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Pneumatic conveying is an attractive option for the transportation of a wide range of bulk materials in various industrial fields due to several features and advantages. However, many pneumatic conveying systems are being operated beyond optimal conditions that result in excessive wastes of energy or even pipeline blockages. Therefore, there is a strong demand to develop new technologies for operating pneumatic conveying systems more reliably in industry. To reduce excessive power consumption and/or prevent pipeline blockage, numerous studies have been undertaken, including pneumatic conveying characteristics, system improvements and modifications, swirling or oscillating air supplies, as well as artificial-intelligence-based technologies. This paper reviews and discusses these technologies and proposes a new promising artificial-intelligence-based approach to operating pneumatic conveying systems under optimal conditions. [ABSTRACT FROM AUTHOR]

Artificial neural network possesses the feature of approximating complicated non-linear functions with arbitrary precision, which makes it possible to predict pressure drop for pneumatic conveying and also develop pneumatic conveying characteristics (PCC) instead of employing traditional mathematical models. In this study, based on the experimental data of polyester pellets and alumina, different artificial neural networks were trained with different algorithms to predict pressure drop or develop PCC curves. Specifically, for polyester pellets, 81 data sets were utilised to train neural networks and another 9 data sets were used for testing purposes. The results show that the neural network trained by the initial steepest descent method yields relatively large deviations, with pressure drop errors in the range -23.1% to 64.2% and a regression coefficient R² of 0.9590. However, the network trained by the Levenberg-Marquardt algorithm produces improved results with errors from -10.8% to 8.0% and an R² of 0.9984. Meanwhile, for alumina, 21 data sets were trained to generate PCC curves with different algorithms. The Bayesian Regularisation algorithm is found to provide a reasonable method for PCC development. [ABSTRACT FROM AUTHOR]

Water spraying is a common method of dust suppression in the industries that rely on bulk materials handling and processing. However, the performance of such spray systems does not always satisfy the design goal of efficient dust control. This can be avoided if the efficiency of a spray system can be predicted accurately at the design stage. This paper outlines the application of CFD-DPM method in predicting the dust suppression efficiency of a water spray system. In this method, the air is treated as a continuum and the droplets and particles are described as a discrete phase. The collision between droplets and dust particles in each computational cell is calculated using the capture efficiency of a single droplet. This model is firstly calibrated and verified using experimental data obtained from a laboratory test rig. The results presented in this paper demonstrate that CFD can be used to predict the dust suppression efficiency of spray system, and this can help to minimise the risk of system design failure. [ABSTRACT FROM AUTHOR]

Belt conveyors are applied to handle bulk materials in industries and pouch belt conveyors (PBC) are also recognized due to their environmental-friendly advantage and additional features. However, the motion resistance calculation method for PBC is not firmly established by researchers. This paper presents a study on predicting the motion resistance of PBC systems, with a focus on the key features and system parameters of two recently installed systems. Using a data-driven dimensional analysis method with a belt conveying characteristics curve, the study analyses and validates the motion resistance prediction of the two PBC systems. The findings make a significant contribution to predicting the motion resistance of PBC systems, with potential applications for future designs and optimizations. [ABSTRACT FROM AUTHOR]

The simulation of complex flows involving mixtures of fluids and granular particulates has been a subject of focus for a variety of industrial applications such as material processing, and slurry transport. In the last decade, there has been an increasing interest in the application of the coupling between SPH and DEM to model such systems. However, only limited integrations of bench-scale calibration of these methods have been conducted in the literature. This research investigates the influence of different parameters on coupled SPH - DEM simulations through a series of bench-scale tests. The current tests are a submerged angle of repose test, and a two-phase rotating drum test. The SPH - DEM calibration study was conducted by first performing DEM calibration on dry material and then performing the SPH - DEM study on the submerged systems. This methodology aims to investigate the impact of calibrated DEM parameters for the dry bulk material on the behaviour of the two-phase mixture. The current results highlight that, with the selected bulk material, the presence of fluid changes the dynamic behaviours of the bulk flow comparing to the dry state. With a set of calibrated dry DEM parameters, the coupling model exhibits a natural prediction of a fully submerged mixture. For a partially submerged mixture, the model requires a compromise to achieve physically sound results. The calibration approach in this research provides a framework to calibrate model parameters for large scale industrial applications using SPH - DEM coupling. [ABSTRACT FROM AUTHOR]

The occurrence of free-surface fluid flows is present in several specific material handling and processing applications such as wet grinding ball mills, slurry transport, and mixing in a shuttle chute. Modelling these flows with free surfaces, changing geometries, and fast flow dynamics can be challenging and inefficient for conventional continuum methods such as finite volume CFD. Because of these shortcomings of conventional gridbased CFD, smoothed particle hydrodynamics (SPH) poses as a viable particle-based numerical approach to gain insights on flows with the mentioned characteristics. This research uses a proposed length scale refinement study scheme to couple and refine the two SPH length scales, particle spacing and smoothing length, at different rates. The goal is to investigate the effectiveness of the proposed scheme on the flow field prediction of a water flow inside a rotating drum with lifters. Another goal is to address how suitable a standard convergence assessment procedure is in reflecting the convergence of the proposed refinement scheme. The investigation was performed by tracking the simulation runtimes, performing qualitative assessments on the velocity field, and using a Richardson Extrapolation procedure recommended by ASME researchers. The study shows that the proposed scheme resulted in an increase in simulation runtime while appeared to alter and diverge the predicted flow field. The Richardson Extrapolation procedure results in misleading metrics that are unsuitable for the case study. The current results serve as a part of a study to investigate the challenges of SPH application in fluid - granular particulate flows. [ABSTRACT FROM AUTHOR]

The belt conveyor is a commonly used equipment for handling bulk materials in industrial applications. This paper presents a comprehensive review of the simulation methods of belt conveyor dynamics behaviour, including both traditional and closed belt conveyors. Additionally, the study compares the coupled simulation methods used by different researchers, guiding the pouch belt conveyors. Finally, the conclusion of this work is summarized and topics of future potential studies on pouch belt conveyor systems are suggested. [ABSTRACT FROM AUTHOR]

The suppression of airborne dust is a significant issue within the resources industry that has been highlighted by the re-emergence of lung diseases such as coal workers' pneumoconiosis and silicosis. A prominent method for the control of airborne dust is the use of water spraying systems to suppress dust and minimise its liberation into the workplace. The challenge with water spraying systems for airborne dust suppression lies largely in the correct selection and operation of nozzles to suit the application. This paper outlines work conducted by the University of Wollongong to investigate the factors affecting the performance of water spraying dust suppression systems. The paper presents innovative experimental methods that have been setup for the testing of nozzles under various dust and airflow conditions. A parametric study has been conducted on the influence of factors such as water flow rate and pressure, droplet size, dust concentration, dust and air velocity where the outcome has been the development of models for the selection of nozzles for effective dust capture characteristics and resistance to the local airflow conditions. It is expected that the models generated can be used by industry for the future design of water spraying dust suppression systems to greatly improve the performance and reliability of such systems. [ABSTRACT FROM AUTHOR]