Modeling and design optimization for membrane filters

Membrane filtration is widely used in many applications, ranging from industrial processes to everyday living activities. With growing interest from both industrial and academic sectors in understanding the various types of filtration processes in use, and in improving filter performance, the past few decades have seen significant research activity in this area. Experimental studies can be very valuable, but are expensive and time-consuming, therefore theoretical studies offer potential as a cost-effective and predictive way to improve on current filter designs. In this work, mathematical models, derived from first principles and simplified using asymptotic analysis, are proposed for: (1) pleated membrane filters, where the macroscale flow problem of Darcy flow through a pleated porous medium is coupled to the microscale fouling problem of particle transport and deposition within individual pores of the membrane; (2) dead-end membrane filtration with feed containing multiple species of physicochemically-distinct particles, which interact with the membrane differently; and (3) filtration with reactive particle removal using porous media composed of chemically active granular materials. Asymptotically-simplified models are used to describe and evaluate the membrane performance numerically and filter design optimization problems are formulated and solved for a number of industrially-relevant scenarios. This study demonstrates the potential of such modeling to guide industrial membrane filter design for a range of applications involving purification and separation

Deep Learning Applications in Industrial and Systems Engineering

Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the regression of real geographic coordinates from aerial photos - showing promising results. Next, continuing on this work, the features and characteristics of the deep aerial geolocalization model are further studied, with implications for future applications and methodological improvements. Lastly, a deep learning model is developed and applied to a difficult rare event problem of predicting failure times in oil and natural gas wells from process and site data. Practical details of applying deep learning to this sort of data are discussed, and methodological principles are proposed

Design of exceptionally strong and conductive Cu alloys beyond the conventional speculation via the interfacial energy-controlled dispersion of gamma-Al2O3 nanoparticles

The development of Cu-based alloys with high-mechanical properties (strength, ductility) and electrical conductivity plays a key role over a wide range of industrial applications. Successful design of the materials, however, has been rare due to the improvement of mutually exclusive properties as conventionally speculated. In this paper, we demonstrate that these contradictory material properties can be improved simultaneously if the interfacial energies of heterogeneous interfaces are carefully controlled. We uniformly disperse γ-Al2O3 nanoparticles over Cu matrix, and then we controlled atomic level morphology of the interface γ-Al2O3 //Cu by adding Ti solutes. It is shown that the Ti dramatically drives the interfacial phase transformation from very irregular to homogeneous spherical morphologies resulting in substantial enhancement of the mechanical property of Cu matrix. Furthermore, the Ti removes impurities (O and Al) in the Cu matrix by forming oxides leading to recovery of the electrical conductivity of pure Cu. We validate experimental results using TEM and EDX combined with first-principles density functional theory (DFT) calculations, which all consistently poise that our materials are suitable for industrial applications.1

A Review on Industrial Augmented Reality Systems for the Industry 4.0 Shipyard

[Absctract]: Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this paper focuses on augmented reality, whose application in the industrial field has led to the concept of industrial augmented reality (IAR). This paper first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it proposed an IAR system architecture that combines cloudlets and fog computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the cloud.This work was supported by the Plant Information and Augmented Reality research line of the Navantia-UDC Joint Research Unit

The Alternate Arm Converter (AAC) - "short-overlap" mode operation - analysis and design parameter selection

This paper presents converter operation principles and theoretical analyses for “short-overlap” mode operation of the Alternate Arm Converter (AAC), which is a type of modular multilevel Voltage Source Converter (VSC) that has been proposed for HVDC transmission applications. Fourier series expressions for the ideal arm current and reference voltage are derived, for the first time, in order to develop an expression for the sub-module capacitance required to give a selected peak-peak voltage ripple of the summed sub-module capacitor voltages in an arm. The DC converter current contains non-negligible low order even harmonics; this is verified by deriving, for the first time, a Fourier series expression for this current. As the DC converter current needs to be filtered to form a smooth DC grid current, a novel DC filter arrangement is proposed, which uses the characteristics of a simplified DC cable model, as well as the capacitance of the DC link and additional DC link damping resistance, in order to form a passive low pass filter. Results obtained from a simulation model, which is based on an industrial HVDC demonstrator, are used in order to verify the presented converter operation principles and theoretical analyses

Energy Transfer from Magnetic Iron Oxide Nanoparticles: Implications for Magnetic Hyperthermia

Magnetic iron oxide nanoparticles (IONPs) have gained momentum in the field of biomedical applications. They can be remotely heated via alternating magnetic fields, and such heat can be transferred from the IONPs to the local environment. However, the microscopic mechanism of heat transfer is still debated. By X-ray total scattering experiments and first-principles simulations, we show how such heat transfer can occur. After establishing structural and microstructural properties of the maghemite phase of the IONPs, we built a maghemite model functionalized with aminoalkoxysilane, a molecule used to anchor (bio)molecules to oxide surfaces. By a linear response theory approach, we reveal that a resonance mechanism is responsible for the heat transfer from the IONPs to the surroundings. Heat transfer occurs not only via covalent linkages with the IONP but also through the solvent hydrogen-bond network. This result may pave the way to exploit the directional control of the heat flow from the IONPs to the anchored molecules─i.e., antibiotics, therapeutics, and enzymes─for their activation or release in a broader range of medical and industrial applications

Energy Transfer from Magnetic Iron Oxide Nanoparticles: Implications for Magnetic Hyperthermia

Magnetic iron oxide nanoparticles (IONPs) have gained momentum in the field of biomedical applications. They can be remotely heated via alternating magnetic fields, and such heat can be transferred from the IONPs to the local environment. However, the microscopic mechanism of heat transfer is still debated. By X-ray total scattering experiments and first-principles simulations, we show how such heat transfer can occur. After establishing structural and microstructural properties of the maghemite phase of the IONPs, we built a maghemite model functionalized with aminoalkoxysilane, a molecule used to anchor (bio)molecules to oxide surfaces. By a linear response theory approach, we reveal that a resonance mechanism is responsible for the heat transfer from the IONPs to the surroundings. Heat transfer occurs not only via covalent linkages with the IONP but also through the solvent hydrogen-bond network. This result may pave the way to exploit the directional control of the heat flow from the IONPs to the anchored molecules─i.e., antibiotics, therapeutics, and enzymes─for their activation or release in a broader range of medical and industrial applications

Shewhart methodology for modelling financial series : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand

Quality management techniques are widely used in industrial applications for monitoring observable process variation. Among them, the scientific notion of Shewhart principles is vital for understating variations in any type of process or service. This study extensively investigates and demonstrates Shewhart methodology for financial data. Extremely heavy tails noted in the empirical distribution of stock returns led to the development of new parametric probability distributions for pricing assets and forecasting market risk. Standard asset pricingmodels have also extended to account the first four (excess) moments in return distributions. These approaches remain complex, but yet they are inadequate for capturing extreme volatility caused by infrequent market events. It is well known that the security markets are always subjected to a certain amount of variability caused by noise-traders and other frictional price changes. Unforeseen events which are happening in the world may lead to hugemarket losses. This research shows that Shewhart methodology for partitioning data into common and special cause variations adds value tomodelling stock returns. Applicability of the proposed method is discussed using several scenarios occurring in an industrial process and a financialmarket. A set of new propositions based on Shewhart methodology is formed for finer description of the statistical properties in stock returns. Research issues which are related to the first four moments, co-moments and autocorrelation in stock returns are identified. New statistical tools such as difference control charts, odd-even analysis and estimates for co-moments are proposed to investigate the new propositions and research issues. Finally, several risk measures are proposed, and considered with respect to investor’s preferences. The research issues are investigated using partitioned data from S&P 500 stocks and the findings show that inmost of the scenarios, contradictory conclusions were made as a result of special cause variations. A modelling approach based on common and special cause variations is therefore expected to lead appropriate asset pricing and portfolio management. New statistical tools proposed in this study can be used to other time series data; a new R-package called QCCTS (Quality Control Charts for Time Series) is developed for this purpose