Residual-Adaptive Data-Driven Robust Preview LQR Control of Scrubber Temperature in the Alberta Taciuk Process Under Nonstationary Disturbances

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.