M. Kümmel

Brief paper: Self-tuning control of a pH-neutralization process

A self-tuning regulator has been used in control of a pH-neutralization process. The process is difficult to control due to great variations in process sensitivity with pH and due to process nonlinearities. Experiments on a pilot plant demonstrate that a self-tuning pH-regulator including exponential forgetting and quadratic optimal control has satisfactory static and dynamic properties even though the process is nonlinear. It is also demonstrated that the self-tuning algorithm can adapt to changes in process conditions, and it may be a useful alternative to traditional PI-controllers.

External carbon source addition as a means to control an activated sludge nutrient removal process

Abstract In alternating type activated sludge nutrient removal processes, the denitrification rate can be limited by the availability of readily-degradable carbon substrate. A control strategy is proposed by which an easily metabolizable COD source is added directly to that point in the process at which denitrification momentarily occurs. This approach serves to increase the denitrification rate on demand, thereby allowing the accumulation of nitrate and nitrite during periods of peak nitrogen loading to be reduced or avoided. A pilot plant demonstration of the control strategy using acetate as COD source is provided, showing a marked improvement in effluent water quality as compared to the uncontrolled case. An examination of the resulting denitrification rates illustrates the direct proportionality between these rates and the rate of COD addition.

Monitoring and control of biological removal of phosphorus and nitrogen by flow-injection analysers in a municipal pilot-scale waste-water treatment plant.

Abstract A flow-injection system is used for monitoring and control of a biological waste-water treatment plant with biological removal of phosphate and nitrate. The waste-water treatment plant is an activated sludge type on a pilot scale, with municipal waste water as the influent. The flow-injection system monitors the concentrations of phosphate, ammonia and nitrate in four places: in the inlet, in the outlet of the anaerobic pretreatment tank, in one of the aeration tanks and in the outlet of the plant. Sampling is carried out via a cross-flow filter system, based on an ultra-filtration membrane. The analysers employ highly pulsating, single-piston liquid chromatographic pumps. Synchronization of injection time and pump pulses eliminates the need for pulse-damping devices and ensures high reproducibility. The chemical methods are based on classical colorimetric methods. The measurement system has been designed with emphasis on long-term stability, low reagent consumption and minimum maintenance. To maintain stable, low flow-rates, on-line degassing has been installed for each reagent. Further, on-line standard calibration is being used to compensate for drift in the sensitivity of the analysers. The system is controlled by a PC, programmed in ASYST. The calibrated data is fed to a programmable logic controller (PLC), which also controls the pilot plant. A supervisory PC, programmed in Factory Link, stores and presents data. The measurements will be used for studies of different control strategies for the plant, e.g., rule-based control.

An analysis of nitrogen removal and control strategies in an alternating activated sludge process

Abstract The biological nitrogen removal in an alternating activated sludge process is described and analyzed using a simplified model of the IAWQ activated sludge model No. 1. In face of the alternating nature of the process, a new analytical approach is developed by introducing the nitrification capacity and denitrification potential concepts into the alternating process analysis. This facilitates a more obvious insight into the nitrogen removal with the development of mathematical relationships between the nitrogen removal efficiency and the process operational conditions. The process performances with different operational conditions and control strategies are presented using this approach. The results show that the total nitrogen removal is strongly dependent on the process load, nitrification rate, denitrification rate, cycle length and DO setpoint etc. and an optimal operation requires a proper match between the nitrification and denitrification. In addition, the different control strategies are evaluated using the new analytic technique and through this their mechanism and effectiveness are better understood.

A novel control strategy for improved nitrogen removal in an alternating activated sludge process—part I. Process analysis

Abstract Increasing demands on discharged water quality have led to the development of activated sludge processes which incorporate the biologically mediated removal of nitrogen and phosphorus. A major obstacle in the development of new control strategies for such processes is the lack of variables which can effectively alter process behavior and can feasibly be manipulated. This two part paper deals with a novel means to improve the nitrogen removal in an alternating type nutrient removal activated sludge process through control of the cycle length. In this first part, an analysis of process dynamics is undertaken. Using a simple model to describe the nitrogen dynamics in the alternating process, the existence of an optimal cycle length as a function of process conditions is demonstrated and explained. A graphical technique is developed which allows quick visualization of nitrogen dynamics under constant process conditions. This also serves as a means to assess whether a selected cycle length is optimal, too long, or too short for a given set of conditions. Based on the findings of this first part, the second part of the paper develops and demonstrates control strategies which serve to automatically adjust the cycle length to compensate for changing process conditions.

Dynamics and identification of a binary distillation column

Abstract Recent work in the area of control design has shown that the degree of gain directionality has a significant impact in achievable robust performance. Thus, it is important to obtain process models which give a satisfactory description of gain directionality. In this paper a parametric identification algorithm is applied to a case study of binary distillation. When the actuators are perturbed simultaneously this approach yields models which give a satisfactory description of the high-and low-gain directions. However, a common practical approach for modeling of distillation columns is to perturb the inputs separately and then combine the models into an overall description of the plant dynamics. It is shown that this method might have the drawback that the obtained model yields a poor description of the low-gain direction in the distillation plant. This indicates that modelling of multivariable processes can be improved by application of multivariable identification algorithm.

A novel control strategy for improved nitrogen removal in an alternating activated sludge process—part II. Control development

Abstract The first part of this two part contribution dealt with an analysis of nitrification and denitrification in an alternating activated sludge nutrient removal process for the case of constant process conditions. The existence of an optimal cycle length, which is a function of the process conditions, was demonstrated and discussed. Based on these findings, this paper examines a control strategy by which the cycle length of an alternating activated sludge nutrient removal process is automatically adjusted in order to compensate for changing process conditions. Two control algorithms are proposed. One involves proportional-integral-derivative (PID) control which requires minimal process information and computational effort. The second is a model based predictive control (MBPC) technique, which introduces a feedforward element into the control strategy. The MBPC technique is examined using a relatively simple process model. The results of experimental demonstrations of these two algorithms in a pilot plant facility indicate their potential towards improving nitrogen removal in the alternating process.

Evaluating estimation of gain directionality: Part 1: Methodology

For multivariable systems the input-output gain depends on the directionality of the inputs. This is denoted gain directionality, and explicit bounds on the possible gain of a system are obtained from singular value decomposition. In order to obtain high performance of multivariable control schemes it is mandatory that the dynamic model describes the gain directionality with sufficient accuracy. Based on this we pose the questions: with what accuracy can the gain directionality be estimated for a given system? Given an identified model what is the quality of the estimate of the gain directionality? This leads to an analysis in which a model set is defined based on a linear reference model, a relative output uncertainty description and the experimental design. The experimental design may include feedback, i.e. closed-loop identification. The error in the estimate of the gain in a given direction is then formulated as an H∞-norm problem. Given this representation the uncertainty on the estimated gain can be obtained by the structured singular value.

Evaluating estimation of gain directionality: Part 2: A case study of binary distillation

Abstract The analysis method presented in part 1 of this paper is applied to case studies of binary distillation using the LV and DV control configurations. Based on this we conclude that insufficient estimation of the maximum and minimum process gains for multivariable systems, i.e. gain directionality, is caused by misalignment of inputs and outputs with the corresponding input and output singular vectors, together with a high condition number. This uncertainty has a significant impact on the achievable control performance of multivariable controllers designed from the identified model. For these classes of MIMO systems we show that the uncertainty in the estimate of gain directionality can be reduced by using multi-input perturbations or preferably closed-loop identification as opposed to single-input perturbations. In the case of closed-loop identification it is sufficient to use simple single-loop proportional control, designed from limited qualitative knowledge about the plant. For standard parametric identification commonly used model evaluation techniques, such as degrees of explanation and the quality of the estimated individual transfer functions, are compared to the methodology presented in part 1. As shown, these standard evaluation techniques cannot be used to conclude whether the gain directionality has been adequately estimated. However, using the proposed method tight bounds are obtained on the quality of the estimated gain directionality. Furthermore, we illustrate that the quality of the estimate of gain directionality has a strong impact on achievable control performance, i.e. a significant error in the estimate of the low-gain direction in the plant will cause significant deterioration of the control performance whenever control action is required in this direction. Thus, by using proper scaling of the plant inputs and outputs the method presented analyses the model quality with respect to the control problem at hand.

Brief papers: A multivariable selftuning regulator to control a double effect evaporator

A multivariable selftuning regulator has been applied in control of a pilot plant double effect evaporator. The selftuning regulator consists of a recursive least squares algorithm combined with a single step optimal control strategy which minimizes a quadratic criterion. The algorithm is very fast and can be adjusted by an external operator in order to obtain satisfactory stationary regulator performance.