ARRTOC: Adversarially Robust Real-Time Optimization and Control

Real-Time Optimization (RTO) plays a crucial role in the process operation hierarchy by determining optimal set-points for the lower-level controllers. However, these optimal set-points can become inoperable due to implementation errors, such as disturbances and noise, at the control layers. To address this challenge, in this paper, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm. ARRTOC draws inspiration from adversarial machine learning, offering an online constrained Adversarially Robust Optimization (ARO) solution applied to the RTO layer. This approach identifies set-points that are both optimal and inherently robust to control layer perturbations. By integrating controller design with RTO, ARRTOC enhances overall system performance and robustness. Importantly, ARRTOC maintains versatility through a loose coupling between the RTO and control layers, ensuring compatibility with various controller architectures and RTO algorithms. To validate our claims, we present three case studies: an illustrative example, a bioreactor case study, and a multi-loop evaporator process. Our results demonstrate the effectiveness of ARRTOC in achieving the delicate balance between optimality and operability in RTO and control

Optimal Load Sharing for Serial Compressors via Modifier Adaptation

This paper investigates the optimal load-sharing problem of serial gas compressors in the presence of plant-model mismatch. The problem is formulated as a static real-time optimization task that is solved via modifier adaptation for interconnected systems. The proposed approach guarantees optimal operation of the plant upon convergence. Furthermore, it is shown how the specific problem structure can be exploited during process operation for the efficient estimation of plant gradients with respect to local inputs. A simulated case study demonstrates the effectiveness of the proposed real-time optimization approach

Model predictive approaches for active surge control in centrifugal compressors

\u3cp\u3eModel predictive control (MPC) techniques are considered for industrial centrifugal compression systems with nonlinear dynamics, to address process and antisurge control for reaching the desired pressure ratio and surge distance. We consider a contractive nonlinear MPC formulation that ensures asymptotic stability of the closed-loop system by imposing the decrease of a quadratic Lyapunov function via an additional constraint. We discuss recursive feasibility and estimate the region of attraction via numerical methods. We also consider alternative MPC formulations, including offset-free linear and nonlinear MPC to handle the effects of disturbances and unmodeled dynamics. The computational efficiency of an approximation based on sequential quadratic programming (SQP), that yields a closed-loop performance comparable to the full nonlinear MPC is also discussed. All of the controllers considered are tested in simulations that emulate a realistic test bench and their computational time is assessed on an industrial Programmable Logic Controller (PLC). Their performance is compared with standard industrial control in nominal and perturbed cases replicating the typical and critical disturbances and model mismatches. The numerical results show that the SQP and nonlinear MPC methods outperform the other controllers in the considered scenarios, based on closed-loop performance metrics for the surge margin, the reference tracking accuracy, and the system actuation, without significantly increasing the computational time.\u3c/p\u3

Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement

As an emerging machine learning technique, lifelong learning is capable of solving multiple consecutive tasks based upon previously accumulated knowledge. Although this is highly desired for streaming process prediction in industry, lifelong learning methods have so far failed to gain applications to mainstream adaptive predictive modeling of time-varying industrial processes. This is because when faced with a new data batch, existing lifelong learning approaches need both input and output data to construct local predictors before knowledge transfer can succeed. But in many process industries, the process output data is hard to measure online and it often takes time to acquire them from off-site lab analysis. This delayed acquisition of target output data makes it challenging to apply lifelong learning and other existing adaptive mechanisms to dynamic industrial processes with delayed process output measurement. To overcome this difficulty, this paper proposes a novel lifelong learning framework that can rapidly predict new data batches with input data only before the arrival of the process output measurement. Specifically, we propose to incorporate process input information into lifelong learning via coupled dictionary learning, to enable the prediction of new batches without target output data. The input feature is linked with a local predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the local predictor for the new batch can be reconstructed by knowledge transfer given only process inputs. Two industrial case studies are used to evaluate the effectiveness of our proposed framework and reveal the intrinsic learning mechanism of our lifelong process modeling to perform knowledge base adaptation