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In this paper, we introduce an innovative multivariable data fusion strategy for adaptive steady-state detection, specifically tailored for the alumina evaporation process. This approach is designed to counteract the production instabilities that often arise from frequent alterations in production conditions. At the core of our strategy is the application of an adaptive denoising algorithm based on the Gaussian filter, which adeptly eliminates erroneous data from selected variables without compromising the fidelity of the original signal. Subsequently, we implement a multivariable R-test methodology, integrated with the adaptive Gaussian filter, to conduct a thorough and precise steady-state detection via data fusion. The efficiency of this method is rigorously validated using actual data from industrial processes.Our findings reveal that this strategy markedly enhances the stability and efficiency (by 10%) of the alumina evaporation process, thereby offering a substantial contribution to the field. Moreover, the versatility of this approach suggests its potential applicability in a wide range of industrial settings, where similar production challenges prevail. This study not only advances the domain of process control but also underscores the significance of adaptive strategies in managing complex, variable-driven industrial operations.

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To address the significant soft measurement errors in traditional static models for evaporation process parameters, which are characterized by continuity and cumulativity, this paper proposes a dynamic correction method for soft measurement models of evaporation process parameters based on the autoregressive moving-average model (ARMA). Initially, the Powell's directional evolution (Powell-DE) algorithm is utilized to identify the autoregressive order and moving average order of the ARMA model. Subsequently, the prediction error of a mechanism-reduced robust least squares support vector machine ensemble model is utilized as input. An error time series prediction model, which compensates for the errors in the autoregressive moving average model, is then applied for dynamic estimation of the prediction error. Finally, an integration strategy using the entropy method is employed to combine the static soft measurement model, based on the mechanism-reduced robust least squares support vector machine, with the dynamic correction soft measurement model, which is based on the error time series compensation of the ARMA model. The new model is analyzed and validated using production data from an alumina plant's evaporation process. Compared to traditional models, the new model demonstrates significantly improved prediction accuracy and is capable of dynamic prediction of evaporation process parameters.

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The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of online detection, we propose the introduction of a soft measurement strategy. First, due to the significant fluctuations in the production process variables and inter-variable coupling, comprehensive grey correlation analysis and kernel principal component analysis are employed to reduce the input dimension and computational complexity of the data, enhancing the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM), with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently, an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal parameters of the LSSVM network. Lastly, on-site industrial data validation indicates that the new model offers superior tracking capabilities and heightened accuracy. It is deemed aptly suitable for the online detection of mother liquor concentration.

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Introduction: The control of infinite-dimensional rigid-flexible robotic arms presents significant challenges, with direct truncation of first-order modal models resulting in poor control quality and second-order models leading to complex hardware implementations. Methods: To address these issues, we propose a fuzzy super twisting mode control method based on approximate inertial manifold dimensionality reduction for the robotic arm. This innovative approach features an adjustable exponential non-singular sliding surface and a stable continuous super twisting algorithm. A novel fuzzy strategy dynamically optimizes the sliding surface coefficient in real-time, simplifying the control mechanism. Results: Our findings, supported by various simulations and experiments, indicate that the proposed method outperforms directly truncated first-order and second-order modal models. It demonstrates effective tracking performance under bounded external disturbances and robustness to system variability. Discussion: The method's finite-time convergence, facilitated by the modification of the nonlinear homogeneous sliding surface, along with the system's stability, confirmed via Lyapunov theory, marks a significant improvement in control quality and simplification of hardware implementation for rigid-flexible robotic arms.