A pulp mill benchmark problem for control: problem description

This work introduces a benchmark problem of a pulping process, including both the fiber line and the chemical recovery area. The complete details of the pulp mill process are presented, including the control objectives, modes of operation, process constraints, measurements and costs. The dynamic model, including the source/binary code of all the unit operations is made available to the academic community as a benchmark for use in process system engineering studies.     

An optimal control based approach to designing plantwide control system architectures

An approach to designing decentralized plantwide control system architectures is presented. The approach is based on splitting the optimal controller gain matrix that results from solving an output optimal control problem into feedback and feedforward parts. These two parts are then used to design and evaluate decentralized control systems. Results for the application of the methodology to a realistic, 4 by 4 reactor with recycle process are given. For this system, the optimal control based approach suggests feedback pairings that are significantly different than those suggested by the steady state RGA. The approach presented can give an indication if MPC is preferred over a decentralized approach to plantwide control. Comparison of the results produced by the best decentralized plantwide system and a model predictive control system are presented.     

Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a ‘new paradigm’ under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants.     

Control of ball mill grinding circuit using model predictive control scheme

Ball mill grinding circuits are essentially multivariable systems with high interaction among process variables. Traditionally grinding circuits are controlled by detuned multi-loop PI controllers that minimize the effect of interaction among the control loops. Detuned controllers generally become sluggish and a close control of the circuit is not possible. Model Predictive Controllers (MPC) can handle such highly interacting multivariable systems efficiently due to its coordinated approach. Moreover, MPC schemes can handle input and output constraints more explicitly and operation of the circuits close to their optimum operating conditions is possible. Control studies on a laboratory ball mill grinding circuit are carried out by simulation with detuned multi-loop PI controllers, unconstrained and constrained model predictive controllers and their performances are compared.     

Design and plantwide control of n-butyl acrylate production process

n-Butyl acrylate is commercially produced from acrylic acid and n-butanol using strong acidic homogeneous catalysts. To overcome problems related to corrosion, catalyst removal from product and catalyst disposal after neutralization, research based on solid catalysis received increased attention in the recent years. However, design and control studies of an entire plant are rare. In this paper, the design and control of two reaction-separation-recycle process alternatives are developed. Both use a fixed-bed reactor with Amberlyst 131 catalyst. The separation of n-butyl acrylate from the mixture with n-butanol and acrylic acid is difficult. One of the process alternatives achieves the separation by distillation at pressure above atmospheric, while the other conveniently employs extractive distillation with ethylene glycol. Both processes are controllable, the control system showing robustness when increase or decrease in production capacity is required, or operating conditions change. Aspen Plus and Aspen Plus Dynamics are used as efficient Computer-Aided Process Engineering tools.     

Plug-and-play model predictive control based on robust control invariant sets

We consider the problem of designing decentralized controllers for large-scale linear constrained systems composed by a number of interacting subsystems. As in Riverso et al. (2013b), (i) the design of local controllers requires limited transmission of information from other subsystems and (ii) the addition/removal of a subsystem triggers the design of local controllers for child subsystems only. These properties enable Plug-and-Play (PnP) operations, and we show how to perform them while preserving global stability of the origin and constraint satisfaction. We improve several aspects of the PnP design procedure proposed in Riverso et al. (2013b) and, using recent results in the computation of Robust Control Invariant (RCI) sets, we show that all critical steps in the design of a local controller can be solved through Linear Programming (LP). Finally, an application of the proposed design procedure to a large-scale mechanical system is presented.     

Systematic top-down economic plantwide control of the cumene process

Plantwide control system design for economic operation over a wide through range (design throughput to maximum throughput) encompassing multiple active constraint regions, is studied for the cumene process. A unique feature of the process is that it recycles the heavy side-product to extinction. A novel top-down control system synthesis approach, where the control objectives for maximum throughput operation are first obtained using steady state optimization followed by control loop pairings with highest priority to economic objectives, is applied. The control structure thus obtained is unconventional with tight active constraint control requiring ‘long’ level loops that maintain the reflux drum and bottom sump levels of a column using the two process fresh feeds. This structure for maximum throughput operation is adapted for economic operation at lower throughputs. Rigorous dynamic simulations show that the structure provides acceptable process regulation for large disturbances despite the long level loops over the entire throughput range. More importantly, no back-off from the active hard equipment capacity constraints also ensure that the loss in throughput from the maximum achievable is negligible. This work is amongst the first reports illustrating the application of the top-down plantwide control system design approach for superior economic performance with robust process stabilization.     

Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem

This paper presents an efficient distributed model predictive control scheme based on Nash optimality, in which the on-line optimization of the whole system is decomposed into that of several small co-operative agents in distributed structures, thus it can significantly reduce computational complexity in model predictive control of large-scale systems. The relevant nominal stability and the performance on single-step horizon under the communication failure are investigated. The Shell heavy oil fractionator benchmark control problem is illustrated to verify the effectiveness of the proposed control algorithm.     

Decentralized predictive thermal control for buildings

This paper studies the problem of decentralized control design for thermal control in buildings, to achieve a satisfactory trade-off between underlying performance and robustness objectives. An output-feedback, model predictive framework is used for decentralized control which is based on a reduced order system representation. It entails the use of decentralized extended state observers to address the issue of unavailability of all states and disturbances. The decision on control architecture selection is based on an agglomerative clustering methodology developed previously [22]. The potential use of the proposed control design methodology is demonstrated in simulation on a multi-zone building, which quantifies the tradeoffs in performance and robustness with respect to the degree of decentralization.     

Throughput and product quality control for a grinding mill circuit using non-linear MPC

A non-linear model-based control architecture for a single-stage grinding mill circuit closed with a hydrocyclone is proposed. The control architecture aims to achieve independent control of circuit throughput and product quality, and consists of a non-linear model predictive controller for grinding mill circuit control, and a dynamic inversion controller to control the fast sump dynamics. A particle filter is used to estimate the mill states, and an algebraic routine is used to estimate the sump states. The observers make use of real-time continuous measurements commonly available on industrial plants. Simulation results show that control objectives can be achieved by the controller despite the presence of measurement noise and disturbances.     

Performance assessment using a model predictive control benchmark

Model predictive control (MPC) technology has been widely implemented throughout the petroleum, chemical, metallurgical and pulp and paper industries over the past three decades. The focus of this paper is the assessment of single-input, single-output MPC schemes against a new performance standard. The proposed MPC benchmark is shown to be useful both as a model diagnostic and as a tuning guide during commissioning. A formal assessment procedure is presented which emphasizes the use of routine operating data plus knowledge of the deadtime to determine when it becomes worthwhile to invest in re-identification of the plant dynamics and re-installation of the MPC application.     

Predictive-repetitive control with constraints: From design to implementation

This paper addresses the design and implementation of predictive-repetitive control systems, including the case when constraints are imposed. The approach is based on a frequency response decomposition technique that detects the dominant frequency components of the reference signals and embeds them in the predictive-repetitive controller. Analysis and design make use of the frequency response of the closed-loop system and the choice of the structure of the repetitive controller is considered as a balance between the reduction of tracking errors and minimization of the effects on performance of unwanted elements, such as model uncertainty. The implementation of operational constraints on the amplitudes of the control signals, their increments and on the outputs is considered using an on-line solution constructed using quadratic programming and the identification of active constraints. Experimental results from application to a 2 degree-of-freedom robotic system are used to illustrate the design and implementation procedures developed.     

A hybrid model predictive control strategy for nonlinear plant-wide control

A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques.     

Overlapping control design for multi-channel systems

This paper deals with the decentralized overlapping control of interconnected systems. The notion of a quotient overlapping fixed mode (QOFM) is first introduced and it is shown that a mode of an interconnected linear time-invariant system can be shifted by means of a general decentralized overlapping controller if and only if it is not a QOFM. It is then asserted that any interconnected system with no unstable QOFM can be stabilized by using an appropriate finite-dimensional linear time-varying controller. It is also shown how the existing results aiming at designing a decentralized controller of a certain type such as generalized sampled-data hold function, finite-dimensional linear time-varying, and sampled-data can be utilized to design a decentralized overlapping controller of a desired form, in order to achieve any design specification. The efficacy of the results is elucidated through two numerical examples.     

一种跟踪问题中的次优非线性预测控制算法

针对跟踪问题中无状态和输入约束的非线性预测控制最优解的求取问题,引入参考输入轨迹的概念,利用Ｓｔｉｒｌｉｎｇ插值公式,将非线性系统模型沿参考输入输出轨迹展开,取其一阶近似,处理成参数已知的线性模型．在此基础上,利用线性系统预测控制理论求解得到原系统的次优控制律．该方法不要求系统模型连续可导,且无需对线性化后的模型参数进行在线辨识,计算量小,易于实现．     

A hybrid feedback for a benchmark problem of idle speed control

The ever increasing demands on passengers' comfort, safety, emissions and fuel consumption imposed by car manufacturers and regulations call for advanced techniques and the use of cycle‐accurate models in automotive control. In this paper, we focus on such approach to the idle speed control. It is natural to resort to hybrid methodologies, because of the rich combination of time and event‐based behaviors exhibited by a controlled engine. A hybrid benchmark problem is considered and addressed first by analyzing the equilibria of the system and then testing a simple hybrid feedback strategy. Copyright © 2009 John Wiley & Sons, Ltd.     

Interactions between control and process design under economic model predictive control

Economic model predictive control (EMPC) is a model-based control scheme that integrates process control and economic optimization, which can potentially allow for time-varying operating policies to maximize economic performance. The manner in which an EMPC operates a process to optimize economics depends on the process dynamics, which are fixed by the process design. This raises the question of how process and EMPC designs interact. Works which have addressed process and control design interactions for steady-state operation have sought to simultaneously develop process designs and control law parameters to find the most profitable way to operate a process that is able to prevent process constraints from being violated and to optimize capital costs in the presence of disturbances. Because EMPC has the potential to operate a process in a transient fashion, this work first focuses on how EMPC and process design interact in the absence of disturbances. Using small-scale process examples, we seek to understand the fundamental nature of the interactions between EMPC and process design, including how these interactions can impact computational complexity of the controller and the design procedure. We subsequently utilize the insights gained to suggest controller design variables which might be considered as decision variables for a simultaneous process and control design problem when disturbances are considered.     

Incorporating state estimation into model predictive control and its application to network traffic control

Model predictive control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. This paper focuses on exploring the inclusion of state estimates and their interaction with constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. Using a gaussian assumption, the original problem is approximated by a standard deterministically-constrained MPC problem for the conditional mean process of the state. The state estimates’ conditional covariances appear in tightening the constraints. ‘Closed-loop covariance’ is introduced to reduce the infeasibility and the conservativeness caused by using long-horizon, open-loop prediction covariances. The resulting control law is applied to a telecommunications network traffic control problem as an example.     

Linear periodic controller design for decentralized control systems

In the decentralized control of linear time-invariant (LTI) systems, a decentralized fixed mode (DFM) is a system mode which is immoveable using an LTI controller, while a quotient DFM (QDFM) is one which is immoveable using any form of nonlinear time-varying compensation. If a system has no unstable DFMs, there are well-known procedures for designing an LTI stabilizing controller; for systems which have unstable DFMs but no unstable QDFMs, we provide a simple design algorithm which yields a linear periodic sampled-data stabilizing controller.