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Ali Chaibakhsh

Ph.D. in Mechanical Engineering


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.



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)


M.Sc. in Mechanical Engineering

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


: Thesis Title

Experimental Fuzzy Modeling and Control of a Supercritical Once Through Boiler

(Supervisor: Prof. A. Ghaffari)


B.S. in Mechanical Engineering

University of Guilan, Rasht, Iran


: Thesis Title

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


Mathematics Diploma

NODET High School Rasht, Iran




Control Systems
Numerical Analysis
Signal Processing
System Control
Heat Transfer
Fuzzy Logic
Control Theory

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

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)


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


Invited Professor

University of Tehran

Fooman Pardis, Guilan, Iran


University of Guilan

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

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


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

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

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



  • 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
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
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
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
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
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
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
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.