Water network operational optimization: Utilizing symmetries in combinatorial problems by dynamic programming

This paper introduces a dynamic programming (DP) approach for solving deterministic combinatorial operational optimization problem of water distribution networks. The implementation of dynamic programming over control domain using permutational symmetries is suggested to replace the state space based DP procedures. To enhance the understanding an application on a ub-network of the water supply and distribution network of the city of Sopron (Hungary) is presented which is sufficiently small to track the (pseudo) state space and approach related quantities

Application of Micro-Genetic Algorithm for Task Based Computing

Abstract — Pervasive computing calls for applications which are often composed from independent and distributed components using facilities from the environment. This paradigm has evolved into task based computing where the application composition relies on explicit user task descriptions. The composition of applications has to be performed at run-time as the environment is dynamic and heterogeneous due to e.g., mobility of the user. An algorithm that decides on a component set and allocates it onto hosts accordingly to user task preferences and the platform constraints plays a central role in the application composition process. In this paper we will describe an algorithm for task-based application allocation. The algorithm uses micro-genetic approach and is characterized by a very low computational load and good convergence properties. We will compare the performance and the scalability of our algorithm with a straightforward evolutionary algorithm. Besides, we will outline a system for task-based computing where our algorithm is used. I

Novel evolutionary methods in engineering optimization—towards robustness and efficiency

Abstract In industry there is a high demand for algorithms that can efficiently solve search problems. Evolutionary Computing (EC) belonging to a class of heuristics are proven to be well suited to solve search problems, especially optimization tasks. They arrived at that location because of their flexibility, scalability and robustness. However, despite their advantages and increasing popularity, there are numerous opened questions in this research area, many of them related to the design and tuning of the algorithms. A neutral technique called Pseudo Redundancy and related concepts such as Updated Objective Grid (UOG) is proposed to tackle the mentioned problem making an evolutionary approach more suitable for ''real world'' applications while increasing its robustness and efficiency. The proposed UOG technique achieves neutral search by objective function transformation(s) resulting several advantageous features. (a) Simplifies the design of an evolutionary solver by giving population sizing principles and directions to choose the right selection operator. (b) The technique of updated objective grid is adaptive without introducing additional parameters, therefore no parameter tuning required for UOG to adjust it for different environments, introducing robustness. (c) The algorithm of UOG is simple and computationally cheap. (d) It boosts the performance of an evolutionary algorithm on high dimensional (constrained and unconstrained) problems. The theoretical and experimental results from artificial test problems included in this thesis clearly show the potential of the proposed technique. In order to demonstrate the power of the introduced methods under "real" circumstances, the author additionally designed EAs and performed experiments on two industrial optimization tasks. Although, only one project is detailed in this thesis while the other is referred. As the main outcome of this thesis, the author provided an evolutionary method to compute (optimal) daily water pump schedules for the water distribution network of Sopron, Hungary. The algorithm is currently working in industry

Calibration of physical models with process data using FIR filtering

Abstract Automatic calibration of physical plant models in the context of monitoring and control of industrial processes is considered. A structure integrating a physical model and estimated FIR filters is proposed. In addition, a finite state FIR structure is proposed to complement the calibrated physical model with a data-driven mapping. The approach is illustrated in simulations using the van der Vusse CSTR benchmark

Fundamental limitations of the decay of generalized energy in controlled (Discrete–Time) nonlinear systems subject to state and input constraints

Abstract This paper is devoted to the analysis of fundamental limitations regarding closed-loop control performance of discrete-time nonlinear systems subject to hard constraints (which are nonlinear in state and manipulated input variables). The control performance for the problem of interest is quantified by the decline (decay) of the generalized energy of the controlled system. The paper develops (upper and lower) barriers bounding the decay of the system’s generalized energy, which can be achieved over a set of asymptotically stabilizing feedback laws. The corresponding problem is treated without the loss of generality, resulting in a theoretical framework that provides a solid basis for practical implementations. To enhance understanding, the main results are illustrated in a simple example

Examination of operational optimization at Kemi district heating network

Model-based minimization of short term operational costs for energy distribution systems is examined. Based on the analogies between mass and energy distribution systems, a direct application of a stochastic optimal control approach was considered, previously developed and applied by the authors to water distribution systems. This paper examines the feasibility of the approach for district heating systems under certainty equivalence, i.e., the uncertain quantities are replaced by their nominal values. Simulations, based on a rough model of a part of the Kemi district heating network, are used to illustrate and validate the modeling and optimization approach. The outcomes show that optimal network loading can be designed with the considered tools. Key words: district heating, dynamic simulation, energy systems, optimization, sustainable urban energ

Algorithms for Composing Pervasive Applications

Pervasive computing environments enable the composition of applications from components allocated across different devices. The applications have to be composed at runtime, to cope with changes in context and resource availability in the environment. In addition, this functionality has to be automated in order to minimize user involvement in application management. We propose two new algorithms which are capable of dynamic allocation of application components to multiple networking devices. These algorithms optimize the selection of the networking devices and the structure of composite applications according to a given criteria, such as minimizing hardware requirements, maximizing the application QoS or other criteria specified by the user. The algorithms are based on generic models. This allows the approach to be used in multiple application domains. We analyze the performance of these algorithms in a simulated environment and suggest a system that utilizes our algorithms for pervasive application composition. 1

Examination of operational optimization at Kemi district heating network

Model-based minimization of short term operational costs for energy distribution systems is examined. Based on the analogies between mass and energy distribution systems, a direct application of a stochastic optimal control approach was considered, previously developed and applied by the authors to water distribution systems. This paper examines the feasibility of the approach for district heating systems under certainty equivalence, i.e., the uncertain quantities are replaced by their nominal values. Simulations, based on a rough model of a part of the Kemi district heating network, are used to illustrate and validate the modeling and optimization approach. The outcomes show that optimal network loading can be designed with the considered tools

Stress reduction of turbine units in hybrid power plants:an operational perspective

Abstract This paper develops an operation strategy for hybrid power production units (turbine integrated with energy storage) participating in the balancing of the power grid. The developed strategy targets the reduction of the mechanical stress of the hybrid’s turbine unit by utilizing the available storage capacity. The problem is addressed using the methods of operations research combined with the receding horizon control approach. The benefits of the operational strategy are demonstrated via numerical simulations