Special Issue "Algorithms and Applications in Dynamic Optimization"
Deadline for manuscript submissions: closed (29 February 2016) | Viewed by 23234
Mathematical programming has proven to be a valuable tool in the process industries for improving both design and operations. However, operational challenges are driving the need for more rigorous models, including those considering system dynamics and distributed properties. As computational capabilities increase, there is continued opportunity to develop reliable problem formulations and efficient solution strategies for optimization of complex processes modeled by differential equations.
Several techniques are available for local optimization of differential-algebraic equations (DAEs). For indirect or optimize-then-discretize methods, optimality conditions are applied to the dynamic optimization problem, resulting in a set of differential equations typically solved numerically. For direct or discretize-then-optimize approaches, the DAE system is discretized prior to the application of optimality conditions. These techniques can be further categorized into sequential, simultaneous, or multiple-shooting techniques. Approaches differ in many aspects, including discretization strategies, optimization algorithms, and computation of gradient information.
Numerous problems in the process industries require optimization of differential-algebraic equations (DAEs), including optimal operation of batch processes, dynamic real-time optimization, optimal load transition, and nonlinear model predictive control (MPC). Furthermore, spatially distributed systems, particulate processes, and other models with internally and externally distributed properties, can be discretized along some dimensions, giving rise to a large set of DAEs. Efficient optimization of DAEs can be challenging for several reasons. Large systems of DAEs, like those arising from spatial discretization, can be computationally intensive, and may necessitate the use of advanced computing hardware. In addition, online applications like nonlinear MPC typically require solutions within a fixed, small time horizon. The challenges inherent in these problem classes illustrate the need for reliable and efficient methods to solve dynamic optimization problems.
The special issue, “Algorithms and Applications in Dynamic Optimization” of the journal Processes, aims to cover recent advances in optimization of complex DAE models. Appropriate contributions include novel solution strategies, and advanced modeling and problem formulations for dynamic optimization of complex process. Applications and solution strategies in global optimization of dynamic systems are also welcomed.
Dr. Carl D. Laird
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.
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- mathematical programming
- dynamic optimization
- global optimization
- ordinary differential equations
- differential algebraic equations
- algorithm complexity
- algorithm efficiency
- dynamic modeling
- model predictive control
- dynamic real-time optimization