Calculation force estimation of algorithm
Publish: 2021-04-18 07:37:44
1.
You can refer to the following, according to some commonly used graphics cards in the Internet bar market, sort out the price and calculation power of a related graphics card, as well as the expected return to the current period, It can be used as a reference:
{rrrrrrr}
power consumption: 243w
computing power: 22.4m
price of graphics card: 1999 yuan
quantity of eth g every 24 hours: 0.015
revenue generated every 24 hours: 24.48 yuan
expected payback time: 81.66 days
power consumption: 159w
computing power: 24.3m
price of graphics card: 1599 yuan Yuan
number of eth g every 24 hours: 0.017
revenue generated every 24 hours: 27.9 yuan
estimated payback time: 57.31 days
total power consumption: 171w
computing power: 24.4m
price: 1999 yuan
number of eth g every 24 hours: 0.017
revenue generated every 24 hours: 27.87 yuan
estimated payback time: 71.73 days
Video card (graphics card) full name display interface card, also known as display adapter, is the most basic configuration of computer, one of the most important accessories. As an important part of the computer host, the graphics card is the equipment of digital to analog signal conversion, and it undertakes the task of output display graphics
the graphics card is connected to the main board of the computer, which converts the digital signal of the computer into an analog signal for the display. At the same time, the graphics card still has the ability of image processing, which can help the CPU work and improve the overall running speed. For those engaged in professional graphic design, graphics card is very important. Civil and military graphics chip suppliers mainly include amd (ultra micro semiconctor) and NVIDIA (NVIDIA). Today's TOP500 computer, including graphics card computing core. In scientific computing, graphics card is called display accelerator
2. Hello, you should be talking about the so-called computing power of some blockchain platforms. Now, the algorithms of these platforms are uneven. The real blockchain platform in China is actually zero. This computing power is calculated according to the user's activity and some other statistical rates.
3. Generally, you can't be hungry or rich, but the iron rice bowl is still very good
4. It's a game of drumming and passing on flowers, buying at a low price, waiting for new people to take orders at a high price to earn a difference. And everyone doesn't think he is the last one to take orders. You pit me, I pit him, and it doesn't bring any real value to the transaction. They don't know what's fun and what's the meaning of making this money
if the price can not be stable and fluctuates too much, bitcoin and other digital currencies will always be a game
however, one thing is that its powerful anonymity and separation from government regulation play a significant role in the dark network.
if the price can not be stable and fluctuates too much, bitcoin and other digital currencies will always be a game
however, one thing is that its powerful anonymity and separation from government regulation play a significant role in the dark network.
5. Now calculators that can calculate calculus in the world all adopt the numerical calculation method, that is, within the display accuracy range of calculators, Newton iteration method and other algorithms are used to convert the calculation of calculus into elementary operation such as addition, subtraction, multiplication, division and power.
6.
as the core technology of fermentation instry, fermentation process control and optimization technology is not only related to the maximum proction capacity of bacteria, but also affects the difficulty of downstream treatment, which is a key technology connecting the preceding and the following in the whole fermentation process. The author of this book has been engaged in the research of on-line detection, analysis, control and optimization of fermentation process for many years. Based on the latest research achievements abroad and the research examples completed by the author himself, this book is written with the advantages of many experts
the whole book systematically and detailedly introces the analysis, control and optimization of fermentation process, especially online detection, online state prediction and pattern recognition, as well as the technology and method of online control and optimization control, and introces new control methods such as fuzzy logic reasoning, artificial neural network model, metabolic network model, etc Optimization, state prediction, pattern recognition and other methods and technologies
this book is suitable for researchers, teachers and engineers engaged in fermentation engineering, bioengineering, biochemical engineering, chemical engineering and other related professional fields, as well as for senior undergraates and postgraates of related majors in Colleges and universities. Contents
Chapter 1 Introction 1
section 1 Characteristics of biological process and basic characteristics of operation, control and optimization of biological process 1
section 2 objectives and research contents of biological process control and optimization 2
section 3 Introction to fermentation process control 4
section 4 state variables of fermentation process Operating variables and measurable variables 6
section 5 various mathematical models for fermentation process control and optimization 7
section 6 Introction to optimization control methods for fermentation process 8
I. optimization control methods based on non structural dynamic models 8
II Optimization control method based on real-time process input and output time series data and black box model 9
references 10
Chapter 2 on line detection technology of biological process parameters 11
section 1 on line measurement of pH 13
I. working principle of pH sensor 13
II. Use of pH sensor 15
section 2 on line measurement of dissolved oxygen concentration 18
I Principle of dissolved oxygen concentration measurement 18
2. Dissolved oxygen electrode 19
3. Use of dissolved oxygen electrode 21
3. Measurement of partial pressure of oxygen and carbon dioxide in fermentor and calculation of respiratory metabolic parameters 23
1. Oxygen analyzer 23
2. Detection of partial pressure of carbon dioxide in tail gas 26
3 Calculation of respiratory metabolic parameters 26
section 4 measurement of oxygen volumetric mass transfer coefficient KLA in fermentor 31
I. sulfite oxidation method 31
II. Dissolved oxygen electrode method 32
III. material balance algorithm 33
IV. dynamic determination method 34
v. sampling polarography 35
VI Determination of kla35 by membrane electrode section 5 on line measurement of cell concentration in fermentor and calculation of specific proliferation rate 36
I. detection method and principle of cell concentration 36
II. On line laser turbidimeter 38
section 6 Application of biosensor in fermentation process 39
I. type and structure principle of biosensor 39
II On line measurement of substrate (glucose, etc.) concentration in fermentor 43
3. Drainage analysis and control (FIA) 45
4. Primary metabolites (ethanol, glucose, etc.) in fermentor On line measurement of organic acid concentration 47
references 48
Chapter 3 control system and control design principle and application of fermentation process 49
section 1 equation of state 49
section 2 typical and basic mathematical models of biological process 51
I. the most basic synthesis and metabolic decomposition reactions of biological process 51
II Typical mathematical model of biological process 55
3. Various yield coefficients and specific reaction rates of fermentation process 57
4. Basic operation mode of bioreactor 62
5. Linearization of equation of state of fermentation process near "ideal operation point" 64
section 3 Laplace transform and anti Laplace transform 67
1 Definition of Laplace transform 68
2. Basic characteristics of Laplace transform and Laplace transform of basic function 68
3. Anti Laplace transform 69
4. Anti Laplace transform of rational function 69
5. Transfer function GP (s) - Laplace function of linear equation of state 69
6 Block diagram and transformation of process transfer function 70
7. Response characteristics of process to changes of input variables 71
section 4 stability analysis of process 74
1. Criteria for process stability 74
2. Classification of stability characteristics of process near equilibrium point 75
3 Analysis of the stability characteristics of continuous stirred bioreactor 77
section 5 feedback control and feedforward control of biological process 79
I. feedforward control of biological process 79
II. Feedforward control methods commonly used in fed batch operation of biological process 80
III. feedback control of biological process 83
IV The combination of feedback control and feedforward control in biological process 84
section 6 design and analysis of PID feedback control system 86
I. performance characteristics of closed loop PID feedback control 86
II. Proportional action 87
III. integral action 88
IV. differential action 89
v. composition characteristics of PID feedback controller 89
VI. stability analysis of feedback control system 89
VII Design and parameter adjustment of feedback control system 91
8. On off feedback control 94
7. Practical application of feedback control system in biological process control; Stat method 95
2. Fed batch culture control with pH change as feedback index pH & 57361; Stat method 98
3. Control of fed batch culture with RQ as feedback index 100
4. Control of fed batch culture with glucose concentration as feedback index 101
5. Control of fed batch culture with metabolic by-proct concentration as feedback index 103
references 105
Chapter 4 optimization control of fermentation process 106
section 1 research content, expression and application of optimization control Characteristics and methods106
section 2 maximum principle and its application in optimal control of fermentation process Numerical solution of maximum principle and its application in optimal control of biological process 116
section 3 Green's theorem and its application in optimal control of fermentation process 121
1. Green's theorem 121
2. Using Green's theorem to solve the shortest time orbit problem of fed batch Culture (fermentation) 122
3 Application of Green's theorem in optimal control of lactic acid bacteria filtration culture 125
4. Computer simulation and experimental results of optimal control of lactic acid bacteria filtration culture by using Green's theorem 128
section 4 genetic algorithm and its application in optimal control of fermentation process 131
1. Introction of genetic algorithm 131
2 Summary of genetic algorithm and its application in optimal control of recombinant E.coli culture 132
3. Application of genetic algorithm in optimal proction of yoghurt polysaccharide 138
references 143
Chapter 5 modeling and state prediction of fermentation process 144
section 1 introction of various mathematical models describing fermentation process 144
1 Non structural dynamic model 145
2. Metabolic network model 146
3. Autoregressive average moving model based on online time series data 146
4. Artificial neural network model 147
5. Orthogonal or polynomial regression model 148
section 2. Modeling methods of non structural dynamic mathematical model 148
1 Using nonlinear programming to determine the model parameters of non structural dynamic mathematical model 148
2. Using genetic algorithm to determine the process model parameters 157
3. Using artificial neural network to model and predict the state of fermentation process 159
1. Neural cell and artificial neural network model 159
2. The type of artificial neural network model 161
3 Error back propagation learning algorithm of artificial neural network 163
4. On line identification of physiological state and concentration change of fermentation process using artificial neural network 167
5. Prediction model of fermentation process state variables using artificial neural network 169
6. Nonlinear regression model using artificial neural network 173
7 Process optimization based on artificial neural network model and genetic algorithm Using Kalman filter to estimate the specific growth rate of bacteria on-line 178
references 180
Chapter 6 online adaptive control of fermentation process 182
section 1 Analysis of autoregressive moving average model based on online time series input and output data 184
I. detailed explanation of autoregressive moving average model 184
II The parameters of the autoregressive moving average (ARMA) model are calculated and determined by the method of successive least squares regression (SLR).
section 2 online adaptive control based on the ARMA model Online adaptive optimal control of specific growth rate of yeast fed batch culture process 193
IV. online adaptive control of lactic acid continuous filtration fermentation process 196
section 3 online optimal control based on autoregressive moving average model 201
I. online optimal control of continuous proction of baker's yeast 201
II On line optimal control of continuous filtration fermentation of lactic acid 205
section 4: on line optimal control based on genetic algorithm On line optimal control based on maximum principle and genetic algorithm Implementation and implementation of fuzzy rules -- Methods of solving fuzzy rules 225
v. composition, design and adjustment of fuzzy logic control system 228
section 2 practical application of fuzzy logic control system in fermentation process 231
I. fuzzy control of yeast fed batch culture 231
II. Fuzzy control of glutamic acid fed batch fermentation 237
III Fuzzy control of coenzyme Q10 fermentation process Fuzzy neural network control system and its practical application in fermentation process 253
3. Fuzzy neural network controller and its application in fermentation process 260
references 268
Chapter 8 process control and optimization using metabolic network model 270
section 1 Analysis of metabolic network model 270
1. Simplification, calculation and solution of metabolic network model 272
2 Utilization of metabolism
as the core technology of fermentation instry, fermentation process control and optimization technology is not only related to the maximum proction capacity of bacteria, but also affects the difficulty of downstream treatment, which is a key technology connecting the preceding and the following in the whole fermentation process. The author of this book has been engaged in the research of on-line detection, analysis, control and optimization of fermentation process for many years. Based on the latest research achievements abroad and the research examples completed by the author himself, this book is written with the advantages of many experts
the whole book systematically and detailedly introces the analysis, control and optimization of fermentation process, especially online detection, online state prediction and pattern recognition, as well as the technology and method of online control and optimization control, and introces new control methods such as fuzzy logic reasoning, artificial neural network model, metabolic network model, etc Optimization, state prediction, pattern recognition and other methods and technologies
this book is suitable for researchers, teachers and engineers engaged in fermentation engineering, bioengineering, biochemical engineering, chemical engineering and other related professional fields, as well as for senior undergraates and postgraates of related majors in Colleges and universities. Contents
Chapter 1 Introction 1
section 1 Characteristics of biological process and basic characteristics of operation, control and optimization of biological process 1
section 2 objectives and research contents of biological process control and optimization 2
section 3 Introction to fermentation process control 4
section 4 state variables of fermentation process Operating variables and measurable variables 6
section 5 various mathematical models for fermentation process control and optimization 7
section 6 Introction to optimization control methods for fermentation process 8
I. optimization control methods based on non structural dynamic models 8
II Optimization control method based on real-time process input and output time series data and black box model 9
references 10
Chapter 2 on line detection technology of biological process parameters 11
section 1 on line measurement of pH 13
I. working principle of pH sensor 13
II. Use of pH sensor 15
section 2 on line measurement of dissolved oxygen concentration 18
I Principle of dissolved oxygen concentration measurement 18
2. Dissolved oxygen electrode 19
3. Use of dissolved oxygen electrode 21
3. Measurement of partial pressure of oxygen and carbon dioxide in fermentor and calculation of respiratory metabolic parameters 23
1. Oxygen analyzer 23
2. Detection of partial pressure of carbon dioxide in tail gas 26
3 Calculation of respiratory metabolic parameters 26
section 4 measurement of oxygen volumetric mass transfer coefficient KLA in fermentor 31
I. sulfite oxidation method 31
II. Dissolved oxygen electrode method 32
III. material balance algorithm 33
IV. dynamic determination method 34
v. sampling polarography 35
VI Determination of kla35 by membrane electrode section 5 on line measurement of cell concentration in fermentor and calculation of specific proliferation rate 36
I. detection method and principle of cell concentration 36
II. On line laser turbidimeter 38
section 6 Application of biosensor in fermentation process 39
I. type and structure principle of biosensor 39
II On line measurement of substrate (glucose, etc.) concentration in fermentor 43
3. Drainage analysis and control (FIA) 45
4. Primary metabolites (ethanol, glucose, etc.) in fermentor On line measurement of organic acid concentration 47
references 48
Chapter 3 control system and control design principle and application of fermentation process 49
section 1 equation of state 49
section 2 typical and basic mathematical models of biological process 51
I. the most basic synthesis and metabolic decomposition reactions of biological process 51
II Typical mathematical model of biological process 55
3. Various yield coefficients and specific reaction rates of fermentation process 57
4. Basic operation mode of bioreactor 62
5. Linearization of equation of state of fermentation process near "ideal operation point" 64
section 3 Laplace transform and anti Laplace transform 67
1 Definition of Laplace transform 68
2. Basic characteristics of Laplace transform and Laplace transform of basic function 68
3. Anti Laplace transform 69
4. Anti Laplace transform of rational function 69
5. Transfer function GP (s) - Laplace function of linear equation of state 69
6 Block diagram and transformation of process transfer function 70
7. Response characteristics of process to changes of input variables 71
section 4 stability analysis of process 74
1. Criteria for process stability 74
2. Classification of stability characteristics of process near equilibrium point 75
3 Analysis of the stability characteristics of continuous stirred bioreactor 77
section 5 feedback control and feedforward control of biological process 79
I. feedforward control of biological process 79
II. Feedforward control methods commonly used in fed batch operation of biological process 80
III. feedback control of biological process 83
IV The combination of feedback control and feedforward control in biological process 84
section 6 design and analysis of PID feedback control system 86
I. performance characteristics of closed loop PID feedback control 86
II. Proportional action 87
III. integral action 88
IV. differential action 89
v. composition characteristics of PID feedback controller 89
VI. stability analysis of feedback control system 89
VII Design and parameter adjustment of feedback control system 91
8. On off feedback control 94
7. Practical application of feedback control system in biological process control; Stat method 95
2. Fed batch culture control with pH change as feedback index pH & 57361; Stat method 98
3. Control of fed batch culture with RQ as feedback index 100
4. Control of fed batch culture with glucose concentration as feedback index 101
5. Control of fed batch culture with metabolic by-proct concentration as feedback index 103
references 105
Chapter 4 optimization control of fermentation process 106
section 1 research content, expression and application of optimization control Characteristics and methods106
section 2 maximum principle and its application in optimal control of fermentation process Numerical solution of maximum principle and its application in optimal control of biological process 116
section 3 Green's theorem and its application in optimal control of fermentation process 121
1. Green's theorem 121
2. Using Green's theorem to solve the shortest time orbit problem of fed batch Culture (fermentation) 122
3 Application of Green's theorem in optimal control of lactic acid bacteria filtration culture 125
4. Computer simulation and experimental results of optimal control of lactic acid bacteria filtration culture by using Green's theorem 128
section 4 genetic algorithm and its application in optimal control of fermentation process 131
1. Introction of genetic algorithm 131
2 Summary of genetic algorithm and its application in optimal control of recombinant E.coli culture 132
3. Application of genetic algorithm in optimal proction of yoghurt polysaccharide 138
references 143
Chapter 5 modeling and state prediction of fermentation process 144
section 1 introction of various mathematical models describing fermentation process 144
1 Non structural dynamic model 145
2. Metabolic network model 146
3. Autoregressive average moving model based on online time series data 146
4. Artificial neural network model 147
5. Orthogonal or polynomial regression model 148
section 2. Modeling methods of non structural dynamic mathematical model 148
1 Using nonlinear programming to determine the model parameters of non structural dynamic mathematical model 148
2. Using genetic algorithm to determine the process model parameters 157
3. Using artificial neural network to model and predict the state of fermentation process 159
1. Neural cell and artificial neural network model 159
2. The type of artificial neural network model 161
3 Error back propagation learning algorithm of artificial neural network 163
4. On line identification of physiological state and concentration change of fermentation process using artificial neural network 167
5. Prediction model of fermentation process state variables using artificial neural network 169
6. Nonlinear regression model using artificial neural network 173
7 Process optimization based on artificial neural network model and genetic algorithm Using Kalman filter to estimate the specific growth rate of bacteria on-line 178
references 180
Chapter 6 online adaptive control of fermentation process 182
section 1 Analysis of autoregressive moving average model based on online time series input and output data 184
I. detailed explanation of autoregressive moving average model 184
II The parameters of the autoregressive moving average (ARMA) model are calculated and determined by the method of successive least squares regression (SLR).
section 2 online adaptive control based on the ARMA model Online adaptive optimal control of specific growth rate of yeast fed batch culture process 193
IV. online adaptive control of lactic acid continuous filtration fermentation process 196
section 3 online optimal control based on autoregressive moving average model 201
I. online optimal control of continuous proction of baker's yeast 201
II On line optimal control of continuous filtration fermentation of lactic acid 205
section 4: on line optimal control based on genetic algorithm On line optimal control based on maximum principle and genetic algorithm Implementation and implementation of fuzzy rules -- Methods of solving fuzzy rules 225
v. composition, design and adjustment of fuzzy logic control system 228
section 2 practical application of fuzzy logic control system in fermentation process 231
I. fuzzy control of yeast fed batch culture 231
II. Fuzzy control of glutamic acid fed batch fermentation 237
III Fuzzy control of coenzyme Q10 fermentation process Fuzzy neural network control system and its practical application in fermentation process 253
3. Fuzzy neural network controller and its application in fermentation process 260
references 268
Chapter 8 process control and optimization using metabolic network model 270
section 1 Analysis of metabolic network model 270
1. Simplification, calculation and solution of metabolic network model 272
2 Utilization of metabolism
7. For primary school students, the cultivation of computing ability is nothing more than the cultivation of three aspects of primary school students' ability: oral arithmetic, written arithmetic and mental arithmetic. Now I will talk about my experience in teaching activities from these three aspects. 1. On the methods of strengthening oral arithmetic. Oral arithmetic, also known as mental arithmetic, is one of the abilities often used in people's daily life. It has a direct effect on the development of students' attention, memory and thinking ability. In my opinion, it is important to develop oral arithmetic in peacetime and insist on it. In the calculation class, 3-4 minutes of oral practice before each class, the form can be lively and diverse, the method should be simple and easy. In the four calculations, students should first learn the addition and subtraction of oral arithmetic, multiplication in the table and corresponding division within 20, which requires accurate and skilled oral arithmetic
2. Written calculation is based on certain calculation rules, using a pen to calculate on paper. Written calculation is helpful for students to find and check the errors in the process of calculation. In order to ensure the accuracy of calculation, it is necessary to guide students to master the checking method in teaching, and cultivate students' good habit of self-conscious checking
3. Strengthen the awareness of estimation and cultivate the ability of estimation. The new curriculum standard puts forward "pay attention to oral calculation, strengthen estimation, and advocate algorithm diversification". Indeed, estimation has important practical value. Countless examples show that the number of times a person estimates the sum proct quotient in a day's activities is far more than the number of times he makes accurate calculations. At the same time, the learning of estimation is of great significance to cultivate students' number sense
mathematics is inseparable from calculation. We should cultivate students' good habit of calculation by calculating in class and practicing every day. The cultivation of habits is also very important. The cultivation of habits is not abstract and invisible. It's real, visible and tangible. Therefore, the cultivation of habits can not be used as a slogan, we need to be down-to-earth and start bit by bit. There are many reasons for calculation errors, and bad calculation habits are the important reasons for calculation errors. Therefore, to cultivate good calculation habits is the main way and measure to improve students' calculation ability: first, to cultivate students' conscientiousness, carefulness, neat writing and standard format. Second, we must emphasize checking calculation after careful calculation. The third is to carry out error correction training regularly. Display the topics that are easy to make mistakes, and compare them with your own reality. If you have something, you can change it. If you have nothing, you can encourage it.
2. Written calculation is based on certain calculation rules, using a pen to calculate on paper. Written calculation is helpful for students to find and check the errors in the process of calculation. In order to ensure the accuracy of calculation, it is necessary to guide students to master the checking method in teaching, and cultivate students' good habit of self-conscious checking
3. Strengthen the awareness of estimation and cultivate the ability of estimation. The new curriculum standard puts forward "pay attention to oral calculation, strengthen estimation, and advocate algorithm diversification". Indeed, estimation has important practical value. Countless examples show that the number of times a person estimates the sum proct quotient in a day's activities is far more than the number of times he makes accurate calculations. At the same time, the learning of estimation is of great significance to cultivate students' number sense
mathematics is inseparable from calculation. We should cultivate students' good habit of calculation by calculating in class and practicing every day. The cultivation of habits is also very important. The cultivation of habits is not abstract and invisible. It's real, visible and tangible. Therefore, the cultivation of habits can not be used as a slogan, we need to be down-to-earth and start bit by bit. There are many reasons for calculation errors, and bad calculation habits are the important reasons for calculation errors. Therefore, to cultivate good calculation habits is the main way and measure to improve students' calculation ability: first, to cultivate students' conscientiousness, carefulness, neat writing and standard format. Second, we must emphasize checking calculation after careful calculation. The third is to carry out error correction training regularly. Display the topics that are easy to make mistakes, and compare them with your own reality. If you have something, you can change it. If you have nothing, you can encourage it.
Hot content