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Set Point Optimization in Multivariable Constrained Predictive Control

Abstract In this paper a procedure is presented to compute, at each sampling time, optimal future value of the controlled variables that can be effectively reached by the process in a given time horizon, taking into account the process dynamics and constraints on inputs and outputs. The optimality is considered in relation to an economic index involving the process variables. These values of the controlled variables are taken as the desired optimum set points, applying a rolling horizon policy. To illustrate the behaviour the system, an application example with a two-inputs, three outputs simulated non-linear chemical reactor is presented.

An MPC-based control structure selection approach for simultaneous process and control design

Abstract An optimization framework that addresses the simultaneous process and control design of chemical systems including the selection of the control structure is presented. Different control structures composed of centralized and fully decentralized predictive controllers are considered in the analysis. The systems dynamic performance is quantified using a variability cost function that assigns a cost to the worst-case closed-loop variability, which is calculated using analytical bounds derived from tests used for robust control design. The selection of the controller structure is based on a communication cost term that penalizes pairings between the manipulated and the controlled variables based on the tuning parameters of the MPC controller and the process gains. Both NLP and MINLP formulations are proposed. The NLP formulation is shown to be faster and converges to a similar solution to that obtained with the MINLP formulation. The proposed methods were applied to a wastewater treatment industrial plant.

Handling infeasibilities in predictive control

During the last years predictive control has received an increasing attention from industry. One of the reasons is that it takes into account the process constraints in a natural way. Nevertheless, there are situations (perturbations, not well defined constraints, etc.) when it is not possible to compute a sequence of future controls such that all the constraints are satisfied, i.e., the problem is not feasible. When this sort of problem appears it is necessary to apply some infeasibility handling procedure that drives the problem to a feasible region. After reviewing briefly some of the different approaches found in the literature, this paper presents a new method for solving the infeasibilities considering a constrained MIMO GPC based controller. The feasibility is recovered applying different techniques or a combination of all of them and the constraints changes are minimised according to a certain criteria. Finally, some computational results are shown.

Evaluation of the recovery of osmotic energy in desalination plants by using pressure retarded osmosis

ABSTRACT The current paper explores the possibility of using pressure retarded osmosis (PRO) as part of the post-treatment of existing desalination plants: a membrane-based PRO system would be used to transform osmotic energy of the retentate into hydraulic pressure; this pressure is then used to generate electricity in a turbine. For this, a source of water with lower osmotic pressure would be needed: municipal or industrial wastewater, brackish water, etc. From the point of view of implementation, except for the PRO membranes, this additional PRO post-treatment uses a small number of additional components, which are similar to those already standards in desalination industry. A model of the process is developed, and some feasibility studies will be discussed, to evaluate the potential for varying mixing rates.

A method to coordinate decentralized NMPC controllers in oxygen distribution networks

Abstract This paper deals with the optimal operation of large scale systems composed by local processes liked by shared resources. A decentralized architecture plus a coordinator, which guarantees the satisfaction of the global constraints of the process, is presented. The decomposition of the control problem into smaller ones is based on Lagrangean decomposition and on price coordination methods to update the prices. A coordination method that allows formulating the price assignment as a control problem is presented besides a formulation based on market behaviour. Both approaches are driven by the difference between the total shared resources available and demanded by the local NMPC controllers. One advantage of this approach is that in the low layer only requires adding an extra term in the cost function of the existing NMPC controllers. Moreover, there is no communication between local controllers, only between each local controller and the coordinator.

Predictive Control of a Process With Variable Dead-Time

Abstract This paper deals with the control of variable-delay processes, where the delay depends on the value of the manipulated variable, which results in a non-linear system difficult to control. As a reference process, the case of a heated tank where the controlled variable is the liquid temperature and the placement of the sensor introduce a transport delay in the control loop, has been considered. This challenging problem is approached from the perspective of predictive control, using the non-linear EPSAC controller.

Performance monitoring of industrial controllers based on the predictability of controller behavior

This paper focuses on performance assessment of industrial controllers. Instead of using process or controller models, it is based on process data collected at regular time intervals. Data analysis includes a set of tests that are reviewed in the paper and implemented in a software system. A methodology based on the concept of the predictability of controller errors is also proposed for performance monitoring. It considers the time series of the error and verifies the existence of predictable patterns beyond the control horizon in each one of the controlled variables of the process. The result of the analysis is given as a performance index. Examples using industrial data from a refinery are provided.

Nested Modifier-Adaptation for RTO in the Otto Williams Reactor

Abstract This paper deals with the problem of uncertainty management in real time optimization (RTO). It proposes a new architecture in the modifier-adaptation methodology, reformulating the algorithm as a nested optimization problem with two layers. Using this approach, it is possible to find a point that satisfies the KKT conditions of a process using an inaccurate model, but unlike the original modifier method, with no need to estimate the experimental gradients of the process. The proposed method has been tested in the Otto Williams Reactor considering structural mismatches and perfect and noisy measurements. The results are compared with the previous modifier adaptation methodology using dual control optimization showing that the method finds a KKT point of the process with the advantage that no experimental gradient information is required and with less sensitivity to process noise.

Improving scenario decomposition algorithms for robust nonlinear model predictive control

Abstract This paper deals with the efficient computation of solutions of robust nonlinear model predictive control problems that are formulated using multi-stage stochastic programming via the generation of a scenario tree. Such a formulation makes it possible to consider explicitly the concept of recourse, which is inherent to any receding horizon approach, but it results in large-scale optimization problems. One possibility to solve these problems in an efficient manner is to decompose the large-scale optimization problem into several subproblems that are iteratively modified and repeatedly solved until a solution to the original problem is achieved. In this paper we review the most common methods used for such decomposition and apply them to solve robust nonlinear model predictive control problems in a distributed fashion. We also propose a novel method to reduce the number of iterations of the coordination algorithm needed for the decomposition methods to converge. The performance of the different approaches is evaluated in extensive simulation studies of two nonlinear case studies.

An improvement-based MILP optimization approach to complex AWS scheduling

Abstract The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times. This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed.