Simulation of thermal dynamics in city-scale district energy grids often becomes computationally prohibitive for long simulation runs. Most current model order reduction methods offer limited interpretability with regards to the nonreduced system and are not in general applicable for varying flow rates, multiple producers, or changing flow directions. This article presents a graph perspective on modeling of district energy networks. Based on spectral graph theory, a novel method for model order reduction of district energy systems is proposed. The method approximates the solution of an optimization problem, minimizing the coefficients of the local truncation error for the advection equation. Furthermore, a method for calculating the intracluster temperature distribution is presented. It is shown that the method can be used to reduce the thermal dynamic model of a city-scale energy grid, resulting in a coarser temporal and spatial resolution, with a significant decrease in simulation time. The relative root-mean-square error (rRMSE) was 2.5% for the temperature in the evaluation scenario, comparing the reduced-order system with the nonreduced system at the instances of the coarser time step.

This manuscript introduces a perturbation-free extremum-seeking control strategy specifically designed to enhance the operational performance of wind turbines under optimal conditions. The strategy employed utilizes a perturbation-free extremum-seeking approach to ensure the wind turbine farm operates at maximum power efficiency while also reducing power output fluctuations. By employing this control system, the wind turbine generators are maintained at their optimal power output level, effectively navigating through disturbances such as tower shadow, wind shear, and the erratic nature of wind speeds without introducing external perturbations. The algorithm is adept at managing the blade pitch angles and the turbines’ rotational speed, ensuring a controlled and efficient operation. Furthermore, this paper outlines a perturbation-free method to optimize energy production and decrease fatigue loads, thereby extending the operational lifespan of a wind turbine. The superior operational efficiency of the wind turbine array generators achieved through this approach underscores its effectiveness. The robustness and effectiveness of this perturbation-free control system are validated through comprehensive simulation tests on an array comprising four wind turbine generators. The outcomes of these simulations solidly back the extremum-seeking control system's proficiency in reaching maximum power output without relying on perturbative methods, highlighting its innovative contribution to wind turbine efficiency optimization.

This paper outlines the application of geometrically constrained extremum-seeking control (ESC) to optimize froth flotation circuits. Froth flotation is a complex process, presenting challenges for model-based optimization. Furthermore, the process depends on stable operations for performance, with PID control often utilized for this purpose. To address the problem of rigorous modeling and provide high adaptability to changing operational conditions, this paper suggests using geometrically constrained extremum-seeking control as a combined approach to reach optimal performance while stabilizing the process. For this purpose, we developed and performed simulations of a 4-cell flotation circuit. The simulations show how the controller can steer the four cells toward a stable (predefined) operation point and drive it toward an (unknown) optimum.

The supply temperature in district heating systems has traditionally been controlled using feedforward – a robust and well-validated approach for district heating networks with few producers and relatively high supply temperatures. The transition towards lower temperature district heating networks allows for efficient reuse of excess heat from, e.g., industrial processes and data centers. Excess heat is often intermittent, cannot always be assumed to be possible to control with a centralized controller, and can cause temperature disturbances in the grid. Closing the loop using PID control is challenging due to the process’s time-varying nature and long time delays. Model Predictive Control (MPC) suffers from a higher complexity, long computational times, and the need for a well-validated and maintained centralized model. The paper suggests a semi-decentralized approach using the Smith predictor with an event-driven assignment of active controllers and sensors. A reduced order model based on a more comprehensive state space model is derived and used for gain scheduling and input–output pairing using the normalized relative gain array. The focus is on temperature disturbance rejection, and appropriate tuning rules and controller structures are suggested. Simulation results show that the proposed control structure can handle various types of temperature disturbances, even in the presence of model estimation errors.

This review aims at assessing the opportunities and challenges of creating and using digital twins for process industrial systems over their life-cycle in the context of estimation and control. The scope is, therefore, to provide a survey on mechanisms to generate models for process industrial systems using machine learning (purely data-driven) and automated equation-based modeling. In particular, we consider learning, validation, and updating of large-scale (i.e., plant-wide or plant-stage but not component-wide) equation-based process models. These aspects are discussed in relation to typical application cases for the digital twins creating value for users both on the operational and planning level for process industrial systems. These application cases are also connected to the needed technologies and the maturity of those as given by the state of the art. Combining all aspects, a way forward to enable the automatic generation and updating of digital twins is proposed, outlining the required research and development activities. The paper is the outcome of the research project AutoTwin-PRE funded by Strategic Innovation Program PiiA within the Swedish Innovation Agency VINNOVA and the academic version of an industry report prior published by PiiA.

Simulation of thermal dynamics in city-scale district energy grids often becomes computationally prohibitive for long simulation runs. Current model order reduction methods offer limited interpretability with regards to the nonreduced system, and are not in general applicable for e.g., varying flow rates, multiple producers, or changing flow directions. This article presents a novel method based on graph theory that approximates the solution of an optimization problem that minimizes the local truncation error for heat transport in the grid. It is shown that the method can be used to reduce the thermal dynamic model of a city-scale energy grid, resulting in a coarser temporal and spatial resolution. The relative root mean square error was 2.3 % for the temperature in the evaluation scenario, comparing the reduced-order system with the nonreduced system at the instances of the coarser time-step.

Decentralized control of MIMO system is still the dominating control strategy in industry for its ease in design and maintenance, despite its known shortcomings in performance. In this article, an online decoupling scheme of MIMO processes using an extremum-seeking approach is proposed. By applying the developed method, at first, the system's loop interactions are estimated in real-time using phasor extremum seeking, and then, a pre-compensator matrix is calculated and augmented to the process by minimizing the loop interactions. The presented online decoupling approach is a model-free method, and only the plant's inputs and outputs are analyzed to provide the decentralized control protocol. Numerical results provide the effectiveness of the developed method for a two-input, two-output (TITO) control system.

Predictive maintenance strategies for the mining sector are of utmost importance considering the automated behavior of industrial systems and the oftentimes inaccessible environment around belt conveyor systems. In this paper, we present a model combining IoT sensors and dead-reckoning modeling, focused on early theoretical work in the field of modeling the behavior of belt conveyor systems to act as a decision support tool in maintenance strategies, by estimating the remaining useful life (RUL) of rotating components in a belt conveyor system. The estimation of RUL is a function of the degradation of the ball bearings in idler rollers due to the forces acting on the rollers during the conveyance of material. The forces occur due to the material loading, the belt weight, roller shell weight, and the idler misalignment load (IML). Furthermore, the dynamics of bulk material during conveyance can be modeled in several ways considering earth pressure theory. A model considering this is derived from the Krausse Hettler method to determine the forces acting on the wing rollers of a thee-roll idler trough set by the notion that the bulk material undergoes active and passive stress states during conveyance. The model is further compared and extended to the works of Sokolovski, to get a bounded delta RUL reduction estimate on the roller bearings in each idler set of a belt conveyor system.

This paper considers the problem of optimizing the operation of Indirect Adiabatic Cooling (IAC) systems with application to data centers. Optimal operation is achieved when the required cooling demand is satisfied at the minimum energy cost. For this purpose, we design a supervisory control system, where the higher layer determines the optimal set‐points for the local controllers by employing an Extremum Seeking Control (ESC) scheme. In particular, we consider a Newton‐like phasor ESC, which augments the derivative estimator underlying the phasor approach to capture also the Hessian of the plant index and then it uses these estimates to steer the system along a Newton‐like direction. The effectiveness of the considered approach is tested in simulation by exploiting a Matlab‐based simulation environment including an IAC system and a computer room.