Robert Piotrowski

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.

An Analog Sub-Miliwatt CMOS Image Sensor With Pixel-Level Convolution Processing

A new approach to an analog ultra-low power medium-resolution vision chip design is presented. The prototype chip performs low-level image processing algorithms in real time. Only a photo-diode, MOS switches and two capacitors are used to create an analog processing element (APE) that is able to realize any convolution algorithm based on a full 3 × 3 kernel. The proof-of-concept circuit is implemented in 0.35 μm CMOS technology, and contains a 64 × 64 SIMD matrix with embedded APEs. The matrix dissipates less than 0.3 mW (less than 0.1 W per APE) of power under 3.3 V supply, and its image processing speed is up to 100 frames/s.

MODELLING AND IDENTIFICATION OF AERATION SYSTEMS FOR MODEL PREDICTIVE CONTROL OF DISSOLVED OXYGEN – SWARZEWO WASTEWATER TREATMENT PLANT CASE STUDY

Abstract Aeration is an important and expensive activity that is carried out during wastewater treatment plant ( WWTP ) operation. The aeration system serves as the actuator to deliver the airflow prescribed by the dissolved oxygen controller. It is on its own a complex dynamic system with nonlinear hybrid dynamics. Recently hybrid model predictive controllers ( HMPC ) were proposed in order to control the system valve and blower operation so that the required airflow can be accurately delivered and the energy cost due to blowing the air minimised. Availability of a suitable model of the aeration system is vital for a successful operation of these controllers. The paper improves the model recently proposed. A practical approach to the model identification and validation is proposed and applied to Swarzewo WWTP.

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.

Nonlinear PI control for dissolved oxygen tracking at wastewater treatment plant

Abstract The paper addresses design, implementation and simulation of a novel type of softly switched Takagi-Sugeno fuzzy PI control system for dissolved oxygen concentration (DO) tracking at wastewater treatment plant (WWTP). The proposed control system is designed, including tuning the PI controllers, entirely based on the experimental data. This control system is validated by simulation.

LOWER – LEVEL CONTROLLER FOR HIERARCHICAL CONTROL OF DISSOLVED OXYGEN CONCENTRATION IN ACTIVATED SLUDGE PROCESSES

Abstract The paper addresses control design for an aeration system within a hierarchical structure for an integrated control of dissolved oxygen in the activated sludge processes. In the paper the hybrid predictive controller for the lower control level is developed and its performance is investigated based on real data records.

CENTRALIZED DISSOLVED OXYGEN TRACKING AT WASTEWATER TREATMENT PLANT: NOWY DWOR GDANSKI CASE STUDY

Abstract The aeration is used in different operations at wastewater treatment plants. The biological processes that are carried out at require sufficiently high dissolved oxygen concentration in order to maintain microorganisms in an activated sludge. The paper proposes a centralized nonlinear model predictive controller of dissolved oxygen tracking and aeration system control. The controller is applied to wastewater treatment plant at Nowy Dwor Gdanski and its performance is compared with the performance of the control system that is currently applied at this site.

Analog CMOS processor for early vision processing with highly reduced power consumption

A new approach to an analog ultra-low power vision chip design is presented. The prototype chip performs low-level convolutional image processing algorithms in real time. The circuit is implemented in 0.35 µm CMOS technology, contains 64 × 64 SIMD matrix with embedded analogue processors APE (Analogue Processing Element). The photo-sensitive-matrix is of 2.2 µm × 2.2 µm size, giving the density of 877 processors per mm2. The matrix dissipates less than 0.4 mW (less than 0.1 µW per processor) of power under 3.3 V supply, and their image processing speed is up to 100 frames/s.

Analysis the Parameters of the Adaptive Controller for Quality Control of Dissolved Oxygen Concentration

The wastewater treatment plant can be treated as large scale complex system. The dissolved oxygen concentration in aerobic zones is considered as the most important control parameters. Air is supplied by aeration system. It is a complex dynamic system. In this paper, the sequencing batch reactor and aeration system are modelled and applied for control systems purposes. The cascade nonlinear adaptive control system, extended by anti-windup filter, is applied. Furthermore, analysis the parameters of the adaptive controller for control quality is examined. Simulation results based on real data records for case study plant are presented. DOI: http://dx.doi.org/10.5755/j01.itc.45.1.9246

Takagi-Sugeno fuzzy model of dissolved oxygen concentration dynamics in a bioreactor at WWTP

The importance of dissolved oxygen (DO) concentration control in aeration tanks of a bioreactor at flow-through wastewater treatment plant (WWTP) can easily be justified by technological requirements as well as simple economics. Firstly, appropriate levels of DO concentration are essential for the vitality of microorganisms that comprise the bioreactor. Secondly, the costs of DO concentration control related to the blower station operation constitute up to 75% of the plant-wide electric bill. This paper addresses a problem of Takagi-Sugeno fuzzy model identification of DO dynamics for control design purposes. In the proposed approach the fuzzy partitions and the corresponding fuzzy sets are identified based on the knowledge of the process. Local model structure is obtained based on a larger well-established cognitive model from which a well-defined parameter set follows. Thereafter, a parameter identification is performed. A case study example of a bioreactor at Kartuzy WWTP illustrates the workflow of the proposed identification algorithm.