Neural-based Monitoring of a Debutanizer. Distillation Column
|
|
- Marybeth Goodman
- 5 years ago
- Views:
Transcription
1 Neural-based Monitoring of a Debutanizer Distillation Column L. Fortuna*, S. Licitra, M. Sinatra, M. G. Xibiliaº ERG Petroli ISAB Refinery, Siracusa, Italy slicitra@ergpetroli.it *University of Catania, DEES, Viale A. Doria 6, tel , Catania, Italy ºUniversity of Messina, Dept. of Mathematics Contrada Papardo, Salita Sperone 31, Messina, Italy mxibilia@ingegneria.unime.it Keywords: Modelling, Neural networks, Virtual sensors, Distillation columns Abstract In this paper a neural approach to distillation columns modelling is described. In particular a Debutanizer colums is considered and a real-time estimate of the butane percentage (C4) in the bottom draw (C5) is obtained by a NARMAX model implemented with a Multi-Layer Perceptron. The analyser of the C4 in C5 percentage used at present, provides a measure after a great and unknown delay, and is therefore not suitable for closed loop control purposes. A neural-based model, acting as a virtual sensor, can therefore represent a suitable strategy in getting a real-time estimation of the C4 in C5 concentration. Neural networks are used both to evaluate the delay of the analyser and to provide the desired real-time estimate of the C4 flow in the bottom draw of the debutanizer, overcoming the analyser s delay. To obtain more accurate results the model is built so that the measured output is used as an input of the model together with the predicted one, suitably delayed. The neural NARMAX model has been determined by using an appropriate set of measurements performed on a plant operating in Sicily (Italy) and is now working on the plant. A comparison between the estimated output and the analyser's measures confirms the validity of the proposed approach.
2 1. Introduction. In the last years an ever growing interest has been given to the production quality standards and to the pollution phenomena in industrial environment. Particular attention has been devoted to petrol-chemical area due to the high risks deriving from the production process and to the high level of quality standard imposed by government laws. In this context, due to the complexity of the considered systems, in general highly non-linear, and to the high number of variables and disturbances, the analytical modelling strategies cannot be easily applied. Great improvements can be obtained approaching the involved measurements and control problems with non conventional techniques, as for example Soft Computing, which allow to obtain non-linear dynamical models joining neural networks, fuzzy logic and genetic algorithm, exploiting human knowledge and measurements performed on the system. In this paper the first part of this monitoring and control strategy is described. In particular a virtual sensor, implemented via a MLP neural network is described. The system obtained can be used as virtual sensor, as parts of control loop or to validate the sensors output, detecting the occurrence of faults in the measurement system. Such an approach allows a great economical advantage, in that a lower number of measuring devices is needed, the measures can be easily validated and a more tight control on the production quality can be obtained. A Soft Computing approach in modelling of distillation columns has been used in [1] and [2] where the benzene percentage of a Splitter Benzene column has been considered. In particular 3 different types of model have been determined: a one-step predictive model to obtain a real-time estimate, in spite of the 15 minutes delay introduced by the chromatograph, a non linear MA model, able to give an approximative value of the benzene percentage in case of faults of the chromatograph, and a three-step predictive NARMAX model, used to share the multistream chromatograph among 3 different columns. The application considered in this paper is the butane concentration neural modelling in the bottom stream of a Debutanizer distillation column operating in the ERGPETROLI ISAB Refinery (Siracusa, Italy). The butane concentration is at present indirectly measured by an analyser which is connected to a different column, the measure is therefore given with a great and unknown delay and cannot be efficiently used in a control loop.
3 As it will be explained in the following, the proposed neural model allows to obtain a real-time estimate of the percentage of C4 in C5, thus overcoming the delay introduced by the present measurement system. 2. The distillation process: debutanizer column. The distillation process allows to separate the mixture coming from the Deethanizer in several components through a sequence of vaporisation and condensation steps [4]. It can be realized in a distillation column, as represented in Fig. 1. TI 040 PRC 011 E107 A/B FRC 015 D104 LRC 008 FRC 018 G.P.L. Splitter Deethanizer P102 A/B P103 A/B 9 TRC 004 TI 037 FRC 016 T102 TI 036 E108 A E108 B FRC 021 Nafta slpitter Fig. 1 : The Debutanizer The column has two draws: top and bottom. The overheads from the column are condensed against cooling water (E107A/B) and enter the debutanizer reflux drum (D104). Liquid from the reflux drum is either returned to the column as reflux (F015RC) or pumped to the C3/C4 splitter (F018RC). The flow to the C3/C4 splitter is normally cascaded from the drum level (L008RC). C3/C4 product flow is typically m 3 /Hr versus a reflux flow of around 200m 3 /Hr. The column pressure (P011RC) usually manipulates the
4 flow through the water condensers (E107A/B), but, in case of very high pressure, the controller can also open the blow down valve (F037R). Temperature at the top of the column is measured (T040I); temperature is measured also in the middle of the column (T004RC) and its variation controls the flow of the vacuum residue against witch the bottom of the column is reboiled (E108A/B) and hence influence the two temperatures (T036I and T037I) measured at the bottom of the column. Liquid is withdrawn from the column under flow control (02F021RC) and routed to the Naphtha splitter. The first step in realizing a virtual sensor of the C4 flow in the bottom draw of the column is to select the correct set of input variables. This has been done by using the knowledge of the experts operating on the system. After an accurate analysis of the involved variables, based on the process knowledge, the most important have been selected as model inputs as follows: u1: (T040I) Top temperature; u2: (P011RC) Top pressure; u3: (F015RC) Top reflux; u4: (F018RC) Top drawn flow; u5: (T004RC) Middle temperature; u6: (T036RC) Bottom temperature; u7: (T037RC) Bottom temperature. The model output is the flow of C4 in the bottom draw: y: (F_C4). Historical plant data covering an operating period of two months were collected from refinery database to build the neural model. The trends of these variables are shown in Fig. 2 The C4 composition in the Debutanizer bottom is not measured at the bottom draw, because the C4 analyser is installed on the overheads of the Naphtha Splitter. We can suppose that the analyser measures all the flow of C4 that comes out from the bottom of the Debutanizer because in the Naphtha Splitter C4 is the lighter
5 component so it will go unbroken in the top of the Naphtha Splitter. The measure provided from the analyser has therefore a great delay with respect to the actual output. Fig.2 The seven input variables and the output of system This delay should be properly estimated in order to use the analyzer output data in building the neural model. From our knowledge about the plant's structure, we can only suppose that the delay of the analyser is in the range [45 min - 1h:30']. 3. Neural Modelling and Numerical Results As well known, a lumped non-linear dynamical system can be represented by a NARMAX model of the following form [3], [6], [7]: y(t) = f [ y1( t-1 ), y1( t-2 ),, y1(t-n 1 ),, y2(t-1),,y2(t-n 2 ),yn(t-1),, yn(t-n n ), u1( t-1), u1(t-m 1 ),, um(t-m m )] (1) where t is the discrete time, y=[y1,,yn] is the n-dimensional output vector, n i (i=1,,n) is the number of delays of the i-th output variables, u=[u1,,um] is the m-dimensional input vector and m i (i=1,,m) is the number of delays of the i-th input. A model is therefore determined by the non linear function f(.) and by the number of input and output delayed samples.
6 Due to the approximation capabilities of MLP neural networks [5] the function f(.) can be suitably represented by a MLP if a sufficient number of measured I/O samples is used to train the network and the correct number of I/O delays are considered [6]. In the considered application the correct delay between the actual and measured output (TD) should be also determined, in order to arrange correctly the measurements and to obtain a suitable training data set. The number of delayed samples to be used for each I/O variable has been determined on the base of both the knowledge of the plant operators and a trial and error phase. A large set of neural networks, differing for the value of TD, the input and output variables delays and for the number of hidden neurons has been trained. Best results have been obtained by considering TD=45 min. As an example of the comparison performed, in Fig 3 the difference between the actual output and the model's output is shown when two different values of TD (45 min and 60 min) are considered to arrange the data set. The model inputs and the network structure are identical in both cases. Fig 3 Difference between actual output and model output when 2 different delays are considered to arrange the data set. Over about 1000 measured data (sampling time is 15 min.), 800 have been used to train the networks while the remaining 200 have been used to test their performance. It can be seen that the performance of the
7 two networks, one trained supposing TD=45 min and the other with TD=60 min, are very different as regards the 200 checking data, indicating that 45 min is a correct estimate of TD. At the end of this trial and error phase good results are obtained using the following input variables: FC_4 * (k+1) = f [T040(k), P011(k), F015(k), F018(k), T004(k-3), T004(k-2), T004(k-1), T004(k), T036(k), T037(k), FC_4 * (k), FC_4 * (k-1)] where k is the discrete time, FC_4 * is the estimated value of the output and the other input values are given by the corresponding measurement instruments. In order to further improve the model performance the following remark can be considered: since the analyser has a sampling time of 15 minutes, a model with only two delayed output samples as inputs, used on-line, cannot use the analyser measured values, because the analyser has not yet provided the two needed samples (TD is 45 min, corresponding to 3 prediction step). So the neural network has to iteratively reconstruct the two estimated output value used in the input vector and the analyser measure is not used at all. Therefore a new model with four delayed output samples has been considered: two samples came from the analyser, while the other two samples (the most recent ones) are computed by the network itself. A new set of 14-x-1 neural network has been therefore trained, with a larger set of data in order to determine the correct number of hidden neurons. About 1500 data have been used to train the network and a different set of 500 data has been used to test the results. The number of hidden neurons has been chosen with a growing strategy, checking the possible occurence of overlearning by computing the output MSE both with the learning and testing data sets. Fig 4 shows the neural network output and the analyser output; the difference between these two trends is shown too. The obtained virtual sensor is presently implemented on the refinery control and monitoring system. Fig. 5 represents the actual versus the model output obtained after the implementation of the model. As can be observed the obtained performance are satisfactory. However a periodic tuning of the network weights has to be done to take into account that the plant is sometimes stopped and restarted from a different working point.
8 Fig 4 Dynamic model output and real output and their difference Fig. 5 On-line performance of the virtual sensor
9 4. Conclusions In the paper the advantages introduced by the neural network modelling approach to a petrol chemical plant are outlined. In particular bottom draw butane concentration of a distillation column is predicted in real time as a function of a suitable set of input variables via a MLP. A delayed value of the output variable, measured by an analyser located on a different column is used to improve the model performance. The choice of the input variables has revealed of fundamental importance for the efficiency of the model and has been made based both on process knowledge and on trial-and-error procedures. The dynamical model allows a real-time control of the bottom product of the debutanizer. The results obtained are satisfactory and suggest the application of neural modelling in solving a number of measurement and control problems in petrolchemical processes. A further improvement can be introduced by using also hybrid metodologies, based on neuro-fuzzy networks and genetic algorithm both in realising virtual sensors and sophisticated control algorithm. References [1] C. Bozzanca, S. Licitra, L. Fortuna, M.G. Xibilia, Neural networks for benzene percentage monitoring in distillation columns, Proc. of Soft Computing (SOCO) 99, pp , Genova, Italy, June [2] M. Bucolo, L. Fortuna, M. Sinatra, S. Graziani, Neuro-fuzzy modelin in petrol-chemical industry, Proc. of the 7-th Mediterranean Conf. On Control and Automation, JUNE 28-30, Haifa, Israel, [3] L. Ljung, T. Soderstrom, Theory and Practice of Recursive Identification, Cambridge, Mass: MIT Press, 1983 [4] R. Perry, Don Green, Perry s chemical engineers handbook, Mc-Graw Hill, 1984 [5] G. Cybenko, Approximation By Superposition of a Sigmoidal Function, Mathematics of Control, Signals, and Systems, Springer-Verlag, pp [6] S. Chen, S.A.Billings and P.M.Grant, Non-Linear system identification using neural networks, Int. Journal of Control, vol. 51, No. 6, ,1990. [7] K. S. Narendra, K. Pathasarathy, Identification and Control of Dynamical System Using Neural Networks, IEEE Trans. on Neural Networks, Vol. 1, No. 1, 1990.
Process Unit Control System Design
Process Unit Control System Design 1. Introduction 2. Influence of process design 3. Control degrees of freedom 4. Selection of control system variables 5. Process safety Introduction Control system requirements»
More informationUsing Neural Networks for Identification and Control of Systems
Using Neural Networks for Identification and Control of Systems Jhonatam Cordeiro Department of Industrial and Systems Engineering North Carolina A&T State University, Greensboro, NC 27411 jcrodrig@aggies.ncat.edu
More informationVC-dimension of a context-dependent perceptron
1 VC-dimension of a context-dependent perceptron Piotr Ciskowski Institute of Engineering Cybernetics, Wroc law University of Technology, Wybrzeże Wyspiańskiego 27, 50 370 Wroc law, Poland cis@vectra.ita.pwr.wroc.pl
More informationMass Transfer Operations I Prof. Bishnupada Mandal Department of Chemical Engineering Indian Institute of Technology, Guwahati
Mass Transfer Operations I Prof. Bishnupada Mandal Department of Chemical Engineering Indian Institute of Technology, Guwahati Module - 5 Distillation Lecture - 5 Fractional Distillation Welcome to the
More informationDYNAMIC SIMULATOR-BASED APC DESIGN FOR A NAPHTHA REDISTILLATION COLUMN
HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 45(1) pp. 17 22 (2017) hjic.mk.uni-pannon.hu DOI: 10.1515/hjic-2017-0004 DYNAMIC SIMULATOR-BASED APC DESIGN FOR A NAPHTHA REDISTILLATION COLUMN LÁSZLÓ SZABÓ,
More informationDistilla l tion n C olum u n
Distillation Column Distillation: Process in which a liquid or vapour mixture of two or more substances is separated into its component fractions of desired purity, by the application and removal of heat
More informationIntelligent Modular Neural Network for Dynamic System Parameter Estimation
Intelligent Modular Neural Network for Dynamic System Parameter Estimation Andrzej Materka Technical University of Lodz, Institute of Electronics Stefanowskiego 18, 9-537 Lodz, Poland Abstract: A technique
More informationA Novel Activity Detection Method
A Novel Activity Detection Method Gismy George P.G. Student, Department of ECE, Ilahia College of,muvattupuzha, Kerala, India ABSTRACT: This paper presents an approach for activity state recognition of
More informationArtificial Neural Networks Francesco DI MAIO, Ph.D., Politecnico di Milano Department of Energy - Nuclear Division IEEE - Italian Reliability Chapter
Artificial Neural Networks Francesco DI MAIO, Ph.D., Politecnico di Milano Department of Energy - Nuclear Division IEEE - Italian Reliability Chapter (Chair) STF - China Fellow francesco.dimaio@polimi.it
More informationECE Introduction to Artificial Neural Network and Fuzzy Systems
ECE 39 - Introduction to Artificial Neural Network and Fuzzy Systems Wavelet Neural Network control of two Continuous Stirred Tank Reactors in Series using MATLAB Tariq Ahamed Abstract. With the rapid
More informationLecture 5: Logistic Regression. Neural Networks
Lecture 5: Logistic Regression. Neural Networks Logistic regression Comparison with generative models Feed-forward neural networks Backpropagation Tricks for training neural networks COMP-652, Lecture
More informationA STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS
A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS Karima Amoura Patrice Wira and Said Djennoune Laboratoire CCSP Université Mouloud Mammeri Tizi Ouzou Algeria Laboratoire MIPS Université
More informationBuilding knowledge from plant operating data for process improvement. applications
Building knowledge from plant operating data for process improvement applications Ramasamy, M., Zabiri, H., Lemma, T. D., Totok, R. B., and Osman, M. Chemical Engineering Department, Universiti Teknologi
More informationStability of Amygdala Learning System Using Cell-To-Cell Mapping Algorithm
Stability of Amygdala Learning System Using Cell-To-Cell Mapping Algorithm Danial Shahmirzadi, Reza Langari Department of Mechanical Engineering Texas A&M University College Station, TX 77843 USA danial_shahmirzadi@yahoo.com,
More informationPredictive Control of a Single Link Flexible Joint Robot Based on Neural Network and Feedback Linearization
Australian Journal of Basic and Applied Sciences, 3(3): 2322-2333, 2009 ISSN 1991-8178 Predictive Control of a Single Link Flexible Joint Robot Based on Neural Network and Feedback Linearization 1 2 1
More informationOptimization and composition control of Distillation column using MPC
Optimization and composition control of Distillation column using M.Manimaran 1,A.Arumugam 2,G.Balasubramanian 3,K.Ramkumar 4 1,3,4 School of Electrical and Electronics Engineering, SASTRA University,
More informationControl of MIMO processes. 1. Introduction. Control of MIMO processes. Control of Multiple-Input, Multiple Output (MIMO) Processes
Control of MIMO processes Control of Multiple-Input, Multiple Output (MIMO) Processes Statistical Process Control Feedforward and ratio control Cascade control Split range and selective control Control
More informationCONTROL OF MULTIVARIABLE PROCESSES
Process plants ( or complex experiments) have many variables that must be controlled. The engineer must. Provide the needed sensors 2. Provide adequate manipulated variables 3. Decide how the CVs and MVs
More informationIncrease of coal burning efficiency via automatic mathematical modeling. Patrick Bangert algorithmica technologies GmbH 1 Germany
Increase of coal burning efficiency via automatic mathematical modeling Patrick Bangert algorithmica technologies GmbH 1 Germany Abstract The entire process of a coal power plant from coal delivery to
More informationProcess Control, 3P4 Assignment 6
Process Control, 3P4 Assignment 6 Kevin Dunn, kevin.dunn@mcmaster.ca Due date: 28 March 204 This assignment gives you practice with cascade control and feedforward control. Question [0 = 6 + 4] The outlet
More informationNonlinear System Identification Based on a Novel Adaptive Fuzzy Wavelet Neural Network
Nonlinear System Identification Based on a Novel Adaptive Fuzzy Wavelet Neural Network Maryam Salimifard*, Ali Akbar Safavi** *School of Electrical Engineering, Amirkabir University of Technology, Tehran,
More informationParameter Identification and Dynamic Matrix Control Design for a Nonlinear Pilot Distillation Column
International Journal of ChemTech Research CODEN (USA): IJCRGG ISSN: 974-429 Vol.7, No., pp 382-388, 24-25 Parameter Identification and Dynamic Matrix Control Design for a Nonlinear Pilot Distillation
More informationMODULE 5: DISTILLATION
MOULE 5: ISTILLATION LECTURE NO. 3 5.2.2. Continuous distillation columns In contrast, continuous columns process a continuous feed stream. No interruptions occur unless there is a problem with the column
More informationFigure 4-1: Pretreatment schematic
GAS TREATMENT The pretreatment process consists of four main stages. First, CO 2 and H 2 S removal stage which is constructed to assure that CO 2 would not exceed 50 ppm in the natural gas feed. If the
More informationVirtual Sensor Technology for Process Optimization. Edward Wilson Neural Applications Corporation
Virtual Sensor Technology for Process Optimization Edward Wilson Neural Applications Corporation ewilson@neural.com Virtual Sensor (VS) Also known as soft sensor, smart sensor, estimator, etc. Used in
More informationMultiple Model Based Adaptive Control for Shell and Tube Heat Exchanger Process
Multiple Model Based Adaptive Control for Shell and Tube Heat Exchanger Process R. Manikandan Assistant Professor, Department of Electronics and Instrumentation Engineering, Annamalai University, Annamalai
More informationNONLINEAR PLANT IDENTIFICATION BY WAVELETS
NONLINEAR PLANT IDENTIFICATION BY WAVELETS Edison Righeto UNESP Ilha Solteira, Department of Mathematics, Av. Brasil 56, 5385000, Ilha Solteira, SP, Brazil righeto@fqm.feis.unesp.br Luiz Henrique M. Grassi
More informationESTIMATING THE ACTIVATION FUNCTIONS OF AN MLP-NETWORK
ESTIMATING THE ACTIVATION FUNCTIONS OF AN MLP-NETWORK P.V. Vehviläinen, H.A.T. Ihalainen Laboratory of Measurement and Information Technology Automation Department Tampere University of Technology, FIN-,
More informationNeural Network Control in a Wastewater Treatment Plant
Neural Network Control in a Wastewater Treatment Plant Miguel A. Jaramillo 1 ; Juan C. Peguero 2, Enrique Martínez de Salazar 1, Montserrat García del alle 1 ( 1 )Escuela de Ingenierías Industriales. (
More informationEngineering Models For Inferential Controls
Petrocontrol Leader in inferential control technology Engineering Models For Inferential Controls By Y Zak Friedman, PhD Principal consultant INTRODUCTION This paper describes a class of inferential control
More informationA Logarithmic Neural Network Architecture for Unbounded Non-Linear Function Approximation
1 Introduction A Logarithmic Neural Network Architecture for Unbounded Non-Linear Function Approximation J Wesley Hines Nuclear Engineering Department The University of Tennessee Knoxville, Tennessee,
More informationDesigning Dynamic Neural Network for Non-Linear System Identification
Designing Dynamic Neural Network for Non-Linear System Identification Chandradeo Prasad Assistant Professor, Department of CSE, RGIT,Koderma Abstract : System identification deals with many subtleties
More informationNon-square open-loop dynamic model of methyl acetate production process by using reactive distillation column
Non-square open-loop dynamic model of methyl acetate production process by using reactive distillation column Ahmad Misfa Kurniawan 1, Renanto Handogo 1,*, Hao-Yeh Lee 2, and Juwari Purwo Sutikno 1 1 Department
More informationKeywords- Source coding, Huffman encoding, Artificial neural network, Multilayer perceptron, Backpropagation algorithm
Volume 4, Issue 5, May 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Huffman Encoding
More informationA recursive algorithm based on the extended Kalman filter for the training of feedforward neural models. Isabelle Rivals and Léon Personnaz
In Neurocomputing 2(-3): 279-294 (998). A recursive algorithm based on the extended Kalman filter for the training of feedforward neural models Isabelle Rivals and Léon Personnaz Laboratoire d'électronique,
More informationFuzzy reliability analysis of washing unit in a paper plant using soft-computing based hybridized techniques
Fuzzy reliability analysis of washing unit in a paper plant using soft-computing based hybridized techniques *Department of Mathematics University of Petroleum & Energy Studies (UPES) Dehradun-248007,
More informationPlantwide Control of Chemical Processes Prof. Nitin Kaistha Department of Chemical Engineering Indian Institute of Technology, Kanpur
Plantwide Control of Chemical Processes Prof. Nitin Kaistha Department of Chemical Engineering Indian Institute of Technology, Kanpur Lecture - 41 Cumene Process Plantwide Control (Refer Slide Time: 00:18)
More informationADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS
ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS RBFN and TS systems Equivalent if the following hold: Both RBFN and TS use same aggregation method for output (weighted sum or weighted average) Number of basis functions
More informationARTIFICIAL NEURAL NETWORK WITH HYBRID TAGUCHI-GENETIC ALGORITHM FOR NONLINEAR MIMO MODEL OF MACHINING PROCESSES
International Journal of Innovative Computing, Information and Control ICIC International c 2013 ISSN 1349-4198 Volume 9, Number 4, April 2013 pp. 1455 1475 ARTIFICIAL NEURAL NETWORK WITH HYBRID TAGUCHI-GENETIC
More informationPROPORTIONAL-Integral-Derivative (PID) controllers
Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process R.Vinodha S. Abraham Lincoln and J. Prakash Abstract Multi-loop (De-centralized) Proportional-Integral- Derivative
More informationA Modified Incremental Principal Component Analysis for On-line Learning of Feature Space and Classifier
A Modified Incremental Principal Component Analysis for On-line Learning of Feature Space and Classifier Seiichi Ozawa, Shaoning Pang, and Nikola Kasabov Graduate School of Science and Technology, Kobe
More informationDISTILLATION. Keywords: Phase Equilibrium, Isothermal Flash, Adiabatic Flash, Batch Distillation
25 DISTILLATION Keywords: Phase Equilibrium, Isothermal Flash, Adiabatic Flash, Batch Distillation Distillation refers to the physical separation of a mixture into two or more fractions that have different
More informationExperimental evaluation of a modified fully thermally coupled distillation column
Korean J. Chem. Eng., 27(4), 1056-1062 (2010) DOI: 10.1007/s11814-010-0205-8 RAPID COMMUNICATION Experimental evaluation of a modified fully thermally coupled distillation column Kyu Suk Hwang**, Byoung
More informationOverview of Control System Design
Overview of Control System Design General Requirements 1. Safety. It is imperative that industrial plants operate safely so as to promote the well-being of people and equipment within the plant and in
More informationShort Term Load Forecasting Based Artificial Neural Network
Short Term Load Forecasting Based Artificial Neural Network Dr. Adel M. Dakhil Department of Electrical Engineering Misan University Iraq- Misan Dr.adelmanaa@gmail.com Abstract Present study develops short
More informationMIMO Identification and Controller design for Distillation Column
MIMO Identification and Controller design for Distillation Column S.Meenakshi 1, A.Almusthaliba 2, V.Vijayageetha 3 Assistant Professor, EIE Dept, Sethu Institute of Technology, Tamilnadu, India 1 PG Student,
More informationNonlinear System Identification using Support Vector Regression
Nonlinear System Identification using Support Vector Regression Saneej B.C. PhD Student Department of Chemical and Materials Engineering University of Alberta Outline 2 1. Objectives 2. Nonlinearity in
More informationDETERMINATION OF OPTIMAL ENERGY EFFICIENT SEPARATION SCHEMES BASED ON DRIVING FORCES
DETERMINATION OF OPTIMAL ENERGY EFFICIENT SEPARATION SCHEMES BASED ON DRIVING FORCES Abstract Erik Bek-Pedersen, Rafiqul Gani CAPEC, Department of Chemical Engineering, Technical University of Denmark,
More informationApproximation Bound for Fuzzy-Neural Networks with Bell Membership Function
Approximation Bound for Fuzzy-Neural Networks with Bell Membership Function Weimin Ma, and Guoqing Chen School of Economics and Management, Tsinghua University, Beijing, 00084, P.R. China {mawm, chengq}@em.tsinghua.edu.cn
More informationA Modified Incremental Principal Component Analysis for On-Line Learning of Feature Space and Classifier
A Modified Incremental Principal Component Analysis for On-Line Learning of Feature Space and Classifier Seiichi Ozawa 1, Shaoning Pang 2, and Nikola Kasabov 2 1 Graduate School of Science and Technology,
More informationReprinted from February Hydrocarbon
February2012 When speed matters Loek van Eijck, Yokogawa, The Netherlands, questions whether rapid analysis of gases and liquids can be better achieved through use of a gas chromatograph or near infrared
More informationMass Transfer Operations I Prof. Bishnupada Mandal Department of Chemical Engineering Indian Institute of Technology, Guwahati
Mass Transfer Operations I Prof. Bishnupada Mandal Department of Chemical Engineering Indian Institute of Technology, Guwahati Module - 5 Distillation Lecture - 6 Fractional Distillation: McCabe Thiele
More information5-7 Organic Chemistry Trilogy
5-7 Organic Chemistry Trilogy.0 A student investigated the viscosity of liquid hydrocarbons. The student used this method:. Measure 40 cm 3 of the liquid hydrocarbon. 2. Pour the liquid hydrocarbon into
More informationMultivariable Process Identification for MPC: The Asymptotic Method and its Applications
Multivariable Process Identification for MPC: The Asymptotic Method and its Applications Yucai Zhu Tai-Ji Control Hageheldlaan 62, NL-5641 GP Eindhoven The Netherlands Phone +31.40.2817192, fax +31.40.2813197,
More informationCOMP9444: Neural Networks. Vapnik Chervonenkis Dimension, PAC Learning and Structural Risk Minimization
: Neural Networks Vapnik Chervonenkis Dimension, PAC Learning and Structural Risk Minimization 11s2 VC-dimension and PAC-learning 1 How good a classifier does a learner produce? Training error is the precentage
More informationCSTR CONTROL USING MULTIPLE MODELS
CSTR CONTROL USING MULTIPLE MODELS J. Novák, V. Bobál Univerzita Tomáše Bati, Fakulta aplikované informatiky Mostní 39, Zlín INTRODUCTION Almost every real process exhibits nonlinear behavior in a full
More informationOverview of Control System Design
Overview of Control System Design Introduction Degrees of Freedom for Process Control Selection of Controlled, Manipulated, and Measured Variables Process Safety and Process Control 1 General Requirements
More informationANN Control of Non-Linear and Unstable System and its Implementation on Inverted Pendulum
Research Article International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347-5161 2014 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet ANN
More informationDesign and Control Properties of Arrangements for Distillation of Four Component Mixtures Using Less Than N-1 Columns
D. M. MÉNDEZ-VALENCIA et al., Design and Control Properties of Arrangements, Chem. Biochem. Eng. Q. 22 (3) 273 283 (2008) 273 Design and Control Properties of Arrangements for Distillation of Four Component
More informationProcess Classification
Process Classification Before writing a material balance (MB) you must first identify the type of process in question. Batch no material (mass) is transferred into or out of the system over the time period
More informationFault Neural Classifier Applied to a Level Control Real System
Fault Neural Classifier Applied to a Level Control Real System Raphaela G. Fernandes, Diego R. C. Silva Universidade Federal do Rio Grande do Norte Natal-RN, Brazil Email: raphaela@dca.ufrn.br, diego@dca.ufrn.br
More informationPRACTICAL CONTROL OF DIVIDING-WALL COLUMNS
Distillation Absorption 2010. Strandberg, S. Skogestad and I.. Halvorsen All rights reserved by authors as per DA2010 copyright notice PRACTICAL CONTROL OF DIVIDING-WALL COLUMNS ens Strandberg 1, Sigurd
More informationAll Rights Reserved. Armando B. Corripio, PhD, P.E., Multicomponent Distillation Column Specifications... 2
Multicomponent Distillation All Rights Reserved. Armando B. Corripio, PhD, P.E., 2013 Contents Multicomponent Distillation... 1 1 Column Specifications... 2 1.1 Key Components and Sequencing Columns...
More informationMODELING WITH CURRENT DYNAMICS AND VIBRATION CONTROL OF TWO PHASE HYBRID STEPPING MOTOR IN INTERMITTENT DRIVE
MODELING WITH CURRENT DYNAMICS AND VIBRATION CONTROL OF TWO PHASE HYBRID STEPPING MOTOR IN INTERMITTENT DRIVE Ryota Mori, Yoshiyuki Noda, Takanori Miyoshi, Kazuhiko Terashima Department of Production Systems
More informationSensing Device for Camless Engine Electromagnetic Actuators
Sensing Device for Camless Engine Electromagnetic Actuators Fabio Ronchi, Carlo Rossi, Andrea Tilli Dept. of Electronics, Computer Science and Systems (DEIS), University of Bologna Viale Risorgimento n.,
More informationNeural Network Nonlinear Modeling of a Common Rail Injection System for a CNG Engine
Neural Network Nonlinear Modeling of a Common Rail Injection System for a CNG Engine BRUNO MAIONE, PAOLO LINO, ALESSANDRO RIZZO Dipartimento di Elettrotecnica ed Elettronica Politecnico di Bari via Re
More information( t) Identification and Control of a Nonlinear Bioreactor Plant Using Classical and Dynamical Neural Networks
Identification and Control of a Nonlinear Bioreactor Plant Using Classical and Dynamical Neural Networks Mehmet Önder Efe Electrical and Electronics Engineering Boðaziçi University, Bebek 80815, Istanbul,
More informationANN and Statistical Theory Based Forecasting and Analysis of Power System Variables
ANN and Statistical Theory Based Forecasting and Analysis of Power System Variables Sruthi V. Nair 1, Poonam Kothari 2, Kushal Lodha 3 1,2,3 Lecturer, G. H. Raisoni Institute of Engineering & Technology,
More informationApproximate Methods Fenske-Underwood-Gilliland (FUG) Method Selection of Two Key Components
Lecture 3. Approximate Multicomponent Methods () [Ch. 9] Approximate Methods Fenske-Underwood-Gilliland (FUG) Method Selection of Two Key Components Column Operating Pressure Fenske Equation for Minimum
More informationFUZZY-NEURON INTELLIGENT COORDINATION CONTROL FOR A UNIT POWER PLANT
57 Asian Journal of Control, Vol. 3, No. 1, pp. 57-63, March 2001 FUZZY-NEURON INTELLIGENT COORDINATION CONTROL FOR A UNIT POWER PLANT Jianming Zhang, Ning Wang and Shuqing Wang ABSTRACT A novel fuzzy-neuron
More informationNeural Network Based Methodology for Cavitation Detection in Pressure Dropping Devices of PFBR
Indian Society for Non-Destructive Testing Hyderabad Chapter Proc. National Seminar on Non-Destructive Evaluation Dec. 7-9, 2006, Hyderabad Neural Network Based Methodology for Cavitation Detection in
More informationCOMPARISON OF DAMPING PERFORMANCE OF CONVENTIONAL AND NEURO FUZZY BASED POWER SYSTEM STABILIZERS APPLIED IN MULTI MACHINE POWER SYSTEMS
Journal of ELECTRICAL ENGINEERING, VOL. 64, NO. 6, 2013, 366 370 COMPARISON OF DAMPING PERFORMANCE OF CONVENTIONAL AND NEURO FUZZY BASED POWER SYSTEM STABILIZERS APPLIED IN MULTI MACHINE POWER SYSTEMS
More informationChapter 4. Problem SM.7 Ethylbenzene/Styrene Column
Background Chapter 4. Problem SM.7 Ethylbenzene/Styrene Column In Problem SM.6 of the HYSYS manual, a modified form of successive substitution, called the Wegstein method, was used to close the material
More informationSystem Identification for Process Control: Recent Experiences and a Personal Outlook
System Identification for Process Control: Recent Experiences and a Personal Outlook Yucai Zhu Eindhoven University of Technology Eindhoven, The Netherlands and Tai-Ji Control Best, The Netherlands Contents
More informationReal Time wave forecasting using artificial neural network with varying input parameter
82 Indian Journal of Geo-Marine SciencesINDIAN J MAR SCI VOL. 43(1), JANUARY 2014 Vol. 43(1), January 2014, pp. 82-87 Real Time wave forecasting using artificial neural network with varying input parameter
More informationEffect of Rule Weights in Fuzzy Rule-Based Classification Systems
506 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 9, NO. 4, AUGUST 2001 Effect of Rule Weights in Fuzzy Rule-Based Classification Systems Hisao Ishibuchi, Member, IEEE, and Tomoharu Nakashima, Member, IEEE
More informationReduced Order Modeling of High Purity Distillation Columns for Nonlinear Model Predictive Control
Reduced Order Modeling of High Purity Distillation Columns for Nonlinear Model Predictive Control Suabtragool Khowinij, Shoujun Bian and Michael A Henson Department of Chemical Engineering University of
More informationA Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average (FARMA) Model
142 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 1, MARCH 2003 A Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average (FARMA) Model Un-Chul Moon and Kwang Y. Lee, Fellow,
More informationPrediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks
Int. J. of Thermal & Environmental Engineering Volume 14, No. 2 (2017) 103-108 Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks M. A. Hamdan a*, E. Abdelhafez b
More informationARTIFICIAL NEURAL NETWORKS گروه مطالعاتي 17 بهار 92
ARTIFICIAL NEURAL NETWORKS گروه مطالعاتي 17 بهار 92 BIOLOGICAL INSPIRATIONS Some numbers The human brain contains about 10 billion nerve cells (neurons) Each neuron is connected to the others through 10000
More informationMODELING USING NEURAL NETWORKS: APPLICATION TO A LINEAR INCREMENTAL MACHINE
MODELING USING NEURAL NETWORKS: APPLICATION TO A LINEAR INCREMENTAL MACHINE Rawia Rahali, Walid Amri, Abdessattar Ben Amor National Institute of Applied Sciences and Technology Computer Laboratory for
More informationProcess modeling and optimization of mono ethylene glycol quality in commercial plant integrating artificial neural network and differential evolution
From the SelectedWorks of adeem Khalfe Winter December 7, 2008 Process modeling and optimization of mono ethylene glycol quality in commercial plant integrating artificial neural network and differential
More informationCHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING. Professor Dae Ryook Yang
CHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING Professor Dae Ryook Yang Spring 2018 Dept. of Chemical and Biological Engineering 11-1 Road Map of the Lecture XI Controller Design and PID
More informationA SEASONAL FUZZY TIME SERIES FORECASTING METHOD BASED ON GUSTAFSON-KESSEL FUZZY CLUSTERING *
No.2, Vol.1, Winter 2012 2012 Published by JSES. A SEASONAL FUZZY TIME SERIES FORECASTING METHOD BASED ON GUSTAFSON-KESSEL * Faruk ALPASLAN a, Ozge CAGCAG b Abstract Fuzzy time series forecasting methods
More informationDistillation is a method of separating mixtures based
Distillation Distillation is a method of separating mixtures based on differences in their volatilities in a boiling liquid mixture. Distillation is a unit operation, or a physical separation process,
More informationARTIFICIAL NEURAL NETWORK PART I HANIEH BORHANAZAD
ARTIFICIAL NEURAL NETWORK PART I HANIEH BORHANAZAD WHAT IS A NEURAL NETWORK? The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided
More informationOnline Identification And Control of A PV-Supplied DC Motor Using Universal Learning Networks
Online Identification And Control of A PV-Supplied DC Motor Using Universal Learning Networks Ahmed Hussein * Kotaro Hirasawa ** Jinglu Hu ** * Graduate School of Information Science & Electrical Eng.,
More informationNeurocomputing 131 (2014) Contents lists available at ScienceDirect. Neurocomputing. journal homepage:
Neurocomputing 131 (214) 59 76 Contents lists available at ScienceDirect Neurocomputing journal homepage: www.elsevier.com/locate/neucom Composition Prediction of a Debutanizer Column using Equation Based
More informationImproved Kalman Filter Initialisation using Neurofuzzy Estimation
Improved Kalman Filter Initialisation using Neurofuzzy Estimation J. M. Roberts, D. J. Mills, D. Charnley and C. J. Harris Introduction It is traditional to initialise Kalman filters and extended Kalman
More informationABSTRACT INTRODUCTION
Design of Stable Fuzzy-logic-controlled Feedback Systems P.A. Ramamoorthy and Song Huang Department of Electrical & Computer Engineering, University of Cincinnati, M.L. #30 Cincinnati, Ohio 522-0030 FAX:
More informationOn the Parametric Study of Lubricating Oil Production using an Artificial Neural Network (ANN) Approach
On the Parametric Study of Lubricating Oil Production using an Artificial Neural Network (ANN) Approach Masood Tehrani 1 and Mary Ahmadi 2 1 Department of Engineering, NJIT, Newark, NJ, USA 07102 2 Depratment
More informationShort-term wind forecasting using artificial neural networks (ANNs)
Energy and Sustainability II 197 Short-term wind forecasting using artificial neural networks (ANNs) M. G. De Giorgi, A. Ficarella & M. G. Russo Department of Engineering Innovation, Centro Ricerche Energia
More informationOne-Hour-Ahead Load Forecasting Using Neural Network
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 17, NO. 1, FEBRUARY 2002 113 One-Hour-Ahead Load Forecasting Using Neural Network Tomonobu Senjyu, Member, IEEE, Hitoshi Takara, Katsumi Uezato, and Toshihisa Funabashi,
More informationA Comparison Between Polynomial and Locally Weighted Regression for Fault Detection and Diagnosis of HVAC Equipment
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com A Comparison Between Polynomial and Locally Weighted Regression for Fault Detection and Diagnosis of HVAC Equipment Regunathan Radhakrishnan,
More informationEfficiently merging symbolic rules into integrated rules
Efficiently merging symbolic rules into integrated rules Jim Prentzas a, Ioannis Hatzilygeroudis b a Democritus University of Thrace, School of Education Sciences Department of Education Sciences in Pre-School
More informationAPPLICATION OF MULTIVARIABLE PREDICTIVE CONTROL IN A DEBUTANIZER DISTILLATION COLUMN. Department of Electrical Engineering
APPLICAION OF MULIVARIABLE PREDICIVE CONROL IN A DEBUANIZER DISILLAION COLUMN Adhemar de Barros Fontes André Laurindo Maitelli Anderson Luiz de Oliveira Cavalcanti 4 Elói Ângelo,4 Federal University of
More informationDESIGN OF AN ADAPTIVE FUZZY-BASED CONTROL SYSTEM USING GENETIC ALGORITHM OVER A ph TITRATION PROCESS
www.arpapress.com/volumes/vol17issue2/ijrras_17_2_05.pdf DESIGN OF AN ADAPTIVE FUZZY-BASED CONTROL SYSTEM USING GENETIC ALGORITHM OVER A ph TITRATION PROCESS Ibrahim Al-Adwan, Mohammad Al Khawaldah, Shebel
More informationIdentification of Nonlinear Systems Using Neural Networks and Polynomial Models
A.Janczak Identification of Nonlinear Systems Using Neural Networks and Polynomial Models A Block-Oriented Approach With 79 Figures and 22 Tables Springer Contents Symbols and notation XI 1 Introduction
More informationNegatively Correlated Echo State Networks
Negatively Correlated Echo State Networks Ali Rodan and Peter Tiňo School of Computer Science, The University of Birmingham Birmingham B15 2TT, United Kingdom E-mail: {a.a.rodan, P.Tino}@cs.bham.ac.uk
More informationNonlinearControlofpHSystemforChangeOverTitrationCurve
D. SWATI et al., Nonlinear Control of ph System for Change Over Titration Curve, Chem. Biochem. Eng. Q. 19 (4) 341 349 (2005) 341 NonlinearControlofpHSystemforChangeOverTitrationCurve D. Swati, V. S. R.
More information