi design points as input/output patterns, the ANN is developed based on supervised evolutionary learning. In the last stage, the optimal solution for adhesives composition considering minimum creep rate is investigated based on ANN and genetic algorithm. The optimal results for creep rate minimization based on proposed approach are reached when large quantities for PUs and for some additives are considered, and when colophony and vinyl resin are not considered on the formulation. The sensitivity of the structural response of footwear adhesives to composition constituents is also studied based on Sobol indices obtained from ANN–Monte Carlo simulation procedure. The performance measured by creep rate of the adhesive joint is very sensitive to the influence of some polyurethanes, and a particular sensitivity to caprolactone types with extremely high crystallization is observed. The sensitivities of the creep rate to the resins colophony and coumarone–indene are also important.

Zero-load predictive model for performance analysis in deflection routing NoCs

We study a static model for 2-D and 3-D networks that accurately represents the average distance travelled by packets under deflection routing, which is a specific form of adaptive routing. The model captures static properties of the network topology and the spatial distribution of traffic, but does not take into account traffic loading and congestion. Even though this static model cannot accurately predict packet latency under high load, we contend that it is a perfect predictor of deflection routing networks’ relative performance under any load condition below saturation, and thus always correctly predicts the optimum network configuration. This is verified through cycle-accurate simulations of congested and uncongested networks with fully adaptive, deflection routing for regular traffic patterns such as uniform random, localised, bursty, and others, as well as irregular patterns in both regular and irregular networks. As the networks with minimal average distance perform best even under high traffic load, the average distance model establishes a robust relation between a static network property, average distance, and network performance under load, providing new insight into network behaviour and an opportunity to identify the optimal network configuration without time-consuming simulations.

Planning Large Single Frequency Networks for DVB-T2

The final coverage and associated performance of a single frequency networks (SFNs) is a joint result of the properties of all transmitters in the SFN. Due to the large number of parameters involved in the process, finding the right configuration is quite complex. The purpose of this paper is to find optimal SFN network configurations for second generation digital terrestrial broadcast system (DVB-T2). Offering more options of system parameters than its predecessor DVB-T, DVB-T2 allows large SFN networks. However, self-interference in SFNs gives rise to restrictions on the maximum intertransmitter distance and the network size. In order to make optimum use of the spectrum, the same frequency can be reused over different geographical areas-beyond the reuse distance to avoid co-channel interference. In this paper, a methodology based on theoretical network models is proposed. A number of network architectures and network reference models are considered here for different reception modes in order to study the effects of key planning factors on the maximum SFN size and minimum reuse distance. The results show that maximum bitrate, network size, and reuse distance are closely related. In addition, it has been found that the guard interval is not the only limiting parameter and that its impact strongly depends on the rest of DVB-T2 mode parameters as well as on the network characteristics (equivalent radiated power, effective height, and intertransmitter distance). Assuming that the carrier to noise ratio requirements are in the vicinity of 20 dB and bitrates over 30 Mb/s, it has been found that the network can be as large as 360 × 360 km (delivering 39.2 Mb/s) or even 720 × 720 km (delivering 37.5 Mb/s). The reuse distance will also have a complex dependency on the DVB-T2 mode and especially the network parameters, ranging from below 100 to 300 km.