Prediction algorithms, thermodynamic

A computer algorithm has been developed for making multi-component mixture calculations to predict (a) thermodynamic properties of liquid and vapor phases (b) bubble point, dew point, and flash conditions (c) multiple flashes, condensations, compression, and expansion operations and (d) separations by distillation and absorption. 

Friese, T. P. Ulbig and S. Schulz. Use of Evolutionary Algorithms for the Calculation of Group Contribution Parameters in Order to Predict Thermodynamic Properties. Part 1 Genetic Algorithms. Comput Chem Eng 22 1559-1572 (1998). 

Shock E. L. and Helgeson H. C. (1988). Calculations of the thermodynamic and transport properties of aqueous species at hight pressures and temperatures Correlation algorithms for ionic species and equations of state predictions to 5Kb and 1000°C. Geochim. Cosmochim. Acta, 52 2009-2036. 

Zuker M, Mathews DH, Turner DH (1999) Algorithms and thermodynamics for RNA secondary structure prediction a practical guide. In Barciszewski J, Clark BTC (eds) RNA biochemistry and biotechnology. NATO ASI series. Kluwer Academic Publishers, Dordrecht, pp 11-43... 

When thermodynamics or physics relates secondary measurements to product quality, it is easy to use secondary measurements to infer the effects of process disturbances upon product quality. When such a relation does not exist, however, one needs a solid knowledge of process operation to infer product quality from secondary measurements. This knowledge can be codified as a process model relating secondary to primary measurements. These strategies are within the domain of model-based control Dynamic Matrix Control (DMC), Model Algorithmic Control (MAC), Internal Model Control (IMC), and Model Predictive Control (MPC—perhaps the broadest of model-based control strategies). 

In RNA bioinformatics, only a few basic algorithms, namely those for structure prediction based on thermodynamic rules, are available as web tools. In addition, RNA structure prediction is a computationally rather demanding process, so that the sequence lengths that can be dealt with on the web are limited. Because of these limitations we recommend that you install the software locally on a computer in your lab (or your laptop). 

Most recent synthesis algorithms are also based upon the principles of the thermodynamic pinch (Linnhoff et al., 1979 Umeda et al., 1978). Recognition of the pinch provided great physical insight into the problem of HEN synthesis. The reader is assumed to be familiar with the principles of the pinch and with general methods for HEN synthesis [e.g., pinch design method (Linnhoff et al., 1982 Linnhoff and Hindmarsh, 1983), structural optimization methods for selection of a minimum set of stream matches (Papoulias and Grossmann, 1983), and determination of the most economical network structure (Floudas et al., 1986) from the predicted matches]. 

Zuker M, Mathews DH, Turner DH, Algorithms and Thermodynamics for RNA Secondary Structure Prediction A Practical Guide. In J Barciszewski ... 

Apart from prediction of MFE structures RNAalifold also implements an algorithm to compute the partition function over all possible (consensus) structures and the thermodynamic equilibrium probability for each possible pair. These basepairing probabilities are useful to see structural alternatives, and to distinguish well-defined regions, where the predicted structure is most likely correct, from ambiguous regions. 

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