István Selek

Short term optimization of district heating network supply temperatures

The increasing challenges in district heating operational optimization are briefly discussed. The paper describes the first steps in a research project on minimization of short term operational costs in a full scale district heating system. Based on a test model describing a part of a real district heating network, and a chosen approximate dynamic programming technique, simulations are used to illustrate and validate the fundamentals of the modelling and optimization approaches. It is concluded that the considered methods provide an adequate set of tools for the design of optimal network loading. The project is currently continuing with building of a more realistic dynamic model of the full-scale energy network and its components.

Optimal (short-term) pump schedule detection for water distribution systems by neutral evolutionary search

The application of neutrality is a straightforward tool to preserve population diversity since it allows the genotype (on the represented search space) to be changed without affecting the corresponding fitness. To implement neutrality the literature suggests representational redundancy (more to one correspondence in genotype-phenotype mapping) although using it as a source of neutrality researchers uniformly reported better or worse results. Instead of applying representational redundancy here the utilization of pseudo redundancy as the source of neutrality is proposed, that is, neutrality is achieved by simple objective-fitness transformation while pseudo redundancy (as another redundancy interpretation) denotes more to one correspondence between objective-fitness domains by objective-fitness mapping. The contribution of this work is specified by the dynamic generational gap model introduced for evolutionary algorithms which appears when elitist strategy is used under neutrality by pseudo redundancy. This paper investigates the influence of dynamic generational gap model on the performance of a micro-genetic algorithm framework applied to achieve least cost water pump control policy for an industrial size water network distribution system. The presented constrained mixed-integer optimization problem is originated from the regional water network of the city of Sopron (60,000 citizens) located in Hungary. Here, the goal is to obtain intra-day pump schedule which minimizes the cost required for operation while satisfies the system constraints (water reservoir level limitations, pump flow and delivery regulations, pump energy consumption limitations) and fulfills the water requirement by the users.

Process control using finite Markov chains with iterative clustering

Abstract Finite state Markov Decision Processes (MDP) for process control are considered. MDP provide robust tools to perform optimization in closed-loop, and their finite state description enables an easy implementation of Bayesian state estimation. An approach to tackle the curse of dimensionality problem, yet retaining the benefits of the finite state MDP in control and estimator design, is proposed. The suggested approach uses iterative re-discretization based on clustering of closed-loop data. An efficient modification of the k-means clustering technique is proposed. The performance of the approach is demonstrated using a challenging benchmark from chemical engineering, the van der Vusse continuous stirred tank reactor control problem. It is shown that the requirements of the benchmark are met, and that the suggested iterated clustering significantly improves the performance. It is concluded that the finite state MDP approach is a viable alternative for small-to-medium scale problems of practical process control and state estimation.

Analysis and tuning of a CFB model using particle filtering

Abstract Analysis and tuning of a circulating fluidized bed (CFB) hot-loop model are considered. Selected model outputs are fitted to measured data by allowing a set of constant-valued parameters to be considered as time-varying. These include the reaction rate, several heat transfer coefficients, and fuel moisture. Estimates of parameter distributions are created using particle filters (PF). PF provide a Bayesian approach utilizing both the system model and measured data. An illustrative example is given using data and model for a full scale plant.

Optimization of Pumping Schedules Using the Genealogical Decision Tree Approach

This paper examines the application of a particle filtering-based optimization technique, the genealogical decision trees (GDT), to a finite horizon pump scheduling problem in a water distribution network. The GDT approach for trajectory tracking is first introduced, and a modified algorithm for minimization of costs during pump sequence optimization is then presented. Several variants of the algorithm are suggested, using the extended end constraint and neutrality. The performance of the optimization in various algorithm and parameter settings is examined in extensive simulations. It was observed that both the extended end constraint and neutrality improved the performance, however the deviation between solutions within a population and between different runs remained uncomfortably large. Finally, a comparison with a number of alternative up-to-date optimization techniques is provided. It was observed that the performance of GDT was adequate, compared with the best available approaches.

Integration of concentrated solar power (CSP) and circulating fluidized bed (CFB) power plants – final results of the COMBO-CFB project

Hybridization of solar energy together with another energy source is an option to provide heat and power reliably on demand. Hybridization allows decreasing combustion related fuel consumption and emissions, assuring stable grid connection and cutting costs of concentrated solar power technology due to shared power production equipment. The research project “Integration of Concentrated Solar Power (CSP) and Circulating Fluidized Bed (CFB) Power Plants” (COMBO-CFB) has been carried out to investigate the technical possibilities and limitations of the concept. The main focus was on the effect of CSP integration on combustion dynamics and on the joint power cycle, and on the interactions of subsystems. The research provides new valuable experimental data and knowhow about dynamic behaviour of CFB combustion under boundary conditions of the hybrid system. Limiting factors for maximum solar share in different hybridization schemes and suggestions for enhancing the performance of the hybrid system are derived.

Tube-based robust MPC for pump scheduling in water distribution systems

This paper proposes a (tube based) robust MPC approach for the class of “well-designed” water distribution systems subject to water demand uncertainties. The underlying mathematical problem is formulated within a robust decision making framework where the operational decision (which is obtained using feedback) is cost efficient and feasible under a range of water demand realizations. An application to the efficient pump scheduling of the water distribution system of the city of Sopron (Hungary) is presented.