Detecting Changes in a Distillation Column by Using a Sequential Probability Ratio Test

AbstractIn chemical plants, a reliable detection of anomalies is important for a safe operation. To this end, a fault detection (FD) method of abnormal operations applicable to a chemical process is presented in this paper. This method couples an Artificial Neural Network-Multi-Layer Perceptron (ANN-MLP) with a statistical module based on the sequential probability ratio test (SPRT) of Wald, for the analysis of the process residuals. To detect a change, this combination uses the mean and the standard deviation of the residual noise obtained from applying a NARX (Nonlinear Auto-Regressive with eXogenous input) model. The FD effectiveness is tested under real abnormal circumstances on a real plant as a distillation column. The experimental results obtained show the relevance of this method for the fast detection and the monitoring of this chemical process

Artificial neural network based modelling and optimization of refined palm oil process

YesThe content and concentration of beta-carotene, tocopherol and free fatty acid is one of the important parameters that affect the quality of edible oil. In simulation based studies for refined palm oil process, three variables are usually used as input parameters which are feed flow rate (F), column temperature (T) and pressure (P). These parameters influence the output concentration of beta-carotene, tocopherol and free fatty acid. In this work, we develop 2 different ANN models; the first ANN model based on 3 inputs (F, T, P) and the second model based on 2 inputs (T and P). Artificial neural network (ANN) models are set up to describe the simulation. Feed forward back propagation neural networks are designed using different architecture in MATLAB toolbox. The effects of numbers for neurons and layers are examined. The correlation coefficient for this study is greater than 0.99; it is in good agreement during training and testing the models. Moreover, it is found that ANN can model the process accurately, and is able to predict the model outputs very close to those predicted by ASPEN HYSYS simulator for refined palm oil process. Optimization of the refined palm oil process is performed using ANN based model to maximize the concentration of beta-carotene and tocopherol at residue and free fatty acid at distillate

Development of surrogate models for distillation trains

El temps d’execució necessari per a la resolució de problemes d’optimització en programes de simulació rigorosos no sol ser asequible, fet que promou l’ús de models de substitució. El desenvolupament d’aquests models aproximats comporta la resolució d’una sèrie de reptes com la càrrega computacional i el risc d’excés d’adequació del model. En el treball presentat, les eines i procediments per a crear, entrenar i validar una xarxa neuronal (ANN) son desenvolupats per a l’entrenament de models de simplificació de simulacions rigoroses. Les eines proposades han estat posades a prova en un cas d’estudi que aborda la síntesis de trens de separació per als productes de la pirólisis del polietilé, centrant-se en les columnes de destil·lació del procés simulades en Aspen-HYSYS. Finalment, dos models ANN que simulen el comportament de la columna respecte una funció que considera els costos de la simulació han estat desenvolupats. El comportament i precisió dels dos models és correspon a l’estudiat en la superfície triada.El tiempo de computación necesario para solucionar problemas de optimización en programas de simulación rigurosos no suele ser asequible, lo que promueve el uso de modelos de sustitución. El desarrollo de estos modelos aproximados conlleva la resolución de una serie de retos como la carga computacional y el riesgo de sobreajuste del modelo. En el presente trabajo, las herramientas y procedimientos para crear, entrenar y validar una red neuronal artificial (ANN), han sido desarrollados para la construcción de modelos simplificados de simulaciones rigurosas. Las herramientas propuestas han sido puestas a prueba en un caso de estudio que aborda la síntesis de trenes de separación para los productos de la pirolisis del polietileno, centrándose en las columnas de destilación del proceso simuladas en Aspen-HYSYS. Finalmente, dos modelos de redes neuronales que simulan el comportamiento de la columna con respecto a una función que considera los costes de la simulación han sido desarrollados. Los dos modelos representan correctamente y con buena precisión la superficie estudiada.The computational time required to solve optimization problems in rigorous simulation programs is usually unaffordable, raising the need to use surrogate models. The development of these approximate models is a challenge that needs to handle the computational burden and risk of over fitting. In the present work, tools and procedures to build, train, and validate an Artificial Neural Network (ANN) are developed to build simplified models of rigorous simulations. The proposed tools are tested with a case study that addresses the synthesis of separation trains for the products of polyethylene pyrolysis, focusing in the distillation columns of the process simulated with Aspen-HYSYS. Finally, two ANN models have been developed to simulate the behaviour of the column regarding a function that considers the costs of the simulation. Both models fit correctly and show good accuracies with respect the surface studied