Kazimierz Duzinkiewicz

Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems

An integrated approach to control of quantity and quality in water supply and distribution systems is proposed. The integrated control consists in optimising the operational cost, meeting a demand on water of desired quality and maintaining the system constraints. This constrained optimising control problem is complex due to nonlinearities, large dimension, output constraints, mixed-integer structure of the variables involved, at least two time scales in the system dynamics and an uncertainty. A sub-optimal two-level hierarchical control structure is proposed that allows incorporating the desired controller functions and yet making the synthesis of these functions possible. The algorithms for implementing the functionalities are proposed and discussed. Detail design of the lower level controller is presented and investigated. The controller performance is validated by simulation.

Genetic Hybrid Predictive Controller for Optimized Dissolved-Oxygen Tracking at Lower Control Level

A hierarchical two-level controller for dissolved-oxygen reference trajectory tracking in activated sludge processes has been recently developed and successfully validated on a real wastewater treatment plant. The upper level control unit generates trajectories of the desired airflows to be delivered by the aeration system to the aerobic zones of the biological reactor. A nonlinear model predictive control algorithm is applied to design this controller. The aeration system itself is a complicated hybrid nonlinear dynamical system. The lower level controller (LLC) forces the aeration system to follow these set-point trajectories, minimizing a cost of energy due to pumping of the air and accounting for system operational limitations such as the limits on the allowed frequency of switching of the blowers and on their capacity. The predictive control is also applied to design the LLC based on a piecewise-linearized hybrid dynamics of the aeration system. Casting the mixed-integer nonlinear optimization problem under heterogeneous constraints due to the limits on the blower switching frequency into the approximated mixed-integer form is done at a cost of introducing large number of auxiliary variables into the lower level predictive controller optimization task. This paper derives another nonlinear hybrid predictive control algorithm for the LLC. It is directly based on the nonlinear hybrid dynamics and logical formulation of the switching constraint. A genetic algorithm is derived with dedicated operators allowing for efficient handling of the switching constraint and nonlinear hybrid system dynamics. The efficiency of the control algorithm is validated by simulation based on real data records.

Data-driven models for fault detection using kernel PCA

Kernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various applications of KPCA are known, this paper explores the possibility to use it for building a data-driven model of a non-linear system-the water distribution system of the Chojnice town (Poland). This model is utilised for fault detection with the emphasis on water leakage detection. A systematic description of the system’s framework is followed by evaluation of its performance. Simulations prove that the presented approach is both flexible and efficient.

Dissolved Oxygen Tracking and Control of Blowers at Fast Time Scale

Abstract Aeration is very important and expensive process in Wastewater Treatment Plant (WWTP). Oxygen is provided as a fundamental component for the biological processes. The aeration system is a complicated hybrid nonlinear dynamic system with faster dynamics compared to internal dynamics of the dissolved oxygen (DO) at the biological reactor. The currently used control systems are far from satisfactory. The achieved energy cost due to blowing the air into the biological reactor aerobic zones is high and the (DO) tracking performance is low. This paper extends newly developed hierarchical hybrid model predictive controller to plants with several aerobic zones supplied by an aeration system of limited capacity. The constraint on the airflow that can be delivered is then active and a multivariable predictive controller at the upper level handles its distribution between the zones. Simulation tests for aeration system at WWTP in Kartuzy are presented.

Simplified, multiregional fuzzy model of a nuclear power plant steam turbine

Power systems, including steam turbines and synchronous generators, are complex nonlinear systems with parameters varying over time. The paper presents the developed simplified, multiregional fuzzy model of the steam turbine of a nuclear power plant turbine generator set and compares the results with a full nonlinear model and commonly used linear input-output model of a steam turbine. The proposed model consist of series of linear input-output models defined for specific steam turbine operating points and one fuzzy switching module with Takagi-Sugeno reasoning interface. Provided simulation results indicate that the response of presented simplified fuzzy multiregional steam turbine model is very close to the reference nonlinear steam turbine model such that the maximum absolute error is around few percent for considered operating point changes, 30-100% of nominal mechanical power. Hence steam turbine model in such form may increase the accuracy of developed control algorithms, which use system model for adaptation or prediction purposes.

Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor

Bearing defect is statistically the most frequent cause of an induction motor fault. The research described in the paper utilized the phenomenon of the current change in the induction motor with bearing defect. Methods based on the analysis of the supplying current are particularly useful when it is impossible to install diagnostic devices directly on the motor. The presented method of rolling-element bearing diagnostics used indirect transformation, namely Clark transformation. It determines the vector of the spatial stator current based on instantaneous current measurements of the induction motor supply phases current. The analysis of the processed measurement data used multilayered, one-directional neural networks, which are particularly attractive due to their nonlinear structure and ability to learn. During the research 40 bearings: undamaged, with damages of three types and various degrees of fault extent, were used. The conducted research proves the efficiency of neural networks for detection and recognition of faults in induction motor bearings. In case of tests of the unknown state bearings, an efficiency approach to failure detection equaled 77%.

Bounding Approach to Parameter Estimation without Priori Knowledge on Model Structure Error

Abstract Parameter estimation of an autoregressive movmg average (ARMA) model is discussed in this paper by using bounding approach. Bounds on the model structure error are assumed unknown, or known but conservative. To reduce this conservatism, a point-parametric model concept is proposed, where there exist a set of model parameters and structure error corresponding to each input. Feasible parameter sets are defined for point-parametric model. Bounded values on the model parameters and structure error can then be computed jointly by tightening the feasible set using observations under deliberately designed input excitations. Finally, a constantly bounded parameter model is established, which can be used for robust control.

Comparison of tuning procedures based on evolutionary algorithm for multi-region fuzzy-logi PID controller for non-linear plant

The paper presents a comparison of tuning procedures for a multi-region fuzzy-logic controller used for nonlinear process control. This controller is composed of local PID controllers and fuzzy-logic mechanism that aggregates local control signals. Three off-line tuning procedures are presented. The first one focuses on separate tuning of local PID controllers gains in the case when the parameters of membership functions of fuzzy-logic mechanism are know a priori. The second one consists of two major steps. In the first step, the local PID controllers gains are calibrated and in the next step the parameters of membership functions of fuzzy-logic mechanism are tuned. The third one focuses on tuning of PID controllers gains and membership functions parameters jointly. These procedures exploit evolutionary algorithm to optimize the performance of the multi-region fuzzy-logic controller with respect to integral quality criterion such as Integral Absolute Error (IAE). Effectiveness of these procedures is verified based on a well known from literature continuous nonlinear pH neutralization reactor model. For comparison a single PID controller tuned for specific work point is also taken into account.

Pipeline system for heat transportation from nuclear power plant — An optimizing approach

Over the last few years heat piping insulation technology and pump systems efficiency have been significantly improved. Reduced thermal losses encourage heat transportation over long distances. It provides an opportunity for increasing thermodynamic efficiency of Nuclear Power Plants (NPPs) that are often located in rural areas because of safety issues. It can be achieved by Combined Heat and Power (CHP) generation, as heat produced in cogeneration mode can now by effectively used for distant District Heating (DH). Methodology for optimal design of Heat Transportation System (HTS) between NPP and DH network is investigated. Static model of HTS has been proposed and used in multi-criteria, hybrid, nonlinear and constrained optimization task in order to minimize construction and operation costs of HTS. These costs are minimized over presumed system lifetime and under variation of annual heat demand within DH area. Moreover, the issues of: terrain elevation profile, variability of ground temperature and insulation aging are taken into account. The methodology was tested on case study example of intended NPP located in Northern Poland.

Multi-nodal PWR reactor model — Methodology proposition for power distribution coefficients calculation

In the paper the multi-nodal Pressurized Water Reactor (PWR) heat transfer model based on Manns model is presented. This model is used for heat transfer from fuel to coolant modelling purposes in a reactor core. The authors expand the approach widely used in literature by defining additional coefficients for heat transfer model. These parameters approximate the power generation distribution in the PWR reactor core according to the reactor control rod bank movement. Authors describe in details the proposed methodology for calculation of introduced power distribution coefficients. In the simplest case, those coefficients may be equal and constant over time and space of a reactor. On the other hand, they can be treated as variables that are time and reactor region dependent. By introducing these specific coefficients, the nodal model of heat transfer gains advanced capabilities that can be efficiently used in design and synthesis of more advanced and complex power control system in nuclear reactor with respect to temperature distribution within the reactor core.