UNIVERSITI PUTRA MALAYSIA DESIGN OF PROFILE CONTROLLER FOR BIOCHEMICAL REACTOR ARASH ASSADZADEH GHARAHVARAN FK 2010 44
DESIGN OF PROFILE CONTROLLER FOR BIOCHEMICAL REACTOR By ARASH ASSADZADEH GHARAHVARAN Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfilment of the Requirements for the Degree of Master of Science September 2010
DEDICATIONS To My ever-encouraging parents for their love and supports ii
Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment of the requirement for the degree of Master of Science ABSTRACT DESIGN OF PROFILE CONTROLLER FOR BIOCHEMICAL REACTOR By ARASH ASADZADEH GHARAHVARAN September 2010 Chair: Professor Sudhanshu Shekhar Jamuar, PhD Faculty: Engineering The biotechnology industry is growing sharply due to progress in understanding of complex biological systems and high demand for chemical and biologically manufactured products. Bioreactors are used for the production of materials like ethanol, or extraction of enzymes from microorganisms, animal or plant cells. In order to maximize the productivity, the bioreactor needs optimal conditions for process parameters such as ph, temperature, and dissolved oxygen (DO). The goal of achieving high performance controller is the answer to these demands. There are many types of process controllers like the ON/OFF controller, PID controller, and controller based on an artificial intelligence. Neural network and PID controllers have been used together to learning system features, and to reduce the residual error as well as to replace currently PID controllers in the proposed design. This research focuses on designing a profile controller for ph, DO, and temperature that affects production of ethanol. The temperature is controlled by the following a iii
cooling agent, and the ph is controlled by adding the appropriate amount of base or acid, while the dissolved oxygen is controlled by changing the speed of the stirrer. The inputs to the bioreactor are cooling agent (F ag ), flow of base (F b ), and stirring speed of the liquid (N stir ). The parameters that need to follow a given profile are temperature, ph, and dissolved oxygen. This thesis presents the use of Inverse Neural Networks (INN) for temperature control of a biochemical reactor and its effect on ethanol production. The process model is derived indicating the relationship between temperature, ph and dissolve oxygen. Using the fundamental model obtained data sets; an inverse neural network has been trained by using the back-propagation learning algorithm. Two types of temperature profile are used to compare the performance of the controllers. The controllers have been simulated to have a quantitative comparison with two types of the controllers and show the effectiveness of the INN controller versus the conventional PID controller. The results obtained by the neural network based INN controller and by PID controller are presented and compared. There is an improvement in performance of INN controller in ISE over PID controller. iv
Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk ijazah Master Sains REKABENTUK BAGI PROFIL PENGAWALAN UNTUK REAKTOR Pengerusi : Fakulti BIOKIMIA Oleh ARASH ASADZADEH GHARAHVARAN September 2010 Professor Sudhanshu Shekhar Jamuar, PhD : Kejuruteraan Industri bioteknologi sedang pesat membangun sejajar dengan kemajuan dalam pemahaman kompleks sistem biologi dan permintaan tinggi dalam pengilangan produk kimia dan biologi. Bioreaktor digunakan bagi pengeluaran bahan-bahan seperti ethanol atau ekstrak enzim-enzim daripada mikroorganisma haiwan, sel-sel haiwan, sel-sel tumbuhan, dan sebagainya. Bagi memaksimumkan produktiviti, bioreaktor memerlukan keadaan yang optimum bagi proses parameter seperti ph, suhu dan oksigen terlarut (DO). Matlmat pencapaian prestasi dalam sistem pengawal adalah jawapan kepada proses-proses ini. Terdapat banyak jenis proses-proses pengawalan seperti pengawal ON/OFF, pengawal PID, dan pengawal yang berdasarkan kepada kecerdasan buatan. Jaringan saraf dan pengawal PID telah digunakan bersama-sama bagi membangunkan kebolehkawalan, ciri-ciri sistem pembelajaran dan untuk mengurangkan tahap kesilapan serta menggantikan satu pengawal PID terkini dalam cadangan rekabentuk. v
Penyelidikan ini tertumpu di dalam mereka satu pengawal bagi ph, DO dan suhu yang menjejaskan penghasilan etanol. Suhu ini dikawal dengan pengaliran satu agen penyejukan dan ph adalah dikawal dengan menambahkan jumlah asas atau asid, manakala oksigen terlarut adalah dikawal dengan menukar kelajuan mengacau. Input-input bagi bioreaktor adalah agen penyejukan ( ), aliran asas ( ), dan kelajuan mengacau cecair ( ). Parameter-parameter yang diperlukan untuk mengikuti profil yang telah diberikan adalah suhu, ph dan oksigen terlarut. Tesis ini membentangkan mengenai penggunaan Inverse Neural Networks (INN) bagi kawalan suhu reactor biokimia dan kesannya kepada penghasilan etanol. Proses ini menunjukkan hubungan antara suhu, ph dan oksigen terlarut. Dengan menggunakan model asas yang merangkumi set-set data, jaringan saraf sonsang yang terlatih dengan menggunakan kaedah algoritma. Dua jenis suhu profil yang digunakan bagi membandingkan prestasi alat-alat pengawal. Alat-alat pengawal telah disimulasikan bagi membuat suatu perbandingan antara dua jenis alat pengawal dan menunjukkan kecekapan alat pengawal INN dengan alat pengawal PID konvensional. Keputusannya adalah dengan memperoleh jaringan saraf alat pengawal INN dan alat pengawal PID yang telah dibentang dan dibandingkan. Terdapat satu peningkatan di dalam alat pengawal INN di dalam penetapan jangka masa alat pengawal PID. vi
ACKNOWLEDGEMENTS I wish to express my sincere gratitude to Professor Dr. Sudhanshu Shekhar Jamuar, the chairperson of my supervisory committee, for his invaluable guidance, patience, understanding, encouragement, and supervision throughout the study until the completion of this thesis. I am also grateful to other members of the supervisory committee, Dr. Samsul, and Dr. Fakhrul for their comments. I would like to express my special thanks to my truly friends, Dr. Mahmood Akhavan Mahdavi, and Mr. Esmaeel Atashpaz for their help and advises at all times, KEE department staff and numerous people who have walked with me along this way. My deep love to my parents, Hosein Asadzadeh and Bibi Mansureh Hoseini Fatemi, for their unconditional supports, assistance, encouragement, and patience throughout this long process of study. Once again, thank you to everyone who has aid me in the completion of this thesis. I am humbly grateful for your care. Sincerely Arash Assadzadeh vii
This thesis was submitted to the Senate of Universiti Putra Malaysia and has been accepted as fulfilment of the requirement for the degree of Master of Science. Members of the Supervisory Committee were as follows: Sudhanshu Shekhar Jamuar, PhD Professor Faculty of Engineering Universiti Putra Malaysia (Chairman) Fakhru l Razi Bin Ahmadun, PhD Professor Faculty of Engineering Universiti Putra Malaysia (Member) Samsul Bahari Mohd Noor, PhD Lecturer Faculty of Engineering Universiti Putra Malaysia (Member) HASANAH MOHD GHAZALI, PhD Professor and Dean School of Graduate Studies Universiti Putra Malaysia Date: viii
DECLARATION I declare that the thesis is my original work except for quotations and citations which have been duly acknowledged. I also declare that it has not been previously, and is not concurrently, submitted for other degree at Universiti Putra Malaysia or at any other institution. ARASH ASADZADEH GHARAHVARAN Date: 20/9/2010 ix
TABLE OF CONTENTS Page DEDICATIONS ABSTRACT ABSTRAK ACKNOWLEDGEMENTS DECLARATION LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS LIST OF NOTATION CHAPTER ii iii Error! Bookmark not defined. vii ix xii xiii xvi xvii Error! Bookmark not defined. 1 INTRODUCTION 1 1.1 Introduction to the Biochemical Reactor 1 1.2 Definition of Biochemical Reactor 2 1.3 Biochemical Reactor Technology 4 1.4 Problem statement 8 1.5 Aim and Objectives 9 1.6 Scope of work 10 1.7 Thesis layout 11 2 A REVIEW OF BIOREACTOR SYSTEM 13 2.1 Introduction 13 2.2 Schematic and Modelling of Bioreactor 14 2.3 Temperature Effects 15 2.4 ph Effects 17 2.5 Dissolved Oxygen Effects in Microorganism 20 2.5.1 Oxygen Transfer 21 2.5.2 Oxygen Demands 22 2.6 Process Parameters 22 2.6.1 Temperature control of bioreactor 22 2.6.2 ph control 23 2.6.3 Control of dissolved oxygen (DO) 24 2.7 Kinetic Pattern of Growth and Product Formation 25 2.8 Bioreactor Controllers 27 2.9 Summary 37 3 METHODOLOGY 39 3.1 Introduction 39 3.2 Mathematical Model of the Biochemical Reactor 39 3.3 Bioreactor Model 41 x
3.4 Model for Dissolved Oxygen 42 3.5 Model for Temperature Profile 44 3.6 Model for ph profile 46 3.7 Dynamics of process 47 3.8 Decoupling 50 3.9 Control Systems Design 51 3.10 Brief Description of Neuron 52 3.11 Activation Function 53 3.12 Training Data Set 54 3.13 Back-propagation Training Algorithm 56 3.14 Direct Inverse Control 60 3.15 Number of Hidden Layer 61 3.16 Number of neurons in each hidden layer 62 3.17 Error Convergence of the Neural Network 63 3.18 Temperature Control System 64 3.19 ph Control System 65 3.20 Dissolved Oxygen Control System 65 3.21 PID Controller Setting (Tuning) 66 3.22 Integral Square Error (ISE) 69 3.23 Implementation of Bioreactor Process in MATLAB 70 3.23.1 Controller Strategy 70 3.23.2 Bioreactor Controller Design 71 3.23.3 Dissolved Oxygen Bioreactor Controller 72 3.23.4 ph Controller 73 3.24 Summary 74 4 RESULTS AND DISCUSSIONS 75 4.1 Introduction 75 4.2 Temperature Profile 75 4.3 ph Profile 77 4.4 Dissolved oxygen profile 78 4.5 Simulation Results of Profile Controller 78 4.5.1 Temperature Responses for First Profile 79 4.5.2 Temperature Responses for Second Profile 82 4.6 Dynamics of Process 86 4.7 Conclusions 89 5 CONCLUSION AND RECOMMENDATION 90 5.1 Conclusion 90 5.2 Research Contribution 91 5.3 Suggestion for Future Work 92 REFERENCES 94 APPENDICES 98 BIODATA OF STUDENT 106 LIST OF PUBLICATIONS 107 LIST OF CORRECTIONS 1 xi