Designing low-carbon fly ash based geopolymer with red mud and blast furnace slag wastes: Performance, microstructure and mechanism.

In order to tackle the environmental problems induced by Portland cement production and industrial solid wastes landfilling, this study aims to develop novel ternary cementless fly ash-based geopolymer by recycling red mud and blast furnace slag industrial solid wastes. The fresh-state properties, mechanical strength, water permeability, phase assemblage and microstructure were systematically investigated to evaluate the performance variation and reveal the hydration mechanism for geopolymers with different mixing proportions. The results showed that a higher slag content or a lower red mud content could result in the higher fluidity and shorter setting time for fresh mixture. The existence of slag promoted the transformation of N-A-S-H to C-A-S-H gel, which contributed to higher compressive strength and better resistance to water penetration. However, an excessive incorporation of 30% red mud may impede the generation of N-A-S-H gel and form more flocculent-like loose hydrates, thus to mildly degrade the mechanical strength and anti-permeability. The synergetic utilization of red much and blast furnace slag in fly ash-based geopolymer led to much less CO2 emission compared with the condition that red much or slag was singly added, which demonstrated prominent environmental advantages for such kind of ternary cementless geopolymer with equivalent mechanical strength.

Online Inspection of Browning in Yali Pears Using Visible-Near Infrared Spectroscopy and Interpretable Spectrogram-Based CNN Modeling.

Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears.

Establishment of online deep learning model for insect-affected pests in "Yali" pears based on visible-near-infrared spectroscopy.

Insect-affected pests, as an important indicator in inspection and quarantine, must be inspected in the imports and exports of fruits like "Yali" pears (a kind of duck head-shaped pear). Therefore, the insect-affected pests in Yali pears should be previously detected in an online, real-time, and accurate manner during the commercial sorting process, thus improving the import and export trade competitiveness of Yali pears. This paper intends to establish a model of online and real-time discrimination for recessive insect-affected pests in Yali pears during commercial sorting. The visible-near-infrared (Vis-NIR) spectra of Yali samples were pretreated to reduce noise interference and improve the spectral signal-to-noise ratio (SNR). The Competitive Adaptive Reweighted Sampling (CARS) method was adopted for the selection of feature modeling variables, while Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Block Attention Module-Convolutional Neural Networks (CBAM-CNN) were used to establish online discriminant models. T-distributed Stochastic Neighbor Embedding (T-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) were used for the clustering and attention distribution display of spectral features of deep learning models. The results show that the online discriminant model obtained by SGS pretreatment combined with the CBAM-CNN deep learning method exhibits the best performance, with 96.88 and 92.71% accuracy on the calibration set and validation set, respectively. The prediction time of a single pear is 0.032 s, which meets the online sorting requirements.

Research on construction method and validity mechanism of robust analysis model in snow peach quality detection based on visible-near infrared spectroscopy.

Visible-near infrared (Vis-NIR) spectra analysis method is widely used in the quality grading of bulk fruits with its rapid, non-destructive, diverse detection modes and flexible modular integration scheme. However, during the online grading of fruits, the random mechanized way of dropping fruit onto the conveyor belt method and the open detection environment led to more spectral abnormal samples, which affect the accuracy of the detection. In this paper, the soluble solids content (SSC) of snow peach is quantitatively analyzed by static and online detection methods. Several spectral preprocessing methods including Norris-Williams Smoothing (NWS), Savitzky-Golay Smoothing (SGS), Continuous Wavelet Derivative (CWD), Multivariate Scattering Correction (MSC), and Variable Sorting for Normalization (VSN) are adopted to eliminate spectral rotation and translation errors and improve the signal-to-noise ratio. Monte Carlo Uninformative Variable Elimination (MCUVE) method is used for the selection of optimal characteristic modeling variables. Partial Least Squares Regression (PLSR) is used to model and analyze the preprocessed spectra and the spectral variables optimized by MCUVE, and the effectiveness of the method is evaluated. Sparse Partial Least Squares Regression (SPLSR) and Sparse Partial Robust M Regression (SPRMR) are used for the construction of robust models. The results showed that the SGS preprocessing method can effectively improve the analysis accuracy of static and online models. The MCUVE method can realize the extraction of stable characteristic variables. The SPRMR model based on SGS preprocessing method and the effective variables has the optimal analysis results. The analysis accuracy of snow peach static model is slightly better than that of online analytical model. Through the test results of the PLSR, SPLSR and SPRMR models by the artificially adding noise test method, it can be seen that the SPRMR method eliminates the influence of abnormal samples on the model during the modeling process, which can effectively improve the anti-noise ability and detection reliability.