Haizeng Liu

To address the problems of fine particle size, high ash content, strong suspension stability, and poor settling performance of coal slurry water in coal preparation plants, enhanced flocculation–sedimentation experiments using combined CaCl₂ and polyacrylamide were conducted, and a particle-scale numerical sensitivity model was employed to investigate microscopic aggregation behavior under different conditions. The coal slurry water was first characterized in terms of water quality, mineral composition, and particle size distribution. Single-factor tests with CaCl₂, nonionic polyacrylamide (PAM), and anionic polyacrylamide (APAM), followed by combined CaCl₂ + PAM and CaCl₂ + APAM tests, were then performed. The results showed that the appropriate dosages of CaCl₂, PAM, and APAM in the single-factor tests were 1.4 g/L, 4 mg/L, and 0.6–1.0 mg/L, respectively. Among the combined systems, CaCl₂ + APAM exhibited superior overall settling performance. At 1.2 g/L CaCl₂ and 0.8 mg/L APAM, the settling rate reached 805 mm/min and the supernatant turbidity decreased to 41.7 NTU. In the numerical model, the particle composition was reconstructed according to the measured particle size distribution, with the 1–5 μm ultrafine fraction retained to improve consistency between the model and the actual coal slurry particle size characteristics. Sensitivity analysis showed that, when the effective particle adhesion parameter increased from 0.2 to 0.8, the mean floc size at 60 s increased from 10.655 to 13.065 μm, the aggregation rate increased from 0.00356 to 0.01034 s⁻¹, and the apparent fractal dimension increased from 1.065 to 1.184, indicating that enhanced effective particle adhesion significantly promotes the formation of stable aggregates. In addition, as the CaCl₂ dosage increased from 0.6 to 2.4 g/L, the corresponding ionic-strength increment increased from 16.219 to 64.877 mmol/L, accompanied by stronger electrostatic screening and increasing trends in the simulated mean floc size and aggregation rate. These results indicate that CaCl₂-induced electrical double-layer screening and polyacrylamide-promoted effective particle adhesion jointly influence the aggregation of coal slurry particles, while the CaCl₂ + APAM system provides favorable solid–liquid separation performance. The findings provide a reference for optimizing combined reagent addition and improving sedimentation treatment of coal slurry water in coal preparation plants.

Flocculation-sedimentation is a widely used solid–liquid separation process in mineral processing and wastewater treatment, yet its optimization remains challenging due to nonlinear interactions among reagents, solids, and operating conditions. This study presents a statistically rigorous multi-response optimization framework combining HuberT robust regression, canonical analysis, and the Derringer desirability function, and demonstrates its application to coal slurry water treatment as a case study. Coal slurry concentration, polyacrylamide (PAM) dosage, and CaCl₂ concentration were used as factors, with supernatant turbidity and initial settling velocity as responses. A central composite design (20 runs) was employed, and second-order models were fitted via HuberT M-estimation to mitigate the influence of potential outliers. Both the ln-transformed turbidity model (R² = 0.9887) and settling velocity model (R² = 0.9908) showed high significance and predictive capability. Canonical analysis confirmed that the turbidity response surface exhibits a true minimum within the design space, while the settling velocity surface has a saddle-point structure. HuberT weight diagnostics identified no downweighted runs for turbidity and two mildly influential runs for settling velocity, confirming overall data consistency. Multi-response optimization via the Derringer desirability function yielded a combined optimum (coal slurry 25.27 g/L, PAM 5.19 mg/L, CaCl₂ 2.07 g/L; desirability D = 0.8981) with predicted turbidity of 29.21 NTU and settling velocity of 13.26 mm/s. The proposed framework may be extended to other flocculation-sedimentation systems requiring simultaneous improvement of multiple, often conflicting, process responses, although this generalizability has not yet been empirically tested beyond the single coal slurry system examined here.

Flocculation-sedimentation is a widely used solid-liquid separation process in mineral processing and wastewater treatment, yet its optimization remains challenging due to nonlinear interactions among reagents, solids, and operating conditions. This study presents a statistically rigorous multi-response optimization framework combining HuberT robust regression, canonical analysis, and the Derringer desirability function, and demonstrates its application to coal slurry water treatment as a case study. Coal slurry concentration, polyacrylamide (PAM) dosage, and CaCl₂ concentration were used as factors, with supernatant turbidity and initial settling velocity as responses. A central composite design (20 runs) was employed, and second-order models were fitted via HuberT M-estimation to mitigate the influence of potential outliers. Both the ln-transformed turbidity model (R² = 0.9887) and settling velocity model (R² = 0.9908) showed high significance and predictive capability. Canonical analysis confirmed that the turbidity response surface exhibits a true minimum within the design space, while the settling velocity surface has a saddle-point structure. HuberT weight diagnostics identified no downweighted runs for turbidity and two mildly influential runs for settling velocity, confirming overall data consistency. Multi-response optimization via the Derringer desirability function yielded a combined optimum (coal slurry 25.27 g/L, PAM 5.19 mg/L, CaCl₂ 2.07 g/L; desirability D = 0.8981) with predicted turbidity of 29.21 NTU and settling velocity of 13.26 mm/s. The proposed framework is generalizable to other flocculation-sedimentation systems requiring simultaneous improvement of multiple, often conflicting, process responses.

This study addresses the timely and accurate measurement of coal ash content by proposing a detection model based on nuclear science technology, which is validated using FLUKA 4-4.0 simulation software. The background provided highlights the fact that coal ash content is a critical sales indicator, and its precise measurement is essential for adjusting production parameters in coal preparation plants. In terms of methodology, this study employs the widely used FLUKA4-4.0 software in the field of nuclear physics to simulate X-ray transmission through coal, investigating the impact of changes in coal type on the accuracy of ash measurements. The results indicate that, when the proportions of high-atomic-number elements in coal remain constant, the ash measurement results are accurate and reliable. However, significant fluctuations occur when these proportions change. The conclusion emphasizes the fact that variations in coal type are the primary cause of inaccuracies in ash measurement, particularly when the ratios of high-atomic-number elements are altered. This research provides a new perspective on the online measurement of coal ash content and offers theoretical support for improving measurement accuracy.

Ash content is an important production indicator of flotation performance, reflecting the current operating conditions of the flotation system and the recovery rate of clean coal. It also holds significant importance for the intelligent control of flotation. In recent years, the development of machine vision and deep learning has made it possible to detect ash content in flotation-recovered clean coal. Therefore, a prediction method for ash content in flotation-recovered clean coal based on image processing of the surface characteristics of flotation froth is studied. A convolutional neural network –long short-term memory (CNN-LSTM) model optimized by Newton–Raphson is proposed for predicting the ash content of flotation froth. Initially, the collected flotation froth video is preprocessed to extract the feature dataset of flotation froth images. Subsequently, a hybrid CNN-LSTM network architecture is constructed. Convolutional neural networks are employed to extract image features, while long short-term memory networks capture time series information, enabling the prediction of ash content. Experimental results indicate that the prediction accuracy on the training set achieves an R value of 0.9958, mean squared error (MSE) of 0.0012, root mean square error (RMSE) of 0.0346, and mean absolute error (MAE) of 0.0251. On the test set, the prediction accuracy attains an R value of 0.9726, MSE of 0.0028, RMSE of 0.0530, and MAE of 0.0415. The proposed model effectively extracts flotation froth features and accurately predicts ash content. This study provides a new approach for the intelligent control of the flotation process and holds broad application prospects.

In froth flotation, one of the pivotal metrics employed to evaluate the flotation efficacy is the clean ash content, given its widely acknowledged status as a paramount gauge of coal quality. Leveraging deep learning and computer vision, our study achieved the dynamic recognition of coal flotation froth, a key element for predicting and controlling the ash content in coal concentrate. A comprehensive dataset, assembled from 90 froth flotation videos, provided 16,200 images for analysis. These images revealed key froth characteristics including bubble diameter, quantity, brightness, and bursting rate. We employed Keras to build a comprehensive deep neural network model, incorporating multiple features and mixed data inputs, and subsequently trained it with a rigorous 10-fold cross-validation strategy. Our model was evaluated using robust metrics including the mean squared error, mean absolute error, and root mean squared error, demonstrating a high precision with respective values of 0.003017%, 0.053385%, and 0.042640%. With this innovative approach, our work significantly enhances the accuracy of ash content prediction and provides an important breakthrough for the intelligent advancement and efficiency of froth flotation processes in the coal industry.

This study successfully achieved high-precision detection of the clean coal ash content in the coal froth flotation domain by integrating deep learning with the likelihood function. Methodologically, a novel data processing and prediction framework was established by combining a deep learning Keras neural network with the likelihood function from probability statistics. The SIFT algorithm was utilized to extract key feature points and descriptors from the images, and keypoint matching and mean-shift clustering algorithms were employed to accurately obtain information on foam motion trajectories and velocities. For parameter optimization, the maximum likelihood estimation was applied to find the optimal parameter estimates of the likelihood function, ensuring enhanced model accuracy. By incorporating the optimized likelihood function parameters into the Keras deep neural network, an efficient prediction model was constructed for the dosage of flotation reagents, froth velocity, and clean coal ash content. The model’s evaluation involved six performance metrics. The experimental results were highly significant, with R² at 0.99997%, RMSE at 0.04458%, MAE at 0.00170%, MAPE at 0.02329%, RRSE at 0.00994%, and MAAPE at 0.00067%.

Liquid concentration detection systems have been widely used in food, chemical, pharmaceutical, and many other industries. When the liquid flows, a large number of vortices will usually be generated, resulting in increased turbulence intensity, which will interfere with the detection of the concentration of the suspension. In this paper, a method for concentration detection by differential pressure based on turbulence elimination is proposed to improve the reliability of concentration detection results. The changes in the internal flow field corresponding to different lengths of the turbulence elimination structure and different inlet angles are analyzed through numerical simulation. Finally, the influence of changes in structure parameters on the accuracy of concentration detection is tested through experiments. The results show that when the length of the turbulence elimination structure is small, the vortex zone inside the concentration detection device changes with the inlet velocity. When the length of the turbulence elimination structure is 150 mm, the vortex zone is basically not affected by the inlet velocity. The stability of the flow field increases with the increase in the inlet angle. When the inlet angle increases to 60°, a stable zone of turbulence will form in the region where Y > 0.4 m. When the length of the turbulence elimination structure is 150 mm and the inlet angle is 60°, the expected experimental results are obtained, and the actual needs of liquid concentration measurement are met.

The online measurement of coal ash has overcome the shortcomings of chemical tests. However, there could be large fluctuations and errors in the results of online ash monitors because of the transient change in coal quality resulting from different geological conditions in the mining process. In this study, to resolve the problems of the dual-energy γ-ray online ash monitor in the Linhuan Coal Preparation Plant, we investigated the internal factors, such as the composition of multimineral and multicoal, and external factors, such as the moisture and impurities, which affect the measurement results of the coal ash monitor. Furthermore, we developed a mathematical model to determine the effect of relevant factors on the coefficient of the online ash monitor, which revealed the relationship between coal composition and the parameters of the ash monitor, ensuring the stable and accurate measurement of ash in clean coal. The method of determining parameters used in the case of coal blending has been applied in the Linhuan Coal Preparation Plant. By comparing with tested ash content, the average absolute error and relative error for daily ash content measured in April are 0.21 and 2.18%, respectively. Meanwhile, it shows certain accuracy and reproducibility while opposed to the daily average absolute error of 0.22 and relative error of 2.39% in May.

The three products hydrocyclone screen (TPHS), a branch of the hydrocyclone, effectively removes the fish-hook effect, which has been used in the industrial field. The current cylindrical screen in the TPHS generates the characteristic flow known as the screen underflow, which has a significant impact on device performance. To investigate the flow behaviour of the fluid passing through the screen, a combination of a dynamic analysis and a numerical simulation was used. The permeating process in the TPHS was abstracted by a simple fan mode in this work to generate the flow-rate equations and the driving-force models. The pressure difference was the driving force for the screen penetration in the ideal fluid, but it also included a viscous force in the viscous fluid. Furthermore, at the same inlet velocity, the viscous fluid had a higher flow rate than the ideal, indicating that the viscosity promoted the fluid penetration. Meanwhile, as the inlet velocity increased, the mass flow of the screen backflow increased, while the corresponding proportion first rose to a peak then dropped and then gradually stabilised. Furthermore, a flow equation for the screen underflow in the TPHS was developed, which is related to the structural parameters (the rotation radius, the length of the cylindrical screen, the aperture size, and the open-area percentage) and the process parameters (the dynamic viscosity of the fluid and the pressure difference between the feed inlet and the screen outlet).