In the heat treatment processes, offline utilization of first-principles models is well-established. These models tend to be complex, computationally demanding, and rely heavily on empirical relations. The fidelity of these models degrades over time due to changes in the process resulting in plant-model mismatch, which is typically attributed to an incorrect constitutive relation of a physical mechanism in the model (i.e. fouling in the heat exchangers). In this paper, we propose two hybrid modeling approaches, namely Sparse Identification of Nonlinear Dynamics with Control and least square estimation, to learn the dynamics of the discrepancy between the measurement data and the simulation model. The hybrid modeling approach is implemented on a heat exchanger network (HEN) example and it is shown that the accuracy of the first principles dynamic model is improved.

The transition to renewable energy sources, particularly sources like wind and solar induces a dependency on weather in the supply side of electrical grids. At the same time, the move to electric mobility with uncontrolled charging induces extra peak loads on these grids. These developments can cause grid congestion or an imbalance between the renewable power supply and the demand. Locally balancing the power supply and demand in grid-connected microgrids can alleviate such issues on the main grid. This paper presents a model based control strategy to address the challenge of locally balancing the power supply and demand in a grid-connected microgrid to avoid reaching the threshold rated power output set for large buildings. The microgrid under consideration consists of photovoltaic power sources and a large fleet of electric vehicle chargers (>150). A model predictive controller is developed that treats the daily vehicle charging as a batch process. Given vehicle charge objectives, the controller utilizes vehicle charger occupancy and photovoltaic power generation forecasting services to distribute power optimally over a fixed period of time. The optimization problem is formulated as a quadratic programming problem and is implemented utilizing open-source Python libraries. The controller was integrated into the control system of a microgrid situated at a corporate office in the Netherlands. The control system oversaw the operation of 174 vehicle chargers. The effectiveness of the model predictive control technology was demonstrated over a three-week period and led to an average daily grid peak power reduction of 59%.

Control of extrusion-based printing is fundamental to improving the traditional open-loop operation of commercial machines. However, the literature on feedback control based on nozzle motion is still limited in comparison with the developments for extrusion dynamics. In this work, we propose a model-based control strategy where the effects of the nozzle speed on the filament width are described by a first-order model, and the model uncertainty is used for robust synthesis. The feedback employs the internal model controller (IMC) to get an approximate inversion of the dynamics and the IMC filter is tuned based on robust performance criteria. The controller was experimentally tested and provided satisfactory results, especially when the parameters were refined depending on the speed regime.

The predictive capacity of models is becoming increasingly relevant due to the increase in the development and deployment of digital twins in several areas of manufacturing. Therefore, it is important to keep the models up to date so that they represent the process reliably. One way to keep these models calibrated is via parameter estimation. However, parameter estimation problems in nonlinearly parametrized systems result in local optima or suffer from high computational costs. In view of the aforementioned limitations, multiple shooting parameter estimation arises as viable method, since it provides a setting to deal with the ill-defined regions of the search space. In this paper, we propose an improvement to the multiple shooting parameter estimation integrating sensitivity analysis. The approach divides the measured operating trajectory into different segments and performs a sensitivity analysis to find the most contributing parameters in each of these segments. In this way, the proposed technique benefits from the selection of a subset of the most sensitive parameters per segment and the computational advantages of the multiple shooting method. The performance of the multiple shooting parameter estimation integrated with sensitivity analysis is tested on a case study, a batch reactive distillation column which is a multistage process.