This article presents an innovative visual method for measuring material mass online by quantified conveyor belt deformation with deep learning, which offers a noncontact and safe alternative to traditional pressure- and radioactivity-based weighing techniques. The correlation between the belt deformation and the carried material mass is further investigated through finite element simulations. Then, a visual weighing method by belt deformation is proposed, comprising a calibration algorithm to construct a measurement model using a gated recurrent unit-based network, and an online measurement algorithm to calculate material mass with the trained network. Finally, a case study is presented to analyze the effect of different dimension configurations and networks. The results validate that the proposed method attains a notable accuracy and is suitable for high-velocity conveyor environments. The demonstrated benefits signify an advancement in visual perception of materials, enabling a new approach for intelligent operation and monitoring in material handling field.

@en

Accurate prediction of mill load parameters is crucial to improving grinding efficiency and saving energy. Traditional prediction models have challenges such as poor interpretability, low prediction efficiency and differences in data distribution. This study innovatively proposed a multi-task prediction model that integrates physical information and domain adaptation. By constructing a physical-data-driven hybrid model, the physical relationship between mill load parameters is embedded into the model as prior knowledge to improve the prediction accuracy of the model. At the same time, multi-task learning is used to predict the material-to-ball volume ratio and the pulp density at the same time, which improves efficiency and reduces repetitive work. The domain adaptation method is introduced to ensure that the model maintains stable prediction performance when the data distribution changes. Laboratory ball mill data verification shows that the proposed model not only improves the prediction accuracy, but also adapts well to variable working conditions, showing significant superiority.

@en

Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-78455-7, published online 06 November 2024 The original version of this Article contained typographical errors in Equations. In Equation 8, where now reads: In Equation 17, where now reads: In Equation 18, where now reads: In Equation 24, where now reads: The original Article has been corrected.

@en

Segregation of granular materials is a critical phenomenon in various industries, such as food processing, pharmaceuticals, and mining. The Discrete Element Method (DEM) is an effective tool for gaining insight into granular segregation by providing particle-level information and the freedom to model mixtures that are often difficult or impossible to achieve through experiments. To ensure realistic material behaviour and correct representation of segregation, it is essential to calibrate the model parameters systematically. However, in the context of multi-component segregation, it is extremely challenging and computationally expensive to consider all parameters in the calibration procedure since interaction parameters between components must also be considered. This work aims to identify the dominant DEM parameters for modelling multi-component segregation during hopper discharge, chute flow and heap formation in a mixture of pellet and sinter. Utilising a representative example of a multi-component mixture with different sizes, densities and shapes used in blast furnaces, the investigation is done for various initial configurations as well as various mass ratios of the mixture. Our findings revealed that, while only particle-geometry interaction parameters dominate the segregation after the hopper and chute flow, particle-particle parameters are also significant for segregation in the heap. We also demonstrated that the downstream segregation is significantly influenced by the segregation upstream. Moreover, we found that the effect of pellet-sinter interactions is negligible. This research provides insights into the dominant DEM parameters, facilitating more efficient and robust calibration of multi-component models in future research endeavours.

@en

Deep-sea mining activities have lately attracted growing interest. Recent research has led to the development of a hydraulic–mechanical hybrid vertical lifting system for deep-sea mining. Predicting the resistance of vertical plug flow in this system is critical for its engineering design. In this paper, an analytical model was developed to determine the resistance of vertical plug flow in a hydraulic–mechanical hybrid vertical lifting system in the presence of coarse particles. A recent model of the stress transmission ratio for pneumatic plug flow was verified and applied to the proposed model of resistance. In addition, it is considered that the co-existence of turbulent and laminar flows in the fluid flowing through the particles provides the necessary pressure gradient. A test setup was built to measure the resistance of vertical plug flow with/without water, along with varying particle diameters, plug lengths, and lifting speeds. Comparisons between the predictions of the analytical model and experimental measurements showed that they deviated by less than 13.6% on average. This shows that the proposed analytical model can be used to predict the resistance of vertical plug flow involving coarse particles.@en

The industrially collected process data usually exhibit non-Gaussian and multi-mode characteristics. Due to sensor failures, irregular disturbances, and transmission problems, there are unavoidable outliers that make the data exhibit heavy-tailed characteristics. To this end, a variational auto-encoder regression method based on the mixture Laplacian distribution (MLVAER) is proposed, by introducing a type-II multivariate Laplacian distribution in the latent variable space for robust modeling, and further extending it to the mixture form to accommodate multi-mode processes, the corresponding reparameterization trick is finally proposed for the mixture form of this distribution for neural network gradient descent training. The model based on this distribution assumption has higher degrees of freedom than the model based on the traditional multivariate Laplace distribution assumption when the network structure is the same. Numerical simulation and experiments on two industrial examples demonstrate that the proposed algorithm reduces the root mean square error by over 15% compared to other algorithms.

@en

Segregation of granular materials is a critical challenge in many industries, often aimed at being controlled or minimised. The discrete element method (DEM) offers valuable insights into this phenomenon. However, calibrating DEM models is a crucial, albeit time-consuming, step. Recently, using machine learning (ML)-based surrogate models (SMs) in the calibration process has emerged as a promising solution. Nevertheless, developing such SMs is challenging due to the high number of DEM simulations required for training. Additionally, choosing a suitable ML model is not trivial. This study aims to develop SMs that effectively link particle-particle and particle-wall DEM interaction parameters to segregation of a multi-component mixture. We evaluate several ML models, ranging from artificial neural networks to ensemble learning, that are trained on a very cost-effective dataset, employing Bayesian optimisation with cross-validation to tune their hyperparameters. Next, we introduce a novel transfer learning (TL)-based approach that leverages knowledge from a few scenarios to handle new “unseen” ones. This method enables the construction of adaptive SMs for unseen scenarios, such as a new initial configuration (IC) of granular mixtures, without the need for a full-sized dataset. Our findings indicate that Gaussian process regression (GPR) efficiently builds accurate SMs on a very small dataset. We also demonstrate that only a few samples are required to build an accurate SM for the unseen IC, which significantly reduces the data preparation burden. By incorporating one and five samples from unseen scenarios to update the TL-GPR-based surrogate model, the SM’s performance (based on ) on unseen scenarios improves by 17 and 47%, respectively. The insights and methodology presented in this study will facilitate and accelerate the development of accurate SMs for DEM calibration, assisting in developing reliable DEM models in a shorter timeframe.@en

Bed permeability is a crucial factor in blast furnace performance which depends on the material distribution achieved through charging. Since a homogeneous bed of pellet and sinter is recommended, it is crucial to understand whether segregation of the pellet-sinter mixture occurs during charging. The Discrete Element Method is useful in this regard; however, simulations of pellet-sinter mixture charging currently lack credibility since pellet-sinter interaction parameters have not yet been calibrated and validated. Determining pellet-sinter interaction parameters will require significant efforts, so it is useful to know whether mixture segregation and the resulting bed permeability are sensitive to these parameters. In this work, we investigate to what extent the restitution coefficient, sliding friction coefficient and rolling friction coefficient between pellet and sinter affect segregation during bed formation and the resulting permeability in terms of porosity using a simplified charging setup. The investigation is done for different mixture compositions and flow velocities, and analysis settings including sample size and sampling directions. We conclude that all parameters affect segregation and porosity, regardless of the composition and velocity. Hence, all mixture parameters including the interaction parameters between the components must be carefully calibrated when developing a model for predicting permeability.

@en

Aiming to address soft sensing model degradation under changing working conditions, and to accommodate dynamic, nonlinear, and multimodal data characteristics, this paper proposes a nonlinear dynamic transfer soft sensor algorithm. The approach leverages time-delay data augmentation to capture dynamics and projects the augmented data into a latent space for constructing a nonlinear regression model. Two regular terms, distribution alignment regularity and first-order difference regularity, are introduced during data projection to address data distribution disparities. Laplace regularity is incorporated into the nonlinear regression model to ensure geometric structure preservation. The final optimization objective is formulated within the framework of partial least squares, and hyperparameters are determined using Bayesian optimization. The effectiveness of the proposed algorithm is demonstrated through experiments on three public datasets.

@en

The inland waterway transport sector is facing increasingly stringent legislation to reduce emissions and improve energy efficiency. Speed planning methods are an attractive option as they provide energy-efficient, timely and emission-reducing voyage planning for ships. However, current methods do not consider dynamic conditions of waterways and traffic. Due to these dynamic navigational conditions the static speed planning methods do not guarantee optimality, nor do they satisfy the constraints of the optimization problem throughout the journey. In this paper we propose an optimization structure that is based on the Model Predictive Control algorithm, which uses the most current information on water depth, water speed and expected delays to re-optimize the speed planning throughout the journey. Through a use case we show a 4.31% energy reduction compared to other speed planning strategies. Additionally, we show that the constraints regarding desired arrival times and safety are satisfied throughout the journey. Therefore, the method proves useful from a logistical, energy, emissions, and safety perspective. Modelling of uncertainties, lock interactions and predictions of waterway conditions will make our method an even more attractive option for speed planning.

@en

The accuracy of high-precision optical processing robots is influenced by various factors, including static error factors and dynamic error factors. These factors pose significant challenges to the deterministic processing of precision optics. This article proposes a pose residual prediction model for optical processing hybrid robots based on deep spatio-temporal graph convolutional neural networks. In this study, we establish a geometric error model for hybrid robots and calibrate the geometric error parameters using an extended Kalman filter to obtain the pose residuals component. To address the complex spatio-temporal interactions between multiple sensor variables in joint space during robot motion, we introduce the non-Euclidean spatio-temporal graph convolutional neural network. This model effectively extracts advanced spatio-temporal interaction features based on a spatio-temporal attention mechanism. Finally, the performance of the proposed method in pose residual prediction was validated through real experiments, and the results demonstrated its advantages over other state-of-the-art methods.

@en

In this study, we investigated autonomous vessel obstacle avoidance using advanced techniques within the Guidance, Navigation, and Control (GNC) framework. We propose a Mixed Integer Linear Programming (MILP) based Guidance system for robust path planning avoiding static and dynamic obstacles. For Navigation, we suggest a multi-modal neural network for perception, demonstrating the identification of obstacle type, position, and orientation using imaging sensors. Additionally, the paper compares an error-based PID control strategy and a Model Predictive Control (MPC) scheme as well. This evaluation aids in better evaluating their performance and determining their applicability within the GNC scheme. We detail the implementation of these systems, present simulation results, and offer a performance evaluation using an experimental dataset. Our findings, analysed through qualitative discussion and quantitative performance indicators, contribute to advancements in autonomous navigation and the control strategies to achieve it.

@en

Segregation or de-mixing is the phenomenon occurring in moving granular materials in which particles with similar properties, e.g., size, density and shape, accumulate. This de-mixing reduces the homogeneity of the mixture which is generally considered undesirable and should be minimised. Despite many experimental and numerical attempts to investigate segregation in relation to different factors, the current literature has several shortcomings. Firstly, most of these studies have considered single-component mixtures, usually with a limited number of particle diameters, while most of the mixtures existing in industry and nature are complex multi-component mixtures. Secondly, a systematic calibration procedure for segregation is often missing while it is crucial for developing a reliable and predictive DEM model. This study proposes a combined global and local calibration strategy for DEM modelling of multi-component segregation. We demonstrate this for an iron ore mixture (i.e., the mixture of pellets and sinter), which is a good example of a multi-component mixture. The model was calibrated not only on the global level but also on the local level and hence it consists of two steps. First, pellets and sinter were individually calibrated on bulk level using the angle of repose measured in a shear box setup. Second, mixtures of pellets and sinter were discharged into a transparent quasi-3D hopper and the segregation index was used to calibrate the interaction parameters between pellets and sinter on a local level. Hereby, image analysis in conjunction with painting pellets have been utilised to measure segregation in a non-invasive manner. We conclude that the initial results of the proposed calibration procedure are promising. To improve it further, we suggest utilizing a more manageable experimental setup, improving the simulation model for the mixture, reducing the number of potential parameter sets, and testing other parameters resulting from single-component calibration.@en

Vessels sailing in a single platoon could reduce resistance from the perspective of the whole platoon and the individual vessel, and contribute to improving energy benefits. Moreover, transportation energy costs and traffic efficiency are essential indicators for measuring waterborne transportation systems. We attempt to minimize transportation energy costs by coordinating platoon formation using a distributed framework of controllers. A large-scale coordinated vessel platooning program is proposed to minimize transportation energy costs and optimize traffic efficiency while guaranteeing safety. The control framework covers routing, energy consumption-dependent cooperative platooning decision and speed optimization based on graph search algorithm, cluster analysis, optimal control approach and model predictive control. Firstly, a local scheduling strategy combined with the leader vessel selection algorithm is adopted. Furthermore, we used cluster analysis to create a series of mergeable vessel platooning sets. Then, we used the mathematical planning method and a two-step hybrid optimal control approach to calculate the improvement and optimization of each vessel platoon's path and speed. Finally, the scalability of the scheduling strategy is elucidated. In a simulation of large scale inland waterborne network, savings surpassed 3.5% when six hundreds vessels participated in the system. These simulation results reveal that the scheduling strategy coordinating vessels into vessel platooning, which improves transportation efficiency as well as descends cost, comparing to a fixed origin route in the waterway network.

@en

Accurately predicting runoff is crucial for managing water resources, preventing and mitigating floods, scheduling hydropower plant operations, and protecting the environment. The hydrological dynamic composite system that forms runoff is complex and random, and seemingly random behavior may be caused by nonlinear variables in a simple deterministic system, which poses a challenge to runoff prediction. In this paper, we construct parallel and multi-timescale reservoirs from a chaotic theory perspective to simulate the stochasticity of chaotic systems. We propose a multi-task-based "Decomposition-Integration-Prediction" (Multi-SDIPC) model for runoff prediction. To validate our research results, we use the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset and compare our proposed model with 10 baseline models. The results show that our model has an average NSE metric of 0.83 and exhibits higher accuracy, better generalization, and greater stability than the other models in multi-step forecasting. Based on our findings, we recommend wider application of the Multi-SDIPC model in different regions of the world for medium or long-term runoff prediction.

@en

This work aims to identify the most influential DEM parameters for modelling multi-component segregation during heap formation, hopper discharge, and chute flow.@en

Bed permeability is a crucial factor in blast furnace efficiency and stability. The Discrete Element Method (DEM) has been used extensively to model material flow in different parts of the furnace and holds great potential for optimizing the permeability. The inherent computational load is the main bottleneck in using this method to provide detailed descriptions of different blast furnace granular phenomena on an industrial scale. In recent years, computing capabilities have been rapidly increasing and more elaborate models are being developed for the furnace as a whole. This paper reviews the recent progress in modelling relevant phenomena related to the burden distribution, and how they affect the bed permeability, using DEM. We conclude that significant efforts have been made in modelling the burden distribution; however, these models generally do not investigate the permeability. Hence, understanding of how the permeability can be optimized still requires significant efforts towards model development.

@en

Segregation control is a challenging yet crucial aspect of bulk material handling processes. The discrete element method (DEM) can offer useful insights into segregation phenomena, provided that reliable models are developed. The main challenge in this regard is finding a good balance between including particle-level details and managing the computational load. This is especially true for industrial applications, where multi-component flows consisting of particles with various irregular shapes and wide size distributions are encountered in huge amounts. In this work, we review the state of the art in DEM modelling of segregation in industrial applications involving the gravity-driven flow of dry, cohesionless granular materials. We start by introducing a novel scientific notation to distinguish between different types of mixtures. Next, we review how parameters for mixture models are determined in the current literature, and how segregation is affected by material, geometric and operational parameters based on these models. Finally, we review existing segregation indices and their applicability to multi-component segregation. We conclude that systematic calibration procedures for segregation models are currently missing in the literature, and realistic models representing multi-component mixtures have not yet been developed. Filling these gaps will pave the way for optimising industrial processes dealing with segregation.@en

This work investigates the proportion of generated fines in a pilot-scale experiment using a belt conveyor and commercial fuel pellets. For this, a belt conveyor with a length of 3.1 m was used and operated at varying conditions: speeds, percentages of material loading on the belt, two combinations of the inclination angle of the belt and the falling height, and a different number of handling steps. We considered a design of experiments approach based on response surface methodology to investigate the effect of different conditions on the potential of fines generation. Moreover, a comparison between the results of the belt conveyor and three common benchmark experimental approaches (tumbling box, rotary impact tester, and mechanical compression test) was made. Results show that the number of handling steps and the combined effect of drop height and inclination angle directly affected the fines generation. However, the tested belt speed range and the level of loading were of lower significance. A polynomial quadratic model was derived based on the regression analysis and showed a high accuracy to predict the proportion of fines. Moreover, the tumbling box method showed good potential to predict the proportion of fines in a belt conveyor when transported several times.

@en

Aiming at the problem of mismatch between real-time data distribution and modeling data distribution caused by the change of working conditions in industrial process, which leads to the performance deterioration of the soft sensor model, a multi-source unsupervised soft sensor method based on joint distribution alignment and mapping structure preservation is proposed. Firstly, the method uses the hypergraph to establish the complex structure of feature and label, and clusters the hypergraph matrix in multiple views to completely construct the class pseudo label; then dynamic distribution alignment is used to adapt marginal distribution and conditional distribution between the data of historical working conditions and the current working conditions, and the hypergraph Laplacian operator is introduced for manifold regularization to prevent the mapping relationship between feature and label from being destroyed; finally, similar working conditions are introduced to further enhance the robustness of the model. The experimental results show that compared with the traditional unsupervised soft sensor methods, the method used in this paper can effectively improve the prediction accuracy of the model.

@en