A novel MINLP-based representation of the original complex model for predicting gasoline emissions

The Environmental Protection Agency (EPA) introduced Reformulated Gasoline (RFG) requirements as a measure to reduce emissions from gasoline-powered vehicles in certain geographic areas. As part of this effort, the EPA developed empirical models for predicting emissions as a function of gasoline properties and established statutory baseline emissions from a representative set of gasolines. All reformulated gasoline requires certification via this model, known as the Complex Model, and all refiners and importers calculate emissions performance reductions from the statutory baseline gasoline. The current representation of the Complex Model is extremely difficult to implement within refinery operations models or to use in combination with models for designer gasoline. RFG and boutique fuels are key driving forces in the North American refining industry.The RFG models introduce increasingly complex constraints with the major limitation that they are implicitly defined through a series of complicated disjunctions assembled by the EPA in the form of spreadsheets. This implicit and cumbersome representation of the emissions predictive models renders rigorous optimization and sensitivity analysis very difficult to address directly. In this paper, we discuss how the federal government requirements for reformulated gasoline can be restated as a set of mixed-integer nonlinear programming (MINLP) constraints with the aid of disjunctive programming techniques. We illustrate the use of this model with two simple example fuel blending problems.     

Playing with Maude

This paper is an introduction to rule-based programming in Maude. We illustrate in particular the use of operator attributes to structure the state of a system, and the difference between equations and rules. We use well-known mathematical games and puzzles for our examples illustrating the expressive power of Maude.     

Controlled linear system with delayed relay output under impulse random disturbances

The paper is devoted to the first order delayed linear system with relay output controlled by the proportional-integral (PI) regulator. The deterministic system exhibits stable oscillations, and, since the system itself is stable, it can be suitable to switch off the controller if there are no disturbances during a long time interval. In the present work, the random disturbances are modelled by a Poisson stream of impulses, and the goal is to determine the instants of switching on (off) of the PI controller. After several assumptions and quantization of the time axis, we construct the new optimal control problem which is successfully solved with the help of the dynamic programming approach.     

基于MATLAB在化工优化设计中的应用

结合化工优化计算的实例，着重介绍如何应用MATLAB7．0提供的优化工具箱来对线性规划及非线性规划模型进行求解。     

An Agent Interaction Based Method for Nonlinear Process Plan Scheduling

This article puts forward a scheduling method for nonlinear process plan shop floor.Task allocation and load bal- ance are realized by bidding mechanism.Though the agent interaction process,the execution of tasks is determined and the coherence of manufacturing decision is verified.The employment of heuristic index can help to optimize the system performance.     

Dynamic Multicast Session Provisioning in WDM Optical Networks with Sparse Splitting Capability

In this work, we study dynamic provisioning of multicast sessions in a wavelength-routed sparse splitting capable WDM network with an arbitrary mesh topology where the network consists of nodes with full, partial, or no wavelength conversion capabilities and a node can be a tap-and-continue (TaC) node or a splitting and delivery (SaD) node. The objectives are to minimize the network resources in terms of wavelength-links used by each session and to reduce the multicast session blocking probability. The problem is to route the multicast session from each source to the members of every multicast session, and to assign an appropriate wavelength to each link used by the session. We propose an efficient online algorithm for dynamic multicast session provisioning. To evaluate the proposed algorithm, we apply the integer linear programming (ILP) optimization tool on a per multicast session basis to solve off-line the optimal routing and wavelength assignment given a multicast session and the current network topology as well as its residual network resource information. We formulate the per session multicast routing and wavelength assignment problem as an ILP. With this ILP formulation, the multicast session blocking probability or success probability can then be estimated based on solving a series of ILPs off-line. We have evaluated the effectiveness of the proposed online algorithm via simulation in terms of session blocking probability and network resources used by a session. Simulation results indicate that our proposed computationally efficient online algorithm performs well even when a fraction of the nodes are SaD nodes.     

Modeling alternative zoning strategies in forest management

To satisfy public demands for environmental values, forest companies are facing the prospect of a reduction in wood supply and increases in costs. Some Canadian provincial governments have proposed intensifying silviculture in special zones dedicated to timber production as the means for pushing out the forest possibility frontiers. In this paper, we compare the traditional two‐zone land allocation framework which includes ecological reserves and integrated forest management zones with the triad — a three‐zone scheme which adds a zone dedicated to intensive timber production. We compare the solutions of the mixed‐integer linear programs formulated under both land‐allocation frameworks. We explore through sensitivity analysis the conditions under which the triad regime can offset the impact on timber production from increased environmental demands. We show that under the realistic conditions characteristic to Coastal British Columbia, higher environmental demands may be satisfied under the triad regime without increasing the financial burdens on the industry or reducing its wood supply. This occurs, however, only if regulatory constraints in timber production zone are flexible.     

GLU♮ embedded in C++: a marriage between multidimensional and object‐oriented programming

The embedding of a small but expressive language of multidimensional functional programming in a well known and widely used language of object‐oriented programming leads to the combination of two radically different programming models. In this paper, we formally define the syntax and semantics of GLU^?, which can be thought of as the multidimensional core of Lucid and GLU, and we describe its implementation as a language embedded in C++. With the aid of a few examples, we argue that the marriage of the two programming models is not only compatible and natural, but also that it produces a new and interesting hybrid language. Copyright © 2004 John Wiley & Sons, Ltd.     

Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.