Logic-Based Outer Approximation for the Design of Discrete-Continuous Dynamic Systems with Implicit Discontinuities

We address the optimization of discrete-continuous dynamic optimization problems using a disjunctive multistage modeling framework, with implicit discontinuities, which increases the problem complexity since the number of continuous phases and discrete events is not known a-priori. After setting a fixed alternative sequence of modes, we convert the infinite-dimensional continuous mixed-logic dynamic (MLDO) problem into a finite dimensional discretized GDP problem by orthogonal collocation on finite elements. We use the Logic-based Outer Approximation algorithm to fully exploit the structure of the GDP representation of the problem. This modelling framework is illustrated with an optimization problem with implicit discontinuities (diver problem).

Logic-Based Outer-Approximation Algorithm for Solving Discrete-Continuous Dynamic Optimization Problems

We present an extension of the logic outer-approximation algorithm for dealing with disjunctive discrete-continuous optimal control problems whose dynamic behavior is modeled in terms of differential-algebraic equations. Although the proposed algorithm can be applied to a wide variety of discrete-continuous optimal control problems, we are mainly interested in problems where disjunctions are also present. Disjunctions are included to take into account only certain parts of the underlying model which become relevant under some processing conditions. By doing so the numerical robustness of the optimization algorithm improves since those parts of the model that are not active are discarded leading to a reduced size problem and avoiding potential model singularities. We test the proposed algorithm using three examples of different complex dynamic behavior. In all the case studies the number of iterations and the computational effort required to obtain the optimal solutions is modest and the solutions are relatively easy to find.

THE TRANSPORT OF HEAT BY FLOWING GROUNDWATER IN A DISCRETE FRACTURE

In typical theoretical or experimental studies of heat migration in discrete fractures, conduction and thermal dispersion are commonly neglected from the fracture heat transport equation, assuming heat conduction into the matrix is predominant. In this study analytical and numerical models are used to investigate the significance of conduction and thermal dispersion in the plane of the fracture for a point and line sources geometries. The analytical models account for advective, conductive and dispersive heat transport in both the longitudinal and transverse directions in the fracture. The heat transport in the fracture is coupled with a matrix equation in which heat is conducted in the direction perpendicular to the fracture. In the numerical model, the governing heat transport processes are the same as the analytical models; however, the matrix conduction is considered in both longitudinal and transverse directions. Firstly, we demonstrate that longitudinal conduction and dispersion are critical processes that affect heat transport in fractured rock environments, especially for small apertures (eg. 100 μm or less), high flow rate conditions (eg. velocity greater than 50 m/day) and early time (eg. less than 10 days). Secondly, transverse thermal dispersion in the fracture plane is also observed to be an important transport process leading to retardation of the migrating heat front particularly at late time (eg. after 40 days of hot water injection). Solutions which neglect dispersion in the transverse direction underestimate the locations of heat fronts at late time. Finally, this study also suggests that the geometry of the heat sources has significant effects on the heat transport in the system. For example, the effects of dispersion in the fracture are observed to decrease when the width of the heat source expands.

Modelling a biomass supply chain through discrete-event simulation

The organizational structure of the companies in the biomass energy sector, regarding the supply chain management services, can be greatly improved through the use of software decision support tools. These tools should be able to provide real-time alternative scenarios when deviations from the initial production plans are observed. To make this possible it is necessary to have representative production chain process models where several scenarios and solutions can be evaluated accurately. Due to its nature, this type of process is more adequately represented by means of event-based models. In particular, this work presents the modelling of a typical biomass production chain using the computing platform SIMEVENTS. Throughout the article details about the conceptual model, as well as simulation results, are provided

Overcoming performance and convergence issues of discrete transform based modeling of crosslinking classical and reversible deactivated radical polymerizations

Population balances of polymer species in terms 'of discrete transforms with respect to counts of groups lead to tractable first order partial differential equations when ali rate constants are independent of chain length and loop formation is negligible [l]. Average molecular weights in the absence ofgelation are long known to be readily found through integration of an initial value problem. The extension to size distribution prediction is also feasible, but its performance is often lower to the one provided by methods based upon real chain length domain [2]. Moreover, the absence ofagood starting procedure and a higher numerical sensitivity hás decisively impaired its application to non-linear reversibly deactivated polymerizations, namely NMRP [3].