This work is concerned with the development of control strategies for the Direct Internal Recycling Loop (DIRL) system, which is an essential part of the Tritium Fuel Cycle (TFC) for the fueling of fusion reactors. As a first step, a control-oriented model is developed that describes dynamic behavior of DIRL with interactions between the torus reactor, buffer and fuel units, and recirculation streams. This model is used to evaluate the controllability, stability of the DIRL, and interactions between input and output variables. Moreover, the direct recycling of isotopes from the exhaust gases is discussed from a control perspective. It is observed that Gas Distribution and Storage (GDS) within the DIRL is associated with significant control challenges due to input-output interactions and competing process objectives. Three control strategies are developed and evaluated for the GDS of the European Demonstration fusion power plant (EU-DEMO): a Multiple Input Multiple Output (MIMO) control scheme, a redesigned GDS configuration with extended input variables enabling decentralised Single Input Single Output (SISO) control, and an extended-input MIMO control scheme addressing protium dilution. All strategies are assessed against three control objectives: maintaining GDS pressure around a prescribed set-point to ensure process safety; regulating the tritium-deuterium fuelling ratio for optimal reactor operation; and managing protium concentration to prevent fuelling dilution.
This paper describes an advanced algorithm for current control in a modular multilevel power converter based on deadbeat model predictive control, which is further enhanced by applying an artificial neural network.
This paper presents a comprehensive literature review of recent advances in DC–DC converter technologies for hydrogen fuel cell power conditioning, with particular emphasis on proton exchange membrane fuel cells (PEMFCs) used in electric vehicle and stationary energy systems. Covering 29 peer-reviewed studies, the review examines progress in converter topologies, control methodologies, and performance optimization. The analysis highlights interleaved boost converters combined with advanced control strategies—such as linear quadratic regulator (LQR) control, fractional-order PID control, and sliding mode control—as the current state-of-the-art, achieving efficiencies above 95% while substantially reducing input current ripple and improving transient response. Emerging research trends include high-gain impedance-network converters enabling wide input voltage ranges, isolated and bidirectional converter architectures that support hybrid energy storage integration, and intelligent optimization-based control approaches incorporating particle swarm optimization (PSO) and model predictive control (MPC). The review further identifies key trade-offs in efficiency, power density, voltage gain capability, and dynamic behavior across converter designs, providing a structured foundation for future development of high-performance fuel cell power conditioning systems.
In the context of water scarcity and tightening environmental requirements, improving the energy efficiency of biological wastewater treatment processes has become particularly important. The aeration tank is one of the most energy-intensive and dynamically complex units, strongly affected by the variability in influent flow and composition. Conventional PID controllers do not provide predictive disturbance compensation and often result in excessive aeration and increased energy consumption. The study proposes an intelligent control approach based on a digital twin, neural network-based influent flow forecasting, and model predictive control (MPC). The digital twin represents a dynamic model of the biological process incorporating key state variables, including substrate, activated sludge, and dissolved oxygen concentrations. The LSTM neural network model is employed to predict the hydraulic load based on historical plant operation data, as well as to compensate for residual nonlinear dynamics that are not represented by the linearized MPC model. The predicted influent flow values are incorporated into the MPC framework as measured disturbances, enabling the generation of anticipatory control actions for the aeration system. The adequacy of the digital twin was validated using operational data from the wastewater treatment facilities of Semey city and was characterized by RMSE = 0.14 mg/L, MAE = 0.09 mg/L, R2 = 0.94, and MAPE = 6.3%. The simulation results demonstrated that, compared with the fuzzy PID controller, the application of MPC reduced the RMSE by 57.1%, decreased the overshoot from 24% to 8%, reduced the integral absolute error (IAE) by 60.9%, and lowered the energy consumption of the aeration system by 21.1%, while maintaining the dissolved oxygen concentration within the permissible operating range. The proposed Advisory MPC architecture is compatible with existing PLC–SCADA systems and can serve as a basis for the gradual digital modernization of wastewater treatment facilities without modifying the existing automation loops.
In this paper, a real-time optimized sigmoid-based energy management system (EMS) is proposed for a fuel cell hybrid vehicle (FCHV). The power demand is dynamically shared among the fuel cell, a lithium-ion battery (LIB), and an ultracapacitor. The sigmoid function is employed to regulate the full cell operation, ensuring higher efficiency. A real-time particle swarm optimization (RT-PSO) algorithm defines both the power distribution and the ultracapacitor frequency response in a multi-objective framework that accounts for source degradation, fuel consumption, and power losses, without relying on prior knowledge of the driving cycle. To reduce computational complexity, the optimization is executed at fixed time steps. Numerical results demonstrate the enhanced performance and real-time feasibility of the proposed strategy, increasing LIB lifetime by factors of 1.41 and 1.74 compared to global and non-optimized strategies, respectively, while reducing operational costs by 16.5% and 27.4%. These results confirm that the proposed EMS is a suitable real-time solution with optimal performance and low computational burden.
With the increasing integration of intelligent sensing, industrial communication networks, and Electromagnetic Waves, Antennas and Propagation technologies in smart process industries, stable control of complex multivariable systems has become essential for reliable information acquisition and distributed decision-making. This study proposes a stable operation strategy for a near-zero discharge evaporation and crystallization system for coal chemical wastewater based on model predictive control (MPC). A discrete state-space model incorporating influent chemical oxygen demand, salt concentration disturbances, liquid level–concentration coupling, and steam network constraints is established to characterize the dynamic behavior of the process. A rolling optimization controller integrating feedforward compensation and quadratic programming is developed to coordinate feed flow, steam regulation, and circulation control under multiple operational constraints. Simulation and industrial validation demonstrate that the proposed strategy reduces liquid level overshoot by 82.4%, decreases steam consumption fluctuation by 66.1%, and maintains stable operation with a water reuse rate above 92.3% under severe disturbance conditions. The results confirm that the MPC-based framework significantly enhances disturbance rejection, robustness, and energy efficiency while providing an effective engineering solution for cyber–physical industrial systems. Furthermore, the proposed architecture offers valuable references for communication-enabled intelligent monitoring, distributed sensing, and industrial automation applications associated with Electromagnetic Waves, Antennas and Propagation technologies.