Automation

Wheel-actuator failure can compromise the trajectory-tracking capability of omnidirectional mobile robots, potentially interrupting material transport in automated warehouses. This study investigates control allocation reconfiguration to maintain trajectory tracking of a four-Mecanum-wheeled mobile robot after a single wheel actuator completely loses its torque-generation capability. Assuming that the failed actuator has already been detected and isolated, the proposed approach retains the nominal trajectory-tracking controller and redistributes the required generalized forces and yaw moment among the remaining actuators. The feasibility of post-fault tracking is examined through the rank of the available allocation matrix and the actuator torque limits. Five operating configurations are compared in simulation, including healthy operation, unreconfigured operation after failure, three-actuator reconfiguration, and two reduced two-actuator configurations. The achieved results demonstrate that, under the investigated simulation conditions, actuator-aware control allocation can preserve both position and orientation tracking after a single actuator failure without retuning the nominal controller, supporting continued robot motion during a post-fault operating interval.

Sulfur recovery units (SRUs) at oil refineries are complex, multi-parameter systems whose Cold Bed Absorption (CBA) reactors operate under incomplete and uncertain process information, which limits conventional control. This study develops an engineering neuro-fuzzy decision support system (DSS) for optimizing sulfur production at the Atyrau oil refinery (Kazakhstan) under uncertainty. The system integrates a mathematical description of the process, whose dynamic part is stated as a general formulation, an expert-derived fuzzy rule base, a first-order Sugeno neuro-fuzzy estimator of sulfur yield and quality, and a multi-criteria fuzzy optimization block based on the Bellman–Zadeh approach, together with an explicit operator-facing decision algorithm and a software interface. The estimators were identified and validated on 200 operating log records of the unit. These records show that the effect of reactor temperature on the sulfur quality index reverses sign with reactor pressure—the correlation is −0.57 below 0.79 kgf/cm² and +0.75 above it—so that a model additive in the two variables cannot represent the relationship: linear regression on temperature and pressure attains a cross-validated R² of only 0.06 for the quality index, whereas the neuro-fuzzy estimator attains 0.83 (RMSE 0.72 points, MAPE 0.74%). A regression with an explicit interaction term on the same inputs attains 0.79; in a paired comparison the neuro-fuzzy estimator had the lower error in every repetition, but the difference is not statistically significant (p = 0.07), and the estimator is retained for its rule-level interpretability and its integration with the decision block. For sulfur yield, where the relationship is close to linear once pressure and throughput are accounted for, the corresponding figures are 0.71 and 0.85 (RMSE 0.33 t/day, MAPE 1.35%). The same ordering is reproduced on an independent hold-out test set, under nested and cluster-blocked cross-validation and after removal of the records lying at the unit’s capacity and regulatory bounds, and the expert rule base was revised into an asymmetric form accordingly. A worked application of the optimization block to three operating situations, with the production plan, quality and capacity constraints enforced, raised the model-predicted degree of joint satisfaction of the yield, quality and energy criteria from 0.08–0.25 to 0.49–0.57; in one of the three, the recommended action is to reduce process severity and lower a reheat duty surrogate by about a third rather than to increase output. The resulting DSS, at present a standalone advisory software prototype, supports the operator in analyzing the current process state, evaluating alternative operating modes and forming recommendations, while the final decision remains with the operator.

Short-term air-quality forecasting is important for environmental monitoring and public-health planning, particularly in Indian cities affected by persistent air pollution. This study presents a next-day air-quality prediction framework for seven pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO, NO_x, and O₃) across 11 Indian cities over the period 2019–2023, using daily observations from the Central Pollution Control Board (CPCB) and meteorological data from NASA POWER. Next-day pollutant concentrations are predicted using historical pollutant measurements, lagged and rolling features, calendar variables, and meteorological covariates available at the feature date. The study documents Variational Quantum Circuit (VQC) configurations implemented using PennyLane and Qiskit, alongside a candidate quantum-feature-augmentation approach for XGBoost. Quantitative evaluation is restricted to the XGBoost model, using mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination $R^2$ over the common chronological final-test target window spanning 28 June 2022 to 31 March 2023. For the evaluated XGBoost predictions, the reported pollutant-specific $R^2$ values range from 0.613 to 0.864. The quantum-circuit configurations and quantum-feature-augmentation approach are presented as candidate methods whose comparative predictive performance remains to be established through evaluation using aligned final-test predictions and observed targets.