Near-infrared analysis has demonstrated superiority in constructing calibration models for evaluating gasoline properties during blending. However, the intricacies of sampling and the time-intensive analysis process frequently pose challenges in obtaining sufficient labeled samples, thereby hindering the establishment of calibration model with the satisfactory performance. Generative adversarial networks (GANs) bridge this gap by generating virtual samples. Nevertheless, existing methods neglect the global distribution of samples and lack explicit mechanisms for generating properties. To expand labeled sample sets sufficiently for constructing precise and reliable calibration models, a regression Wasserstein deep convolutional generative adversarial network with divergence (RWDCGAN-div) is proposed. Wasserstein divergence is designed in the RWDCGAN-div framework to broaden the global spatial distribution of labeled samples and consistent convolutional neural networks are utilized to simplify model construction. Regression models are integrated to facilitate the discriminator learn the relationship between spectra and properties, thereby guiding the generator in producing spectra and corresponding properties with explicit mechanisms. The effectiveness of the proposed method is verified through prediction experiments based on gasoline blending process.

Unmanned aerial vehicles (UAVs) can offer higher spatial resolution images compared to satellites. In this experiment, an L-band microwave radiometer named PoLRa was equipped on an UAV to detect surface soil moisture and a new soil moisture retrieval algorithm was developed for it. Compared with the algorithm provided by suppliers of PoLRa, the new algorithm can significantly improve the accuracy of soil moisture and enhance spatial details. A comparison with ground-based soil moisture measurements showed that the UAV's spatial resolution reached 10 meters with an accuracy of 0.08 m3/m3. This demonstrates significant advantages for monitoring soil moisture at the farmland scale, making it applicable to precision agriculture, flash flood warnings, and drought monitoring in the future.

Drought has a significant impact on water resources and socio-economic development, with evapotranspiration being an important component. Based on total water storage changes observed by GRACE missions and precipitation data obtained by GPM missions, this study estimates monthly anomalies of evapotranspiration through water balance method. The results were verified and analyzed in the Yangtze River Basin. The analysis reveals that during summer drought events, evapotranspiration generally decreases instead of increases, and there is a mutual relationship between evapotranspiration and hydrological and meteorological factors. This study establishes a framework for analyzing ET using GRACE data and water balance methods, providing a reference for future research.

Soil texture is a fundamental soil property and serve as a crucial input to many Land Surface Models (LSMs). However, current soil texture datasets used in LSMs are usually extrapolated from in-situ scale geological surveys, which may contain high uncertainties due to the mismatch in spatial scales. Here, we propose a method to optimize several currently existing soil texture datasets by using a long-term satellite soil moisture dataset. The optimized soil texture datasets may provide an opportunity to improve land surface simulations in LSMs.

Effective fault diagnosis can be obtained by using multiblock global orthogonal projections to latent structures (MBGOPLS), but there exist certain limitations in this method in terms of block division intelligence and model robustness to outliers. Although these issues can be addressed by developing correlation analysis, such as mutual information and copula-correlation, the complex coupling relationship between variables has not been fully examined. In this study, a robust MBGOPLS is proposed to intelligently diagnose faults using a relatively stable model. First, a double hierarchical clustering method is established, in which internal hierarchical clustering is performed based on Euclidean distance to discretize the samples. Subsequently, external hierarchical clustering is adopted based on mutual information distance to divide variables into different blocks, which results in intelligent block division while suppressing the influence of outliers. Second, a robust block regression coefficient matrix (BRCM) is obtained by employing joint$\ell _{2,1}$-norm minimization on the BRCM and prediction error of the correlation between the block input and output. Furthermore, BRCM is integrated into the MBGOPLS framework. Therefore, the proposed method is robust to outliers while retaining the diagnostic properties of MBGOPLS. Finally, the proposed method is applied in a numerical case and an actual thermal power plant. The results verify the method’s applicability and superiority.Note to Practitioners—With increasing large-scale, complex and intelligent to achieve anticipant performance, thermal power plant is prone to faults that can lead to unplanned outages. Meanwhile, complex operation mechanism and environment, and diverse data acquisition sensors make the coupling relationship between variables complex, and collected data often contain numerous outliers, which happens frequently in modern industrial process. Therefore, fault diagnosis is critical to modern industrial process, and the diagnosis accuracy and robustness are the main challenges. This forces us to ensure the block division accuracy and model robustness when using multiblock-based fault diagnosis technology. This paper proposes a robust MBGOPLS to intelligently diagnose faults of thermal power plant using a relatively stable model. Additionally, the proposed method can be extended to fault diagnosis for other large-scale processes.

Near-infrared (NIR) spectroscopy plays a critical role in online analysis of difficult-to-measure properties of petrochemicals. In industrial applications, a calibration model among NIR spectra and properties must be established. However, it is a challenge to obtain a precision NIR model in the majority of petrochemical processes since industrial data present strong non-Gaussian and uncertainty characteristics. To deal with these problems, in the present work a probabilistic regression modeling method based on a biased minimax probability machine (BMPM) is proposed, without assuming any specific distributions for the data, in this article. In addition, a multiple locally weighted updating approach with a new supervised similarity distance is introduced to cope with process changes. The greatest advantage of the proposed approach is that it has superior capability in dealing with uncertainties and variations. The effectiveness of the method is illustrated through its application in an actual gasoline blending process and a simulated fermentation process.