S. Cristea

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.

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

Logic Embedded NMPC of a Solar Air Conditioning Plant

This paper presents a hybrid controller for a solar air conditioning plant, located at the University of Seville, Spain, and used as a benchmark for the HYCON NoE of the European Union (EU). The plant uses two sources of energy: solar and gas, plus a set of accumulation tanks and an absorption tower to provide conditioned air to a university building. The hybrid character comes from the discrete decisions that have to be taken in relation with the energy source in use, which is selected by means of on/off valves, plus the continuous nature of the process and other manipulated variables. The control aim is to operate the conditioned air system using the smaller possible amount of energy from gas and maintaining other variables close to their set points. The hybrid control is based on a model predictive control strategy developed with the objective of dealing with the mixed discrete-continuous nature of the process in an efficient way. A novel approach incorporating an internal model with embedded logic control is used to transform the hybrid problem in a continuous-nonlinear one where NMPC can be applied. The paper presents results obtained both, in simulation and in the real system.

Hybrid control of a mixed continuous-batch process

Abstract This paper deals with a control problem related with a mixed continuous-batch process, a pilot plant of our Lab, where both continuous decisions and scheduling takes place. We tried to find a solution in the framework of non-linear model predictive control formulating the control problem by means of an hybrid model in terms of integer and continuous variables. As the system must be controlled in real-time, mixed integer optimization algorithms proved to be too slow, so, an alternative formulation in terms of real variables was set up. The paper describes the process, the control problem formulation, and the optimization alternatives and provides results of some test for evaluation of the proposed approach.

Hybrid model predictive control of a sugar end section

Abstract This paper deals with the MPC control of an industrial hybrid process where continuous and batch units operate jointly: the crystallization section of a sugar factory. The paper describes a plant-wide predictive controller that takes into account, both, the continuous objectives and manipulated variables, as well as the ones related to the scheduling of the batch units. The MPC is formulated avoiding the use of integer variables, so that a NLP optimization technique could be applied. Simulation results of the controller operation are provided *

Hybrid NMPC Control of a Sugar House

Plant-wide control is attracting considerable interest, both as a challenging research field and because of its practical importance. It is a topic [1] characterized by complexity in terms of the number and type of equipments involved, diversity of aims, and lack of adequate models and control policies. In this paper, the MPC control of the final part of a beet sugar factory, the so-called sugar house or sugar end, where sugar crystals are made, is presented. Perhaps the most characteristic aspect of its operation is that batch and continuous units operate jointly, which introduce the need for combining on-line scheduling with continuous control. As such, it is a hybrid process that requires non-conventional control techniques. The paper presents a methodology and a predictive controller that takes into account both, the continuous objectives and manipulated variables, as well as the ones related to the discrete operation and logic of the batch units, and, at the end, simulation results of the controller operation are provided.

Nonlinear Predictive Control in the LHC Accelerator

Abstract 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) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation maintains the magnets temperature at about 1.9xa0K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.

Optimum Operation of a Beer Filtration Process

This paper deals with the optimum operation of a beer filtration process that uses membranes for this task. Due to fouling, the operation requires cleaning, which damages the membranes, and creates a discontinuous operation. The optimal economic operation can be defined in terms of minimizing the number of chemical cleanings, as well as the use of energy, when processing a certain amount of beer in a given time. The problem is hybrid in nature, due to the discontinuities created by the cleanings. The corresponding optimization problem is formulated in the framework of predictive control but integrating the economic operation as target of the controller and different time scales. Also, instead of using binary variables for representing the discontinuities, the problem employs a sequential approach, embedding them in the dynamic simulation of the process model combined with a control parameterization that allows computing the solution in terms of the continuous variables that represent its degrees of freedom. Results of the optimal operation are presented.

Run-to-run convergence analysis of model-based policy iteration algorithms for experimental optimization of batch processes

Convergence analysis of iterative identification-optimization schemes is a key issue in modeling for optimization of batch processes. In this work, it is formally shown that for convergence is sufficient to guarantee that parametric uncertainty is increasingly reduced on a run-to-run basis. Convergence of a policy iteration algorithm to an optimal policy which satisfies the Hamilton-Jacobi-Bellman equation is thus assured as long as parametric uncertainty is iteratively reduced such that the performance prediction mismatch is driven to zero. The integration of global sensivity analysis with confidence interval boostrapping in the design of a convergent algorithm for model-based policy iteration is proposed. A simple bioprocess is used to exemplify run-to-run improvement.