On the prediction of psd in antisolvent mediated crystallization processes based on fokker-planck equations

A phenomenological model for the description of antisolvent mediated crystal growth processes is presented. The crystal size growth dynamics is supposed to be driven by a deterministic growth factor coupled to a stochastic component. Two different models for the stochastic component are investigated: a Linear and a Geometric Brownian motion terms. The evolution in time of the particle size distribution is then described in terms of the Fokker-Planck equation. Validations against experimental data are presented for the NaCl-water-ethanol anti-solvent crystallization system. It was found that a proper modeling of the stochastic component does have an impact on the model capabilities to fit the experimental data. In particular, the GBM assumption is better suited to describe the antisolvent crystal growth process under examination

Optimal strategies to control particle size and variance in antisolvent crystallization operations using deep RL

Solution crystallization operations have complex dynamics that are typically lumped into two competing processes namely nucleation and growth. Mathematical models can be used to describe these two processes and their effect on the crystal population when subject to variables like temperature, addition of anti-solvent, etc. To ensure that the crystals meet specific performance objectives, the models need to be solved and the control variables need to be optimized. This has largely been done until now using algorithms from dynamic programming or optimal control theory. Recently, however, it has been shown that learning frameworks like Reinforcement Learning can solve large optimization problems efficiently while offering distinct advantages. In this work, we explore the possibility of computing the optimal profiles of a semi-batch crystallizer to control the mean size and variance using four different deep RL algorithms. The performance on one of the tasks is evaluated experimentally on the anti-solvent crystallization of NaCl in a water-ethanol system

Evaporative CO2 cooling using microchannels etched in silicon for the future LHCb vertex detector

The extreme radiation dose received by vertex detectors at the Large Hadron Collider dictates stringent requirements on their cooling systems. To be robust against radiation damage, sensors should be maintained below -20 degree C and at the same time, the considerable heat load generated in the readout chips and the sensors must be removed. Evaporative CO2 cooling using microchannels etched in a silicon plane in thermal contact with the readout chips is an attractive option. In this paper, we present the first results of microchannel prototypes with circulating, two-phase CO2 and compare them to simulations. We also discuss a practical design of upgraded VELO detector for the LHCb experiment employing this approach.Comment: 12 page

Control of a natural gas liquid recovery plant in a GSP unit under feed and composition disturbances

Recent technological improvements have driven the rapid increase in natural gas production from unconventional reservoirs. The heaviest hydrocarbon fraction of this fossil fuel, the so-called natural gas liquids (NGL), have greater economic interest justifying the attention on its separation process from the raw gas. Various process schemes have been developed and studied for the NGL recovery, including the conventional, cold residue recycle (CRR), and the gas subcooled process (GSP). This study aims to assess different control strategies for a GSP unit and determine the most appropriate and effective process control scheme. For this, the dynamic responses for each control scheme are evaluated by changing feed flow rate and composition. The main targets are the achievement of 84% ethane recovery and low levels of methane impurity at the bottom of the demethanizer column. Due to the high cost of composition analyzers and the high delays introduced by composition controllers under the presence of flow disturbances, the control goals are reached by the knowledge of on-line temperature measurements. This is done by considering different temperature control structures such as the direct temperature control and cascade control, plus a pressure compensator. The results are compared, in presence of composition disturbances, with the action of a hybrid cascade control that uses in-line delayed concentration measurements to update the controller reference at each sampling period. Here, the hybrid and the simple cascade controls show the best control performance

Machine learning for monitoring and control of NGL recovery plants

In this contribution, the monitoring and control problem of the natural gas liquids (NGL) extraction process is addressed by exploiting a data-driven approach. The cold residue reflux (CRR) process scheme is considered and simulated by using the process simulator Aspen HYSYS®, with the main targets of the achievement of 84% ethane recovery and low levels of methane impurity at the bottom of the demethanizer column. The respect of product quality is obtained by designing a proper control strategy that uses a data-driven approach based on a neural network to estimate the unmeasured outputs. The performance of the controlled system is assessed by simulating the process under various input conditions evaluating different control structures such as direct control and cascade control of the temperature in the column

Effect of the suspension composition on the microstructural properties of high velocity suspension flame sprayed (HVSFS) Al2O3 coatings

Seven different Al2O3-based suspensions were prepared by dispersing two nano-sized Al2O3 powders (having analogous size distribution and chemical composition but different surface chemistry), one micron-sized powder and their mixtures in a water+isopropanol solution. High velocity suspension flame sprayed (HVSFS) coatings were deposited using these suspensions as feedstock and adopting two different sets of spray parameters. The characteristics of the suspension, particularly its agglomeration behaviour, have a significant influence on the coating deposition mechanism and, hence, on its properties (microstructure, hardness, elastic modulus). Dense and very smooth (Ra ~ 1.3 μm) coatings, consisting of well- flattened lamellae having a homogeneous size distribution, are obtained when micron-sized (~1 -2 μm) powders with low tendency to agglomeration are employed. Spray parameters favouring the break-up of the few agglomerates present in the suspension enhance the deposition efficiency (up to >50%), as no particle or agglomerate larger than ~2.5 μm can be fully melted. Nano-sized powders, by contrast, generally form stronger agglomerates, which cannot be significantly disrupted by adjusting the spray parameters. If the chosen nanopowder forms small agglomerates (up to few microns), the deposition efficiency is satisfactory and the coating porosity is limited, although the lamellae generally have a wider size distribution, so that roughness is somewhat higher. If the nanopowder forms large agglomerates (on account of its surfacechemistry), poor deposition efficiencies and porous layers are obtained. Although suspensions containing the pure micron-sized powder produce the densest coatings, the highest deposition efficiency (~70%) is obtained by suitable mixtures of micron-and nano-sized powders, on account of synergistic effect

The Portrayal of Complementary and Alternative Medicine in Mass Print Magazines Since 1980

Objectives: The objectives of this study were to examine and describe the portrayal of complementary and alternative medicine (CAM) in mass print media magazines. Design: The sample included all 37 articles found in magazines with circulation rates of greater than 1 million published in the United States and Canada from 1980 to 2005. The analysis was quantitative and qualitative and included investigation of both manifest and latent magazine story messages. Results: Manifest analysis noted that CAM was largely represented as a treatment for a patient with a medically diagnosed illness or specific symptoms. Discussions used biomedical terms such as patient rather than consumer and disease rather than wellness. Latent analysis revealed three themes: (1) CAMs were described as good but not good enough; (2) individualism and consumerism were venerated; and (3) questions of costs were raised in the context of confusion and ambivalence

Structural–Functional Relationship of the Ribonucleolytic Activity of aIF5A from Sulfolobus solfataricus

The translation factor IF5A is a highly conserved protein playing a well-recognized and well-characterized role in protein synthesis; nevertheless, some of its features as well as its abundance in the cell suggest that it may perform additional functions related to RNA metabolism. Here, we have undertaken a structural and functional characterization of aIF5A from the crenarchaeal Sulfolobus solfataricus model organism. We confirm the association of aIF5A with several RNA molecules in vivo and demonstrate that the protein is endowed with a ribonuclease activity which is specific for long and structured RNA. By means of biochemical and structural approaches we show that aIF5A can exist in both monomeric and dimeric conformations and the monomer formation is favored by the association with RNA. Finally, modelling of the three-dimensional structure of S. solfataricus aIF5A shows an extended positively charged surface which may explain its strong tendency to associate to RNA in vivo