Anas Alanqar

Integrating production scheduling and process operation via economic model predictive control

Managing production schedules and tracking time-varying demand of certain products while optimizing process economics are subjects of central importance in industrial applications. Production schedule following is generally required for a small subset of the total process state vector. Motivated by this, the present work proposes an approach that targets achieving the desired production schedule while maximizing economics using economic model predictive control (EMPC), which is a feedback control approach that optimizes plant economics in a receding horizon fashion. Conditions for closed-loop stability are derived for a general class of nonlinear systems. The proposed EMPC scheme was applied to a chemical process example where the product concentration was requested to follow a certain production schedule. Simulation results demonstrate that the proposed EMPC was able to maintain closed-loop stability, achieve the desired production schedule, and maximize plant economics.

Simultaneous control of safety constraint sets and process economics using economic model predictive control

Maintaining safe operation of chemical processes is of paramount importance in process systems and control engineering, and is ideally achieved while maximizing profit. It has long been argued that process safety is fundamentally a process control problem, yet few research efforts have attempted to integrate process safety and control. Economic model predictive control (EMPC) has attracted significant attention recently due to its ability to optimize process operation from an economic perspective. However, there is very limited work on the problem of integrating safety considerations with EMPC. Motivated by the above considerations, this work presents an EMPC methodology that adjusts in real-time the size of the safety sets in which the process state should reside in order to ensure safe process operation and feedback control of the process state while optimizing economics via time-varying process operation. Recursive feasibility and closed-loop stability are established for a sufficiently small EMPC sampling period. The proposed method is demonstrated with a chemical process example.

Economic model predictive control of nonlinear process systems using multiple empirical models

Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. Motivated by this, in the present work, Lyapunov-based economic model predictive control (LEMPC) is designed with multiple linear empirical models. The different models are used to more accurately predict the behavior of a nonlinear system over a larger state-space region compared to using a single empirical linear model only. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages.

Process safeness index: Its definition and use in economic model predictive control to ensure process operational safety

In this work, we propose initial steps in the first systems safety approach that coordinates the control and safety systems through a common metric (a Safeness Index) and develop a controller formulation that incorporates this index. Specifically, this work presents an economic model predictive control (EMPC) scheme that utilizes a Safeness Index function as a hard constraint to define a safe region of operation termed the safety zone. Under the proposed EMPC, the closed-loop state of a nonlinear process can be guaranteed to enter the safety zone in finite time while maximizing the process economics. The proposed design is demonstrated using a chemical process example.

Elucidating and handling effects of valve-induced nonlinearities in industrial feedback control loops

Abstract In this work, we investigate the effects of various types of valve behavior (e.g., linear valve dynamics and stiction) on the effectiveness of process control in a unified framework based on systems of nonlinear ordinary differential equations that characterize the dynamics of closed-loop systems including the process, valve, and controller dynamics. By analyzing the resulting dynamic models, we demonstrate that the responses of the valve output and process states when valve behavior cannot be neglected (e.g., stiction-induced oscillations in measured process outputs) are closed-loop effects that can be difficult to predict a priori due to the coupled and typically nonlinear dynamics of the process-valve model. Subsequently, we discuss the implications of this closed-loop perspective on the effects of valve dynamics in closed-loop systems for understanding valve behavior compensation techniques and developing new ones. We conclude that model-based feedback control designs that can account for process and valve constraints and dynamics provide a systematic method for handling the multivariable interactions in a process-valve system, where the models in such control designs can come either from first-principles or empirical modeling techniques. The analysis also demonstrates the necessity of accounting for valve behavior when designing a control system due to the potentially different consequences under various control methodologies of having different types of valve behavior in the loop. Throughout the work, a level control example and a continuous stirred tank reactor example are used to illustrate the developments.

Error-triggered on-line model identification in economic model predictive control

Economic model predictive control (EMPC) is a feedback control technique that employs real-time dynamic optimization to find optimal control actions with respect to a cost function representing the plant economics. The model used in EMPC must be able to capture the important dynamics of the plant. In industry, it may be difficult in many applications to obtain a first-principles model of the process, in which case a linear empirical model constructed using process data may be used as the process model within an EMPC. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state-space, restricting an EMPC to operate in a small region within which the potential of EMPC for improving process profit is not realized. For this reason, we present a scheme for expanding the level sets used to design state constraints in Lyapunov-based economic model predictive control (LEMPC) with linear empirical models to improve the process profit, incorporating on-line updates of the linear empirical model triggered by model prediction errors quantified by a moving horizon error detector. A chemical process example illustrates that the proposed LEMPC can maintain closed-loop stability of the process and bring the performance close to that which would be obtained if the nonlinear process model were used in LEMPC.