Formation and behavior of composite CO2 hydrate particles in a high-pressure water tunnel facility

Sinking CO₂ composite particles consisting of seawater, liquid CO₂, and CO₂ hydrate were produced by a coaxial flow injector fed with liquid CO₂ and artificial seawater. The particles were injected into a high-pressure water tunnel facility to permit determination of their settling velocities and dissolution rates. Injections were performed at fixed pressures approximately equivalent to 1200-m, 1500-m, and 1800-m depths and at temperatures varying from approximately 2 to 5 °C. Immediately after injection, the cylindrical particles were observed to break away from the injector tip and often aggregated into sinking clusters. The seawater flow in the tunnel was then adjusted in a countercurrent flow mode to suspend the particles in an observation window so that images of the particles could be recorded for later analysis. The flow would often break or cause rearrangement of some of the clusters. Selected individual particles and some clusters were studied until they became too hydrodynamically unstable to follow. In general, the flow required to suspend clusters or individual particles decreased with time as the particles dissolved. For example, one particle was produced and observed for over 6 min at an average pressure of 15.022 MPa and an average temperature of 5.1 °C. Its sinking rate, determined from the flow required for stabilization, changed from 37.2 to 3.3 mm/s over this time. Particle sinking rates were compared to correlations from the literature for uniform cylindrical objects. Reasonable agreement was observed for short times; however, the observed decrease in sinking velocity with time was greater than that predicted by the correlations for longer times. Particle dissolution rates, based on changes in diameter, were also determined and varied from 5 to . A pseudo-homogeneous mass transfer model was used to predict single-particle dissolution rates. Good agreement was achieved between experimental dissolution data and the modeling results.     

Analysis of mixing during melt-melt blending in twin screw extruders using reactive polymer tracers

Mixing during melt-melt blending of segregated polypropylene melt streams in a co-rotating twin screw extruder was experimentally investigated. The mixing limited reaction between two polymer reactive tracers, which are terminally functionalized polyolefin oligomers, was used to determine the mixing performance of a kneading block section. The selected functional groups were succinic anhydride and a primary amine, and Fourier-Transform Infrared Spectrometry (FT-IR) was used to determine the anhydride conversion. In the absence of interfacial tension, the reaction conversion was directly related to the amount of interfacial area generated. Experiments were completed to study the effects of operating conditions, kneading block design, and polymer material properties. The screw speed effect was observed to be non-linear because of competing contributions from shear rate, residence time, channel fill, and viscous heating. The mixing performance of kneading blocks backed by a reverse conveying element was observed to follow the trend of: forward > reverse > neutral. For each kneading block design, the mixing performance decreased with an increase in polymer viscosity.     

An approach to mechanistic event recognition applied on monitoring organic matter depletion in SBRs

A fundamental practice in process engineering is monitoring the state dynamics of a system. Unfortunately, observability of some states is related to high costs, time, and efforts. The mechanistic event recognition (MER) aims to detect an event (defined as a change of the system with specific significance to the operation of the process) that cannot be directly observed but has some predictable effect on the dynamics of the systems. MER attempts to apply fault diagnosis techniques using mechanistic “recognition” models to describe the process. A systematic method for building recognition models using optimal experimental design tools is presented. As proof of concept, the MER approach to detect organic matter depletion in sequencing batch reactors, measuring only ammonia, dissolved oxygen, and nitroxides is applied. The event, that is, consumption of organic matter to a level below 50 gCOD/m³, was successfully detected even though microbial activity is known to continue after organic matter depletion. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3460–3472, 2014     

Prediction of the molecular and polymer solution properties of LDPE in a high-pressure tubular reactor using a novel Monte Carlo approach

In the present work, a novel kinetic/topology Monte Carlo algorithm is developed for the prediction of molecular, topological and solution properties of highly branched low-density polyethylene (LDPE), produced in a high-pressure multi-zonal tubular reactor. It is shown that the combined kinetic/topology MC algorithm can provide comprehensive information regarding the distributed molecular and topological properties of LDPE (i.e., molecular weight distribution, short- and long-chain branching distributions, joint molecular weight-long chain branching distribution, branching order distribution, seniority/priority distributions, etc.) The molecular/topological results obtained from the MC algorithm are then introduced into a random-walk molecular simulator to calculate the solution properties of LDPE (i.e., the mean radius of gyration, R_g, and the branching factor, g) in terms of the chain length of the branched polyethylene. The validity of the commonly applied approximation regarding the random scission of highly branched polymer chains is assessed by a direct comparison of the average molecular properties of LDPE (i.e., number and weight average molecular weights), calculated by the combined kinetic/topology MC algorithm, with the respective predictions obtained by the commonly applied method of moments (MOM). Through this comparison it is demonstrated that the ambiguous implementation of the random scission reaction in the MOM formulation can result in erroneous predictions of the weight average molecular weight and MWD of LDPE. Finally, the effects of two key process parameters, namely, the polymerization temperature profile and the solvent concentration, on the molecular, topological and polymer solution properties of LDPE produced in a multi-zonal tubular reactor are investigated.     

Modeling the effect of chemotaxis on glioblastoma tumor progression

Tumor progression depends on the intricate interplay between biological processes that span the molecular and macroscopic scales. A mathematical agent‐based model is presented to describe the 3‐D (three‐dimensional) progression of a brain tumor type (i.e., glioblastoma multiforme) as the collective behavior of individual tumor cells whose fate is determined by intracellular signaling pathways (i.e., MAPK pathway) that are governed by the temporal‐spatial distribution of key biochemical cues (i.e., growth factors, nutrients). The model is used to investigate how tumor growth and invasiveness depend on the response of migrating tumor cells to chemoattractants. Simulation results suggest that individual cell sensitivity to chemical gradients is necessary to generate in silico tumors with the irregular shape and diffusive tumor‐stroma interface characteristic of glioblastomas. In addition, vascular network damage influences tumor growth and invasiveness. The results quantitatively recapitulate the central role that nutrient availability and signaling proteins have on tumor invasive properties. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by Graphic Processor Units

In this paper the feasibility of adopting Graphic Processor Units towards real-time emotion aware computing is investigated for boosting the time consuming computations employed in such applications. The proposed methodology was employed in analysis of encephalographic and electrodermal data gathered when participants passively viewed emotional evocative stimuli. The GPU effectiveness when processing electroencephalographic and electrodermal recordings is demonstrated by comparing the execution time of chaos/complexity analysis through nonlinear dynamics (multi-channel correlation dimension/D2) and signal processing algorithms (computation of skin conductance level/SCL) into various popular programming environments. Apart from the beneficial role of parallel programming, the adoption of special design techniques regarding memory management may further enhance the time minimization which approximates a factor of 30 in comparison with ANSI C language (single-core sequential execution). Therefore, the use of GPU parallel capabilities offers a reliable and robust solution for real-time sensing the user's affective state.     

Simulation of reactive injection molding

This paper deals with the computer simulation of those aspects of Reactive Injection Molding (RIM) dealing with the non-isothermal and transient flow of a chemically reacting mixture into a mold cavity, and the in situ polymerization (“curing”) which follows mold filling. Linear polyurethane systems were considered. The purpose of this simulation work is to investigate the effects of the operating, chemical, and rheological variables on the length and the stability of the RIM process, as well as the quality of the resulting product. Since the flowing fluid mixture is reactive, there is a need to know the thermal history of each of the flowing fluid particles. For this reason the “fountain” flow at the fluid-air interface is considered in a heuristic fashion.