FINAL YEAR PROJECT

FINAL YEAR PROJECT(FYP-CCB 4612)MODELLING FOR COMPOSITION NON-ISOTHERMAL CSTR BY USING FUZZY LOGIC Name: Raihan Fikri Bin RoslanID:15613Supervisor: Nasser Mohamed RamliCourse: Chemical Engineering (CE)Universiti Teknologi PETRONAS (UTP)

CONTENT2BACKGROUND STUDY

Typical Reactor INTRODUCTIONPROFIT $

NEED TO HAVE AN EFFECTIVE CONTROL SYSTEM !! ACCURATE MATHEMATICAL MODEL Inchemical engineering,chemical reactorsare vessels designed to containchemical reactions;

Chemical engineers design reactors to maximize net present value for the given reaction n. Designers ensure that the reaction proceeds with the highest efficiency towards the desired output product, producing the highest yield of product while requiring the least amount of money to purchase and operate.

To do so, we need to have an efficient control system , by controlling the parameters of the reactors.

When dealing with chemical reactions and reactor designing, there are various parameters that is needed to be take care off: TemperatureConcentration Feed flow rate and etc.

In order to gain this high demands ; we need to have an effective control system;

An efficient control of the product concentration in CSTR can be achieved only through accurate model.

Obtaining a mathematical model for a complex system is complex and time consuming as it often requires some assumptions such as defining an operating point and doing linearization about that point and ignoring some system parameters, etc.

This fact has recently led the researchers to exploit the AI techniques using neural and fuzzy tools in modelling complex systems utilizing solely the input-output data sets.

3BACKGROUND STUDY

Typical Reactor Fuzzy Logic ConceptINTRODUCTION

Fuzzy logic provides a practicable way to understand and manually influence the mapping behavior .

Generally, fuzzy logic uses simples rules to describe the system of interest rather than the analytical complex equation. This will make easier to implement.

Beside, there is advantages by using this techniques due to its robustness and speed. It shows that this is the best solution for system modelling and control.

And this project requires the Matlab Simulink Software.

Fuzzy logic= getting the computers to make a decision like a human brain

Uses : fuzzy sets and fuzzy rules

Fuzzy sets the degree of truthFuzzy rules if & then - inference

4BACKGROUND STUDY

Fuzzy Logic ConceptFUZZY SET FUZZY RULESFuzzy logic provides a practicable way to understand and manually influence the mapping behavior .

Generally, fuzzy logic uses simples rules to describe the system of interest rather than the analytical complex equation. This will make easier to implement.

Beside, there is advantages by using this techniques due to its robustness and speed. It shows that this is the best solution for system modelling and control.

And this project requires the Matlab Simulink Software.

Fuzzy logic= getting the computers to make a decision like a human brain

Uses : fuzzy sets and fuzzy rules

Fuzzy sets the degree of truthFuzzy rules if & then - inference

5BACKGROUND STUDY SlowFast

Speed = 0Speed = 1bool speed; get the speed if ( speed == 0) {// speed is slow} else {// speed is fast}

TRADITIONAL REPRESENTATION OF LOGICFuzzy logic provides a practicable way to understand and manually influence the mapping behavior .

Generally, fuzzy logic uses simples rules to describe the system of interest rather than the analytical complex equation. This will make easier to implement.

Beside, there is advantages by using this techniques due to its robustness and speed. It shows that this is the best solution for system modelling and control.

And this project requires the Matlab Simulink Software.

Fuzzy logic= getting the computers to make a decision like a human brain

Uses : fuzzy sets and fuzzy rules

Fuzzy sets the degree of truthFuzzy rules if & then - inference

6BACKGROUND STUDY

FUZZY LOGIC REPRESENTATION

SlowestFastestSlowFast[ 0.0 0.25 ][ 0.25 0.50 ][ 0.50 0.75 ][ 0.75 1.00 ]FUZZY SETS Fuzzy logic provides a practicable way to understand and manually influence the mapping behavior .

Generally, fuzzy logic uses simples rules to describe the system of interest rather than the analytical complex equation. This will make easier to implement.

Beside, there is advantages by using this techniques due to its robustness and speed. It shows that this is the best solution for system modelling and control.

And this project requires the Matlab Simulink Software.

Fuzzy logic= getting the computers to make a decision like a human brain

Uses : fuzzy sets and fuzzy rules

Fuzzy sets the degree of truthFuzzy rules if & then - inference

7BACKGROUND STUDY

SlowestFastest

float speed; get the speed if ((speed >= 0.0)&&(speed < 0.25)) {// speed is slowest} else if ((speed >= 0.25)&&(speed < 0.5)) {// speed is slow}else if ((speed >= 0.5)&&(speed < 0.75)) {// speed is fast}else // speed >= 0.75 && speed < 1.0 {// speed is fastest}

Slow

FastFUZZY LOGIC REPRESENTATION Fuzzy logic provides a practicable way to understand and manually influence the mapping behavior .

Generally, fuzzy logic uses simples rules to describe the system of interest rather than the analytical complex equation. This will make easier to implement.

Beside, there is advantages by using this techniques due to its robustness and speed. It shows that this is the best solution for system modelling and control.

And this project requires the Matlab Simulink Software.

Fuzzy logic= getting the computers to make a decision like a human brain

Uses : fuzzy sets and fuzzy rules

Fuzzy sets the degree of truthFuzzy rules if & then - inference

8Lots of refineries running towards the conventional control system in the plant process in which several of qualities had been ignored. Development of efficient control system requires an accurate model with a complex model equation. A lot assumption has been made due to the lacking of information about on the reaction take places in the reactor.

PROBLEM STATEMENT Problem 1 Conventional control system ( PID control ) ~ is very old technology of control process and several of qualities had been ignored , such as robustness: are designed to function properly provided that uncertain parameters or disturbances are found within some (typicallycompact) . Robust methods aim to achieve robust performance and/orstabilityin the presence of bounded modeling errors.

Problem 2 To achieve the efficient control system requires an accurate model with a complex model equation : This will lead time constraint and gives a lot of assumption that will been made .

Problem 3 Mainly many people had done the modelling without knowing the reaction takes places ( experimentation ) , they just assume a lot of assumption in order to archive their objectives.

9To develop a simulation model for CSTR by using fuzzy logic ( Matlab Simulation ) specifically in finding its compositionTo analyze and justify the effectiveness the fuzzy logic in CSTR by comparing with the conventional control.

OBJECTIVES & SCOPE STUDY10LITERATURE REVIEW CONTINOUS STIRRED TANK REACTOR (CSTR) REVIEW By M.J. Willis (March, 2000) Continuous stirred tank reactor models

The mathematical model of non-isothermal reactor of CSTR

CompositionTemperature

These are the mathematical model of non-isothermal reactor that I had been refer to this article:

11AUTHORDATETITLE FINDINGS/ COMMENTS Yazdani, Movahed and Mahmoudzadeh2013Controller based on Imperialist Competitive AlgorithmFuzzy Concept was designed in the such way to minimize the value of Sum of Square Error (SSE) ~ close to the actual set point. Kratmuller M.2009The adaptive control of Nonlinear system based on T-S-K Fuzzy logic The results shows that by using the concept of Fuzzy logic, doesnt require accurate mathematical model of the system.ARTIFICIAL INTELLIGENCE (AI) TECHNIQUE : FUZZY LOGIC REVIEW LITERATURE REVIEW

12AUTHORDATETITLE FINDINGS/ COMMENTSSuja Malar & Thyagarajan 2009Modelling reactor by using AI techniquesThe result shows that by combination of fuzzy logic and neural network can improve the performance of the control system.LITERATURE REVIEW

Conclusion : Fuzzy logic has given an excellent tracking and regulation of performance compared the othersIt can address a complex control problems without having accurate model equation13By using the Matlab Simulation SoftwareTo generate a sample of baseline or set point data to be tested and used as benchmark later on. These are the assumptions that has been made : Heat loss process negligible with constant mixture density and heat capacityAll are being perfectly mixedThe exit stream has same concentration and temperature with entire reactor liquidOverall heat transfer constant with no energy balance The reactor is a flat bottom vertical cylinder and the jacket is around the outside and the bottom

PROJECT METHODOLOGY

The mathematical equation for CSTR

14

PROJECT METHODOLOGY

PROJECT METHODOLOGY

Project Start-Up Literature analysisMethodology ( Matlab )Data gathering and analysisResult Documentation and report Project End PROJECT ACTIVITIES

Gantt chartKey MilestoneGANTT CHART & KEY MILESTONES

WE ARE HERE To develop a simulation model of CSTR non isothermal condition by using the fuzzy logic ( Simulink, Matlab ) focusing on the composition in the reactor based on the objectives given.To study the effectiveness of the modern controls in CSTR and compare which are the most efficient and stableCan brings more benefits on the development of industrial process control system

CONCLUSION

erka, P., et al. (2015). "Liability for damages caused by artificial intelligence." Computer Law & Security Review 31(3): 376-389.Chang, W.-D. (2013). "Nonlinear CSTR control system design using an artificial bee colony algorithm." Simulation Modelling Practice and Theory 31: 1-9.Heredia-Molinero, M. C., et al. (2014). "Feedback PID-like fuzzy controller for pH regulatory control near the equivalence point." Journal of Process Control 24(7): 1023-1037.Jayakumar, N. S., et al. (2014). "Experimental and modeling of a non-isothermal CSTR to find out parameter regions and conditions causing input multiplicity for acid catalyzed hydrolysis of acetic anhydride." Chemometrics and Intelligent Laboratory Systems 135: 213-222.Gonzlez, B., et al. (2015). "Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition." Expert Systems with Applications 42(14): 5839-5847.Mohd Ali, J., et al. (2015). "Artificial Intelligence techniques applied as estimator in chemical process systems A literature survey." Expert Systems with Applications 42(14): 5915-5931.Shareef, H., et al. (2015). "A novel approach for fuzzy logic PV inverter controller optimization using lightning search algorithm." Neurocomputing 168: 435-453.Suja Malar, R. M. and T. Thyagarajan (2009). "Modelling of continuous stirred tank reactor using artificial intelligence techniques." International Journal of Simulation Modelling 8(3): 145-155.vandov, Z., et al. (2006). "Dynamic behaviour of a CSTR with reactive distillation." Chemical Engineering Journal 119(2-3): 113-120.Yazdani, A. M., et al. (2013). "Controller Design for Non-Isothermal Reactor Based on Imperialist Competitive Algorithm." International Journal of Computer Theory and Engineering: 478-483.

REFERENCES

THANK YOU MODELLING FOR COMPOSITION NON-ISOTHERMAL CSTR BY USING FUZZY LOGIC