Self-optimizing Control Research Articles

Shaping of Molecular Weight Distribution Using Self-Optimizing Control Based on Moments

Shaping of Molecular Weight Distribution Using Self-Optimizing Control Based on Moments

Optimal switching of MPC cost function for changing active constraints

Model predictive control (MPC) allows for dealing with multivariable interactions, known future changes and dynamic satisfaction of constraints. Standard MPC has a cost function that aims at keeping selected controlled variables at constant setpoints. This work considers systems where the steady-state optimal active constraints change during operation. This situation is not handled optimally by standard MPC which uses fixed controlled variables for the unconstrained degrees of freedom. We propose a simple framework that detects the constraint changes and updates the controlled variables accordingly. The unconstrained controlled variables are chosen to be the reduced cost gradients, which when controlled to zero minimizes the steady-state economic cost. In this paper, the nullspace method for self-optimizing control is used to estimate the cost gradient using a static combination of the measurements. This estimated gradient is also used for detecting the current set of active constraints, which in particular allows for giving up constraints that were previously active. The proposed framework, here referred to as “region-based MPC”, is shown to be optimal for linear constrained systems with a quadratic economic cost function, and it allows for good economic performance in nonlinear systems in a neighborhood of the considered design points.

A new data-driven distributed power grid load storage resource-wide area voltage self-optimization control method for distribution system

Abstract Different from the conventional energy distribution network, which solely relies on energy distribution, the novel distribution system gradually exhibits a complex and interconnected form of various resources, such as source network load storage. These resources possess characteristics of “multi-point, wide area, and small amount” distribution. Investigating the potential for wide-area voltage regulation by using distributed resources like renewable energy power generation, distributed energy storage, and flexible loads is crucial for constructing a highly integrated source network load storage in the new type of distribution system. This paper proposes a novel data-driven method for optimizing control of wide-area voltage through distributed power network load storage resources. By comprehensively and cooperatively controlling renewable energy power generation, energy storage systems, and flexible loads, this approach enhances the quality of wide-area voltage in the distribution network while ensuring optimal utilization of energy storage capacity and minimizing network losses. The effectiveness and advanced nature of this method are demonstrated by applying it to an urban distribution network example. Compared to traditional voltage regulation methods, this approach aligns better with the requirements for voltage regulation in the new type of distribution network.

Optimal measurement-based cost gradient estimate for feedback real-time optimization

This work presents a simple and efficient way of estimating the steady-state cost gradient Ju based on available uncertain measurements y. The main motivation is to control Ju to zero in order to minimize the economic cost J. For this purpose, it is shown that the optimal cost gradient estimate for unconstrained operation is simply Jˆu=H(ym−y∗) where H is a constant matrix, ym is the vector of measurements and y∗ is their nominally unconstrained optimal value. The derivation of the optimal H-matrix is based on existing methods for self-optimizing control and therefore the result is exact for a convex quadratic economic cost J with linear constraints and measurements. The optimality holds locally in other cases. For the constrained case, the unconstrained gradient estimate Jˆu should be multiplied by the nullspace of the active constraints and the resulting “reduced gradient” controlled to zero.

Dynamic controlled variables based dynamic self-optimizing control

Self-optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant values can achieve optimization effects, translating the process optimization problem into a process control problem. Currently, self-optimizing control is widely applied to steady-state optimization problems. However, the development of process systems exhibits a trend towards refinement, highlighting the importance of optimizing dynamic processes such as batch processes and grade transitions. This paper formally introduces the self-optimizing control problem for dynamic optimization, termed the dynamic self-optimizing control problem, extending the original definition of self-optimizing control. A novel concept, ”dynamic controlled variables” (DCVs), is proposed, and an implicit control policy is presented based on this concept. The paper theoretically analyzes the advantages and generality of DCVs compared to explicit control strategies and elucidates the relationship between DCVs and traditional controllers. Moreover, this paper puts forth a data-driven approach to designing self-optimizing DCVs, which considers DCV design as a mapping identification problem and employs deep neural networks to parameterize the variables. Three case studies validate the efficacy and superiority of DCVs in approximating multi-valued and discontinuous functions, as well as their application to dynamic optimization problems with non-fixed horizons, which traditional self-optimizing control methods are unable to address.

Multi-Function Collaborative Flexible Source-Load Intelligent Interactive Operation Control Technology for Industrial Communities

Different from the traditional power supply mode of only distribution network, the new energy distribution network gradually presents a strong coupling and strong correlation between the "source - network - load - energy storage" multi-energy system structure. Its various types of resources have the characteristics of wide regional distribution of "multiple points and wide areas". According to the characteristics of distribution network, this project studies the voltage self-optimizing control strategy in a wide range of distribution network with multi-source coordination. Through the coordinated regulation of multiple decentralized systems such as new energy power generation, energy storage and flexible load, the voltage quality in a large range is improved under the premise of guaranteeing energy storage capacity. Through the analysis of an urban distribution network, the results show that the proposed algorithm is feasible and advanced. Compared with the conventional regulation mode, this mode is more suitable for the regulation requirements of distribution network.

Global self-optimizing control of batch processes

This work considers to achieve near-optimal operation for a class of batch processes by employing self-optimizing control (SOC). Comparing with a continuous one, a batch process exhibits stronger nonlinearity with dynamics because of the non-steady operation condition. This necessitates a global version of SOC to achieve satisfactory performance. Meanwhile, it also makes the existing global SOC (gSOC) not directly applicable to batch processes due to the causality amongst variables. Therefore, it is necessary to extend the original gSOC to batch processes. In addition to the nonconvexity challenge of the original gSOC problem, the new extension for batch processes has to face even more challenges. Particularly, the causality due to dynamics of batch processes brings in structural constraints on controlled variables (CVs), making a CV selection problem even more difficult. To address these challenges, the gSOC problem is recast in a vectorized formulation and it is proved that the structural constraints considered are linear in the vectorized formulation. Moreover, a novel shortcut method is proposed to efficiently find sub-optimal but more transparent solutions for this problem. The effectiveness of the new approach is validated through a case study of a fed-batch reactor, where CVs are constructed through a combination matrix with a repetitive structure, resulting in a simple SOC scheme. This simplicity facilitates the implementation of the SOC approach and enhances its practical applicability and robustness.

Self-Optimization Control for Alkaline Water Electrolyzers Considering Electrolyzer Temperature Variations

Self-Optimization Control for Alkaline Water Electrolyzers Considering Electrolyzer Temperature Variations

Integrated process design and self-optimizing control: A probabilistic approach

This work introduces a probabilistic-based integrated process design and self-optimizing control approach grounded in a steady-state model, aiming to strike a balance between solution reliability and optimality. The proposed approach offers a practical and cost-effective solution by considering disturbances as random variables with known probability distribution functions. To propagate these disturbances to process outputs, the unscented transform method is utilized alongside rigorous first-principles process models. Additionally, probability levels are assigned to process constraints based on user-defined specifications, providing flexibility to tailor the design to desired risk confidence levels. The effectiveness and benefits of the approach are demonstrated through an application on an extractive distillation process with pre-concentration.

Towards maximum efficiency in heat pump operation: Self-optimizing defrost initiation control using deep reinforcement learning

Towards maximum efficiency in heat pump operation: Self-optimizing defrost initiation control using deep reinforcement learning

Generalized Global Self-Optimizing Control for Chemical Processes Part I. The Existence of Perfect Controlled Variables and Numerical Design Methods

Generalized Global Self-Optimizing Control for Chemical Processes Part I. The Existence of Perfect Controlled Variables and Numerical Design Methods

Integrated design and self-optimizing control of extractive distillation process with preconcentration

Despite increasing incentives, the practice of separating process design and control tasks remains prevalent. Current integrated approaches, when coupled with controller design, necessitate significant budget allocation towards developing suitable dynamic models. To achieve broader industry acceptance, a time-scale separation paradigm is desired, one that separates economic optimization and controller design.In this study, we present a systematic integration of self-optimizing control concepts into the plant design phase and investigate their impact on subsequent operational performance. The key highlights of our work are as follows: (1) Simultaneous inclusion of process structure variables, process parameter variables, control structure variables, and control parameter variables within the integrated formulation. (2) Independent consideration of both economic and control attributes as objectives, resulting in a trade-off between process and control objectives. We illustrate the proposed integrated formulation through a case study on an extractive distillation process with preconcentration.

Machine-vision-based Online Self-optimizing Control System for Line Marking Machines

Machine-vision-based Online Self-optimizing Control System for Line Marking Machines

Self-Optimizing Control Strategy for Distributed Parameter Systems

Real-time optimization (RTO) has been a popular approach for addressing optimal operation by repetitively optimizing online. However, this approach is impractical for distributed parameter systems (DPSs) due to its expensive computational cost. Self-optimizing control (SOC) has emerged as a promising alternative strategy, which achieves acceptable optimality by controlling the designed controlled variables (CVs) at constant set points without reoptimizing. Motivated by this, the existing lumped parameter SOC method is for the first time expanded to a typical class of DPSs based on a high-fidelity discrete-space model. Starting from an infinite-order system itself, the SOC problem is constructed as the minimal loss functional regarding the CVs through the operator operation and variational method. To obtain an operational analytical solution of CVs, the optimization problem is reformulated based on the discrete-space system derived by the finite difference. Considering the variety of sensor and actuator placements, the CVs are analytically designed with the minimal static loss through the stacking matrix approach. Finally, a case study of a plug-flow reactor is presented to demonstrate the feasibility of the proposed approach to achieve optimal operation for DPSs.

Self-Optimizing Control System to Maximize Power Extraction and Minimize Loads on the Blades of a Wind Turbine

This research proposes a methodology for designing and testing a self-optimizing control (SOC) algorithm applied to a wind energy conversion system (WECS). The SOC maximizes WECS power output and reduces the mechanical stress of the wind turbine (WT) blades by optimizing a multiobjective cost function. The cost function computation uses a combined blade element momentum (BEM) and thin-wall beam (TWB) model for calculating wind the turbine power output and blades’ stress. The SOC deployment implies a low computational cost due to an optimization space reduction via a matrix projection applied to a measurement vector, based on a prior offline calculation of a projection matrix, H. Furthermore, the SOC optimizes the operation of the WECS in the presence of uncertainty associated with the wind speed variation by controlling a linear combination of measured variables to a set point. A MATLAB simulation of a wind turbine model allows us to compare the WECS operating with the SOC, a baseline classic control system (BCS), and a nonlinear model predictive controller (NMPC). The SOC algorithm is evaluated in terms of power output, blades’ stress, and computational cost against the BCS and NMPC. The power output and blades’ stress performance of the SOC algorithm are compared with that of the BCS and NMPC, showing a significant improvement in both cases. The simulation results demonstrate that the proposed SOC can effectively optimize a WECS operation in real time with minimal computational costs.

A simplified economic model predictive control for a gas lifted oil production network based on modifier adaptation

We propose a novel simplified economic model predictive controller (EMPC) for supervisory control of an offshore petroleum production network composed of two wells, a riser, and a separator. The proposed controller uses a gradient-and-modifier-based methodology to mimic a nonlinear EMPC (it considers a nonlinear process model and cost function), while only solving quadratic programming. We alter the modifier adaptation to depend on an estimator and employ slack variables to improve feasibility. Tips for appropriate design, to improve stability and robustness, are presented. Since the tested facility is subject to multiple disturbances, we also use a regulatory controller to stabilize the system. Finally, we compare our method to a linear EMPC and self-optimizing control in various scenarios considering disturbances, noise, and model inconsistencies. The proposed controller always tends towards the economic optimal point, presenting similar nominal performance as the others advanced controllers and better robustness.

Efficiency Maximization of a Direct Internal Reforming Solid Oxide Fuel Cell in a Two-Layer Self-Optimizing Control Structure.

Control system configuration is essential for the efficiency performance of a solid oxide fuel cell (SOFC). In this paper, we aim to report a novel two-layer self-optimizing control (SOC) system for the efficiency maximization of a direct internal reforming SOFC, where the efficiency index is defined as the profit of generated electricity penalized by carbon (CO2) emission. Based on the lumped-parameter model of the SOFC, comprehensive evaluations are carried out to identify the optimal controlled variables (CVs), the control of which at constant set-points can optimize the efficiency, in spite of operating condition changes. In the lower SOC layer, we configure single variables as the CVs. The results show that the stack temperature is the active constraint which should be controlled to maintain the cell performance. In addition, the outlet hydrogen composition is identified as the optimal CV. This result differs from several previous proposals, such as methane composition. In the presence of operating condition changes, the set-point of hydrogen composition is further automatically adjusted by the upper SOC layer, where a linear combination of the SOFC measurements is configured as the CV, giving negligible efficiency losses. The cascaded two-layer SOC structure is able to maximize the SOFC efficiency and reduce carbon emission without using online optimization techniques; meanwhile, it allows for smooth and safe operations. The validity of the new scheme is verified through both static and dynamic evaluations.

Enhanced control structure design for an industrial off-gas system: Simple reconfigurations benefit the economy

For control structure design of the industrial off-gas benchmark system, application of the Skogestad's state-of-art design procedure has suggested the scrubber inlet pressure (Psi in the roaster) and one of the fan speeds (Nfan1 or N_fan2 in the furnace) as the self-optimizing controlled variables CVs. In this study, we stress and advocate the gSOC-plus-BAB approach as an enhanced design toolkit for the classical and systematical design procedure. The gSOC (global self-optimizing control) is able to efficiently solve measurement combinations as CVs with improved economic performances, while the BAB (branch and bound) algorithm serves to fast screen promising measurement subsets for large-scale problems. Using the enhanced design for the off-gas system, our new findings are to control the combination of roaster's ID fan outlet pressures, 0.494PIDfan1+0.506PIDfan2 (setpoint: −257.04 Pa), and the furnace's fan pressure difference, PIDfan1- PIDfan2 (setpoint: 0). Such simple reconfigurations can dramatically reduce the average economic loss by 53.3% for the roaster and even achieve perfect optimal control for the furnace. Both steady state and dynamic evaluations are carried out to validate the reconfigured control structures.© 2017 Elsevier Inc. All rights reserved.

Lagrange-Based Global Self-Optimizing Control for Constraint Activeness Varying Processes

Lagrange-Based Global Self-Optimizing Control for Constraint Activeness Varying Processes

Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization

Extremum seeking controllers have been investigated for multivariable data-driven energy optimization in heat pumps. In particular, proportional–integral extremum seeking control (PI-ESC) has demonstrated potential for significant acceleration compared to other ESC variants for nonlinear closed-loop control systems. A barrier to PI-ESC’s utilization in self-optimizing control is the fact that the PI-ESC algorithm is fragile. That is, unless the PI-ESC gains (e.g., controller gains, estimator gains) are carefully tuned, small perturbations to these gains can render the closed-loop unstable. Since arbitrary combinations of PI-ESC gains can result in instabilities, we propose a failure-robust Bayesian optimization (FRBO) algorithm that computes PI-ESC gains that ensure the closed-loop system can be driven rapidly to the optimum, while identifying and avoiding regions in the space of PI-ESC gains that are likely to result in instabilities (i.e., failures). The FRBO-tuned PI-ESC is shown to result in rapid closed-loop convergence to optimal values both on benchmark examples and a production-level model of an air conditioning system.