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

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

A More Precise Extraction of |V_{cb}| in HQEFT of QCD

The more precise extraction for the CKM matrix element |V_{cb}| in the heavy quark effective field theory (HQEFT) of QCD is studied from both exclusive and inclusive semileptonic B decays. The values of relevant nonperturbative parameters up to order 1/m^2_Q are estimated consistently in HQEFT of QCD. Using the most recent experimental data for B decay rates, |V_{cb}| is updated to be |V_{cb}| = 0.0395 \pm 0.0011_{exp} \pm 0.0019_{th} from B\to D^{\ast} l \nu decay and |V_{cb}| = 0.0434 \pm 0.0041_{exp} \pm 0.0020_{th} from B\to D l \nu decay as well as |V_{cb}| = 0.0394 \pm 0.0010_{exp} \pm 0.0014_{th} from inclusive B\to X_c l \nu decay.Comment: 7 pages, revtex, 4 figure

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

Stochastic analysis of a heterogeneous micro-finite element model of a mouse tibia

Finite element (FE) analysis can be used to predict bone mechanical environments that can be used for many important applications, such as the understanding of bone mechano-regulation mechanisms. However, when defining the FE models, uncertainty in bone material properties may lead to marked variations in the predicted mechanical environment. The aim of this study is to investigate the influence of uncertainty in bone material property on the mechanical environment of bone. A heterogeneous FE model of a mouse tibia was created from micro computed tomography images. Axial compression loading was applied, and all possible bone density-modulus relationships were considered through stochastic analysis. The 1st and 3rd principal strains (ε and ε ) and the strain energy density (SED) were quantified in the tibial volume of interest (VOI). The bounds of ε , ε , and SED were determined by the bounds of the density-modulus relationship; the bone mechanical environment (ε , ε , and SED) and the bone density-modulus relationship exhibit the same trend of change; the relative percentage differences caused by bone material uncertainty are up to 28%, 28%, and 21% for ε , ε , and SED, respectively. These data provide guidelines on the adoption of bone density-modulus relationship in heterogeneous FE models. [Abstract copyright: Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

Mobile Application to support fuel-efficient driving through situation awareness

Abstract. Situation awareness is usually conceptualized as design and implementation principles for safety critical industries like aviation or military. Finland was one of the first countries in the world to establish an intelligent transport systems (ITS) strategy in 2009. Increasing the situation awareness in traffic is regarded as one of the means to implement the strategy. In the theoretical part of this thesis, we explore the use of situation awareness and context awareness in intelligent transport systems. Particularly, the thesis focuses on summarizing proper design and evaluation principles to provide situation awareness support for fuel efficient driving. These guidelines were exploited in implementing a mobile application, called Driving Coach Mobile Application in the practical part of the thesis. The purpose of the application is to provide awareness to the drivers about how they can save fuel. Driving Coach Mobile Application’s accordance of design and implementation principles to situation awareness support is validated by user study with simulated data focused on usability, usefulness and fuel efficiency awareness support. The results of this thesis can be used in fleet management planning, city planning as well as in personal driving, for example.Tilannetietoinen mobiilisovellus polttoainetaloudellisen ajamisen tueksi. Tiivistelmä. Turvallisuuskriittisissä teollisuuden osa-alueissa kuten ilmailussa tai sotilaallisessa toiminnassa, eri toimijoiden tilannetietoisuuden parantamiseen tähtäävät suunnittelu- sekä toteutusperiaatteet ovat olleet merkittävässä roolissa jo pitkään. Suomi oli maailman ensimmäisiä maita, jotka julkistivat älykkään liikenteen strategian jo vuonna 2009. Tilannetietoisuuden parantaminen liikenteessä on edelleen eräs tämän strategian toimeenpanomuoto. Tämän työn teoreettisessa osassa tutkitaan avulla tilannetietoisuuden sekä toimintatilanteesta tietoisuuden soveltamista älyliikenteessä. Erityisesti tarkastellaan suunnittelu- sekä evaluointiperiaatteita polttoainetalouden tehokkuuden lisäämiselle tilannetietoisuuden avulla. Työn käytännön osuudessa sovellettiin näitä periaatteita mobiilisovelluksen toteuttamiseksi. Mobiilisovellus tukee kuljettajien polttoainetehokkaampaa ajamista. Sovellus testattiin käytettävyyden, hyödyllisyyden sekä polttoainetehokkaan ajamisen tuen suhteen. Sovellusta voidaan käyttää esimerkiksi kaupunkisuunnittelussa, autokannan toiminnan tarkkailemisessa tai vaikka henkilökohtaisen ajotavan arvioinnissa

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