Lei Zhao

Accurate regulation of the outlet hydrocarbon-vapor temperature in the Alberta Taciuk Process scrubber is challenged by nonlinear coupling, strong thermal inertia, transport delays, and nonstationary operating conditions. This paper develops a residual-adaptive robust preview linear quadratic regulator (RA-RPLQR) that integrates GRU-based process modeling with optimal control. A gated recurrent unit (GRU) trained on industrial time-series data captures nonlinear temporal relationships and provides one-step-ahead temperature predictions without requiring a detailed first-principles model. Automatic differentiation extracts local time-varying Jacobians with respect to the manipulated inputs, connecting the learned dynamics to the LQR design and enabling the control model to be updated as operating conditions change. Measurable upstream disturbances with available lead time are incorporated through preview feedforward compensation. Model uncertainty and unmeasured disturbances are represented through a robust linear-quadratic dynamic-game formulation, in which the unknown-disturbance channel is adjusted online using a recursively filtered GRU prediction residual. Feedback and preview gains are obtained through a finite-horizon generalized Riccati recursion. Actuator magnitude and rate limits are enforced through command-level feasibility projection, together with back-calculation anti-windup compensation during saturation. Closed-loop simulations constructed from previously unseen industrial operating trajectories from the same scrubber show improved tracking, disturbance attenuation, and actuator smoothness compared with PID, conventional LQR, and Adaptive LQR within the evaluated scenarios. The proposed method achieves an integral absolute error of 56.00 and an integral time-weighted absolute error of 1516.39 while reducing transient deviations and actuator movement.

Conventional T-junction pipelines may suffer from persistently elevated turbulent dissipation and insufficient radial mixing, which has motivated the exploration of alternative junction configurations and their corresponding operating maps. In this study, an industrial I–L junction used for methane transport was numerically investigated using the unsteady Reynolds-averaged Navier–Stokes (URANS) approach in conjunction with the realisable k − ϵ turbulence model. By systematically varying the momentum ratio, the flow and mixing characteristics were comprehensively analysed. With increasing M R , the flow regime underwent a pronounced transition from an impinging-jet mode to a deflected/refracted-jet mode. The outlet non-uniformity decreased monotonically in the downstream direction. Compared with the conventional T-junction, the I–L configuration exhibited a faster decay of high-energy regions and a lower sustained dissipation level, suggesting a reduced risk of vibration-induced fatigue without compromising mixing performance. Based on these momentum-ratio-dependent characteristics, a practical operating guideline is proposed, in which V i = 2.5 was identified as a representative balanced operating condition.

In scrubbing tower (ST) systems, outlet vapor temperature regulation is subject to strict thermal constraints, requiring the bottom temperature to remain below a critical safety threshold. Transient overshoots can rapidly trigger pyrolytic coking, foul mass-transfer packed beds and spray nozzles, and ultimately force complete production shutdowns. Due to feedstock compositional drifts, high thermal inertia, and significant transport delays, high-fidelity predictive identification is essential for proactive early warning and for overcoming the limitations of reactive feedback control. To address these bottlenecks, this paper introduces an offset-free, hard-constrained, data-driven adaptive optimal control paradigm, designated as the improved GRU-coupled conjugate gradient linear quadratic regulator (IGRUCG-LQR). First, by constructing an augmented state space embedded with integral error, the proposed paradigm eliminates permanent tracking offsets induced by long-term nonstationary drifts. Second, automatic differentiation is used to extract the time-varying Jacobian matrix of a gated recurrent unit (GRU) online, thereby tracking the nonlinear evolution of the underlying thermodynamic baseline with high fidelity. To manage the critical trade-off between strict actuator saturation and short real-time sampling intervals, the conjugate gradient (CG) method is fused with a hard-boundary projection operator, enforcing physical constraints with high computational efficiency and without complex matrix inversions. Experimental validation on a real-world industrial dataset demonstrates that the proposed paradigm secures the safety baseline while achieving high-resolution transient tracking. Furthermore, it significantly suppresses high-frequency valve chattering to mitigate mechanical fatigue, establishing a solid theoretical and engineering foundation for the prolonged stable operation of safety-critical processes. The proposed framework achieves an Integral Absolute Error (IAE) of 86.66 and an Integral Time Absolute Error (ITAE) of 4064.49.

This study aims to improve the outlet temperature performance of a pneumatic heat-generating blower and investigate the influence of turbulence on the outlet temperature. Based on the heat generation mechanism and structural principle, mathematical models are developed for key components including the impeller and flow channel. The Kriging surrogate model and NSGA-II multi-objective genetic algorithm are adopted to optimize the aerodynamic performance responses of the impeller structural parameters. After comprehensive analysis, an optimal parameter combination is selected from the Pareto solution set for CFD numerical simulation. The results show that the optimization effectively improves the outlet temperature and turbulent kinetic energy distribution. The numerical results agree well with the optimization outcomes, verifying the reliability and accuracy of the proposed method. These findings provide a reference for the multi-physics coupled optimal design of blower blades.