Darko Vrečko

A new modified Smith predictor: the concept, design and tuning.

The paper presents a new modified Smith predictor (MSP) for processes with a long time delay. The MSP appears as an extension of the double controller-scheme (DCS) proposed by Tian and Gao. The important feature of the MSP is that the trade-off between disturbance rejection and robustness to variations in process parameters can be adjusted by means of a single free parameter. The main contribution of the paper concerns tuning of the MSP, which relies on a combination of magnitude optimum criterion with process parameterisation based on multiple integrals of the open-loop step response. In a simulation study the performance of the MSP is compared with that of two known controllers for time delay systems, i.e. DCS of Tian and Gao and Hägglunds predictive PI controller. The results show the advantage of the MSP compared to the two other controllers.

Benchmark simulation models, quo vadis?

As the work of the IWA Task Group on Benchmarking of Control Strategies for wastewater treatment plants (WWTPs) is coming to an end, it is essential to disseminate the knowledge gained. For this reason, all authors of the IWA Scientific and Technical Report on benchmarking have come together to provide their insights, highlighting areas where knowledge may still be deficient and where new opportunities are emerging, and to propose potential avenues for future development and application of the general benchmarking framework and its associated tools. The paper focuses on the topics of temporal and spatial extension, process modifications within the WWTP, the realism of models, control strategy extensions and the potential for new evaluation tools within the existing benchmark system. We find that there are major opportunities for application within all of these areas, either from existing work already being done within the context of the benchmarking simulation models (BSMs) or applicable work in the wider literature. Of key importance is increasing capability, usability and transparency of the BSM package while avoiding unnecessary complexity.

A plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing in wastewater treatment process models.

There is a growing interest within the Wastewater Treatment Plant (WWTP) modelling community to correctly describe physico-chemical processes after many years of mainly focusing on biokinetics. Indeed, future modelling needs, such as a plant-wide phosphorus (P) description, require a major, but unavoidable, additional degree of complexity when representing cationic/anionic behaviour in Activated Sludge (AS)/Anaerobic Digestion (AD) systems. In this paper, a plant-wide aqueous phase chemistry module describing pH variations plus ion speciation/pairing is presented and interfaced with industry standard models. The module accounts for extensive consideration of non-ideality, including ion activities instead of molar concentrations and complex ion pairing. The general equilibria are formulated as a set of Differential Algebraic Equations (DAEs) instead of Ordinary Differential Equations (ODEs) in order to reduce the overall stiffness of the system, thereby enhancing simulation speed. Additionally, a multi-dimensional version of the Newton-Raphson algorithm is applied to handle the existing multiple algebraic inter-dependencies. The latter is reinforced with the Simulated Annealing method to increase the robustness of the solver making the system not so dependent of the initial conditions. Simulation results show pH predictions when describing Biological Nutrient Removal (BNR) by the activated sludge models (ASM) 1, 2d and 3 comparing the performance of a nitrogen removal (WWTP1) and a combined nitrogen and phosphorus removal (WWTP2) treatment plant configuration under different anaerobic/anoxic/aerobic conditions. The same framework is implemented in the Benchmark Simulation Model No. 2 (BSM2) version of the Anaerobic Digestion Model No. 1 (ADM1) (WWTP3) as well, predicting pH values at different cationic/anionic loads. In this way, the general applicability/flexibility of the proposed approach is demonstrated, by implementing the aqueous phase chemistry module in some of the most frequently used WWTP process simulation models. Finally, it is shown how traditional wastewater modelling studies can be complemented with a rigorous description of aqueous phase and ion chemistry (pH, speciation, complexation).

Modeling, Identification, and Validation of Models for Predictive Ammonia Control in a Wastewater Treatment Plant—A Case Study

The aim of this work is to develop the ammonia models that could be used for model predictive control (MPC) of nitrification process in a wastewater treatment plant. First, a reduced nonlinear model is presented, which is based on expression for nitrification reaction rate in activated sludge model No. 1 and modified for attached biomass processes, while second, a linear black-box model is shown. The data used for model identification were collected during several weeks of experiments on a real plant so that good identification data were obtained. The designed models were validated based on open loop simulations and predictions. Validation results show that the reduced nonlinear model performs better compared to the linear model, however, both models show relatively large errors compared to the real plant data. Hence, a closed loop simulation study was performed to see the differences between the performance of model predictive controller using previously estimated linear and nonlinear models and a standard proportional integral (PI) controller. From the simulation study results it was seen that in spite of relatively large model errors the MPC algorithms give better results in terms of ammonia removal compared to the PI controller, while MPC with the nonlinear model shows additional improvements over the MPC with the linear model.

Multivariable Predictive Control of an Activated Sludge Process with Nitrogen Removal

Abstract In the paper multivariable predictive control of the wastewater treatment benchmark is presented. The goal of the predictive control is to improve daily disturbance rejection of the plant in relation to nitrogen removal. A simple Dynamic Matrix Controller (DMC) is used as a predictive controller. The multivariable linear black-box model for predictive control is identified in the open-loop using subspace identification method. The model interactions between inputs and outputs and conditioning are analysed. Simulation results show that the chosen predictive controller gives lower violations of the effluent ammonia limit compared to PI controllers at increased energy consumption for aeration.

Automatic guidance of an aircraft using predictive control in a visual servoing scheme

Abstract In this paper we have designed and implemented a predictive automatic flight controller for an airplane that takes as input the estimated pose obtained from the visual data and provides control outputs for an inner-loop augmented aircraft. In order to make the predictive controller suitable for flight control, it had to be modified. The most important modification was the introduction of the short term reference trajectories, linking the time-based and the position-based control strategy. Furthermore a controller switching mechanism was introduced to enable the aircraft to fly at different flight conditions. The parameters of the controller have been derived by experimental optimization. The tests have shown good overall tracking and disturbance rejection properties; the aircraft is able to perform various maneuvers and the touchdown in all the tested weather (wind, turbulence) conditions.

Evaluation of Different Nitrogen Control Strategies for a Combined Pre- and Post-Denitrification Plant

Abstract In the paper different nitrogen control strategies are proposed and tested on a simulation model of a combined pre- and post-denitrification plant. The plant configuration corresponds to the Domžale-Kamnik wastewater treatment plant that will be upgraded for nitrogen removal using MBBR (moving bed biofilm reactor) technology. The aim of the study is to find an optimal control strategy in terms of required effluent quality and operating (i.e. carbon and aeration) costs. The tested control strategies address aeration control, internal recirculation control, and external carbon dosage control, and are based on PI and feedforward control algorithms. Simulation results indicate that the nitrate PI controller that manipulates external carbon flow-rate and the ammonia PI controller that manipulates oxygen concentration in the aerobic reactors give the best performance with respect to the effluent quality and operating costs. In addition, it was shown that the control authority of the internal recirculation flow-rate was rather limited as the internal recycle flow-rate on the real plant can be increased only up to 200% of the average influent flow-rate. Hence, no improvement of effluent quality could be achieved with internal recycle control. While the improvement of effluent quality with the proposed overall control scheme is small if compared to the basic control scheme with optimal set-points, the energy savings are quite significant reaching up to 40%.

Model-Based Control of the Ammonia Nitrogen Removal Process in a Wastewater Treatment Plant

This chapter investigates what benefits could be achieved from model-based ammonia nitrogen controllers in wastewater treatment plants (WWTPs) in comparison with PI controllers. The controllers addressed are a model-based feedforward-feedback controller and a model predictive controller (MPC). Validation of the controllers was performed both in simulation and on a real plant in the context of various performance criteria related to an effluent ammonia limit violation and air consumption. The simulation results indicate that the feedforward-feedback controller and MPC result in reduced air consumption compared to the PI controller while achieving the same or better ammonia removal. When model-based ammonia controllers are applied to a full scale pilot plant they have to be simplified and properly accommodated. The application of controllers confirmed that the feedforward-feedback controller and MPC give better results than the PI controller. The main reason for this lies in the fact that they use the additional measurable disturbance of the influent ammonia. Interestingly, at the real plant poorer results are obtained with the MPC than with the feedforward-feedback controller. The explanation for this could be associated with the various limitations in applying a more complex MPC in a real environment.

Control of nutrient removing activated sludge system

Abstract The objective of this paper is to evaluate several control algorithms for nitrogen removal using a simulation benchmark of a pre-denitrifying activated sludge process. Various PI and feedforward controllers are evaluated and compared with advanced multivariable and nonlinear model predictive control, which uses a perfect process model, perfect measurements and perfect knowledge of the disturbances. The simulation results indicate that PI nitrate and feedforward-PI ammonia control closely imitates the optimal operation strategy as the operating costs are only slightly higher compared to the case when model predictive control is applied. It appears that the improvement is thus more related to control structure, i.e. where the sensors and the actuators are located, than to the control algorithms.

Oxygen Control of Bioreactor Model Using Fuzzy Processed PID Controller Outputs

Abstract Control of oxygen concentration in a non-linear continuous bioreactor model (CBRM), using fuzzy signal processing of PID controller outputs is presented. The parameters of the PID controllers were tuned by means of Magnitude Optimum Multiple Integration (MOMI) method. The closed-loop results showed that the proposed approach could also be successfully used in highly non-linear processes.