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Announce This monograph is a summary of the author's series of research achievements in the principle and technology of soft sensing of pulp properties. The online measurement of pulp properties, including pulp kappa number, pulp kappa number, pulp whiteness, residual ink content of pulp, pulp concentration, pulp fiber combination and paper strength during the cooking process, is an important parameter in the production control of pulp and paper making process, which directly affects the quality of pulp and paper and the normal operation of production. Therefore, the research and development of pulp property measurement technology is a hot topic in the world, and many achievements and commercialized measurement instruments and methods have also been made, among which the research and application of soft measurement technology is the most eye-catching. This monograph summarizes the achievements made at home and abroad. On the basis of the latest research achievements of the author and his team, it systematically discusses the principle of soft measurement of pulp properties, the derivation of the mathematical model of soft measurement of pulp properties, and introduces the composition and implementation methods of soft measurement technology of pulp properties (soft instrument), A monograph on soft sensing technology for pulp properties from theory to practice.
Chapter 1 Introduction
1.1 Information technology of paper industry
1.1.1 Development of industrial information technology system
1.1.2 Composition of industrial information technology system
1.1.3 The new generation of information technology in paper industry - modern integrated process system (CIPS)
1.1.4 Challenges and opportunities of pulp and paper industry in integrated optimization development
1.2 Overview of special sensor technology for paper industry
1.2.1 Pulp Kappa number online sensor
1.2.3 Optical pulp concentration sensor
1.2.4 Deinked pulp residual ink sensor
1.2.5 Characteristics of optical measurement of pulp properties
1.2.6 Intelligent sensor system
1.3 Soft sensing technology of pulp Kappa number and adaptive reasoning control of cooking process
1.3.1 Soft sensing technology
1.3.2 Soft sensing technology and virtual instrument
1.3.3 Adaptive inferential control of cooking process
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Chapter 2 Establishment of Soft Sensing Mathematical Model of Kraft Pulp Kappa Number
2.1 Relationship between Kappa number and lignin content in pulp
2.1.1 Current understanding of the relationship between Kappa number and lignin content in pulp
2.1.2 New relationship between Kappa number of pulp and lignin content of pulp
2.2 Delignification during kraft cooking reaction kinetics 2.2.1 Reaction mechanism of delignification during kraft cooking
2.2.2 Initial Delignification
2.2.3 Bulk Delignification
2.2.4 Residual Delignification
2.3 Evaluation and analysis of representative kraft cooking process pulp Kappa number mathematical model
2.3.1 Chari model and Lin model
2.3.2 Hatton model
2.3.3 MoDoCell model
2.3.4 Kerr model
2.4 Establishment of a new soft sensing model for pulp Kappa number in kraft cooking process
2.4.1 The starting point of establishing a new soft sensing model for pulp Kappa number in kraft cooking process
2.4.2 Basic mathematical model of new pulp Kappa number model
2.4.3 Establishment of mathematical model of effective alkali concentration in sulfate cooking process
2.4.4 Deduction of Kappa number mathematical model of pulp during sulfate batch cooking
2.4.5 New method for determining the starting point of a large amount of delignification stage
2.5 Validation and improvement of new pulp Kappa number model for kraft cooking process
2.5.1 Verification of new model
2.5.2 Improvement of new pulp Kappa number model in kraft cooking process
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Chapter 3 Establishment of Kappa number mathematical model for deep delignification Lo_SoljdSTM cooking process
3.1 Establishment of Kappa number mathematical model for deep delignification Lo_SolidsTM cooking process
3.1.1 Deep delignification Lo_SolidsTM cooking mechanism
3.1.2 Establishment of mathematical model of pulp Kappa number in Lo_solidsTM cooking process
3.1.3 Model validation
3.2 A simplified model of Kappa value soft sensing in cooking process
3.2.1 Conditions to be satisfied by Kappa value soft sensor model
3.2.2 Model simplification
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Chapter 4 Influence of model building method and data processing on soft sensor
4.1 Factors affecting soft sensor performance
4.1.1 Classification of soft sensing technology
4.1.2 Factors affecting soft sensing performance
4.2 Influence of auxiliary measurement variable selection on Kappa number soft sensing in cooking process
4.2.1 Auxiliary measurement variables of Kappa value soft sensing in cooking process
4.2.2 Calculation method of comprehensive H factor based on average Kappa value in batch cooking process
4.3 Soft sensing method for Kappa number of batch cooking process based on artificial intelligence
4.3.1 Model based fuzzy reasoning method and its application in the soft sensing of Kappa number in cooking process
4.3.2 artificial neural network Application of MATLAB in soft sensing modeling of pulp Kappa number in cooking process 4.3.3 Application of hybrid modeling method based on empirical model and error compensation model in soft sensing of Kappa number in cooking process
4.4 Data pre-processing for contradictory data and abnormal data
4.4.1 Data preprocessing of Kappa value soft sensor modeling in cooking process
4.4.2 Discovery of contradictory data based on process mechanism and cluster analysis
4.5 Design of prediction error estimator to improve the prediction accuracy of soft sensor
4.5.1 Error correction method for improving prediction accuracy of soft sensor
4.5.2 Design of prediction error estimator
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Chapter 5 Development of Online Soft Sensing System for Kappa Number of Pulp and Prediction System for Cooking Endpoint in Sulfate Cooking Process
5.1 Development of online soft sensing system of pulp Kappa number and prediction system of cooking end point in laboratory kraft cooking process
5.1.1 Software development for online soft measurement of pulp Kappa number in laboratory cooking process
5.1.2 Composition of online soft sensing system for pulp Kappa number in laboratory cooking process
5.1.3 Validation of prediction results of end point of laboratory sulfate batch cooking
5.1.4 Validation of prediction results of Lo_SolidsTM cooking endpoint in laboratory
5.1.5 Conclusion
5.2 Development of online soft sensing of pulp Kappa number and prediction system of cooking end point in industrial sulfate cooking process
5.2.1 Composition of the Kappa number soft sensing and cooking endpoint prediction system for industrial cooking process
5.2.2 Software composition design of Kappa value soft measurement and cooking endpoint prediction system in cooking process
5.2.3 Development of software for Kappa value soft measurement and cooking endpoint prediction system in cooking process
5.2.4 Functions and features of Kappa value soft sensing and cooking endpoint prediction system software
5.3 Application examples of pulp Kappa number soft sensing and cooking endpoint prediction system in the cooking process
5.3.1 Soft sensing and end-point prediction system of pulp Kappa number in the cooking process of Fujian Nanping Paper Mill
5.3.2 Soft sensing and endpoint prediction system of pulp Kappa number in the cooking process of Guangxi Hezhou Paper Mill
5.4 Production process oriented technical support system
5.4.1 Functional composition of production process technical support system
5.4.2 Design and development of technical support system for cooking section
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Chapter 6 On line Spectral Measurement of Kappa Number of Pulp in Cooking Process
6.1 On line spectral measurement of pulp Kappa number during kraft cooking
6.1.1 On line measurement of pulp Kappa number during kraft cooking near infrared spectroscopy Band selection 6.1.2 Establishment of mathematical model for on-line measurement of pulp Kappa number by near-infrared spectroscopy during kraft cooking
6.2 On line spectral measurement of pulp Kappa number during sulfite cooking
6.2.1 Sulfite Establishment of theoretical model for on-line measurement of pulp Kappa number in cooking process 6.2.2 Selection of spectral bands for on-line measurement of pulp Kappa number during sulfite cooking
6.2.3 Relationship between lignin content and absorbance in sulfite cooking liquor
6.3 Establishment of prediction model for cooking end point of sulfite cooking process
6.3.1 Establishment of theoretical prediction model
6.3.2 Establishment of theoretical experimental prediction model
6.4 Application example of cooking end point prediction system in sulfite cooking process
6.4.1 Application site and cooking process conditions
6.4.2 Determination of cooking endpoint prediction parameters
6.4.3 Effect of prediction and control of cooking end point
6.4.4 Construction of prediction system for cooking end point in sulfite cooking process
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Chapter 7 Reflectance Spectrum Characteristics of Pulp and Measurement of Kappa Number of Pulp
7.1 Optical properties of lignin in pulp and measurement of pulp Kappa number
7.1.1 Application of optical properties of lignin
7.1.2 Basic principle of measuring Kappa number of pulp by spectral method
7.1.3 Key problems to be solved in the research of pulp Kappa number online sensor
7.2 Basic principle of dispersion (reflection) measurement
7.2.1 Relationship between lignin content in pulp and light reflectance
7.2.2 The key problems that should be solved when the light dispersion (reflection) method is used to measure the Kappa number of pulp online
7.3 Determination of characteristic wavelength for on-line measurement of pulp Kappa number
7.3.1 Scattering spectrum test of sulfate pulp in the ultraviolet visible region
7.3.2 Effect of pulp concentration (moisture) on reflection spectrum of pulp
7.3.3 Selection of wavelength for measuring Kappa number reflection spectrum of pulp
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