Experiment and computational fluid dynamics simulation of in-depth system hydrodynamics in dual-bed gasifier

Dual-bed gasifier is a new gasifier system with separated combustion and gasification zones. The two-zone separation makes it possible to increase calorific value of the producer gas. In order to develop and improve the process operation, understanding of system dynamics and parameters that describe the in-depth hydrodynamics are essential. Computational fluid dynamics is a tool that can be used to explain the complex multiphase system behavior. The considered dual-bed gasifier had 3.00 m height and the maximum width diameters of riser and downcomer were 0.14 and 0.40 m, respectively. Conservation equations of mass, momentum, energy and species for each phase were solved coupling with the kinetic theory of granular flow using ANSYS FLUENT version 12.1. Here, two-dimensional simulation had been successfully determined the flow pattern and chemical reaction corresponding with actual experimental and theoretical data. The calculated results of the solid volume fraction in the riser section showed the bubbling and slugging flow patterns. The product gas composition and gas temperature inside dual-bed gasifer reflected the advantages for this type of reactor over the other conventional gasifiers. The system turbulences were firstly explored in dual-bed system which were normal Reynolds stresses and granular temperatures. For the effect of interphase exchange coefficient model, the pressure-loop using drag force model proposed by Gidaspow was in good agreement with the experiment than the ones proposed by Wen-Yu and Syamlal-O'Brien.     

Precipitation in solutions containing mixtures of synthetic anionic surfactant and soap. I. Effect of sodium octanoate on hardness tolerance of sodium dodecyl sulfate

The effect of sodium octanoate (SO) and pH on the precipitation of sodium dodecyl sulfate (SDS) with calcium (hardness tolerance) in water was investigated. SO can exist as octanoate anion (O⁻) or as the protonated nonionic fatty acid (HO) with the HO/O⁻ ratio increasing with decreasing pH. At intermediate pH levels, SO or SO/SDS systems are composed of mixtures of anionic and nonionic surfactants, resulting in enhancement of micelle formation due to nonideal mixed micelle formation. Above the critical micelle concentration (CMC), the highest hardness tolerance at each pH level occurs at a mole ratio of 80∶20 SDS/SO due to synergism in the SDS/SO mixtures. This synergism also results in an increase in hardness tolerance of SDS with decreasing pH above the CMC.     

Input/output linearization with a two-degree-of-freedom scheme for uncertain nonlinear processes

This work presents a new method to control processes with unmeasured input disturbance and random noise parametric uncertainty. The developed method takes advantage of a two-degree-of-freedom control structure in which setpoint regulation and load disturbance rejection are integrated in the controller synthesis. Input/output linearization is selected to provide the setpoint tracking ability. For disturbance rejection, the high-gain technique is used to compensate for the effect of the uncertainty. The control performance of the method is evaluated through numerical simulation of continuous stirred tank reactors with uncertainty. The simulation results show that both unmeasured disturbance and parametric uncertainty can be effectively compensated for by the proposed control method.     

In Vitro Assay of Iron in Fortified Rice Analogues

A simple in vitro assay for the determination of soluble iron in fortified rice analogue/natural rice blends is described. The soluble iron, measured as an indicator of iron bioavailability, is quantified by FerroZine? colorimetry of the supernatant obtained after simulated gastric (pepsin) and intestinal (pancreatin) digestions. Method suitability was defined by assessments of linearity (R ² average=0.9999±0.0001, and low standard residual average=+0.6±0.9 %; n=17), precision (day-to-day RSD values [n=3 days] ranged from 5.3 % to 75 % for concentrations from 0.017 to 0.585 mg per 100 g of fortified rice analogue/natural rice blend), accuracy (spike recovery=97.5±0.4 %, n=4, spiking level=0.2 to 0.8 mg/l; and linear response of the digest plot [R ²=1.0000] vs. the standard solution plot [R ²=1.0000]), and selectivity (absence of interference from Ca, Mg, Mn, and Zn; reagent blank correction for minimal bias by Cu). The assay provides for a reliable quantification of the soluble iron in fortified rice analogue/natural rice blends. The assay is regarded not as a substitute for in vivo determination of iron absorption, but instead as a tool for identifying formulation, extrusion, and storage issues that are worthy of formal in vivo study. In this connection, application of the assay to fortified rice analogue/natural rice blends and to experimental rice flour preparations has identified potential agents of iron absorption enhancement (e.g., NaFeEDTA at only 2 % of total iron), and of iron absorption inhibition (zinc).     

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.     

A method of sensor fault detection and identification

A method of Bayesian belief network (BBN)-based sensor fault detection and identification is presented. It is applicable to processes operating in transient or at steady-state. A single-sensor BBN model with adaptable nodes is used to handle cases in which process is in transient. The single-sensor BBN model is used as a building block to develop a multi-stage BBN model for all sensors in the process under consideration. In the context of BBN, conditional probability data represents correlation between process measurable variables. For a multi-stage BBN model, the conditional probability data should be available at each time instant during transient periods. This requires generating and processing a massive data bank that reduces computational efficiency. This paper presents a method that reduces the size of the required conditional probability data to one set. The method improves the computational efficiency without sacrificing detection and identification effectiveness. It is applicable to model- and data-driven techniques of generating conditional probability data. Therefore, there is no limitation on the source of process information. Through real-time operation and simulation of two processes, the application and performance of the proposed BBN method are shown. Detection and identification of different sensor fault types (bias, drift and noise) are presented. For one process, a first-principles model is used to generate the conditional probability data, while for the other, real-time process data (measurements) are used.     

Measurement of ac cataphoresis

An apparatus and techniques are described for measurements of ac cataphoresis. The system uses a fiber optics light pipe driven along the discharge tube axis by a synchronous clock drive system. The collimated light signal is directed to a spectrometer whose output is processed using various methods to obtain desired types of information, including longitudinal time averaged scans of the discharge tube, longitudinal gated scans, and gated single cycle observations. Using these techniques one is able to study the time development of the ac effect as well as the presence of ionization waves. Examples are shown using noble gas mixtures of the various sampling procedures.