David Shan-Hill Wong

Accurate Equation of State Predictions at High Temperatures and Pressures Using the Existing UNIFAC Model

Abstract Orbey, H., Sandler, S.I. and Wong D.S.H., 1993. Accurate equation of state predictions at high temperatures and pressures using the existing UNIFAC model. Fluid Phase Equilibria , 85:41-54. A recently developed mixing rule for cubic equations of state is combined with the UNIPAC group contribution method to yield a completely predictive thermodynamic model for the vapor-liquid and liquid-liquid phase behavior of slightly and highly non-ideal mixtures over large ranges of temperature and pressure. We find that the quality of predictions is very good for systems for which UNIFAC provides an accurate low pressure description. The mixing rule + UNIFAC model developed is an accurate and very simple extension of the UNIFAC group contribution method to high temperatures and high pressures. Further, since existing UNIFAC parameters are used, no refitting of experimental data is needed.

Effect of Interaction Multiplicity on Control System Design for a MTBE Reactive Distillation Column

Abstract The control strategy of a reactive distillation for synthesis of MTBE is investigated. Although steady state multiplicities occur in the column, a linear control is still possible since a controlled and manipulated variable-pairing scheme that exhibits a sufficiently large range of near linear relations can be found, if we operate at constant reflux ratio. Reboiler duty is used to control the temperature of a stage just below the reaction section and near the top of the stripping section. Stoichiometric balance is controlled by a feed ratio plus internal composition control scheme, using a control valve installed on the C4 feed-line as the manipulated variable. Such a scheme is capable of maintaining the desirable steady state that achieves high product purity and reactant conversion. However, a similar scheme that uses a control valve installed on the methanol feed-line as the manipulated variable shows severe oscillation. It is caused by multiplicity in the interaction between the temperature and stoichiometric control loops.

Performance Analysis of EWMA Controllers Subject to Metrology Delay

Metrology delay is a natural problem in the implementation of advanced process control scheme in semiconductor manufacturing systems. It is very important to understand the effect of metrology delay on performance of advanced process control systems. In this paper, the influences of metrology delay on both the transient and asymptotic properties of the product quality are analyzed for the case when a linear system with an initial bias and a stochastic autoregressive moving average (ARMA) disturbance is under an exponentially weighted moving average (EWMA) run-to-run control. Tuning guidelines are developed based on the study of numerical optimization results of the analytical closed-loop output response. In addition, effective metrology delay of a variable time delay system is analyzed based on the resampling technique implemented to a randomized time delay system. A virtual metrology technique is a possible solution to tackle the problem of metrology delay. The tradeoff between additional error of virtual metrology and reduction in time delay is studied. The results are illustrated using an example of control of the tungsten deposition rate in a tungsten chemical-vapor deposition reactor. The basic conclusion is that metrology delay is only important for processes that experience nonstationary stochastic disturbance. In such a case, use of virtual metrology is justified if the error of the virtual metrology method is less than the error caused by stochastic process noise. The accuracy of the virtual metrology noise with respect to the traditional metrology is not critical, provided that the error due to metrology is much less than that due to process disturbances.

Fault Detection Based on Statistical Multivariate Analysis and Microarray Visualization

In this work, a statistical method is proposed to mine out key variables from a large set of variables recorded in a limited number of runs through a multistage multistep manufacturing process. The method employed well-known single variable or multivariable techniques of discrimination and regression but also presented a synopsis of analysis results in a colored map of p-values very similar to a DNA microarray. This framework provides a systematic method of drawing inferences from the available evidence without interrupting the normal process operation. The proposed concept is illustrated by two industrial examples.

Development of adaptive soft sensor based on statistical identification of key variables

An adaptive data-driven soft sensor is derived based on systematic dynamic key variables selection of a process system. The key variables are captured using statistical approaches. The on-line plant measurements can be directly selected as key features to estimate the tardily-detected quality variables. The statistical method adopted is the standard stepwise linear regression. The linear model is adapted as the on-line/off-line quality data becomes available. The adaptation of the model is implemented by standard Kalman filtering theory. The key variables are re-selected in case of new scenarios arrive and are detected by the soft senor. The real time data from an industrial O-xylene purification column is implemented to demonstrate the validity of the approach. Many different scenarios are simulated through an industrial standard dynamic simulator. The simulation results also showed the approach is adequate for the industrial applications. Copyright

Layered protonated titanate nanosheets synthesized with a simple one-step, low-temperature, urea-modulated method as an effective pollutant adsorbent.

A simple one-step, low-temperature, urea-modulated method is developed for the synthesis of layered protonated titanate nanosheets (LPTNs). Urea serves as an indirect ammonium ion source, and the controlled supply of the ammonium ion slows the crystalline formation process and enables the production of the LPTNs from amorphous intermediate through aging-induced restructuring. The resulting LPTNs exhibit excellent adsorption capacities for methylene blue and Pb(2+) because of their high specific surface areas and excellent ion-exchange capability. Intercalation of Pb(2+) into the interlayer space of the LPTNs is evidenced by the relevant X-ray diffraction patterns on perturbation of the layered structure. The LPTNs prove to be a promising adsorbent in wastewater treatment for adsorption removal of metal ions or cationic organic dyes.

Steady-state design of thermally coupled reactive distillation process for the synthesis of diphenyl carbonate

Abstract Diphenyl carbonate, a precursor in the production of polycarbonate, is traditionally synthesized by the transesterification reaction of dimethyl carbonate and phenol. In this study, phenyl acetate was used instead of phenol to react with dimethyl carbonate and yield diphenyl carbonate, due to its higher reaction conversion and the absence of side reactions and azeotropes. A plant-wide process with a reactive distillation (RD) column and a separation column was optimized by minimizing the total annual cost. The performance of the thermal coupling between these two columns was also investigated. RD with thermal coupling was demonstrated to provide better energy efficiency than conventional RD. The remixing phenomenon associated with thermodynamic inefficiency in conventional distillation sequences could be greatly reduced by implementing thermal coupling between columns. Reactant concentrations that were closer to stoichiometric balance in the reaction zone were given for the thermally coupled RD column.

Single-crystalline mesoporous ZnO nanosheets prepared with a green antisolvent method exhibiting excellent photocatalytic efficiencies

Single-crystalline mesoporous ZnO nanosheets were prepared with a green, deep eutectic solvent based antisolvent process followed by calcination at 300 °C. The antisolvent procedure led to formation of nanosheets of a mixed composition of zinc carbonate hydroxide and ZnO, calcination of which generated the product. The product nanosheets were ultrathin with a thickness of around 10 nm and pore sizes increasing from less than 10 nm to several tens nanometers with increasing calcination time and temperature. These nanosheets exhibited excellent photocatalytic efficiencies as good as that of a commercial photocatalyst, P-25 TiO2 in terms of photocatalytic degradation of a model pollutant, methylene blue. The success is attributable to the structural advantages of the nanosheets of being ultrathin, mesoporous, and single-crystalline.

Dynamic Transcript Profiling of Candida albicans Infection in Zebrafish: A Pathogen-Host Interaction Study

Candida albicans is responsible for a number of life-threatening infections and causes considerable morbidity and mortality in immunocompromised patients. Previous studies of C. albicans pathogenesis have suggested several steps must occur before virulent infection, including early adhesion, invasion, and late tissue damage. However, the mechanism that triggers C. albicans transformation from yeast to hyphae form during infection has yet to be fully elucidated. This study used a systems biology approach to investigate C. albicans infection in zebrafish. The surviving fish were sampled at different post-infection time points to obtain time-lapsed, genome-wide transcriptomic data from both organisms, which were accompanied with in sync histological analyses. Principal component analysis (PCA) was used to analyze the dynamic gene expression profiles of significant variations in both C. albicans and zebrafish. The results categorized C. albicans infection into three progressing phases: adhesion, invasion, and damage. Such findings were highly supported by the corresponding histological analysis. Furthermore, the dynamic interspecies transcript profiling revealed that C. albicans activated its filamentous formation during invasion and the iron scavenging functions during the damage phases, whereas zebrafish ceased its iron homeostasis function following massive hemorrhage during the later stages of infection. Most of the immune related genes were expressed as the infection progressed from invasion to the damage phase. Such global, inter-species evidence of virulence-immune and iron competition dynamics during C. albicans infection could be crucial in understanding control fungal pathogenesis.

Computational modeling with forward and reverse engineering links signaling network and genomic regulatory responses: NF-κB signaling-induced gene expression responses in inflammation

BackgroundSignal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Diverse cellular stimuli create a wide variety of transcription factor activities through signal transduction pathways, resulting in different gene expression patterns. Understanding the relationship between external stimuli and the corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to identify the physiological consequences of environmental stimuli.ResultsWe proposed a systematic approach that combines forward and reverse engineering to link the signal transduction cascade with the gene responses. To demonstrate the feasibility of our strategy, we focused on linking the NF-κB signaling pathway with the inflammatory gene regulatory responses because NF-κB has long been recognized to play a crucial role in inflammation. We first utilized forward engineering (Hybrid Functional Petri Nets) to construct the NF-κB signaling pathway and reverse engineering (Network Components Analysis) to build a gene regulatory network (GRN). Then, we demonstrated that the corresponding IKK profiles can be identified in the GRN and are consistent with the experimental validation of the IKK kinase assay. We found that the time-lapse gene expression of several cytokines and chemokines (TNF-α, IL-1, IL-6, CXCL1, CXCL2 and CCL3) is concordant with the NF-κB activity profile, and these genes have stronger influence strength within the GRN. Such regulatory effects have highlighted the crucial roles of NF-κB signaling in the acute inflammatory response and enhance our understanding of the systemic inflammatory response syndrome.ConclusionWe successfully identified and distinguished the corresponding signaling profiles among three microarray datasets with different stimuli strengths. In our model, the crucial genes of the NF-κB regulatory network were also identified to reflect the biological consequences of inflammation. With the experimental validation, our strategy is thus an effective solution to decipher cross-talk effects when attempting to integrate new kinetic parameters from other signal transduction pathways. The strategy also provides new insight for systems biology modeling to link any signal transduction pathways with the responses of downstream genes of interest.