Loading Assets...

Ali Chaibakhsh

Ph.D. in Mechanical Engineering

ABOUT ME

Ali Chaibakhsh Langroudi - Ph.D. in Mechanical Engineering

Ali Chaibakhsh is an associate professor in Mechanical Engineering at the University of Guilan (since 2009). He received his B.Sc. degree in 2002 from the University of Guilan in Mechanical Engineering, Rasht, Iran, and his M.Sc. and Ph.D. degrees in 2004 and 2009 from K.N. Toosi University of Technology, Tehran, Iran under supervision of Prof. Ali Ghaffari.  His research interests are intelligent systems including neural networks, fuzzy logic, and soft computing techniques and their applications in industrial processes. His special expertise areas are within modeling, control and fault diagnosis of thermal power plants.

 

Download Resume

EDUCATION

Ph.D. in Mechanical Engineering

K.N. Toosi University of Technology, Tehran, Iran

2009 - 2004

: Thesis Title

Advanced Modeling and Control of Once-through Steam Power Plant

(Supervisor: Prof. A. Ghaffari)

kntu_en2

M.Sc. in Mechanical Engineering

K.N. Toosi University of Technology, Tehran, Iran

2002-2004

: Thesis Title

Experimental Fuzzy Modeling and Control of a Supercritical Once Through Boiler

(Supervisor: Prof. A. Ghaffari)

kntu_en2

B.S. in Mechanical Engineering

University of Guilan, Rasht, Iran

1998-2002

: Thesis Title

Design and Manufacturing of Tennis Robot Based on Counter Rotating Wheels System

r_38_160521140419

Mathematics Diploma

NODET High School Rasht, Iran

1991-1998

untitled

SKILLS

Simulations
100
Matlab
100
Simulink
100
AutoCAD
100
Control Systems
100
Numerical Analysis
100
Modeling
100
Programming
100
Signal Processing
100
Optimization
100
System Control
100
Automation
100
Robotics
100
Heat Transfer
100
Instrumentation
100
Fuzzy Logic
100
Control Theory
100
LabVIEW
100

EXPERIENCE & Qualifications

University Lecturer at Department of Mechanical Engineering in University of Guilan

* Undergraduate Courses:

  • Automatic Control
  • Industrial Control
  • Measurement Systems
  • Mechanical Vibration
  • Hydraulic and Pneumatic Systems
  • Engineering Software

* Graduate Courses:

  • Adaptive Control
  • Advanced Measurement
  • Vibration of Continues Systems
  • M.Sc. Seminar
  • Analysis of Engineering Experiments
  • Robust Control
  • System Identification and Parameter Estimation
  • Probability, Stochastic and Random Variables
  • Multivariable Control Systems
since 2008
2001-2001

Summer Trainee

Iran-Lahijan Steering & Suspension Parts Mfg. Co

– (3 months)Lahijan, Guilan, Iran

This company was established in 1982. The field of activities are automotive, motorcar accessories & supplies, car parts and accessories, vehicle sales, vehicle services business activities.
-manufacturing processes.
- engineering design

Teaching Assistance

K. N. Toosi University of Technology

– (4 years 9 months)Tehra, Iran

Instructor: Prof. Ali Ghaffari
at Department of Mechanical Engineering:
- Automatic Control (Under-graduate )
- Advanced Control (Graduate)

2003-2008
2003-2009

Head of Research and Development

BehinehSazan FSD Co.

– (6 years 2 months)Rasht, Guilan, Iran

Behineh Sazan Faanavaran Sanaat Deylam was established in 2003 (Reg. 8755)
This company involves a vast area from Research and consulting issues to technical inspection, manufacturing and conducting constructive projects.

Senior Research Associate

Advanced Robotics & Automated Systems (ARAS)

– (6 years 3 months)K.N. Toosi University of Technology, Tehran, Iran

ARAS Research group is a center of excellence in the field of robotics and automated systems. The topmost goal of ARAS Research group is to apply research results toward industrial application.

Control System Renovation of Neka Power Plant: Investigation and comparing the control logic of two different generations of power plants,
TAVANIR (Iran Power Generation, Transmission and Distribution Management Company)

- Identification and Modeling of the 4 x 440 MW Power Plants with Benson Type Once-Through Boiler (Neka Power Plant, Mazandaran, Iran)
- Identification and Modeling of the 4 x 380 MW Power Plants with Drum Type Boiler (Arak Power Plant, Markazi, Iran)
- Ranking Project Risks on Industrial Power Plants
- Tuning of Conventional Control System for a 440 MW Fossil Fuel Unit
- Designing a New DEB Coordinated Boiler-Turbine Control System for Power Plant with Once-Through Boiler
- Developing Power Plant Simulator for Dynamical Behavior Studies and Operator Training Purposes
- Introducing New Instruments and Control Systems for a Turbine-Generator Unit
- Performance Assessment of the New Turbine Control System for a Once-Through Boiler-Turbine unit
- Economical Analysis and Energy Management in Implementing New Power Plant Control System

2003-2009
2009-2011

Invited Professor

University of Tehran

–

Fooman Pardis, Guilan, Iran

Supervisor

University of Guilan

–

The Students' Science Club, Uiversity of Guilan, Rasht, Guilan, Iran

2010-2012
since 2011

Training Specialist

Iranian Organization for Engineering Order of Agriculture and Natural Resources

Rasht, Guilan, Iran

Training fresh environmental engineers for operation and maintaining wastewater treatment plant.

Head of Designing and Engineering Department

Behsazan Aab o Khak (Consulting Engineering)

Guilan Science & Technology Park, Rasht, Guilan, Iran

Established in 2005 for research, designing and constructing of water and wastewater treatment plant.

since 2007
since 2011

Technical Protection and Safety Advisor

The Center of Research and Training for Occupational Safety and Health

Rasht, Guilan, Iran

Industrial Boilers and Pressure Vessels inspection services

http://crtosh.mcls.gov.ir

Training Specialist

The Institute for Energy and Hydro Technology (IEHT)

Rasht, Guilan. Iran

The Institute for Energy and Hydro Technology (IEHT) began the modern stage of training in the Ministry of Energy in 1980. The Institute takes action to meet water and power industries educational and research needs within the framework and the regulations of the Ministry of Energy and the Ministry of Science, Research and Technology in short-term training courses in order to promote skills and capabilities of the water and power industries personnel, Graduate and postgraduate programs in collaboration with the University of Applied science and Technology, and research and development in energy and hydro technologies.

since 2010
since 2012

Senior Engineer

Pak Aab Novin Deylam (Engineering and Consulting)

Technical Protection and Safety Advisor

               Industrial Boilers and Pressure Vessels Safety

The Center of Research and Training for Occupational Safety and Health Tehran, Iran
since 2012
2004-2006

Senior Research Associate at Advanced Robotics & Automated Systems

K.N. Toosi University of Technology,

Control System Renovation of Neka Power Plant: Investigation and comparing the control logics of two different generations of power plants. TAVANIR (Iran Power Generation, Transmission and Distribution Management Company)

2004-2006                     Neka Power Plant Co.                      Mazandaran, Iran.

2004-2006                      Shazand Power Plant Co                   Arak, Iran.

University Lecturer, Assistant Professor

Dep. of Mechanical Engineering      Rasht, Guilan, Iran

Since 2009
Since 2008

Mechanical Engineering in University of Guilan

University Lecturer

University Lecturer at Department of Mechanical Engineering in University of Guilan

Guilan Training Center

Professional Training Courses

Professional Training Courses at the Institute for Energy and Hydro Technology – Guilan Training Center 

  • General Instrumentation
  • Process and Instruments drawing (P&ID) for Thermal Power Plants
  • Control Loops for Thermal Power Plants
  • A Tutorial on General Equipments for Steam Power Plants
  • Steam Boilers for Thermal Power Plants
  • Principles of Thermodynamics for Thermal Power Plants Applications
  • A Tutorial on General Equipments for Combined Cycles Power Plants
  • Pump, Fan and Compressor
  • Maintenance and Troubleshooting of the Power Plant’s Boilers
  • Maintenance and Troubleshooting of Steam Turbines
  • Professional Training Courses at Iranian Organization for Engineering Order of Agriculture and Natural Resources - Guilan Province (2012)
  • Instrumentation for Modern Wastewater Treatment Plants
Since 2010
2009-2011

University of Tehran - Fooman Pardis

Invited Professor

Invited Professor at the Department of Chemical Engineering, University of Tehran - Fooman Pardis 

Computer Programming by C#

K.N. Toosi University of Technology

Teaching Assistance

(Teaching Assistance in Control Systems (Graduate and Under Graduate Courses   at Dep. of Mechanical Engineering in K.N. Toosi University of Technolog

2003-2008

PORTFOLIO

  • ALL
  • journal Papers
Feedback-Feedforward Control System Design and Optimizing the Performance of Crude Oil Fired Heater Furnace Using Genetic Algorithm for Abnormal Conditions Management
In this study, feedback-feedforward control system design and optimizing the performance of crude oil furnace process was investigated in order to be recovered from possible abnormal conditions. First, by developing an accurate nonlinear analytical model, the effects of changes in input parameters and operating conditions on the system’s outputs were determined. Then, in order to eliminate the effects of disturbances on furnace, a feedback- feedforward control system for combustion management was suggested, where its performances were optimized genetic algorithm (GA). In addition, to enhance the thermal stability and to maintain product quality, output difference temperature control system was considered for load distribution between furnace’s streams. Also, in order to recover the furnace from abnormal conditions due to burners’ failures, a supervisory system was designed to change the firing rate setpoints. With respect to different failure scenarios, the optimal burners’ firing rate were captured by applying genetic algorithms to the system model. A multilayer perceptron neural network was employed as the core of the controller to interpolate between different conditions. The obtained results indicate the superior performances of the designed control systems.
Feedback-Feedforward Control System Design and Optimizing the Performance of Crude Oil Fired Heater Furnace Using Genetic Algorithm for Abnormal Conditions Management
http://mme.modares.ac.ir/article_14543.html
Fault diagnosis of an industrial once-through benson boiler by utilizing adaptive neuro-fuzzy inference system
Nowadays, the increasing request to have more electric power and the growing complexity of advanced thermal power systems, make it ever more important to improve the performance and reliability of the systems. Hence, an attention is concentrated on fault diagnosis systems to compensate the adverse effects automatically, under conditions of noisy measurement. In order to improve the proficiency of process monitoring and increase accuracy of fault diagnosis (FD) for the once-through Benson type boiler, this article proposed a data driven method based on the configuration of six adaptive neuro fuzzy inference systems (ANFIS). In the proposed structure, due to strong interaction between measurements each ANFIS classifier has been developed to diagnose one particular fault. Finally to evaluate the effectiveness and performance of the proposed FD system against 6 major faults of once-through Benson type boiler under conditions of noisy measurement, different set of test scenarios have been performed.
Fault diagnosis of an industrial once-through benson boiler by utilizing adaptive neuro-fuzzy inference system
http://ieeexplore.ieee.org/document/7482930/
Genetic Optimizing of Hard Computing vs Soft Computing for MR Damper Modeling and Proposing an Invertible Pseudo Static Model
To describe nonlinear behavior of MR dampers as established semi-active devices employed to control vibrations, various models have been proposed which could be classified in hard and soft computing fields. However, only some could mimic hysteretic and highly dynamic characteristics of MR dampers appropriately directly and inversely which is a principle control attribute; more precisely, choosing a qualified invertible model plays a prominent role in a semiactive control, which has not come into sharp focus so far. Thus in this article, first, some best-proposed hard computing (parametric) MR damper models are chosen and identified by genetic optimization under the same conditions. Second, two fuzzy-genetic and neuro-fuzzy models using soft computing techniques are constructed. Then a pseudo static model is proposed, which unlike to accurate dynamic models, have no differential equations and is invertible. Finally, all models subjected to filtered Iranian and foreign earthquakes would be compared. During all phases, experimental data is generated utilizing a benchmark program equipped with large-scale MR dampers, which is proposed by American Society of Civil Engineering (ASCE). Comparisons bring two results: the fuzzy-genetic model is more precise than hard computing ones; and the proposed model performs more effectively than dynamic ones, as it not only demonstrates desirable accuracy and much higher rate, but could easily be inverted.
Genetic Optimizing of Hard Computing vs Soft Computing for MR Damper Modeling and Proposing an Invertible Pseudo Static Model
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwi9womB5u7PAhXCQBQKHQ3CD-MQFggeMAA&url=http%3A%2F%2Fceej.aut.ac.ir%2F%3F_action%3DshowPDF%26sc%3D1%26article%3D562%26_ob%3Da35e5521da09a67520a60ffa4a60ca97%26fileName%3Dfull_text.pdf&usg=AFQjCNEPnx5XKIJEisHJUJrJYMXP-RrTCg
FOULING DETECTION INSIDE THE TUBES OF CRUDE OIL PREHEAT FURNACE USING OPTIMIZED SUPPORT VECTOR MACHINES
In this study, an application of support vector machines is presented for fouling detection and estimating the amount of deposit layer development and tube blockage percentage at the radiation section of the crude oil preheat furnace. Crude oil preheat furnaces are the main elements in processing crude oil in distillation towers, which always suffer from fouling and its consequent risks. In order to predict fouling inside the tubes, first by considering independent input parameters effecting the furnace performance and by using a dynamic model of a particular furnace, the behaviors of the furnace in unusual conditions were simulated. The effects of fouling type and its location inside the tubes were considered on the thermal performances and pressure drops of the furnace. In the second part, based on the different fouling scenarios, a fouling detection mechanism was designed. The operational conditions such as pressure drop inside the tubes, temperatures of the tubes and temperatures of the crude oil were employed for fouling detection and evaluating the thickness of deposits. The obtained results indicated the accuracy and feasibility of the proposed approach.
FOULING DETECTION INSIDE THE TUBES OF CRUDE OIL PREHEAT FURNACE USING OPTIMIZED SUPPORT VECTOR MACHINES
http://en.journals.sid.ir/ViewPaper.aspx?ID=471281
Crude oil direct fired furnace model
In this study, an accurate mathematical model was developed in order to describe the thermal behaviours of a crude oil preheat furnace and to predict the outlet temperature of the crude process at different operating conditions. Based on basic heat and mass transfer rules, and thermodynamic relations, all sub-sections of furnaces including the combustion system, the convection and radiation sections were modelled. The crude process flow was considered as the mixture of 21 different components. The empirical correlations for crude process were adopted for estimating the physical properties of components and the heat transfer coefficients of process fluid for single-phase and two-phase flow regimes at the convection and radiation sections, respectively. The effects of flame height and combustion process conditions were also considered on the furnace dynamics. Available information from operational, geometrical variables and design values were used to define the parameters of the models. In order to show the feasibility and accuracy of the proposed modelling approach, the performances of the developed model were evaluated by comparing its responses with the designed values (on design simulation). Finally, sensitivity analyses were performed by perturbing the model's inputs from nominal conditions to guarantee the capability of the developed model for long-term simulations. Obtained results indicate that the developed model for a direct fired furnace can be used for transient performance analysis at different operating conditions and real-time simulation experiments in MATALB® Simulink environment.
Crude oil direct fired furnace model
http://www.sciencedirect.com/science/article/pii/S1359431115002069
Optimal selection of parameters of a hybrid model of vehicle/passenger for prediction of head injury in front crash
Nowadays, a great number of researches are being done by scientists to provide various models that can predict the passenger injuries in crashes. In this paper, a hybrid model of vehicle and passenger is proposed to predict the head acceleration in the front crash. A lumped mass model with 12-degree-of-freedom (DOF) is first used to predict the behavior of vehicle in front crash. In this model, any member of vehicle is modeled as a lumped mass and connected to the other members through some springs and dampers. The unknown coefficients of such model are obtained using genetic algorithm to minimize the deviation between the results of experimental and suggested model. The parameters of model are established by experimental results of a real world car, namely the HONDA ACCORD2011, in an accident velocity of 48 km/h. Also, the validity of the proposed model is checked by experimental results of the mentioned vehicle at two other crash velocities of 40 km/h, and 56 km/h. The results show that the proposed model is an efficient framework for preliminary design of both structure and parameter design of vehicle to improve its crash worthiness. Moreover, a multi-body dynamic model of driver is proposed to predict the head injury in front crash. The seat acceleration which has been calculated using the vehicle model is considered as input of this model.
Optimal selection of parameters of a hybrid model of vehicle/passenger for prediction of head injury in front crash
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwjwsKyW5-7PAhUGnBoKHWIHB7IQFggtMAI&url=http%3A%2F%2Fwww.sid.ir%2Ffa%2FVEWSSID%2FJ_pdf%2F23813931509.pdf&usg=AFQjCNGovLKf24oTHCLBVmCi5u2TMpwt9g
Long-Term Prediction of Biological Wastewater Treatment Process Behavior via Wiener-Laguerre Network Model
A Wiener-Laguerre model with artificial neural network (ANN) as its nonlinear static part was employed to describe the dynamic behavior of a sequencing batch reactor (SBR) used for the treatment of dye-containing wastewater. The model was developed based on the experimental data obtained from the treatment of an effluent containing a reactive textile azo dye, Cibacron yellow FN-2R, by Sphingomonas paucimobilis bacterium. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was . In order to adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics.
Long-Term Prediction of Biological Wastewater Treatment Process Behavior via Wiener-Laguerre Network Model
https://www.hindawi.com/journals/ijce/2014/248450/abs/
Orthonormal basis function fuzzy systems for biological wastewater treatment processes modeling
In this paper, fuzzy models with orthonormal basis functions (OBF) framework are employed for modeling the nonlinear dynamics of biological treatment processes. These models are consisting of a linear part describing the system dynamics (Laguerre filters) followed by a non-linear static part (fuzzy system). The training procedure contains of two main steps: 1) obtaining the optimum time-scale and the order of truncated Laguerre network as the linear part and 2) defining membership functions, corresponding rules and adjusting the consequent parameters of fuzzy system as the nonlinear part. A comparison between the responses of the developed model and the original plant was performed in order to validate the accuracy of the developed model.
Orthonormal basis function fuzzy systems for biological wastewater treatment processes modeling
http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-d53f41dc-004d-45fe-92ea-0521e608b3bc
THERMO-HYDRAULIC BEHAVIOR MODELING OF PASSIVE HEAT TRANSFER ENHANCEMENT TECHNIQUES USING A SOFT COMPUTING APPROACH
The main aim of this research is to demonstrate effectiveness of soft computing techniques in thermo-hydraulic behavior modeling of passive heat transfer enhancement (HTE) techniques. An artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), two effective modeling methods, have been used to model Nusselt numbers and friction factors of wire coil and twisted tape inserts in various flow regimes. The experimental data sets were utilized for training and validation of these models, and their results were compared with the corresponding correlations. The mean relative error (MRE) between the predicted results and experimental data of ANN and ANFIS models were found to be less than 3% and 1.5% for thermo-hydraulic behavior modeling of wire coil and twisted tape inserts, respectively. Depending on model complexity, performance of both ANN and ANFIS models was found to be superior to that of the corresponding power-law regressions. Hence, application of the soft computing approach to predict the performance of thermal systems in engineering applications is recommended.
THERMO-HYDRAULIC BEHAVIOR MODELING OF PASSIVE HEAT TRANSFER ENHANCEMENT TECHNIQUES USING A SOFT COMPUTING APPROACH
http://www.tandfonline.com/doi/abs/10.1080/00986445.2012.762624
A Model-Based Coordinated Control Concept for Steam Power Plants
An application of model-based coordinated control concept for improving the performance and maneuverability of once-through power plants is presented. In this structure, neuro-fuzzy-based Hammerstein models are employed as the core of feedforward (FF) controller to generate the reference trajectories for the plant’s subsystems. The setpoints for fuel, electrical power, and steam pressure are provided by FF controller with respect to load demands. In order to diminish the effect of disturbances and to compensate deviations between variables and corresponding feedforward setpoints, feedback controllers are considered. The error signal defined by the deviation of desired boiler output pressure from its actual value is used to compensate the demand signals for turbine and boiler. The performance of the proposed control system is compared with that of a conventional turbine-follower and also turbine-follower coordinated control modes during load changes. The results indicate the effectiveness and load tracking capability of the proposed control system at different operating conditions.
A Model-Based Coordinated Control Concept for Steam Power Plants
https://www.hindawi.com/journals/je/2013/808506/abs/
Neuro-Fuzzy Modeling of Heat Recovery Steam Generator
In this paper, an application of dynamic neuro-fuzzy systems is presented for modeling the subsystems of the heat recovery steam generator (HRSG). The dynamic neuro-fuzzy models were developed based on the formal NARX models topology. The clustering techniques were employed to define the structure of the fuzzy models by dividing the entire operating regions into smaller subspaces. The optimal cluster centers and corresponding membership functions are captured by FCM, where the parameters of consequent were adjusted by recursive LSE method. A comparison between the responses of the proposed models and the responses of the plants ware preformed, which validates the accuracy and performance of the modeling approach.
Neuro-Fuzzy Modeling of Heat Recovery Steam Generator
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwj55bC16O7PAhXBPBQKHYjoCEIQFggjMAE&url=http%3A%2F%2Fwww.icmlc.org%2Ficmlc2011%2F016_icmlc2011.pdf&usg=AFQjCNElH3aWIG1Ojl3CCreP2KE7MKPcGg
Modelling and long-term simulation of a heat recovery steam generator
Developing accurate non-linear dynamical models for heat recovery steam generator (HRSG) units is presented in this article. The common non-linear autoregressive with exogenous input (NARX) system topology was employed to develop the neuro-fuzzy models based on the experimental data taken during field experiments. In this structure, the non-linear behaviours of the HRSG unit can be characterized through interpolation of local linear models associated with different operating regions via fuzzy inference mechanism. The operating regimes were recognized by applying a genetic algorithm-based fuzzy clustering technique to the prepared data sets. The structures of the fuzzy models are defined with respect to the obtained optimal cluster centres and the corresponding membership functions. The parameters of fuzzy rules were adjusted by recursive least-squares estimation method to fit the model responses to real data. The performances of developed models were evaluated by performing a comparison between the model responses and the responses of the real plant. In addition, the stability of the developed models was assessed by perturbing the model inputs from the nominal values. This guarantees the long-term simulation capabilities of the developed models. A comparison between the responses of the corresponding models and the models obtained from some recent modelling approaches was performed to show the advantages of the developed models. The results show the accuracy and reliability of the developed models at transient and steady-state conditions.
Modelling and long-term simulation of a heat recovery steam generator
http://www.tandfonline.com/doi/abs/10.1080/13873954.2012.698623
Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process
In this paper, an application of a neuro-fuzzy modeling approach is presented in order to characterize the essential behavior of enzymatic esterification processes. The accuracy of the developed model was validated by comparing the response of the model and actual experimental data. The simulation results showed good generalization of the proposed model and its ability to predict the reaction yield, where the error of prediction for training data was less than 3%, and for validating and testing data less than 3 and 1.5%, respectively. A model-based optimization was performed to obtain the best operating conditions by using genetic algorithm. A fair comparison between the optimization results obtained from simulation experiments and laboratory data indicated the accuracy and feasibility of the proposed approach for estimating the optimal profiles in biotechnological processes. This can further facilitate up-scaling of the process by selecting the appropriate combinations of potential manufacturing parameters
Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process
http://search.proquest.com/openview/0280e2be1cbb8b25c9673f5f3056d01b/1?pq-origsite=gscholar
Experimental Fuzzy Modelling and Control of a Steam Power Plant Boiler
Increasing the use of electricity and the need for more and safer power generation has motivated investigation into new control methods resulting in better performance. Better system performance means increase in power generation efficiency, also decrease in the maintenance costs. To design suitable controllers, adequate information about the system dynamics is required, which in turn has motivated the methods of system identification and simulation studies of power plants. In this paper, simple first-order models are developed for the subsystems of a subcritical once-through boiler, based on the principles of thermodynamics and energy—mass balance, together with parameter estimation routines. These routines are applied on the experimental data obtained from a complete set of field experiments. However, since most processes in a boiler are categorized as multi-input and multi-output systems, mathematical boiler models, which are derived from physical structure and parameters estimation routines, lead to a time-consuming procedure, and employing such models in control algorithms becomes very complex. Therefore, to improve the dynamics modelling, a concise multilayer neuro fuzzy model of the boiler is developed. Next, these two models are compared based on the performance of the real system. This comparison validates the accuracy of both original and neuro fuzzy models, while the latter can be successfully employed in simulation studies, and to design modern model-based control systems. Finally, a new Fuzzy P2 ID controller is developed to use for superheaters temperature control. Simulation results show very good performance of this controller in terms of more accurate and less fluctuation in the temperature of corresponding subsystems, compared to the existing classic controllers.
Experimental Fuzzy Modelling and Control of a Steam Power Plant Boiler
http://www.tandfonline.com/doi/abs/10.1080/02286203.2009.11442545
Steam turbine model
In order to characterize the transient dynamics of steam turbines subsections, in this paper, nonlinear mathematical models are first developed based on the energy balance, thermodynamic principles and semi-empirical equations. Then, the related parameters of developed models are either determined by empirical relations or they are adjusted by applying genetic algorithms (GA) based on experimental data obtained from a complete set of field experiments. In the intermediate and low-pressure turbines where, in the sub-cooled regions, steam variables deviate from prefect gas behavior, the thermodynamic characteristics are highly dependent on pressure and temperature of each region. Thus, nonlinear functions are developed to evaluate specific enthalpy and specific entropy at these stages of turbines. The parameters of proposed functions are individually adjusted for the operational range of each subsection by using genetic algorithms. Comparison between the responses of the overall turbine-generator model and the response of real plant indicates the accuracy and performance of the proposed models over wide range of operations. The simulation results show the validation of the developed model in term of more accurate and less deviation between the responses of the models and real system where errors of the proposed functions are less than 0.1% and the modeling error is less than 0.3%.
Steam turbine model
http://www.sciencedirect.com/science/article/pii/S1569190X08001196
A simulated model for a once-through boiler by parameter adjustment based on genetic algorithms
In this paper based on the physical rules, thermodynamics principles and energy mass balance, the simulation models are developed and applied to electrical power generating plants in order to characterize the essential dynamic behavior of the boiler subsystems and to use the corresponding models for the power plant processes. These models are developed for a sub-critical once through Benson type boiler based on the experimental data obtained from a complete set of field experiments. An optimization approach based on genetic algorithm (GA) is executed to estimate the model parameters and fit the models response on the real system dynamics. Comparison between the responses of the corresponding models with the response of the real plants validates the accuracy and performance of modeling approach. A similar comparison between the responses of these models with linear parametric models shows the effectiveness and feasibility of the developed model in term of more accurate and less deviation between the responses of the models and the corresponding subsystems.
A simulated model for a once-through boiler by parameter adjustment based on genetic algorithms
http://www.sciencedirect.com/science/article/pii/S1569190X07000731
RANKING PROJECT RISKS USING MADM METHODOLOGIES
With respect to uncertainties in projects and the need to optimum allocation of resources, Risk management (RM) is considered as an important phase of the project management (PM) process. As well, ranking risk’s (RR) of the project is the critical step of the risk analysis phase in a RM. Risk ranking is the process of prioritizing the risks with the use of some criterions. In most standard methods only two indexes, probability of occurrence and consequence of risks, are being used in the risk analysis and risk ranking. Some other indexes such as “uncertainty of estimates” and “ability to response” have been used by some other researchers. There are both qualitative and quantitative methods to rank different risks of a project. Within this article, various MADM methodologies, as quantitative approaches, are studied in order to rank the risks of the projects. Each MADM methodology has its own limitations and attributes, and the decision maker cannot use them in all decision-making problems. Using MADM methodology to priority different alternatives of a decision problem needs to consider both the characteristics of the chosen methodology and attributes of the problem itself. Despite, reaching to wrong priorities of the alternatives would be unavoidable. Therefore, different MADM methods are studied from point of using them in a RR problem. Appropriate MADM methods for this purpose are discussed in this paper. It is shown that only compensatory methods can be used in a RR problem. Finally, a real world case study, ranking risks of a power plant renovation project, is presented. To solve the problem, four criteria are considered; including probability of occurrence, consequence of risk, ability to response, and uncertainty of estimates. The TOPSIS algorithm is implemented to solve the problem in the case study. It is suggested to implement more studies to find the most suitable MADM method for the purpose of ranking project risks.
RANKING PROJECT RISKS USING MADM METHODOLOGIES
http://en.journals.sid.ir/ViewPaper.aspx?ID=106764
Soft computing approach for modeling power plant with a once-through boiler
In this paper, a soft computing approach is presented for modeling electrical power generating plants in order to characterize the essential dynamic behavior of the plant subsystems. The structure of the soft computing method consists of fuzzy logic, neural networks and genetic algorithms. The measured data from a complete set of field experiments is the basis for training the models including the extraction of linguistic rules and membership functions as well as adjusting the other parameters of the fuzzy model. The genetic algorithm is applied to the modeling approach in order to optimize the procedure of the training. Comparison between the responses of the proposed models with the responses of the plants validates the accuracy and performance of the modeling approach. A similar comparison between the responses of these models with the models obtained based on the thermodynamical and physical relations of the plant shows the effectiveness and feasibility of the developed model in terms of more accurate and less deviation between the responses of the models and the corresponding subsystems.
Soft computing approach for modeling power plant with a once-through boiler
http://www.sciencedirect.com/science/article/pii/S0952197606001928

SOCIAL CONNECT

CONTACT

Ali Chaibakhsh Langroudi, Ph.D.

Department of Mechanical Engineering

University of Guilan, Rasht, Guilan, Iran

ZIP: 41996-13769      POB: 41635-3756

Tel: +98-13-33690055

Fax: +98-13-33690271

URL: http://staff.guilan.ac.ir/chaibakhsh/?lg=1

Email: chaibakhsh@guilan.ac.ir

Latest Comments

Flickr Photos