C. de Prada

Non-linear Predictive Control of Dissolved Oxygen in the Activated Sludge Process

Abstract This paper considers the control of dissolved oxygen concentration in a wastewater treatment plant. Since the system is nonlinear and multivariable in nature, a complex control system should be postulated. However, under certain conditions it is feasible to decompose the control problem into separate multi-input single output sub-systems. In this case, the control loops of substrate and dissolved oxygen concentrations in the bioreactor can be independently handled and thus, more effectively regulated. A non-linear predictive control algorithm is proposed to satisfy the process demands. Robustness of operation and ease of constraint incorporation make the long range predictive control philosophy particularly atractive. Dissolved oxygen is regulated by a series of aerators with fixed speed motors. It is the sequence of motor on/off switches which must be determined in order to control the dissolved oxygen level. Furthermore, several constraints must be satisfied in order to avoid mechanical or technical problems. The non-linear predictive control algorithm has been developed to predict the series of motor switches which will result in tight dissolved oxygen regulation whilst also satisfying these constraints. The results obtained show that the proposed controller may be a good alternative to the present control philosophy.

Tight robust interval observers: An LP approach

This paper provides a solution based on linear programming to the problem of designing observers that ensures guaranteed bounds on the estimated states. Firstly, considering linear systems without uncertainties, we provide a complete solution for the existence of interval observers having minimal l1-norm of the interval error. Secondly, new type of observers involving dilatation functions, are introduced in order to deal with the robust estimation of systems that are possibly nonlinear and subject to uncertainties. A new methodology is provided for the design and characterization of tight robust interval observers. All the proposed conditions are expressed in term of linear programming.

Nonlinear Predictive Control of processes with variable time delay. A temperature control case study

Material or fluid transportation is a commonly encountered phenomenon in industrial applications, generating variable time delay that makes the design of feedback control loops more difficult. This paper investigates the applicability of MPC (Model Predictive Control) strategies to this type of processes. The experimental setup consists of a heated tank, of which the outlet temperature (measured at a certain distance from the tank) is controlled by manipulating the outlet flow. The nonlinear EPSAC (Extended Prediction Self-Adaptive Control) approach is used, which reduces the complexity of nonlinear optimization to iterative quadratic programming. It is shown that developing a process model in which dynamics are decoupled from the variable time delay leads to a Smith predictor-like control structure, that allows the proper operation of the control loop with fixed control parameters. The performance of the predictive controller is compared on the pilot plant to the performance of classic control approaches for systems with time delay.

Development of a real-time expert system for wastewater treatment plants control

Abstract A real-time expert system to control wastewater treatment plants is presented. The software has been developed in the G2 environment. It contains: an interface that permits on-line acquisition of plant data using G2 standard interface (GSI), a predictive control algorithm for dissolved oxygen (DO) control, and a graphical interface between the expert system and the operator. The dissolved oxygen control is performed using a non-linear predictive control algorithm, that has been developed to satisfy quality constraints whilst reducing energy demands. The algorithm uses data obtained from the plant by hardware sensors, and software which recursively estimates the oxygen uptake rate. All these elements are integrated in a knowledge base that includes a set of diagnosis, detection, prediction and operation rules, making the system capable of handling a wide number of usual (where predictive control can be useful) and unusual situations (where quantitative and qualitative information must be considered.

Aeration control of a wastewater treatment plant using hybrid NMPC

In the operation of wastewater treatment plants a key variable is dissolved oxygen (DO) content in the bioreactors. As oxygen is consumed by the microorganisms, more oxygen has to be added to the water in order to comply with the required minimum dissolved oxygen concentration. This is done using a set of aerators working on/off that represents most of the plant energy consumption. In this paper a hybrid nonlinear predictive control algorithm is proposed, based on economic and control aims. Specifically, the controller minimizes the energy use while satisfying the time-varying oxygen demand of the plant and considering several operation constraints. A parameterization of the binary control signals in terms of occurrence time of events allows the optimization problem to be re-formulated as an nonlinear programming (NLP) problem at every sampling time. Realistic simulation results considering real perturbations data sets for the inlet variables are presented.

Hybrid Control of a Supermarket Refrigeration Systems

This paper presents a non-linear model predictive control (NMPC) of a supermarket refrigeration system. This is a hybrid process involving switching nonlinear dynamics and discrete events, on/off manipulated variables, like valves and compressors, continuous controlled variables like goods temperatures and finally, several operation constraints. The hybrid controller is based on a parameterization of the on/off control signals in terms of time of occurrence of events instead of using directly binary values, on this way, we can reformulate the optimization problem as a NLP problem. A rigorous model of a real supermarket refrigeration system provided by Danfoss is presented as well as results of the hybrid controller operating on it. The paper describes the hybrid process, presents the control problem formulation and provides some results of the proposed approach and comparisons with the traditional control.

Non-linear Predictive Control for a Distillation Column

In this paper an alternative approach to non-linear predictive control is presented. It is based on iterative linearisation of the model response so that the same closed loop responses as in the pure non-linear approach are obtained but with reduced computation times and more efficient optimisation tools. The method is applied to a high purity distillation column and some results are presented showing the behaviour of the proposed algorithm.

Nonlinear predictive control in the LHC accelerator

This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the Inner Triplet Heat Exchanger Unit (IT-HXTU) prototype of the LHC particle accelerator being built at CERN, operating at a temperature of about 1.9 K. The development includes a state estimator with a receding horizon estimation procedure to improve the regulator predictions. Copyright

MLD systems: modeling and control. Experience with a pilot process

We present preliminary results of the modeling and control of a hydraulic pilot process, currently under construction at the Laboratory of Automatics of the ISA Departament of Universidad de Valladolid. The system is described by linear inequalities involving both, real and integer variables, and the dynamical and logical decisions are heavily inter dependant. Hence the characterization as a mixed logical dynamical (MLD) system. The model obtained is particularly suited to apply a model based predictive control strategy to command the system. Results of a simulation of the closed loop system are feature.

Knowledge based process control supervision and diagnosis: the AEROLID approach

Abstract The artificial intelligence incidence in process control, although an active area in the researchers community and even with some implementations at industrial environment, is not sufficiently evaluated in numerical terms for the long term. The present article shows such an evaluation of a knowledge based system, developing supervisory control tasks in the sugar production from sugar-beet, and paying particular attention to fault detection and diagnosis. A way of conceiving supervision for continuous processes is presented and supported with this industrial application. The expert system carrying out supervisory tasks operates in a VAX ® workstation, directly over the distributed control system. The expert system development tool is G2 ® which has real-time facilities. Although the core system was developed in G2, it also consists of some external modules because it combines both analytical and artificial intelligence problem resolution techniques. The global architecture, as well as the implementation details of the modules necessary for fault identification, are presented altogether with the experimental results obtained from the factory field.