Development of a Fast Gas-Solid Flow Simulation for Control of the Pneumatic Conveying System on Air Seeders

Limitations of the pneumatic conveying system are an obstacle to the improvement of air seeding technology. Operators often run conveying velocities far above the minimum requirement. This is common because lower conveying velocities - which could reduce waste, energy consumption and hydraulic requirements - put the system at risk of blockages and non-uniform distribution. Furthermore, new precision technologies such as variable rate application and sectional control introduce imbalances to the highly coupled and distributed conveying system. Incorporating adaptive control mechanisms has been theorized as a potential means of improving conveying system performance. Real-time prediction of conveying system flow conditions is a prerequisite for the proposed control strategies. There is limited existing research regarding control and modeling for air seeders or similar pneumatic conveying systems. While there is extensive research for multiphase flow modeling, few examples prioritize computational efficiency to the extent that real-time simulation is feasible. Application to control dictates that computational speed, in addition to accuracy, is essential. The purpose of this research was to identify, develop and validate a method for predicting flow conditions within a pneumatic conveying system that is suitable for control applications. A low-computational cost, one-dimensional model and simulation have been developed for fast prediction of bulk multiphase flow conditions within the pneumatic conveying system. The model is a simplified form of the Eulerian-Eulerian (two-fluid) equations for fluid-particle flows. The differential model equations were discretized via the finite volume method and solved using computational fluid dynamics techniques. Specifically, the SIMPLER algorithm for the solution of coupled equations was used. The simulation program, which employs the numerical methods to obtain solutions to the discrete equations, was implemented in MATLAB®. Experimental data were collected using a laboratory apparatus which approximated a straight horizontal pneumatic conveying line. The inner diameter of the experimental conveying line was 57.4 mm. Spherical plastic particles with a mean diameter of 3.56 mm were conveyed. Testing consisted of dilute flows only that were relevant for air seeding conditions. Experiments covered air velocities of 20 to 30 m/s and mass loadings of 0.84 to 4.68. Recorded data included steady-state and transient measurements for fluid pressure and bulk particle velocity. The experimental data were used to validate simulation results. The accuracy of the model for steady-state conditions was acceptable for sufficiently dilute and well-developed flow. The simulation predicted experimental fluid pressure within 6% in all tests. For moderate mass loadings, simulation error for particle velocity was below 10%. At higher mass loadings, accuracy for particle velocity began to deteriorate and an error of > 25% was observed. Analysis of the model’s accuracy for transient conditions was inconclusive. Evidence suggested that transient simulation results may be quite good. However, limitations of the continuous equations and experimental factors complicated the analysis, preventing a definitive verdict regarding transient accuracy. Simulation performance with respect to computing time was excellent. Simulation results were found to be relatively insensitive to the size of time and spatial step used, allowing for the program to execute in less time than was being simulated. The fastest execution recorded required 5.0 sec to simulate 60 sec of transient flow, and results deviated minimally from higher resolution simulations. Results indicated the potential for optimization between speed and accuracy. While the simplified model only calculates a limited number of bulk flow properties, it delivered timely results with reasonable accuracy and with relatively low computational effort. Assessment of the developed model and simulation has concluded a suitable potential for control application. Acceptable accuracy and computing speed were obtained to justify further development efforts. The prescribed methodology provides a foundation for future expansion and improvement. There is potential to incorporate fast multiphase flow simulation into control infrastructure to improve the performance of the air seeder conveying system

Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting

Die sensorgestützte Sortierung bietet zukunftsweisende Lösungen für die Trennung von körnigen Materialien. Die derzeit in solchen Systemen verwendeten Zeilensensoren liefern nur eine einzige Beobachtung jedes Objekts und keine Daten über dessen Bewegung. Jüngsten Studien zufolge hat die Verwendung einer Flächenkamera das Potenzial, sowohl den Charakterisierungs- als auch den Trennungsfehler in einem Sortierprozess zu verringern. Ein prädiktiver Tracking-Ansatz auf der Grundlage von Kalman-Filtern ermöglicht die Schätzung der verfolgten Pfade und die Parametrisierung eines individuellen Bewegungsmodells für jedes Objekt in einem Multiobjekt-Tracking-System. Während sich frühere Studien auf physikalisch motivierte Bewegungsmodelle konzentrierten, hat sich gezeigt, dass moderne Ansätze des maschinellen Lernens genauere Vorhersagen ermöglichen. In diesem Beitrag beschreiben wir die Entwicklung eines prädiktiven Trackingsystems auf Basis neuronaler Netze. Der neue Algorithmus wird auf ein experimentelles Sortiersystem und auf ein numerisches Modell des Sortierers angewendet. Zwar erreicht der neue Ansatz noch nicht ganz die Sortierqualität der bestehenden Ansätze, jedoch ermöglicht er die Anwendung von prädiktivem Tracking, ohne dass hierfür Expertenwissen oder ein grundlegendes Verständnis der Parametrisierung des Partikelbewegungsmodells erforderlich sind

Continuous direct compression as manufacturing platform for sustained release tablets

This study presents a framework for process and product development on a continuous direct compression manufacturing platform. A challenging sustained release formulation with high content of a poorly flowing low density drug was selected. Two HPMC grades were evaluated as matrix former: standard Methocel CR and directly compressible Methocel DC2. The feeding behavior of each formulation component was investigated by deriving feed factor profiles. The maximum feed factor was used to estimate the drive command and depended strongly upon the density of the material. Furthermore, the shape of the feed factor profile allowed definition of a customized refill regime for each material. Inline NIRs was used to estimate the residence time distribution (RTD) in the mixer and monitor blend uniformity. Tablet content and weight variability were determined as additional measures of mixing performance. For Methocel CR, the best axial mixing (i.e. feeder fluctuation dampening) was achieved when an impeller with high number of radial mixing blades operated at low speed. However, the variability in tablet weight and content uniformity deteriorated under this condition. One can therefore conclude that balancing axial mixing with tablet quality is critical for Methocel CR. However, reformulating with the direct compressible Methocel DC2 as matrix former improved tablet quality vastly. Furthermore, both process and product were significantly more robust to changes in process and design variables. This observation underpins the importance of flowability during continuous blending and die-filling. At the compaction stage, blends with Methocel CR showed better tabletability driven by a higher compressibility as the smaller CR particles have a higher bonding area. However, tablets of similar strength were achieved using Methocel DC2 by targeting equal porosity. Compaction pressure impacted tablet properties and dissolution. Hence controlling thickness during continuous manufacturing of sustained release tablets was crucial to ensure reproducible dissolution. (C) 2017 Elsevier B.V. All rights reserved

Experimental quantification and modelling of attrition of infant formulae during pneumatic conveying

Infant formula is often produced as an agglomerated powder using a spray drying process. Pneumatic conveying is commonly used for transporting this product within a manufacturing plant. The transient mechanical loads imposed by this process cause some of the agglomerates to disintegrate, which has implications for key quality characteristics of the formula including bulk density and wettability. This thesis used both experimental and modelling approaches to investigate this breakage during conveying. One set of conveying trials had the objective of establishing relationships between the geometry and operating conditions of the conveying system and the resulting changes in bulk properties of the infant formula upon conveying. A modular stainless steel pneumatic conveying rig was constructed for these trials. The mode of conveying and air velocity had a statistically-significant effect on bulk density at a 95% level, while mode of conveying was the only factor which significantly influenced D[4,3] or wettability. A separate set of conveying experiments investigated the effect of infant formula composition, rather than the pneumatic conveying parameters, and also assessed the relationships between the mechanical responses of individual agglomerates of four infant formulae and their compositions. The bulk densities before conveying, and the forces and strains at failure of individual agglomerates, were related to the protein content. The force at failure and stiffness of individual agglomerates were strongly correlated, and generally increased with increasing protein to fat ratio while the strain at failure decreased. Two models of breakage were developed at different scales; the first was a detailed discrete element model of a single agglomerate. This was calibrated using a novel approach based on Taguchi methods which was shown to have considerable advantages over basic parameter studies which are widely used. The data obtained using this model compared well to experimental results for quasi-static uniaxial compression of individual agglomerates. The model also gave adequate results for dynamic loading simulations. A probabilistic model of pneumatic conveying was also developed; this was suitable for predicting breakage in large populations of agglomerates and was highly versatile: parts of the model could easily be substituted by the researcher according to their specific requirements

Electrostatic Sensors – Their Principles and Applications

Over the past three decades electrostatic sensors have been proposed, developed and utilised for the continuous monitoring and measurement of a range of industrial processes, mechanical systems and clinical environments. Electrostatic sensors enjoy simplicity in structure, cost-effectiveness and suitability for a wide range of installation conditions. They either provide unique solutions to some measurement challenges or offer more cost-effective options to the more established sensors such as those based on acoustic, capacitive, optical and electromagnetic principles. The established or potential applications of electrostatic sensors appear wide ranging, but the underlining sensing principle and resultant system characteristics are very similar. This paper presents a comprehensive review of the electrostatic sensors and sensing systems that have been developed for the measurement and monitoring of a range of process variables and conditions. These include the flow measurement of pneumatically conveyed solids, measurement of particulate emissions, monitoring of fluidised beds, on-line particle sizing, burner flame monitoring, speed and radial vibration measurement of mechanical systems, and condition monitoring of power transmission belts, mechanical wear, and human activities. The fundamental sensing principles together with the advantages and limitations of electrostatic sensors for a given area of applications are also introduced. The technology readiness level for each area of applications is identified and commented. Trends and future development of electrostatic sensors, their signal conditioning electronics, signal processing methods as well as possible new applications are also discussed

Dynamic viscoplastic granular flows: A persistent challenge in gas-solid fluidization

Fluidization is a prime example of complex granular flows driven by fluid-solid interactions. The interplay of gravity, particle-particle and fluid-particle forces leads to a rich spectrum of hydrodynamic behavior. A number of complex mathematical formulations exist to describe granular flows. At a macroscopic scale, Eulerian models based on the Kinetic Theory of Granular Flow (KTGF) have been successfully employed to simulate dilute and moderately dense systems, such as circulating fluidized bed reactors. However, their applications to dense flows are challenging, because sustained particle contacts are important. As solid fraction rises, the behavior of granular media responds dramatically to particle properties and changes in concentration. Lacking a coherent transition between formulations of dilute, dense and quasi-static flow behavior, kinetic models are incapable of describing how microstructure emerges and affects the rheology. The behavior of transitional granular flows, such as pulsed fluidized beds, for which the particulate phase transitions between the viscous and plastic regimes, are good reminders of this limitation. In recent years, tremendous effort has been devoted to finding new ways to describe the effects of sustained solids friction and dense flow rheology. This article provides a perspective on this matter from the viewpoint of gas-solid fluidization and discusses advances in describing the dilute-to-dense transition in a continuum framework. Four innovative approaches prevail to extend or supersede the existing kinetic theory: (i) including effective restitution coefficients, (ii) coupling local granular rheological correlations, (iii) introducing rotational granular energy, and (iv) combining non-local laws. While their reliability is still far from that of a Eulerian-Lagrangian approach, they lay a promising foundation for developing a rigorous description of granular media that merges the classical frameworks of continuous fluid and soil mechanics. The progress of continuum formulations does not compete with multi-scale modeling platforms with an applied focus. Ultimately, combining both is a prerequisite to developing new solid stress models that will improve not only the performance of macroscopic models, but also our understanding of granular physics

Deep Learning based Prediction of Clogging Occurrences during Lignocellulosic Biomass Feeding in Screw Conveyors

Over the last decades, there have been substantial government and private sector investments to establish a commercial biorefining industry that uses lignocellulosic biomass as feedstock to produce fuels, chemicals, and other products. However, several biorefining plants experienced material conveyance problems due to the variability and complexity of the biomass feedstock. While the problems were reported in most conveyance unit operations in the biorefining plants, screw conveyors merit special attention because they are the most common conveyors used in biomass conveyance and typically function as the last conveyance unit connected to the conversion reactors. Thus, their operating status affects the plant production rate. Therefore, detecting emerging clogging events and, ultimately, proactively adjusting operating conditions to avoid downtime is crucial to improving overall plant economics. One promising solution is the development of sensor systems to detect clogging to support automated decision-making and process control. In this study, two deep learning based algorithms are developed to detect an imminent clogging event based on the current signature and vibration signals extracted from the sensors connected to the benchtop screw conveyor system. The study focuses on three biomass materials (switchgrass, loblolly pine, and hybrid poplar) and is designed around three research objectives. The first research objective examines the relationship between the occurrence of clogging in a screw conveyor and the current and vibration signals on the different feedstocks to establish the presence of clogging event fingerprint that could be exploited in automated decision-making and process-control. The second research objective applies two deep learning algorithms to the current and vibration signals to detect the imminent occurrence of clogging and its severity for decision making with an optimization procedure. The third objective examines the robustness of the optimized deep learning algorithm to detection imminent clogging events when feedstock properties (size distribution and moisture contents) vary. In the long-term, the early clogging detection methodology developed in this study could be leveraged to develop smart process controls for biomass conveyance