Transient detection with an application to a chemical process

We propose a sequential test procedure for transient detections in a stochastic process which can be expressed as an autoregressive moving average (ARMA) model. Preliminary analysis shows that if an ARMA(p,q) time series exhibits a transient behavior, then its residuals behave as an ARMA(Q,Q) process, where Q ≤ p + q. Based on this fact, we derive a new sequential test to determine when a transient behavior occurs in a given ARMA time series. Simulation experiments conducted in this study show that the proposed test can detect the occurrence of a transient in the ARMA model. We also apply the proposed method to detect transient changes in the pH of an erythromycin salt.     

Neural networks to model dynamic systems with time delays

An algorithm is proposed to identify a neural network model that represents a nonlinear dynamic system with a multivariate time delay response. The algorithm consists of two major parts. The first one identifies the time delay vector for a given neural network structure. This task is accomplished by using an exhaustive integer enumeration algorithm that minimizes a statistical parameter to assess the performance of the neural network model. The second part uses a cross-validation strategy to identify the best neural network model. Since the structure that models a nonlinear system is usually unknown, the identification strategy consists of selecting several neural network structures and identifying the best time delay vector for each network. The modeling process starts with the simplest structure and progressively the complexity of the network is increased to end up with a complex structure. Finally, the network that offers the simplest structure with the best network performance is the one that exhibits the appropriate neural network structure with the corresponding optimal time delay vector. The Monte Carlo simulation technique was used to test the performance of the algorithm under the presence of linear and nonlinear relationships among several variables of dynamic systems and with a different time delay applied to each input variable. The introduced algorithm is used to detect a chemical reaction delay among enriched amyl acetate, acetic acid, water, and the pH of erythromycin sail. An appropriate neural network model was designed to model the pH of the erythromycin during a continuous extraction process. To the best of the authors knowledge the proposed algorithm is the only one currently available to identify time delay interactions in the multivariate input output variables of a system. The major drawback of the introduced algorithm is that it becomes very slow as the number of system inputs increases. This algorithm works efficiently in a system that involves five inputs or less.     

THERMODYNAMIC STABILITY OF SOLID PHARMACEUTICAL FORMULATIONS

This paper deals with the application of rigorous stability analysis and predict time for degradation of solid pharmaceutical formulations. The stability analysis in the form of the tangent-plane criterion is used to predict the stability of certain solid formulations. The Michelsen vector which indicates instability when the sum of its components is greater than unity is differentiated with respect to time and the results are used to predict when instability sets in. The total degradation time is the sum of the time for instability to set in and the time for the mole fraction to reach a certain value.