The complexity of processes in the modern industry (many kinetics and fluid dynamic variables) makes to think in few data due to high costs for experimental data collection. Therefore, it is important to consider statistical models, such as the partial least square regression (PLSR) method that provide an adequate solution to the multicollinearity in regression models, due to the minimum requirements in terms of measurement scales, sample size, and residual distribution. This work has shown for first time the application of the PLSR method to a fixed bed catalytic reactor for cracking of t-pentene-2 to obtain light olefins. Results show a correlation of R2predictive > 0.85, which was validated with other experimental data of the same chemical process indicating the robustness of the method as a powerful statistical tool to infer the behavior in chemical process. The importance of this research lies to present the use of PLSR in engineering applications as such rating and design of chemical processes as a novel alternative method to predictive behaviors with few experimental data tests.

{"references": ["Wang Z, OA El\u2013Seoud, WJ Baader, EL Bastos (2017), \"Encyclopedia of physical organic chemistry, chapter practical chemical kinetics in solution\", John Wiley & Sons, Inc", "Pt\u00e1\u010dek P, Opravil T, \u0160oukal F (2018), \"Introducing the effective mass of activated complex and the discussion on the wave function of this instanton, Chapter: A brief introduction to the history of chemical kinetics\", IntechOpen", "Igarashi A (2005), \"Catalytic reaction engineering toward green chemical processes\", Journal of Chemical Engineering of Japan, Volume 38, Issue 10, pp. 779\u2212784", "Wang X, Kruger U, Lennox B (2003), \"Recursive partial least squares algorithms for monitoring complex industrial processes\", Control Engineering Practice, Volume 11, Issue 6, pp. 613\u2212632", "Ferrer A, Aguado D, Vidal-Puig S, et al. (2008), \"PLS: A versatile tool for industrial process improvement and optimization\", Applied Stochastic Models in Business and Industry, Volume 24, Issue 6, pp. 551\u2212567"]}