Grid-connected power converters encounter significant stability challenges during weak grid faults, when conventional PI-based controllers exhibit an oscillatory response and poor fault-ride-through performance. This paper addresses this problem by replacing the conventional outer PI controllers that regulate DC-link and PCC voltages with an offset-free data-driven predictive controller. The developed algorithm leverages either pre-fault or fault-time data to construct input-output predictors, yielding offset-free control without the need for physics-based modelling. Simulation results show that pre-fault offset-free DPC doubles the critical equivalent grid impedance that can be handled and reduces the root mean squared error during faults by a factor of 40, while maintaining computation times comparable to conventional PI control. These findings demonstrate that the developed offset-free data predictive controller offers a simple, robust, and computationally efficient alternative to conventional control, significantly enhancing fault-ride-through capabilities of converters in weak grids.
In human-centered flexible manufacturing systems under Industry 5.0, some human operations can be performed through cooperation among multiple workers. The selected worker number affects both processing time and total labor input, so scheduling involves three coupled decision dimensions: operation sequencing, resource assignment, and worker number selection. This study investigates a multi-objective flexible job shop scheduling problem with multi-worker cooperation, processing times that vary with the selected worker number, and transportation time. A mathematical model is formulated to minimize makespan and total labor input while describing the diminishing returns from multi-worker cooperation. To solve this problem, a preference-conditioned heterogeneous graph reinforcement learning method is proposed. The scheduling process is formulated as a Markov decision process, and a heterogeneous graph is used to encode operation precedence, resource states, station worker capacity, and transportation information. Based on this graph representation, a hierarchical policy network is designed with an operation-resource pair selection actor and a worker number selection actor to handle the additional worker number decision for human operations. Furthermore, the objective preference weight is embedded in both the state representation and the reward function, enabling a single model to generate multi-objective schedules under different objective preferences. Experiments on 130 instances constructed from a welding and assembly workshop show that the proposed method has advantages over ten classic and recent algorithms across multiple evaluation metrics. Preference interpolation and ablation analyses show that the preference input and hierarchical policy design help improve solution set quality. An industrial order case study further demonstrates that the method can generate executable schedules and has the potential to make fast decisions after disturbances.
The high penetration of renewable energy reduces system inertia, leading to instability and decreased power system reliability. In this context Virtual Synchronous Generator emerges as a method that can provide virtual inertia to improve system stability. The trade-off between frequency response and power output of VSG requires a strategy to adapt its virtual inertia. This paper proposes a reinforcement learning-based method with fast reward shaping capability to enhance the system’s learning and stability. The results of the proposed method are implemented through MATLAB/Simulink and compared with other methods to demonstrate the effectiveness.
Construction and demolition waste (CDW) is a major global waste stream. Coarse aggregates from CDW are widely reused, but recycled concrete fines (RCf, <80 µm) remain underutilised, often ending up in landfills or used in low-value applications. Carbonation is a promising treatment to valorise RCf as a supplementary cementitious material (SCM), converting reactive calcium-bearing phases into calcium carbonate while permanently sequestering CO2. Hence, this study explores the semi-dry accelerated carbonation of RCf generated in a concrete recycling facility in the Netherlands, focusing on the influence of the initial moisture content, varied from 0 to 50 wt%. Pre-wetting the RCf prior to carbonation significantly influenced calcium carbonate formation, with higher moisture levels promoting calcite formation. Samples preconditioned with 10–50 wt% moisture demonstrated enhanced CO2 uptake and a pronounced increase in specific surface area, from ∼ 7 m2/g for raw RCf to up to ∼ 20 m2/g for carbonated samples at intermediate moisture content. CO2 uptake increased with increasing moisture, whereas reactivity did not follow the same trend. The 50 wt% condition achieved the highest CO2 uptake but the lowest R3 reactivity among the carbonated samples, while intermediate moisture (∼20 wt%) led to the highest specific surface area and favourable R3 reactivity and was selected for paste validation. Selected carbonated and uncarbonated RCf were subsequently used to replace 20% of the cement in paste mixtures and were compared against a limestone reference. RCf carbonated at 20 wt% moisture achieved a 13.4% increase in 28-day compressive strength compared with the limestone reference. These results demonstrate that RCf, often considered low-value waste, can be valorised as an SCM through moisture-controlled carbonation. Their use supports CO2 utilisation in cementitious systems. It contributes to more circular construction practices, while offering a critical perspective on the application of mineral carbonation to materials produced in full-scale waste treatment plants.
This study investigates the near-threshold fatigue crack growth behaviour of heat-affected zone (HAZ) microstructures in welded structural steels with nominal yield strength of 235MPa and 355MPa. Quantifying the influence of these microstructures is important for reliable assessment of the remaining fatigue life of welded structures. The microstructures of the HAZ subregions were experimentally simulated on material samples representative for bridge structures with a Gleeble thermal-mechanical simulator after which compact tension specimens were extracted. The fatigue crack growth tests were performed with ΔK-decreasing procedure at load ratios of R=0.1 and R=0.5. The crack length was monitored using the alternating current potential drop technique. At R=0.1, the simulated HAZ subregions of both steels exhibited lower crack growth rates and higher long crack threshold stress intensity factors, than their respective base metals. This increased resistance was attributed primarily to enhanced roughness-induced crack closure. At R=0.5, no significant differences were observed between the simulated HAZ subregions and the corresponding base metals, consistent with the suppression of crack closure effects at higher load ratios.
