Sensitivity Analysis for the Thermal Stability Criteria of Hydrogen Peroxide

Chemical reactors carrying on exothermic processes may undergo to runaway reactions. To prevent from this hazard, Early Warning Detection Systems (EWDSs) can be used in industry, because they allow the on-line detection at an early stage of the runaway. The stability criterion of Hub and Jones is frequently implemented in EWDSs. Despite its simplicity during the detection step (the criterion is based just on measurements of the temperatures inside the reactor and jacket), the effectiveness in distinguishing between dangerous and non-dangerous occurrence is strongly affected by the presence of noise in the monitored signals. Furthermore, the numerical methodology for the calculation of the derivative of the measured signals may be of great importance for the definition of alarm conditions. In this paper, the sensitivity analysis of the performances of Hub and Jones criterion with respect to the Savitzky and Golay smoothing filter degree is discussed. The analysis is applied to experimental data on the decomposition of hydrogen peroxide 35%wt carried out in a Thermal Screening Unit.

[1]  V. Casson,et al.  Hydrogen Peroxide Decomposition Analysis by Screening Calorimetry Technique , 2012 .

[2]  V. Casson,et al.  Screening Analysis for Hazard Assessment of Peroxides Decomposition , 2012 .

[3]  Joseph P. Zbilut,et al.  On-line runaway detection in isoperibolic batch and semibatch reactors using the divergence criterion , 2004, Comput. Chem. Eng..

[4]  Fernanda Strozzi,et al.  A comparative analysis between temperature and pressure measurements for early detection of runaway initiation , 2004 .

[5]  William H. Press,et al.  Numerical Recipes in Fortran 77: The Art of Scientific Computing 2nd Editionn - Volume 1 of Fortran Numerical Recipes , 1992 .

[6]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[7]  H. H. Madden Comments on the Savitzky-Golay convolution method for least-squares-fit smoothing and differentiation of digital data , 1976 .

[8]  L. Hub,et al.  Early on‐line detection of exothermic reactions , 1986 .

[9]  Eugeniusz Molga,et al.  No more runaways in fine chemical reactors , 2004 .

[10]  Y. Leong Yeow,et al.  A general method of computing the derivative of experimental data , 2006 .

[11]  William H. Press,et al.  Numerical Recipes in FORTRAN - The Art of Scientific Computing, 2nd Edition , 1987 .

[12]  Fernanda Strozzi,et al.  On-Line Runaway Detection in Batch Reactors Using Chaos Theory Techniques. , 1999 .

[13]  Eugeniusz Molga,et al.  Safety and Runaway Prevention in Batch and Semibatch Reactors—A Review , 2006 .

[14]  Schreiber,et al.  Noise reduction in chaotic time-series data: A survey of common methods. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[15]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .