Optimum design of networks that use operational amplifiers

Optimal transport network design for both traffic safety and risk equity considerations

The choice of an optimal neural network design for a given problem is addressed. A relationship between optimal network design and statistical model identification is described. A derivative of Akaike's information criterion (AIC) is given. This modification yields an information statistic which can be used to objectively select a ;best' network for binary classification problems. The technique can be extended to problems with an arbitrary number of classes.

Pipeline transport is the major means for large‐scale and long‐distance CO2 transport in a CO2 capture and sequestration (CCS) project. But optimal design of the pipeline network remains a challenging problem, especially when considering allocation of intermediate sites, like pump stations, and selection of pipeline routes. A superstructure‐based mixed‐integer programming approach for optimal design of the pipeline network, targeting on minimizing the overall cost in a CCS project is presented. A decomposition algorithm to solve the computational difficulty caused by the large size and nonlinear nature of a real‐life design problem is also presented. To illustrate the capability of our models. A real‐life case study in North China, with 45 emissions sources and four storage sinks, is provided. The result shows that our model and decomposition algorithm is a practical and cost‐effective method for pipeline networks design. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2442–2461, 2014

This paper examines the optimum backbone network for microcellular communications using subcarrier multiplexed optical fiber link.

Abstract. This is the second part of a two-part paper considering a measurement network design based on a stochastic Lagrangian particle dispersion model (LPDM) developed by Marek Uliasz, in this case for South Africa. A sensitivity analysis was performed for different specifications of the network design parameters which were applied to this South African test case. The LPDM, which can be used to derive the sensitivity matrix used in an atmospheric inversion, was run for each candidate station for the months of July (representative of the Southern Hemisphere winter) and January (summer). The network optimisation procedure was carried out under a standard set of conditions, similar to those applied to the Australian test case in Part 1, for both months and for the combined 2 months, using the incremental optimisation (IO) routine. The optimal network design setup was subtly changed, one parameter at a time, and the optimisation routine was re-run under each set of modified conditions and compared to the original optimal network design. The assessment of the similarity between network solutions showed that changing the height of the surface grid cells, including an uncertainty estimate for the ocean fluxes, or increasing the night-time observation error uncertainty did not result in any significant changes in the positioning of the stations relative to the standard design. However, changing the prior flux error covariance matrix, or increasing the spatial resolution, did. Large aggregation errors were calculated for a number of candidate measurement sites using the resolution of the standard network design. Spatial resolution of the prior fluxes should be kept as close to the resolution of the transport model as the computing system can manage, to mitigate the exclusion of sites which could potentially be beneficial to the network. Including a generic correlation structure in the prior flux error covariance matrix led to pronounced changes in the network solution. The genetic algorithm (GA) was able to find a marginally better solution than the IO procedure, increasing uncertainty reduction by 0.3 %, but still included the most influential stations from the standard network design. In addition, the computational cost of the GA compared to IO was much higher. Overall the results suggest that a good improvement in knowledge of South African fluxes is available from a feasible atmospheric network, and that the general features of this network are invariable under several reasonable choices in a network design study.

Reported here are the statistical results leading to the design of an optimum oceanographic network in the interior midlatitude North Pacific from 30 to 50°N, the primary function of which is to detect the generation and evolution of large-scale temperature anomalies in both the surface and subsurface layers of the upper 50 m of ocean. The method used in this optimum network design is based on linear least-squares estimation developed by Gandin (1963), wherein it is necessary to determine the first and second statistical moments (i.e., mean and covariance distribution, respectively) of the variable field, leading to the estimation of the dominant space and time scales of variability, as well as the signal-to-noise ratio. Having determined this information on the statistical structure of the thermal field in the interior midlatitude North Pacific (i.e., Lx = 1500 km, Ly = 1000 km, T = 10 months, S/N = 0.5), the minimum sampling density (i.e., 1 station per 200 km square per month) and maximum instrument error (a lσ accuracy of 0.2°C) are defined, necessary to detect the large-scale thermal variability. This latter information is then used in the actual construction of an oceanographic network, where since January 1976 XBT systems (having a lσ accuracy of 0.07°C) have been placed aboard 22 ships of opportunity that ply the trade routes between the west coast of North America and Japan. Examples of temperature anomaly maps, constructed monthly from 300 XBT's taken randomly over the region 30–50°N, 140–150°E, are presented. As a check on map reliability, the surface and subsurface temperature maps produced by this XBT network are compared with surface maps constructed by ship-injection temperature and subsurface maps constructed from research vessel XBT data.

The optimum design of heat exchanger networks is considered in two stages. In the first, the optimum configuration for fixed values of the continuous variables is determined using an implicit enumeration algorithm. In the second stage the optimization of continuous variables of the network is performed solving a large scale nonlinear programme. The proposed method for discrete variables lessens considerably the computational effort for solving the combinatorial problem in the first stage. The results show the importance of optimizing the continuous variables of these networks.

This paper studies the optimum design of beam networks modeled with Timoshenko beams. To account for multiple load cases, an auxiliary optimal control problem is introduced. Optimal distributed control problems for Timoshenko beam networks are solved through the associated optimality system, where the shape functional of the network is defined by the optimal value of the control cost. For control problems exhibiting the turnpike property, the optimum network design is carried out using the steady-state beam model and the corresponding steady-state control problem. A domain decomposition method is adopted to handle topological changes, while the Steklov–Poincaré operator is used to reformulate the beam network model as an interface problem on subdomain boundaries. This approach is applicable under additional assumptions on the network loading. Consequently, the topological derivative of the Steklov–Poincaré operator is incorporated into the optimality system of the control problem, enabling sensitivity analysis with respect to topological changes. The topological derivative of the cost functional with respect to the size of small cycles is derived and computed. Finally, numerical experiments are presented to illustrate and corroborate the analytical results.

Optimal design of inter-plant hydrogen networks with intermediate headers of purity and pressure

In this paper, we have considered an optimal design and provisioning of WDM networks for the grooming of sub-wavelengths traffic requests. As higher layer electronic ports, such as transceivers and optical splitters are dominant cost factors of an optical network, it is essential to reduce their numbers of use while grooming the multicast traffic requests into high bandwidth light-trees. This paper provides an optimal cost design of WDM networks with multicast traffic grooming under static traffic demand. We develop a unified framework for the optimal provisioning of different practical scenarios of multicast traffic grooming in a static traffic scenario. In this study, we design an Integer linear Programming (ILP) formulation for multicast traffic grooming to minimize the cost associated with the higher layer electronic ports such as transceivers, splitters and wavelengths, and simultaneously maximize the bandwidth utilization of the network. We propose a heuristic algorithm called Efficient Light-Tree based Multicast Traffic Grooming (ELT-MTG) algorithm to achieve scalability in large size optical networks. Simulations are conducted on several standard well-known WDM mesh networks to study the design cost (based on number of transceivers, optical splitters and wavelengths) used in the networks. The result, thus obtained by comparison, helped us to conclude that the proposed approach ELT-MTG gives better performance than well-known existing logical-first sequential routing with single-hop grooming (LFSEQSH) and logical-first sequential routing with multi-hop grooming (LFSEQMH) algorithms in a static traffic environment.

Combining reinforcement learning with mathematical programming: An approach for optimal design of heat exchanger networks

LP-based solution strategies for the optimal design of industrial water networks with multiple contaminants

This paper presents cross entropy (CE) optimization for optimal design of water distribution networks (WDN) under demand uncertainty. In design of WDNs, it is desired to achieve a minimum cost WDN that provides higher reliability in meeting the demands. To achieve these goals, an optimization model is formulated for design of WDNs with an objective of minimizing the total cost of WDN subject to meeting the nodal demands at a specified system reliability, mass conservation and other physical constraints. The uncertainty in future water demands is modeled using the theory of fuzzy random variable (FRV). The water demand at each node is assumed to be following a normal distribution with a fuzzy mean, and 10 % (or 20 %) of the fuzzy mean as its standard deviation. The water demand is represented as a triangular fuzzy number with the random demand as its kernel, and the interval of ±5 % (or ±10 %) variation of the random demand as its support for two scenarios. The fuzzy random system reliability (R) of WDNs is defined on the basis of necessity measure to assess system performance under fuzzy random demands and crisp head requirements. The latin hypercube sampling method is adopted for sampling of uncertain demands. The methodology is applied to two WDNs, and optimization models are solved through cross entropy optimization for different levels of reliability, and generated tradeoffs between the cost and R. On comparing the solutions obtained with the proposed methodology with earlier reported solutions, it is noted that the proposed method is very effective in producing robust optimal solutions. On analyzing the tradeoffs between reliability and costs, the results show that negligence of uncertainty can lead to under design of the WDNs, and the cost increases steeply at higher levels of reliability. The results of the two case studies demonstrate that the presented CE based methodology is effective for fuzzy-probabilistic design of WDNs.

This work proposes a multiperiod mixed integer nonlinear programming model for the optimal design of agricultural water networks, which involves the optimal reuse of resources, such as water, fertilizers, and pesticides. This model is based on a new superstructure that includes attractive options for the use, reuse, recycle, regeneration, and storage for the considered resources. The proposed model incorporates simultaneously the maximization of the net annual profit and the minimization of the environmental impact. The profit is formed by the economic incomes due to the sale of crops minus the corresponding capital and operating costs associated with the fresh water, fertilizer, storage, piping, pumping, and water treatment. Whereas the environmental impact is determined through the Eco-indicator 95 and the water footprint to evaluate the effect over the global warming, acidification, eutrophication, green water footprint, gray water footprint, and blue water footprint. Two case studies from the Mexican state of Sinaloa were solved with the proposed model. The operating, economic, and design parameters are representative for the state of Sinaloa in Mexico; and the results are presented in three-dimensional Pareto diagrams. From these diagrams, it is possible to find the optimal relationship between profit, eco-indicator, and water footprint.

The problems and solutions of the optimal design of levelling networks have been discussed in this paper. An optimization modelling based on Quadratic Programming was implemented and evaluated. Onc...