Chlorate Formation in Chlorine Dioxide Delignification—An Analysis via Elementary Kinetic Modeling

Abstract

Elementary kinetic modeling was used to study the mechanism of chlorate formation in chlorine dioxide delignification. Reaction conditions reflecting typical industrial processes (T = 50°C, pH 1.5–4) were examined. Fe mediated Cl(III) decomposition and a reaction between hypochlorous acid and chlorous acid (or their equilibrium counterparts) were found to be the major reaction routes responsible for chlorate formation at pH < 3. The latter route accounts for chlorate formation at pH ≥ 3. The rate of chlorous acid (HClO₂) self-decomposition was too slow either to compete against the other routes (pH < 3) or to yield notable amounts of chlorate within the given time frame (pH ≥ 3). The results suggest that chlorate formation could be suppressed, without adverse effects on chlorine dioxide regeneration, by aiming for end pH 3–3.5, ensuring a moderate chloride ion concentration and by favoring concentrated solutions/suspensions.     

A Model for Chlorine Dioxide Delignification of Chemical Pulp

Abstract

A phenomenon based model for chlorine dioxide delignification of chemical pulp is introduced. The pulp suspension environment is modeled using the concept of two liquid phases, one inside and the other external to the fiber wall. Physico-chemical processes taking place during delignification are implemented with thermodynamic, mass transfer and reaction kinetic models. A broad library of chemical reactions is introduced. Inclusion of each reaction is justified. The model response is tested against experimental laboratory delignification results (o-delignified birch pulp). The experimental data consists of kappa number, hexenuronic acid, inorganic oxy-chlorine compound, and organochlorine (AOX, OX) measurements at several time points during five delignification experiments. The model predictions are mainly in good agreement with the experimental results. The predictions regarding hypochlorous acid driven processes (HexA removal, organochlorine formation, chlorite and chlorate concentration) are somewhat incoherent, indicating that knowledge regarding the intermediately formed hypochlorous acid is presently insufficient.     

Adhesion, spreading and osteogenic differentiation of mesenchymal stem cells cultured on micropatterned amorphous diamond, titanium, tantalum and chromium coatings on silicon

It was hypothesized that human mesenchymal stromal cell (hMSC) can be guided by patterned and plain amorphous diamond (AD), titanium (Ti), tantalum (Ta) and chromium (Cr) coatings, produced on silicon wafer using physical vapour deposition and photolithography. At 7.5 h hMSCs density was 3.0–3.5× higher (P < 0.0003, except Ti) and cells were smaller (68 vs. 102 μm, P 0.000006–0.02) on patterns than on silicon background. HMSC-covered surface of the background silicon was lower on Ti than AD patterns (P = 0.015), but at 5 days this had reversed (P = 0.006). At 7.5 h focal vinculin adhesions and actin cytoskeleton were outgoing from pattern edges so cells assumed geometric square shapes. Patterns allowed induced osteogenesis, but less effectively than plain surfaces, except for AD, which could be used to avoid osseointegration. All these biomaterial patterns exert direct early, intermediate and late guidance on hMSCs and osteogenic differentiation, but indirect interactions exist with cells on silicon background.     

Mining Software History to Improve Software Maintenance Quality: A Case Study

To keep the Windows operating system stable and secure, Microsoft constantly updates it. However, any update can cause a software regression—an undesired change in the system's stable parts. A key technique for fighting regressions is thorough testing of all updates, which is costly. A statistical model that estimates the risk for updates on the basis of their characteristics makes testing more efficient. Training this model requires collecting data on a large number of fixes made in previous versions of Windows. The Binary Change Tracer tool gets this information from the disparate data sources.     

Automatic performance prediction of multithreaded programs: a simulation approach

The performance of multithreaded programs is often difficult to understand and predict. Multiple threads engage in synchronization operations and use hardware simultaneously. This results in a complex non-linear dependency between the configuration of a program and its performance. To better understand this dependency a performance prediction model is used. Such a model predicts the performance of a system for different configurations. Configurations reflect variations in the workload, different program options such as the number of threads, and characteristics of the hardware. Performance models are complex and require a solid understanding of the program’s behavior. As a result, building models of large applications manually is extremely time-consuming and error-prone. In this paper we present an approach for building performance models of multithreaded programs automatically. We employ hierarchical discrete-event models. Different tiers of the model simulate different factors that affect performance of the program, while interaction between the model tiers simulates mutual influence of these factors on performance. Our framework uses a combination of static and dynamic analyses of a single representative run of a system to collect information required for building the performance model. This includes information about the structure of the program, the semantics of interaction between the program’s threads, and resource demands of individual program’s components. In our experiments we demonstrate that models accurately predict the performance of various multithreaded programs, including complex industrial applications.