The stickiness curves of dairy powder : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Bioprocess Engineering at Massey University

Powder stickiness problems encountered during spray drying are important to the dairy industry. Instantaneous stickiness is a surface phenomena that is caused by exceeding the glass transition temperature of the amorphous sugar in the powder, usually lactose in dairy powders. Instantaneous stickiness occurs at a certain temperature above the Tg of amorphous lactose and has been denoted as the critical "X" value. Whether powder particles are sticky or not depends on whether there is enough liquid flow on the surface between the particles. Two particles stick to each other when there is enough liquid flow to form a bridge between them after the contact. This project aimed to measure the instantaneous sticky point conditions for various dairy powders and to relate these to the operating conditions to give a commerical outcome for the dairy industry. The particle-gun rig was developed to simulate the conditions in the spray drier and the ducting pipe and cyclone. The stickiness of powder particles occurs after a short resident time in the particle-gun. Thus, stickiness is a surface phenomenon and the point of adhesion is the instantaneous sticky point. The amount of deposit on the plate was measured at a temperature, with increasing relative humidity. At a particular temperature and relative humidity, the powder stuck to the stainless steel plate instantaneously. This was observed by a sudden change in % deposition on a % deposition verse RH plot. The T-Tg plot and stickiness curve profile were developed to determine the critical "X" value for the dairy powders. The critical 'X' value is the temperature which exceeds the Tg of amorphous lactose when instantaneous stickiness occurs. The critical "X" values tor various dairy powders including WMP, SMP, MPC, whey protein, buttermilk, white cheese powder and GLUMP powder were found to be 33-49°C. 37-42°C. 42-51C. 50°C, 37-39°C, 28.5°C, and 40.7°C respectively. In addition, the slope of the trend line in the T-Tg plot, indicates how quickly the particular powder becomes sticky once the instantaneous sticky point has been exceeded. The particle-gun rig demonstrated that powders with greater than 30% amorphous lactose are more likely to cause blockage than powders with less than 30%. Both the critical 'X' value and the slope are unique to the powder. The stickiness curve was used to relate the powder surface stickiness condition with the drier outlet temperature and relative humidity. It was recommended to operate at conditions below the stickiness curve for a powder to avoid any chamber or cyclone blockages caused by stickiness. The slope enables a decision to be made about how close to the critical point a plant should be run for a particular powder. The inlet air temperature or concentrate feeding rate can be used to move the operating conditions towards or away from the stickiness curve, according to the operating situations

Grain refinement of DC cast magnesium alloys with intensive melt shearing

A new direct chill (DC) casting process, melt conditioned DC (MC-DC) process, has been developed for the production of high quality billets/slabs of light alloys by application of intensive melt shearing through a rotor-stator high shear device during the DC casting process. The rotor-stator high shear device provides intensive melt shearing to disperse the naturally occurring oxide films, and other inclusions, while creating a microscopic flow pattern to homogenize the temperature and composition fields in the sump. In this paper, we report the grain refining effect of intensive melt shearing in the MC-DC casting processing. Experimental results on DC casting of Mg-alloys with and without intensive melt shearing have demonstrated that the MC-DC casting process can produce magnesium alloy billets with significantly refined microstructure. Such grain refinement in the MC-DC casting process can be attributed to enhanced heterogeneous nucleation by dispersed naturally occurring oxide particles, increased nuclei survival rate in uniform temperature and compositional fields in the sump, and potential contribution from dendrite arm fragmentation

Refining grain structure and porosity of an aluminium alloy with intensive melt shearing

The official published version of the article can be obtained at the link below.Intensive melt shearing was achieved using a twin-screw machine to condition an aluminium alloy prior to solidification. The results show that intensive melt shearing has a significant grain-refining effect. In addition, the intensive melt shearing reduces both the volume fraction and the size of porosity. It can reduce the density index from 10.50% to 2.87% and the average size of porosity in the samples solidified under partial vacuum from around 1 mm to 100 μm.Financial support was obtained from the EPSRC and the Technology Strategy Board

A Machine Learning Enhanced Scheme for Intelligent Network Management

The versatile networking services bring about huge influence on daily living styles while the amount and diversity of services cause high complexity of network systems. The network scale and complexity grow with the increasing infrastructure apparatuses, networking function, networking slices, and underlying architecture evolution. The conventional way is manual administration to maintain the large and complex platform, which makes effective and insightful management troublesome. A feasible and promising scheme is to extract insightful information from largely produced network data. The goal of this thesis is to use learning-based algorithms inspired by machine learning communities to discover valuable knowledge from substantial network data, which directly promotes intelligent management and maintenance. In the thesis, the management and maintenance focus on two schemes: network anomalies detection and root causes localization; critical traffic resource control and optimization. Firstly, the abundant network data wrap up informative messages but its heterogeneity and perplexity make diagnosis challenging. For unstructured logs, abstract and formatted log templates are extracted to regulate log records. An in-depth analysis framework based on heterogeneous data is proposed in order to detect the occurrence of faults and anomalies. It employs representation learning methods to map unstructured data into numerical features, and fuses the extracted feature for network anomaly and fault detection. The representation learning makes use of word2vec-based embedding technologies for semantic expression. Next, the fault and anomaly detection solely unveils the occurrence of events while failing to figure out the root causes for useful administration so that the fault localization opens a gate to narrow down the source of systematic anomalies. The extracted features are formed as the anomaly degree coupled with an importance ranking method to highlight the locations of anomalies in network systems. Two types of ranking modes are instantiated by PageRank and operation errors for jointly highlighting latent issue of locations. Besides the fault and anomaly detection, network traffic engineering deals with network communication and computation resource to optimize data traffic transferring efficiency. Especially when network traffic are constrained with communication conditions, a pro-active path planning scheme is helpful for efficient traffic controlling actions. Then a learning-based traffic planning algorithm is proposed based on sequence-to-sequence model to discover hidden reasonable paths from abundant traffic history data over the Software Defined Network architecture. Finally, traffic engineering merely based on empirical data is likely to result in stale and sub-optimal solutions, even ending up with worse situations. A resilient mechanism is required to adapt network flows based on context into a dynamic environment. Thus, a reinforcement learning-based scheme is put forward for dynamic data forwarding considering network resource status, which explicitly presents a promising performance improvement. In the end, the proposed anomaly processing framework strengthens the analysis and diagnosis for network system administrators through synthesized fault detection and root cause localization. The learning-based traffic engineering stimulates networking flow management via experienced data and further shows a promising direction of flexible traffic adjustment for ever-changing environments

Influences of magnetic coupling process on the spectrum of a disk covered by the corona

Recently, much attention has been paid to the magnetic coupling (MC) process, which is supported by very high emissivity indexes observed in Seyfert 1 galaxy MCG-6-30-15 and GBHC XTE J1650-500. But the rotational energy transferred from a black hole is simply assumed to be radiated away from the surrounding accretion disk in black-body spectrum, which is obviously not consistent with the observed hard power-law X-ray spectra. We intend to introduce corona into the MC model to make it more compatible with the observations. We describe the model and the procedure of a simplified Monte Carlo simulation, compare the output spectra in the cases with and without the MC effects, and discuss the influences of three parameters involved in the MC process on the output spectra. It is shown that the MC process augments radiation fluxes in the UV or X-ray band. The emergent spectrum is affected by the BH spin and magnetic field strength at the BH horizon, while it is almost unaffected by the radial profile of the magnetic field at the disk. Introducing corona into the MC model will improve the fitting of the output spectra from AGNs and GBHCs.Comment: 15 pages, 5 figures, accepted by A&

Affine equivariant rank-weighted L-estimation of multivariate location

In the multivariate one-sample location model, we propose a class of flexible robust, affine-equivariant L-estimators of location, for distributions invoking affine-invariance of Mahalanobis distances of individual observations. An involved iteration process for their computation is numerically illustrated.Comment: 16 pages, 4 figures, 6 table

Modeling singular mineralization processes due to fluid pressure fluctuations

Mineralization in the Earth's crust can be regarded as a singular process resulting in large amounts of mass accumulation and element enrichment over short time or space scales. The elemental concentrations modeled by fractals and multifractals show self-similarity and scale-invariant properties. We take the view that fluid-pressure variations in response to earthquakes or fault rupture are primarily responsible for changes in solubility and trigger transient physical and chemical variations in ore-forming fluids that enhance the mineralization process. Based on this general concept, we investigated mineral precipitation processes driven by rapid fluid pressure reductions by coupling mineralization to a cellular automaton model to reveal the nonlinear mechanism of the orogenic gold mineralization process using simulation. In the model, fluid pressure can increase to the rock failure condition, which was set as lithostatic pressure at a depth of 10 km (270 MPa), due to either porosity reduction or dehydration reactions. Rapid drops in pressure resulting from fault rupture or local hydrofracture may induce repeated gold precipitation. The geochemical patterns generated by the model evolve from depletion to enrichment patterns, and from spatially random to spatially clustered structures quantified by multifractal models and geostatistics. Results show how metal elements self-organize to form high metal concentration patterns displaying self-similarity and scale-invariance. These transitions are attributed to the growth and coalescence of sub-networks with different fluid pressures up to the percolation threshold, resulting in a wide range of fluid pressure reductions and gold precipitation in the form of clusters. The results suggest that cyclic evolution of fluid pressure and its effects on gold precipitation systems can effectively mimic the repeated mineralization superposition process, and generate complex geochemical patterns characterized by a multifractal model. The nonlinear behavior exhibits scale-invariance and self-organized critical threshold, where mineral phase separations result from fluid pressure reductions associated with fault failure