ANFIS based Distillation Column Control

Sivakumar, R.; Balu, Korra

doi:10.5120/1538-141

Cited by 27 publications

(11 citation statements)

References 9 publications

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“…The three most significant variables were selected and applied to develop an ANFIS model. ANFIS is a robust tool favored by researchers for modeling (Al-Ghandoor and Samhouri 2009;Petković et al 2012a, b;Petković and Ćojbašić 2012), making predictions (Hosoz et al 2011;Gocić et al 2015b;Sivakumar and Balu 2010) and control in engineering systems (Kurnaz et al 2010;Ravi et al 2011;Khoshnevisan et al 2015;Petković et al 2012a, b;Tian and Collins 2005). ANFIS facilitates a fuzzy modeling procedure to gather data (Aldair and Wang 2011) and it can also be used to organize fuzzy inference systems using input/output data pairs.…”

Section: Introductionmentioning

confidence: 98%

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

et al. 2015

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In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to predict potato production in Iran. Data related to potato yield from 2010 to 2011 was collected from 50 potato producers in Hamedan, Iran. The resulting ANFIS network has an input layer with eight neurons and an output layer with a single neuron (potato yield). The energy inputs were manual labor, diesel, chemical fertilizers, and manure from farm animals, chemicals, machinery, water, and seed. The most significant and influential inputs were selected from the eight initial inputs and the ANFIS network was used to choose the parameters that have the most influence on potato yield. A new ANFIS model was created after the three most influential parameters were selected. The new ANFIS model was then utilized to estimate yield using the three energy inputs. Next, the ANFIS model results were compared with the results from the support vector regression (SVR) technique. The end results revealed that ANFIS provided more accurate predictions and had the capacity to generalize. The Pearson correlation coefficient (r) for ANFIS potato yield prediction was 0.9999 in the training and testing phases, while the SVR model had a correlation coefficient of 0.8484 in training and 0.9984 in testing.Resumen En este estudio se desarrolló un sistema de inferencia adaptativa de lógica difusa (ANFIS) para predecir la producción de papa en Irán. Se colectaron datos relacionados con el rendimiento de papa de 2010 a 2011 de 50 productores en Hamedan, Irán. La red ANFIS resultante tiene una capa de insumos con ocho neuronas y una capa de salidas con una única neurona (rendimiento de papa). Los insumos de energía fueron mano de obra, diésel, fertilizantes químicos y estiércol de animales de granja, químicos, maquinaria, agua y semilla. Se seleccionaron los insumos más significativos y de influencia de los ocho insumos iniciales, y se usó la red ANFIS para escoger los parámetros que tienen la mayor influencia en el rendimiento de papa. Se creó un nuevo modelo ANFIS después que se seleccionaron los tres parámetros de mayor influencia. Entonces se utilizó el nuevo modelo ANFIS para estimar rendimiento usando los tres insumos de energía. Después, los resultados del modelo ANFIS se compararon con los resultados de la técnica de regresión de vector de respaldo (SVR). Los resultados finales revelaron que ANFIS suministró predicciones más precisas y tuvo la capacidad de generalizar. El coeficiente de correlación de Pearson (r) para la predicción del rendimiento de papa por ANFIS fue 0.9999 en las fases de formación y de prueba, e el modelo SVR tuvo un coeficiente de correlación de 0.8484 en formación y 0.9984 en prueba.

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Section: Introductionmentioning

confidence: 98%

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

et al. 2015

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“…ANFIS (Jang, 1993;Shamshirband et al, 2015), a hybrid intelligent system that increases the capability of learning and adapting automatically has been used by researchers for many different purposes in a variety of engineering systems such as in modeling (Al-Ghandoor & Samhouri, 2009;Petkovi c, Issa, Pavlovi c, Pavlovi c, & Zentner, 2012, 2014Singh, Kainthola, & Singh, 2012), for prediction (Hosoz, Ertunc, & Bulgurcu, 2011;Kariminia & Piri et al, 2015;Sivakumar & Balu, 2010) and for control (Areed, Haikal, & Mohammed, 2010;Kurnaz, Cetin, & Kaynak, 2010;Ravi, Sudha, & Balakrishnan, 2011;Tian & Collins, 2005). This neuro-adaptive learning methodology allows the fuzzy modeling process to obtain information regarding the data gathered (Aldair & Wang, 2011;Dastranj, Ebroahimi, Changizi, & Sameni, 2011).…”

Section: Input and Output Variablesmentioning

confidence: 98%

Evaluating the legibility of decorative arabic scripts for Sultan Alauddin mosque using an enhanced soft-computing hybrid algorithm

Saberi

Motamedi

Shamshirband

et al. 2016

Computers in Human Behavior

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“…Many variables, such as column pressure, temperature, size, and diameter are determined by the properties of the feed and the desired products. Fig 1 depicts the basic distillation column existing in the literature [1,[5][6][7] .…”

Section: Process Descriptionmentioning

confidence: 99%

“…In the literature, a number of controlling procedures are proposed and implemented to operate the process loops in a safer working region. In real time cases, the widely preferred controllers are PI/PID, since these controller structures are simple to understand and easy to implement [5][6][7][8][9][10][11][12] .…”

Section: Introductonmentioning

confidence: 99%

“…The distillation process is a really a complex unit with a series of actions or steps taken to achieve a particular end product with enhanced accuracy and purity. It is a series of interrelated tasks that, together, transform inputs into outputs [5][6][7] . Chemical, physical, electrical or mechanical procedures are the steps involved in industrial processes to aid in the manufacturing of an item, mostly carried out on a very large scale.…”

Section: Introductonmentioning

confidence: 99%

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Multi-loop PID Controller Design for Distillation Column using Firefly Algorithm

Saroja¹,

R²,

Meena³

et al. 2017

IJET

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Abstract-The main motivation of this manuscript is to develop a PID tuning procedure to improve the performance of the benchmark distillation column models using the heuristic firefly algorithm. A decentralised PID tuning approach is developed to design an independent PID controller for the top and bottom product loop. Minimization of a multi-objective function is chosen to guide the search process of the controller tuning procedure. The performance of the proposed approach is confirmed by evaluating the time domain parameters and the error values. From the experimental results, the main observation is that, proposed approach offers satisfactory result on the proposed approach and helps to achieve a reduced values of the time domain and error values. The proposed approach proves its efficiency in a benchmark Two Input Two Output (TITO) system. In future, this approach can be adopted to design controller for the multi-loop and non-linear systems existing in the process control literature. The proposed approach can also be compared against other heuristic approaches existing in the literature.

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ANFIS based Distillation Column Control

Cited by 27 publications

References 9 publications

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

Evaluating the legibility of decorative arabic scripts for Sultan Alauddin mosque using an enhanced soft-computing hybrid algorithm

Multi-loop PID Controller Design for Distillation Column using Firefly Algorithm

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