Caking behaviour of spray-dried milk powders:using scanning probe microscopy to study nanoscale surface properties and material composition

Spray drying is widely used to manufacture many powdered products, with the drying process parameters having significant influence over the final powder's surface properties and propensity for unwanted caking. In most cases caking experiments are performed on bulk powders, but especially in multi-component powders, it is often difficult to interpret these results, where interaction effects between particles can be complex. Here the technique of scanning probe microscopy is used to characterize the nanoscale properties of spray dried model milk powders in order to investigate the surface properties of the powders.

Amino acids as cryoprotectants for liposomal delivery systems

Liposomes provide an efficient delivery system for solubilisation and delivery of both small and macro molecules. However, they suffer from the disadvantage of instability when stored as aqueous dispersions. Cryoprotection of the liposomal systems provides an effective approach to overcome poor stability whilst maintaining formulation characteristics, although, the formulation of a freeze-dried product requires the consideration of not only the selection of an appropriate cryoprotectant, but also needs careful consideration of the processing parameters including pre-freezing conditions, primary and secondary drying protocols along with optimisation of cryoprotectant concentration. This current work investigates the application of amino acids as potential cryoprotectants for the stabilisation of liposomes, and results indicate that amino acids show biphasic nature of stabilisation with 4 mol of cryoprotectant per mole of the lipid exhibiting optimum cryoprotection. The investigations of process parameters showed that the pre-freezing temperatures below the glass transition of the amino acids followed by drying for over 6 h resulted in stable formulations. Studies investigating the efficiency of drug retention showed that the cryoprotection offered by lysine was similar to that shown by trehalose, suggesting that amino acids act as effective stabilisers. ESEM analysis was carried out to monitor morphology of the rehydrated liposomes. © 2007 Elsevier B.V. All rights reserved.

Heat and mass transfer in membrane distillation used for desalination with slip flow

A theoretical model for the transport phenomena in an air gap membrane distillation is presented. The model is based on the conservation equations for the mass, momentum, energy and species within the feed water solution as well as on the mass and energy balances on the membrane sides. The slip flow occurs due to the hydrophobic properties of the membrane. The slip boundary condition applied on the feed saline solution-membrane interface is taken into consideration showing its effects on process parameters particularly permeate flow, heat transfer coefficient and thermal efficiency. The theoretical model was validated with available experimental data and was found to be in good agreement especially when the slip condition is introduced. Increasing slip length from zero to 200 μm was found to increase the permeate flux and the thermal efficiency by 33% and 1.7% respectively.

Uniform design for the optimization of Al2O3 nanofilms produced by electrophoretic deposition

Surface modification by means of nanostructures is of interest to enhance boiling heat transfer in various applications including the organic Rankine cycle (ORC). With the goal of obtaining rough and dense aluminum oxide (Al2O3) nanofilms, the optimal combination of process parameters for electrophoretic deposition (EPD) based on the uniform design (UD) method is explored in this paper. The detailed procedures for the EPD process and UD method are presented. Four main influencing conditions controlling the EPD process were identified as nanofluid concentration, deposition time, applied voltage and suspension pH. A series of tests were carried out based on the UD experimental design. A regression model and statistical analysis were applied to the results. Sensitivity analyses of the effect of the four main parameters on the roughness and deposited mass of Al2O3 films were also carried out. The results showed that Al2O3 nanofilms were deposited compactly and uniformly on the substrate. Within the range of the experiments, the preferred combination of process parameters was determined to be nanofluid concentration of 2 wt.%, deposition time of 15 min, applied voltage of 23 V and suspension pH of 3, yielding roughness and deposited mass of 520.9 nm and 161.6 × 10− 4 g/cm2, respectively. A verification experiment was carried out at these conditions and gave values of roughness and deposited mass within 8% error of the expected ones as determined from the UD approach. It is concluded that uniform design is useful for the optimization of electrophoretic deposition requiring only 7 tests compared to 49 using the orthogonal design method.

Influence of cutting parameters and tool wear on martensitic stainless steel surface integrity after a face milling process

Surface finish is one of the most relevant aspects of machining operations, since it is one of the principle methods to assess quality. Also, surface finish influences mechanical properties such as fatigue behavior, wear, corrosion, etc. The feed, the cutting speed, the cutting tool material, the workpiece material and the cutting tool wear are some of the most important factors that affects the surface roughness of the machined surface. Due to the importance of the martensitic 416 stainless steel in the petroleum industry, especially in valve parts and pump shafts, this material was selected to study the influence of the feed per tooth and cutting speed on tool wear and surface integrity. Also the influence of tool wear on surface roughness is analyzed. Results showed that high values of roughness are obtained when using low cutting speed and feed per tooth and by using these conditions tool wear decreases prolonging tool life. Copyright © 2009 by ASME.

Regression with input-dependent noise: A Gaussian process treatment

Gaussian processes provide natural non-parametric prior distributions over regression functions. In this paper we consider regression problems where there is noise on the output, and the variance of the noise depends on the inputs. If we assume that the noise is a smooth function of the inputs, then it is natural to model the noise variance using a second Gaussian process, in addition to the Gaussian process governing the noise-free output value. We show that prior uncertainty about the parameters controlling both processes can be handled and that the posterior distribution of the noise rate can be sampled from using Markov chain Monte Carlo methods. Our results on a synthetic data set give a posterior noise variance that well-approximates the true variance.

Adding constrained discontinuities to Gaussian process models of wind fields

Gaussian Processes provide good prior models for spatial data, but can be too smooth. In many physical situations there are discontinuities along bounding surfaces, for example fronts in near-surface wind fields. We describe a modelling method for such a constrained discontinuity and demonstrate how to infer the model parameters in wind fields with MCMC sampling.

Computer control of chemical process plants with special reference to distillation

A sieve plate distillation column has been constructed and interfaced to a minicomputer with the necessary instrumentation for dynamic, estimation and control studies with special bearing on low-cost and noise-free instrumentation. A dynamic simulation of the column with a binary liquid system has been compiled using deterministic models that include fluid dynamics via Brambilla's equation for tray liquid holdup calculations. The simulation predictions have been tested experimentally under steady-state and transient conditions. The simulator's predictions of the tray temperatures have shown reasonably close agreement with the measured values under steady-state conditions and in the face of a step change in the feed rate. A method of extending linear filtering theory to highly nonlinear systems with very nonlinear measurement functional relationships has been proposed and tested by simulation on binary distillation. The simulation results have proved that the proposed methodology can overcome the typical instability problems associated with the Kalman filters. Three extended Kalman filters have been formulated and tested by simulation. The filters have been used to refine a much simplified model sequentially and to estimate parameters such as the unmeasured feed composition using information from the column simulation. It is first assumed that corrupted tray composition measurements are made available to the filter and then corrupted tray temperature measurements are accessed instead. The simulation results have demonstrated the powerful capability of the Kalman filters to overcome the typical hardware problems associated with the operation of on-line analyzers in relation to distillation dynamics and control by, in effect, replacirig them. A method of implementing estimator-aided feedforward (EAFF) control schemes has been proposed and tested by simulation on binary distillation. The results have shown that the EAFF scheme provides much better control and energy conservation than the conventional feedback temperature control in the face of a sustained step change in the feed rate or multiple changes in the feed rate, composition and temperature. Further extensions of this work are recommended as regards simulation, estimation and EAFF control.

Adding constrained discontinuities to Gaussian process models of wind fields

Gaussian Processes provide good prior models for spatial data, but can be too smooth. In many physical situations there are discontinuities along bounding surfaces, for example fronts in near-surface wind fields. We describe a modelling method for such a constrained discontinuity and demonstrate how to infer the model parameters in wind fields with MCMC sampling.

Binocular contrast interactions:Dichoptic masking is not a single process

To decouple interocular suppression and binocular summation we varied the relative phase of mask and target in a 2IFC contrast-masking paradigm. In Experiment I, dichoptic mask gratings had the same orientation and spatial frequency as the target. For in-phase masking, suppression was strong (a log-log slope of ∼1) and there was weak facilitation at low mask contrasts. Anti-phase masking was weaker (a log-log slope of ∼0.7) and there was no facilitation. A two-stage model of contrast gain control [Meese, T.S., Georgeson, M.A. and Baker, D.H. (2006). Binocular contrast vision at and above threshold. Journal of Vision, 6: 1224-1243] provided a good fit to the in-phase results and fixed its free parameters. It made successful predictions (with no free parameters) for the anti-phase results when (A) interocular suppression was phase-indifferent but (B) binocular summation was phase sensitive. Experiments II and III showed that interocular suppression comprised two components: (i) a tuned effect with an orientation bandwidth of ∼±33° and a spatial frequency bandwidth of >3 octaves, and (ii) an untuned effect that elevated threshold by a factor of between 2 and 4. Operationally, binocular summation was more tightly tuned, having an orientation bandwidth of ∼±8°, and a spatial frequency bandwidth of ∼0.5 octaves. Our results replicate the unusual shapes of the in-phase dichoptic tuning functions reported by Legge [Legge, G.E. (1979). Spatial frequency masking in human vision: Binocular interactions. Journal of the Optical Society of America, 69: 838-847]. These can now be seen as the envelope of the direct effects from interocular suppression and the indirect effect from binocular summation, which contaminates the signal channel with a mask that has been suppressed by the target. © 2007 Elsevier Ltd. All rights reserved.

Digital control system programming from process control diagrams

A graphical process control language has been developed as a means of defining process control software. The user configures a block diagram describing the required control system, from a menu of functional blocks, using a graphics software system with graphics terminal. Additions may be made to the menu of functional blocks, to extend the system capability, and a group of blocks may be defined as a composite block. This latter feature provides for segmentation of the overall system diagram and the repeated use of the same group of blocks within the system. The completed diagram is analyzed by a graphics compiler which generates the programs and data structure to realise the run-time software. The run-time software has been designed as a data-driven system which allows for modifications at the run-time level in both parameters and system configuration. Data structures have been specified to ensure efficient execution and minimal storage requirements in the final control software. Machine independence has been accomodated as far as possible using CORAL 66 as the high level language throughout the entire system; the final run-time code being generated by a CORAL 66 compiler appropriate to the target processor.

Estimating parameters in stochastic systems:a variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein–Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.

Setting parameters in MRP for the effective management of bought-out inventory in a JIT assembly environment

In recent years, UK industry has seen an explosive growth in the number of `Computer Aided Production Management' (CAPM) system installations. Of the many CAPM systems, materials requirement planning/manufacturing resource planning (MRP/MRPII) is the most widely implemented. Despite the huge investments in MRP systems, over 80 percent are said to have failed within 3 to 5 years of installation. Many people now assume that Just-In-Time (JIT) is the best manufacturing technique. However, those who have implemented JIT have found that it also has many problems. The author argues that the success of a manufacturing company will not be due to a system which complies with a single technique; but due to the integration of many techniques and the ability to make them complement each other in a specific manufacturing environment. This dissertation examines the potential for integrating MRP with JIT and Two-Bin systems to reduce operational costs involved in managing bought-out inventory. Within this framework it shows that controlling MRP is essential to facilitate the integrating process. The behaviour of MRP systems is dependent on the complex interactions between the numerous control parameters used. Methodologies/models are developed to set these parameters. The models are based on the Pareto principle. The idea is to use business targets to set a coherent set of parameters, which not only enables those business targets to be realised, but also facilitates JIT implementation. It illustrates this approach in the context of an actual manufacturing plant - IBM Havant. (IBM Havant is a high volume electronics assembly plant with the majority of the materials bought-out). The parameter setting models are applicable to control bought-out items in a wide range of industries and are not dependent on specific MRP software. The models have produced successful results in several companies and are now being developed as commercial products.

Computer aided process planning (CAPP) for flat rolling of copper alloys

The manufacture of copper alloy flat rolled metals involves hot and cold rolling operations, together with annealing and other secondary processes, to transform castings (mainly slabs and cakes) into such shapes as strip, plate, sheet, etc. Production is mainly to customer orders in a wide range of specifications for dimensions and properties. However, order quantities are often small and so process planning plays an important role in this industry. Much research work has been done in the past in relation to the technology of flat rolling and the details of the operations, however, there is little or no evidence of any research in the planning of processes for this type of manufacture. Practical observation in a number of rolling mills has established the type of manual process planning traditionally used in this industry. This manual approach, however, has inherent drawbacks, being particularly dependent on the individual planners who gain their knowledge over a long span of practical experience. The introduction of the retrieval CAPP approach to this industry was a first step to reduce these problems. But this could not provide a long-term answer because of the need for an experienced planner to supervise generation of any plan. It also fails to take account of the dynamic nature of the parameters involved in the planning, such as the availability of resources, operation conditions and variations in the costs. The other alternative is the use of a generative approach to planning in the rolling mill context. In this thesis, generative methods are developed for the selection of optimal routes for single orders and then for batches of orders, bearing in mind equipment restrictions, production costs and material yield. The batch order process planning involves the use of a special cluster analysis algorithm for optimal grouping of the orders. This research concentrates on cold-rolling operations. A prototype model of the proposed CAPP system, including both single order and batch order planning options, has been developed and tested on real order data in the industry. The results were satisfactory and compared very favourably with the existing manual and retrieval methods.

GIS linked environmental process models

In recent years there has been a great effort to combine the technologies and techniques of GIS and process models. This project examines the issues of linking a standard current generation 2½d GIS with several existing model codes. The focus for the project has been the Shropshire Groundwater Scheme, which is being developed to augment flow in the River Severn during drought periods by pumping water from the Shropshire Aquifer. Previous authors have demonstrated that under certain circumstances pumping could reduce the soil moisture available for crops. This project follows earlier work at Aston in which the effects of drawdown were delineated and quantified through the development of a software package that implemented a technique which brought together the significant spatially varying parameters. This technique is repeated here, but using a standard GIS called GRASS. The GIS proved adequate for the task and the added functionality provided by the general purpose GIS - the data capture, manipulation and visualisation facilities - were of great benefit. The bulk of the project is concerned with examining the issues of the linkage of GIS and environmental process models. To this end a groundwater model (Modflow) and a soil moisture model (SWMS2D) were linked to the GIS and a crop model was implemented within the GIS. A loose-linked approach was adopted and secondary and surrogate data were used wherever possible. The implications of which relate to; justification of a loose-linked versus a closely integrated approach; how, technically, to achieve the linkage; how to reconcile the different data models used by the GIS and the process models; control of the movement of data between models of environmental subsystems, to model the total system; the advantages and disadvantages of using a current generation GIS as a medium for linking environmental process models; generation of input data, including the use of geostatistic, stochastic simulation, remote sensing, regression equations and mapped data; issues of accuracy, uncertainty and simply providing adequate data for the complex models; how such a modelling system fits into an organisational framework.