A step-wise incremented Fourier series model for chemical reactivity prediction
In this paper, chemical reactivity is modeled as a time series of events defined by a reactant's concentration decay measured at consecutive discrete time periods. Since traditional time series techniques such as ARIMA and current Artificial Neural Networks require large data sets that are typically not available for chemical reactions, we developed a Step Wise Incremented Fourier Series (SWIFS) algorithm to model and predict nonlinear short time series. The application of SWIFS to experimental data from first- and second-order reactions produced a significant improvement in prediction accuracy over traditional integrated rate laws. In forward-time prediction, SWIFS has achieved significantly higher prediction accuracy with first- and second-order chemical reactions data. SWIFS also proved more robust in terms of error propagation caused by the effect of the size of the estimation set. The proposed SWIFS model also outperformed rate law models in backwards-time prediction. The ability of SWIFS to provide high accuracy in predicting chemical reactions may have beneficial implications on the efficiency of industrial production of chemicals as well as on the effective control of hazardous materials degradation. Copyright (c) 2007 John Wiley & Sons, Ltd.
fourier series chemical reactivity time series artificial neural networks integrated rate laws