An industrially relevant formulation of a distributed model predictive control algorithm based on minimal process information
Abstracts:Plant-wide control implies advanced supervisory algorithms to maintain desired performance in the involved coupled sub-systems. The dynamical interactions among these sub-systems can vary with the operating point, material properties and disturbances present in the process. Recirculating loops introduce additional phenomena in the dynamic response, further challenging the control tasks. Complex process dynamics may be linear parameter varying (LPV) and may be difficult, if not impossible, to identify properly. In this context, maintaining global performance is a challenge one must undertake with limited information at hand. This paper investigates the trade-off between the complexity of the implementation and achieved performance, using supervisory predictive control with limited information shared, applied on a test-bench representative for process control industry. The robustness of the proposed algorithms is tested against a nominal scenario in which the prediction model is fully identified, with complete information exchange. Experimental tests are performed on a test-bench process characterized by strong interactions, and the results illustrate the usefulness of this work.
Economic model predictive control based on a periodicity constraint
Abstracts:This paper addresses a novel economic model predictive control (MPC) formulation based on a periodicity constraint to achieve an optimal periodic operation for discrete-time linear systems. The proposed control strategy does not rely on forcing the terminal state by means of a terminal equality constraint and hence it does not require a priori knowledge of a periodic steady trajectory. Instead, at each sampling time step the economic cost function is optimized based on a periodicity constraint over all the periodic trajectories that include the current state. The recursive feasibility and the closed-loop convergence to a periodic steady trajectory are discussed. Moreover, an optimality certificate of this steady trajectory is provided based on the Karush–Kuhn–Tucker (KKT) optimality conditions. Finally, an application to a well-known water distribution network benchmark is presented to demonstrate the proposed economic MPC in which the closed-loop simulation results obtained with a linear model and a virtual–reality simulator are both provided.
Real-time Optimization with persistent parameter adaptation using online parameter estimation
Abstracts:One of the major drawbacks of traditional Real-time Optimization (RTO) is the steady-state wait before estimating the parameters. This paper proposes an alternative solution called Real-time Optimization with Persistent Adaptation (ROPA), which integrates on-line parameter estimation in the optimization cycle, avoiding the SS detection step. Essentially, the idea is to use transient information to update the steady-state economic optimization problem and, then, by continuously solving it, the calculated optimal solution would reach the actual plant steady-state optimum in a given time horizon. ROPA provides an intermediary solution between static and dynamic optimization schemes. While it approximates the optimal trajectory, ROPA design enables the application of techniques to plant-wide optimization and the use of well-established static RTO commercial solutions. The new methodology benefits are illustrated with a case study, in which the traditional RTO and ROPA schemes are applied to the Williams–Otto reactor. Their performance is compared based on profit loss and deviation from the actual optimal decisions. The results show that the refinement of the prediction capacity by decreasing the time between two sequential optimization leads to a better economic performance and enhances the disturbance detection of the optimization cycle.
Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes
Abstracts:In this paper, a new data-driven model-free adaptive sliding mode control is discussed for discrete-time nonlinear processes with tracking error constraint. Furthermore, a novel transformed error method together with a new sliding mode control framework is studied to ensure the tracking error converges to a predefined region all the time. Meanwhile, the proposed controller can guarantee that the convergence rate is not smaller than a pre-assigned constant and maximum steady-state error is less than an arbitrarily small prespecified value all the time, which is more effective in the complex industrial processes. The effectiveness of the considered strategy is validated by the simulation examples.
A data-driven optimal control approach for solution purification process
Abstracts:Solution purification holds a critical position in hydrometallurgy. With its inherent complexity and the mixed raw material supply, solution purification process exhibits various working conditions, and has nonlinear, time-varying dynamics. At current stage, a comprehensive and precise model of a solution purification process is still costly to obtain. More specifically, the model structure could be derived by applying physical and chemical principles, while the accurate model parameters cannot be obtained under certain working conditions due to reasons like insufficient data samples. This, in turn, introduces obstacles in achieving the optimal operation. In order to circumvent the modeling difficulty, this paper proposes a ‘Process State Space’ descriptive system to re-describe the optimal control problem of solution purification process, accordingly establishes a two-layer receding horizon framework for developing a data-driven optimal control of solution purification process. In the optimal control scheme, on the ‘optimization’ layer, by utilizing the ‘multiple-reactors’ characteristic of solution purification process, a ‘gradient’ optimization strategy is proposed to transform the dosage minimization problem into obtaining the optimal variation gradient of the outlet impurity concentrations along the reactors. On the ‘control’ layer, a model-free input constrained adaptive dynamic programming algorithm is devised and applied to calculate the optimal dosages for each reactor by learning from the real-time production data. Case studies are performed to illustrate the effectiveness and efficiency of the proposed approach. The results and problems need future research are also discussed.
A mechanistic fault detection and isolation approach using Kalman filter to improve the security of cyber physical systems
Abstracts:This paper seeks to understand and identify ways in which fault detection and isolation (FDI) methods can be utilized to enhance the cyber-security of cyber-physical systems (CPS). In this work, we have used state estimation to validate relations between process variables, termed invariants, thereby detect the onset of attacks. Multiple attack scenarios were studied and the proposed state estimation method was found to excel in detecting attacks conducted on sensors within the system; provided that not all sensors are compromised during the attack. However, the proposed methods for subsequent isolation and correction faced limitations due to delays or lack of information to pinpoint the attacked component. We demonstrated our proposed approach on a well-instrumented pilot scale water treatment plant equipped with controllers.
A data-driven adaptive multivariate steady state detection strategy for the evaporation process of the sodium aluminate solution
Abstracts:The evaporation process of sodium aluminate solution is one of the main processes for alumina production. Due to uncertainty of production environment and existence of random measurement noise, process condition is changed frequently, which easily leads to unstable production. In this paper, an adaptive multivariate steady state detection strategy is proposed for the evaporation process of sodium aluminate solution. In the proposed strategy, steady state detection variables are selected firstly according to partial correlation analysis between the concentration of the outlet mother liquor and its major influencing variables. Then, an improved K-means clustering algorithm is presented to identify the outliers which would affect steady state detection results. By analyzing changes in velocity and acceleration of variables, a steady state evaluation index is defined with quantitative trend information extraction. The flexibility and effectiveness of the proposed steady state detection strategy are validated by industrial data of sodium aluminate solution evaporation process
A novel hybrid of auto-associative kernel regression and dynamic independent component analysis for fault detection in nonlinear multimode processes
Abstracts:With modern industrial processes becoming larger and more complex, we should consider their nonlinear and multimode characteristics carefully for accurate process monitoring and fault detection. In this paper, a novel hybrid of two data-driven techniques—auto-associative kernel regression (AAKR) and dynamic independent component analysis (DICA)—is proposed for fault detection of nonlinear multimode processes. AAKR is a nonparametric multivariate technique; it can effectively deal with nonlinearity and multimodality of target systems by real-time local modeling in accordance with query vectors. Residuals obtained from AAKR usually deviate from Gaussian distribution (i.e., they are non-Gaussian), and there exist auto- and cross-correlations between them. The proposed method detects process faults by applying DICA to the residuals; DICA can capture useful statistical information hidden in the residuals. The validity and effectiveness of the proposed method are illustrated through three popular benchmark problems such as a three-variable multimodal process, a three-variable nonlinear process, and Tennessee Eastman process; the proposed method is also compared with several comparison methods The experimental results demonstrate the superiority of the proposed method, which achieves the best detection rates with reasonable false alarm rates.
Adaptive model predictive control for a dual-hormone artificial pancreas
Abstracts:We report the closed-loop performance of adaptive model predictive control (MPC) algorithms for a dual-hormone artificial pancreas (AP) intended for patients with type 1 diabetes. The dual-hormone AP measures the interstitial glucose concentration using a subcutaneous continuous glucose monitor (CGM) and administers glucagon and rapid-acting insulin subcutaneously. The discrete-time transfer function models used in the insulin and glucagon MPCs comprise a deterministic part and a stochastic part. The deterministic part of the MPC model is individualized using patient-specific information and describes the glucose-insulin and glucose-glucagon dynamics. The stochastic part of the MPC model describes the uncertainties that are not included in the deterministic part of the MPC model. Using closed-loop simulation of the MPCs, we evaluate the performance obtained using the different deterministic and stochastic models for the MPC on three virtual patients. We simulate a scenario including meals and daily variations in the model parameters for two settings. In the first setting, we try five different models for the deterministic part of the MPC model and use a fixed model for the stochastic part of the MPC model. In the second setting, we use a second-order model for the deterministic part of the MPC model and estimate the stochastic part of the MPC model adaptively. The results show that the controller is robust to daily variations in the model parameters. The numerical results also suggest that the deterministic part of the MPC model does not play a major role in the closed-loop performance of MPC. This is ascribed to the availability of feedback and the poor prediction capability of the model, i.e. the large disturbances and model-patient mismatch. Moreover, a second order adaptive model for the stochastic part of the MPC model offers a marginally better performance in closed-loop, in particular if the model-patient mismatch is large.
An intelligent detection method for bulking sludge of wastewater treatment process
Abstracts:Prediction of bulking sludge is a matter of growing importance around the world. In this study, to detect bulking sludge of wastewater treatment process (WWTP), an intelligent detection method, using a self-organizing recurrent radial basis function neural network (SORRBFNN) and a cause variables identification (CVI) algorithm, was developed to detect the fault points and the fault variables of bulking sludge. For this intelligent detection method, first, the structure and parameters of SORRBFNN were updated by an information-oriented algorithm (IOA) and an improved Levenberg-Marquardt (LM) algorithm to improve the prediction accuracy of the sludge volume index (SVI) from the water qualities. Second, the CVI algorithm was designed to allow a quick revealing of the cause variables of bulking sludge with high accuracy. And the intelligent detection method was tested on the measured data from a real WWTP. Experimental results confirmed the attractiveness and effectiveness of the proposed intelligent detection method.