Cocoa Butter Saturated with Supercritical Carbon Dioxide: Measurements and Modelling of Solubility, Volumetric Expansion, Density and Viscosity

International audienceThe use of supercritical carbon dioxide technology for lipid processing has recently developed at the expense of traditional processes. For designing new processes the knowledge of thermophysical properties is a prerequisite. This work is focused on the characterization of physical and thermodynamic properties of CO2-cocoa butter (CB) saturated mixture. Measurements of density, volumetric expansion, viscosity and CO2 solubility were carried out on CB-rich phase at 313 and 353 K and pressures up to 40 MPa. The experimental techniques have previously been compared and validated. Density measurements of CB and CB saturated with CO2, were performed using the vibrating tube technology at pressures ranging from 0.1 to 25 MPa. Experimental values correlated well with the modified Tait equation. CO2 solubility measurements were coupled to those of density in the same pressures ranges. At 25 MPa, the solubility of CO2 is 31.4 % and 28.7 % at 313 and 353 K. Phase behaviour was investigated using a view cell in order to follow the expansion of the CB-rich phase with the rise in pressure. Volumetric expansion up to 47 % was measured and correlated to the CO2 solubility. Phase inversion was observed at 313 K and 40 MPa. Lastly, we developed an innovative falling ball viscometer for high pressure measurements. Viscosity measurements were carried out up to 25 MPa showing a viscosity reduction up to 90 % upon CO2 dissolution. These results were correlated with two empirical models. A new model here presented, was favourably compared with the Grunberg and Nissan model. All the experimental results are consistent with the available literature data for the CB-CO2 mixture and other fat systems. This work is a new contribution to the characterization of physical and thermodynamic behaviour of fats in contact with CO2 which is necessary to design supercritical fluid processes for fats processing

Development of Characterization Techniques of Thermodynamic and Physical Properties Applied to the CO2-DMSO Mixture

International audienceThis work is focused on the development of new characterization techniques of physical and thermodynamic properties. These techniques have been validated using the binary system DMSO-CO2 for which several studies of characterization have been well documented. We focused on the DMSO-rich phase and we carried out measurements of volumetric expansion, density, viscosity and CO2 solubility at 298.15, 308.15 and 313.15 K and pressures up to 9 MPa. The experimental procedures were compared and validated with the available literature data on SC-CO2-DMSO system. We made density and CO2 solubility measurements, using respectively the vibrating tube technology and two static analytical methods. Lastly, we developed an innovative falling body viscosimeter for high pressure measurements. All the measurements made are in good agreement with the already published data in spite of very different experimental techniques. This work is a contribution to the understanding of the DMSO-CO2 binary as it implements new viscosity data. Moreover, it opens new perspectives about the determination of the properties of other systems such as polymers-CO2 and fats-CO2, which are essential for supercritical process design such as extraction, crystallization, chromatography and synthesis reaction

Hybrid Rules with Well-Founded Semantics

A general framework is proposed for integration of rules and external first order theories. It is based on the well-founded semantics of normal logic programs and inspired by ideas of Constraint Logic Programming (CLP) and constructive negation for logic programs. Hybrid rules are normal clauses extended with constraints in the bodies; constraints are certain formulae in the language of the external theory. A hybrid program is a pair of a set of hybrid rules and an external theory. Instances of the framework are obtained by specifying the class of external theories, and the class of constraints. An example instance is integration of (non-disjunctive) Datalog with ontologies formalized as description logics. The paper defines a declarative semantics of hybrid programs and a goal-driven formal operational semantics. The latter can be seen as a generalization of SLS-resolution. It provides a basis for hybrid implementations combining Prolog with constraint solvers. Soundness of the operational semantics is proven. Sufficient conditions for decidability of the declarative semantics, and for completeness of the operational semantics are given

Efficient Parallel Statistical Model Checking of Biochemical Networks

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture

Generating Random Logic Programs Using Constraint Programming

Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions. We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint to control the independence structure of the underlying probability distribution. We also provide a combinatorial argument for the correctness of the model, show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems. Our model allows inference algorithm developers to evaluate and compare the algorithms across a wide range of instances, providing a detailed picture of their (comparative) strengths and weaknesses.Comment: This is an extended version of the paper published in CP 202

Population dynamics and identification of efficient strains of Azospirillum in maize ecosystems of Bihar (India)

Information on inoculum load and diversity of native microbial community is an important prerequisite for crop management of microbial origin. Azospirillum has a proven role in benefiting the maize (Zea mays) crop in terms of nutrient (nitrogen) supply as well as plant growth enhancement. Bihar state has highest average national maize productivity although fertilizer consumption is minimum, indicating richness of Azospirillum both in terms of population and diversity in soils. An experiment was planned to generate basic information on Azospirillum population variation in maize soils under different agricultural practices and soil types of Bihar, to identify suitable agricultural practices supporting the target microorganism and efficient Azospirillum strain(s). No tillage, growing traditional maize cultivar, land use history (diara soil having history of maize cultivation), soil organic carbon (>1%) and intercrop with oat supported prevalence of Azospirillum in maize rhizosphere. Native Azospirillum population varied from 1 million to 1 billion/g soil under diverse agricultural practices and soil types. Such richness, however, does not necessarily mean that artificial inoculation of Azospirillum is not required in Bihar soils as 92% of Azospirillum isolates (50 isolates) were poor in nitrogen-fixing ability and 88% were poor on IAA production. Efficient strains of Azospirillum based on growth (three), acetylene reduction assay (three), IAA production (three), broad range of pH (two) and temperature tolerance were identified. The findings suggested that maize crop in Bihar should be inoculated in universal mode rather than site-specific mode

Reversibility in Chemical Reactions

open access bookIn this chapter we give an overview of techniques for the modelling and reasoning about reversibility of systems, including outof- causal-order reversibility, as it appears in chemical reactions. We consider the autoprotolysis of water reaction, and model it with the Calculus of Covalent Bonding, the Bonding Calculus, and Reversing Petri Nets. This exercise demonstrates that the formalisms, developed for expressing advanced forms of reversibility, are able to model autoprotolysis of water very accurately. Characteristics and expressiveness of the three formalisms are discussed and illustrated

Constraint solving in uncertain and dynamic environments - a survey

International audienceThis article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 (Ninth International Conference on Principles and Practice of Constraint Programming) in Kinsale, Ireland. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments

Using a logical model to predict the growth of yeast

<p>Abstract</p> <p>Background</p> <p>A logical model of the known metabolic processes in <it>S. cerevisiae </it>was constructed from iFF708, an existing Flux Balance Analysis (FBA) model, and augmented with information from the KEGG online pathway database. The use of predicate logic as the knowledge representation for modelling enables an explicit representation of the structure of the metabolic network, and enables logical inference techniques to be used for model identification/improvement.</p> <p>Results</p> <p>Compared to the FBA model, the logical model has information on an additional 263 putative genes and 247 additional reactions. The correctness of this model was evaluated by comparison with iND750 (an updated FBA model closely related to iFF708) by evaluating the performance of both models on predicting empirical minimal medium growth data/essential gene listings.</p> <p>Conclusion</p> <p>ROC analysis and other statistical studies revealed that use of the simpler logical form and larger coverage results in no significant degradation of performance compared to iND750.</p